import numpy
import pandas as pd
import pytest
from pymilvus import DataType

from base.client_base import TestcaseBase
from utils.util_log import test_log as log
from common import common_func as cf
from common import common_type as ct
from common.common_type import CaseLabel, CheckTasks

prefix = "collection"
exp_name = "name"
exp_schema = "schema"
exp_num = "num_entities"
exp_primary = "primary"
default_schema = cf.gen_default_collection_schema()
default_binary_schema = cf.gen_default_binary_collection_schema()


class TestCollectionParams(TestcaseBase):
    """ Test case of collection interface """

    @pytest.fixture(scope="function", params=ct.get_invalid_strs)
    def get_none_removed_invalid_strings(self, request):
        if request.param is None:
            pytest.skip("None schema is valid")
        yield request.param

    @pytest.fixture(scope="function", params=ct.get_invalid_strs)
    def get_invalid_type_fields(self, request):
        if isinstance(request.param, list):
            pytest.skip("list is valid fields")
        yield request.param

    @pytest.fixture(scope="function", params=cf.gen_all_type_fields())
    def get_unsupported_primary_field(self, request):
        if request.param.dtype == DataType.INT64:
            pytest.skip("int64 type is valid primary key")
        yield request.param

    @pytest.fixture(scope="function", params=ct.get_invalid_strs)
    def get_invalid_dim(self, request):
        if request.param == 1:
            request.param = 0
        yield request.param

    @pytest.mark.tags(CaseLabel.L0)
    def test_collection(self):
        """
        target: test collection with default schema
        method: create collection with default schema
        expected: assert collection property
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        self.collection_wrap.init_collection(c_name, schema=default_schema,
                                             check_task=CheckTasks.check_collection_property,
                                             check_items={exp_name: c_name, exp_schema: default_schema, exp_num: 0,
                                                          exp_primary: ct.default_int64_field_name})
        assert c_name, _ in self.utility_wrap.list_collections()

    @pytest.mark.tags(CaseLabel.L0)
    def test_collection_empty_name(self):
        """
        target: test collection with empty name
        method: create collection with a empty name
        expected: raise exception
        """
        self._connect()
        c_name = ""
        error = {ct.err_code: 1, ct.err_msg: "value  is illegal"}
        self.collection_wrap.init_collection(c_name, schema=default_schema, check_task=CheckTasks.err_res,
                                             check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("name", [[], 1, [1, "2", 3], (1,), {1: 1}, None])
    def test_collection_illegal_name(self, name):
        """
        target: test collection with illegal name
        method: create collection with illegal name
        expected: raise exception
        """
        self._connect()
        error = {ct.err_code: 1, ct.err_msg: "`collection_name` value {} is illegal".format(name)}
        self.collection_wrap.init_collection(name, schema=default_schema, check_task=CheckTasks.err_res,
                                             check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("name", ["12-s", "12 s", "(mn)", "中文", "%$#", "a".join("a" for i in range(256))])
    def test_collection_invalid_name(self, name):
        """
        target: test collection with invalid name
        method: create collection with invalid name
        expected: raise exception
        """
        self._connect()
        error = {ct.err_code: 1, ct.err_msg: "Invalid collection name: {}".format(name)}
        self.collection_wrap.init_collection(name, schema=default_schema, check_task=CheckTasks.err_res,
                                             check_items=error)

    @pytest.mark.tags(CaseLabel.L0)
    def test_collection_dup_name(self):
        """
        target: test collection with dup name
        method: create collection with dup name and none schema and data
        expected: collection properties consistent
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        collection_w = self.init_collection_wrap(name=c_name, check_task=CheckTasks.check_collection_property,
                                                 check_items={exp_name: c_name, exp_schema: default_schema})
        self.collection_wrap.init_collection(collection_w.name)
        assert collection_w.name == self.collection_wrap.name
        assert collection_w.schema == self.collection_wrap.schema
        assert collection_w.num_entities == self.collection_wrap.num_entities
        assert collection_w.name, _ in self.utility_wrap.list_collections()[0]

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_dup_name_with_desc(self):
        """
        target: test collection with dup name
        method: 1. default schema with desc 2. dup name collection
        expected: desc consistent
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        schema = cf.gen_default_collection_schema(description=ct.collection_desc)
        collection_w = self.init_collection_wrap(name=c_name, schema=schema,
                                                 check_task=CheckTasks.check_collection_property,
                                                 check_items={exp_name: c_name, exp_schema: schema})
        self.collection_wrap.init_collection(c_name,
                                             check_task=CheckTasks.check_collection_property,
                                             check_items={exp_name: c_name, exp_schema: schema})
        assert collection_w.description == self.collection_wrap.description

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_dup_name_new_schema(self):
        """
        target: test collection with dup name and new schema
        method: 1.create collection with default schema
                2. collection with dup name and new schema
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        self.init_collection_wrap(name=c_name, check_task=CheckTasks.check_collection_property,
                                  check_items={exp_name: c_name, exp_schema: default_schema})
        fields = [cf.gen_int64_field(is_primary=True)]
        schema = cf.gen_collection_schema(fields=fields)
        error = {ct.err_code: 0, ct.err_msg: "The collection already exist, but the schema isnot the same as the "
                                             "schema passed in"}
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_dup_name_new_primary(self):
        """
        target: test collection with dup name and new primary_field schema
        method: 1.collection with default schema
                2. collection with same fields and new primary_field schema
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        int_field_one = cf.gen_int64_field()
        int_field_two = cf.gen_int64_field(name="int2")
        fields = [int_field_one, int_field_two, cf.gen_float_vec_field()]
        schema = cf.gen_collection_schema(fields, primary_field=int_field_one.name)
        collection_w = self.init_collection_wrap(name=c_name, schema=schema,
                                                 check_task=CheckTasks.check_collection_property,
                                                 check_items={exp_name: c_name, exp_schema: schema,
                                                              exp_primary: int_field_one.name})
        new_schema = cf.gen_collection_schema(fields, primary_field=int_field_two.name)
        error = {ct.err_code: 0, ct.err_msg: "The collection already exist, but the schema isnot the same as the "
                                             "schema passed in"}
        self.collection_wrap.init_collection(c_name, schema=new_schema, check_task=CheckTasks.err_res,
                                             check_items=error)
        assert collection_w.primary_field.name == int_field_one.name

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_dup_name_new_dim(self):
        """
        target: test collection with dup name and new dim schema
        method: 1. default schema 2. schema with new dim
        expected: raise exception
        """
        self._connect()
        new_dim = 120
        c_name = cf.gen_unique_str(prefix)
        collection_w = self.init_collection_wrap(name=c_name, check_task=CheckTasks.check_collection_property,
                                                 check_items={exp_name: c_name, exp_schema: default_schema})
        schema = cf.gen_default_collection_schema()
        new_fields = cf.gen_float_vec_field(dim=new_dim)
        schema.fields[-1] = new_fields
        error = {ct.err_code: 0, ct.err_msg: "The collection already exist, but the schema isnot the same as the "
                                             "schema passed in."}
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error)
        dim = collection_w.schema.fields[-1].params['dim']
        assert dim == ct.default_dim

    @pytest.mark.tags(CaseLabel.L2)
    def test_collection_dup_name_invalid_schema_type(self, get_none_removed_invalid_strings):
        """
        target: test collection with dup name and invalid schema
        method: 1. default schema 2. invalid schema
        expected: raise exception and
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        collection_w = self.init_collection_wrap(name=c_name, check_task=CheckTasks.check_collection_property,
                                                 check_items={exp_name: c_name, exp_schema: default_schema})
        error = {ct.err_code: 0, ct.err_msg: "Schema type must be schema.CollectionSchema"}
        schema = get_none_removed_invalid_strings
        self.collection_wrap.init_collection(collection_w.name, schema=schema,
                                             check_task=CheckTasks.err_res, check_items=error)
        assert collection_w.name == c_name

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_dup_name_same_schema(self):
        """
        target: test collection with dup name and same schema
        method: dup name and same schema
        expected: two collection object is available
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        collection_w = self.init_collection_wrap(name=c_name, schema=default_schema,
                                                 check_task=CheckTasks.check_collection_property,
                                                 check_items={exp_name: c_name, exp_schema: default_schema})
        self.collection_wrap.init_collection(name=c_name, schema=default_schema,
                                             check_task=CheckTasks.check_collection_property,
                                             check_items={exp_name: c_name, exp_schema: default_schema})
        assert collection_w.name == self.collection_wrap.name

    @pytest.mark.tags(CaseLabel.L0)
    def test_collection_none_schema(self):
        """
        target: test collection with none schema
        method: create collection with none schema
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        error = {ct.err_code: 0, ct.err_msg: "Should be passed into the schema"}
        self.collection_wrap.init_collection(c_name, schema=None, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L0)
    def test_collection_invalid_type_schema(self, get_none_removed_invalid_strings):
        """
        target: test collection with invalid schema
        method: create collection with non-CollectionSchema type schema
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        error = {ct.err_code: 1, ct.err_msg: "schema type must be schema.CollectionSchema"}
        self.collection_wrap.init_collection(c_name, schema=get_none_removed_invalid_strings,
                                             check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_invalid_type_fields(self, get_invalid_type_fields):
        """
        target: test collection with invalid fields type, non-list
        method: create collection schema with non-list invalid fields
        expected: exception
        """
        self._connect()
        fields = get_invalid_type_fields
        error = {ct.err_code: 1, ct.err_msg: "The fields of schema must be type list"}
        self.collection_schema_wrap.init_collection_schema(fields=fields,
                                                           check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_with_unknown_type(self):
        """
        target: test collection with unknown type
        method: create with DataType.UNKNOWN
        expected: raise exception
        """
        self._connect()
        error = {ct.err_code: 0, ct.err_msg: "Field type not support <DataType.UNKNOWN: 999"}
        self.field_schema_wrap.init_field_schema(name="unknown", dtype=DataType.UNKNOWN,
                                                 check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("name", [[], 1, (1,), {1: 1}, "12-s"])
    def test_collection_invalid_type_field(self, name):
        """
        target: test collection with invalid field name
        method: invalid string name
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        field, _ = self.field_schema_wrap.init_field_schema(name=name, dtype=5, is_primary=True)
        vec_field = cf.gen_float_vec_field()
        schema = cf.gen_collection_schema(fields=[field, vec_field])
        error = {ct.err_code: 1, ct.err_msg: "expected one of: bytes, unicode"}
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("name", ["12-s", "12 s", "(mn)", "中文", "%$#", "a".join("a" for i in range(256))])
    def test_collection_invalid_field_name(self, name):
        """
        target: test collection with invalid field name
        method: invalid string name
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        field, _ = self.field_schema_wrap.init_field_schema(name=name, dtype=DataType.INT64, is_primary=True)
        vec_field = cf.gen_float_vec_field()
        schema = cf.gen_collection_schema(fields=[field, vec_field])
        error = {ct.err_code: 1, ct.err_msg: "Invalid field name"}
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_none_field_name(self):
        """
        target: test field schema with None name
        method: None field name
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        field, _ = self.field_schema_wrap.init_field_schema(name=None, dtype=DataType.INT64, is_primary=True)
        schema = cf.gen_collection_schema(fields=[field, cf.gen_float_vec_field()])
        error = {ct.err_code: 1, ct.err_msg: "You should specify the name of field"}
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("dtype", [6, [[]], {}, (), "", "a"])
    def test_collection_invalid_field_type(self, dtype):
        """
        target: test collection with invalid field type
        method: invalid DataType
        expected: raise exception
        """
        self._connect()
        error = {ct.err_code: 0, ct.err_msg: "Field type must be of DataType"}
        self.field_schema_wrap.init_field_schema(name="test", dtype=dtype, is_primary=True,
                                                 check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L2)
    def test_collection_field_dtype_float_value(self):
        """
        target: test collection with float type
        method: create field with float type
        expected:
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        field, _ = self.field_schema_wrap.init_field_schema(name=ct.default_int64_field_name, dtype=5.0,
                                                            is_primary=True)
        schema = cf.gen_collection_schema(fields=[field, cf.gen_float_vec_field()])
        error = {ct.err_code: 0, ct.err_msg: "Field type must be of DataType"}
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L0)
    def test_collection_empty_fields(self):
        """
        target: test collection with empty fields
        method: create collection with fields = []
        expected: exception
        """
        self._connect()
        error = {ct.err_code: 0, ct.err_msg: "Must be have a primary key field"}
        self.collection_schema_wrap.init_collection_schema(fields=[], primary_field=ct.default_int64_field_name,
                                                           check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_dup_field(self):
        """
        target: test collection with dup field name
        method: Two FieldSchema have same name
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        field_one = cf.gen_int64_field(is_primary=True)
        field_two = cf.gen_int64_field()
        schema = cf.gen_collection_schema(fields=[field_one, field_two, cf.gen_float_vec_field()])
        error = {ct.err_code: 0, ct.err_msg: "duplicated field name"}
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error)
        assert not self.utility_wrap.has_collection(c_name)[0]

    @pytest.mark.tags(CaseLabel.L0)
    @pytest.mark.skip(reason="waiting for required int primary field")
    @pytest.mark.parametrize("field", [cf.gen_float_vec_field(), cf.gen_binary_vec_field()])
    def test_collection_only_vector_field(self, field):
        """
        target: test collection just with vec field
        method: create with float-vec fields
        expected: raise exception
        """
        self._connect()
        error = {ct.err_code: 0, ct.err_msg: "Must be have a primary key field"}
        self.collection_schema_wrap.init_collection_schema([field], check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_multi_float_vectors(self):
        """
        target: test collection with multi float vectors
        method: create collection with two float-vec fields
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        fields = [cf.gen_int64_field(is_primary=True), cf.gen_float_vec_field(), cf.gen_float_vec_field(name="tmp")]
        schema = cf.gen_collection_schema(fields=fields, auto_id=True)
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.check_collection_property,
                                             check_items={exp_name: c_name, exp_schema: schema})

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_mix_vectors(self):
        """
        target: test collection with mix vectors
        method: create with float and binary vec
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        fields = [cf.gen_int64_field(is_primary=True), cf.gen_float_vec_field(), cf.gen_binary_vec_field()]
        schema = cf.gen_collection_schema(fields=fields, auto_id=True)
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.check_collection_property,
                                             check_items={exp_name: c_name, exp_schema: schema})

    @pytest.mark.tags(CaseLabel.L0)
    def test_collection_without_vectors(self):
        """
        target: test collection without vectors
        method: create collection only with int field
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        schema = cf.gen_collection_schema([cf.gen_int64_field(is_primary=True)])
        error = {ct.err_code: 0, ct.err_msg: "The schema must have vector column"}
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_without_primary_field(self):
        """
        target: test collection without primary field
        method: no primary field specified in collection schema and fields
        expected: raise exception
        """
        self._connect()
        int_fields, _ = self.field_schema_wrap.init_field_schema(name=ct.default_int64_field_name, dtype=DataType.INT64)
        vec_fields, _ = self.field_schema_wrap.init_field_schema(name=ct.default_float_vec_field_name,
                                                                 dtype=DataType.FLOAT_VECTOR, dim=ct.default_dim)
        error = {ct.err_code: 0, ct.err_msg: "Must be have a primary key field"}
        self.collection_schema_wrap.init_collection_schema([int_fields, vec_fields],
                                                           check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_is_primary_false(self):
        """
        target: test collection with all is_primary false
        method: set all fields if_primary false
        expected: raise exception
        """
        self._connect()
        fields = [cf.gen_int64_field(is_primary=False), cf.gen_float_field(is_primary=False),
                  cf.gen_float_vec_field(is_primary=False)]
        error = {ct.err_code: 0, ct.err_msg: "Must be have a primary key field"}
        self.collection_schema_wrap.init_collection_schema(fields, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("is_primary", ct.get_invalid_strs)
    def test_collection_invalid_is_primary(self, is_primary):
        """
        target: test collection with invalid primary
        method: define field with is_primary=non-bool
        expected: raise exception
        """
        self._connect()
        name = cf.gen_unique_str(prefix)
        error = {ct.err_code: 0, ct.err_msg: "Param is_primary must be bool type"}
        self.field_schema_wrap.init_field_schema(name=name, dtype=DataType.INT64, is_primary=is_primary,
                                                 check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("primary_field", ["12-s", "12 s", "(mn)", "中文", "%$#", "a".join("a" for i in range(256))])
    def test_collection_invalid_primary_field(self, primary_field):
        """
        target: test collection with invalid primary_field
        method: specify invalid string primary_field in collection schema
        expected: raise exception
        """
        self._connect()
        fields = [cf.gen_int64_field(), cf.gen_float_vec_field()]
        error = {ct.err_code: 0, ct.err_msg: "Must be have a primary key field"}
        self.collection_schema_wrap.init_collection_schema(fields=fields, primary_field=primary_field,
                                                           check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("primary_field", [[], 1, [1, "2", 3], (1,), {1: 1}, None])
    def test_collection_non_string_primary_field(self, primary_field):
        """
        target: test collection with non-string primary_field
        method: primary_field type is not string
        expected: raise exception
        """
        self._connect()
        fields = [cf.gen_int64_field(), cf.gen_float_vec_field()]
        error = {ct.err_code: 0, ct.err_msg: "Must be have a primary key field"}
        self.collection_schema_wrap.init_collection_schema(fields, primary_field=primary_field,
                                                           check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_not_existed_primary_field(self):
        """
        target: test collection with not exist primary field
        method: specify not existed field as primary_field
        expected: raise exception
        """
        self._connect()
        fake_field = cf.gen_unique_str()
        fields = [cf.gen_int64_field(), cf.gen_float_vec_field()]
        error = {ct.err_code: 0, ct.err_msg: "Must be have a primary key field"}

        self.collection_schema_wrap.init_collection_schema(fields, primary_field=fake_field,
                                                           check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L0)
    def test_collection_primary_in_schema(self):
        """
        target: test collection with primary field
        method: specify primary field in CollectionSchema
        expected: collection.primary_field
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        schema = cf.gen_default_collection_schema(primary_field=ct.default_int64_field_name)
        self.collection_wrap.init_collection(c_name, schema=schema)
        assert self.collection_wrap.primary_field.name == ct.default_int64_field_name

    @pytest.mark.tags(CaseLabel.L0)
    def test_collection_primary_in_field(self):
        """
        target: test collection with primary field
        method: specify primary field in FieldSchema
        expected: collection.primary_field
        """
        self._connect()
        fields = [cf.gen_int64_field(is_primary=True), cf.gen_float_field(), cf.gen_float_vec_field()]
        schema, _ = self.collection_schema_wrap.init_collection_schema(fields)
        self.collection_wrap.init_collection(cf.gen_unique_str(prefix), schema=schema)
        assert self.collection_wrap.primary_field.name == ct.default_int64_field_name

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_unsupported_primary_field(self, get_unsupported_primary_field):
        """
        target: test collection with unsupported primary field type
        method: specify non-int64 as primary field
        expected: raise exception
        """
        self._connect()
        field = get_unsupported_primary_field
        vec_field = cf.gen_float_vec_field(name="vec")
        error = {ct.err_code: 1, ct.err_msg: "Primary key type must be DataType.INT64."}
        self.collection_schema_wrap.init_collection_schema(fields=[field, vec_field], primary_field=field.name,
                                                           check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.xfail(reason="#6093")
    def test_collection_multi_primary_fields(self):
        """
        target: test collection with multi primary
        method: collection with two primary fields
        expected: raise exception
        """
        self._connect()
        int_field_one = cf.gen_int64_field(is_primary=True)
        int_field_two = cf.gen_int64_field(name="int2", is_primary=True)
        # error = {ct.err_code: 0, ct.err_msg: "Primary key field can only be one"}
        schema, _ = self.collection_schema_wrap.init_collection_schema(fields=[int_field_one, int_field_two,
                                                                               cf.gen_float_vec_field()])
        # check_task=CheckTasks.err_res, check_items=error)
        log.debug(schema)
        collection_w, _ = self.collection_wrap.init_collection(name="c", schema=schema)
        log.debug(collection_w.schema)

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_primary_inconsistent(self):
        """
        target: test collection with different primary field setting
        method: 1. set A field is_primary 2. set primary_field is B
        expected: raise exception
        """
        self._connect()
        int_field_one = cf.gen_int64_field(is_primary=True)
        int_field_two = cf.gen_int64_field(name="int2")
        fields = [int_field_one, int_field_two, cf.gen_float_vec_field()]
        error = {ct.err_code: 0, ct.err_msg: "Primary key field can only be one"}
        self.collection_schema_wrap.init_collection_schema(fields, primary_field=int_field_two.name,
                                                           check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_primary_consistent(self):
        """
        target: test collection with both collection schema and field schema
        method: 1. set A field is_primary 2.set primary_field is A
        expected: verify primary field
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        int_field_one = cf.gen_int64_field(is_primary=True)
        schema = cf.gen_collection_schema(fields=[int_field_one, cf.gen_float_vec_field()],
                                          primary_field=int_field_one.name)
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.check_collection_property,
                                             check_items={exp_name: c_name, exp_schema: schema})

    @pytest.mark.tags(CaseLabel.L0)
    @pytest.mark.parametrize("auto_id", [True, False])
    def test_collection_auto_id_in_field_schema(self, auto_id):
        """
        target: test collection with auto_id in field schema
        method: specify auto_id True in field schema
        expected: verify schema's auto_id
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        int_field = cf.gen_int64_field(is_primary=True, auto_id=auto_id)
        vec_field = cf.gen_float_vec_field(name='vec')
        schema, _ = self.collection_schema_wrap.init_collection_schema([int_field, vec_field])
        assert schema.auto_id == auto_id
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.check_collection_property,
                                             check_items={exp_name: c_name, exp_schema: schema})

    @pytest.mark.tags(CaseLabel.L0)
    @pytest.mark.parametrize("auto_id", [True, False])
    def test_collection_auto_id_in_collection_schema(self, auto_id):
        """
        target: test collection with auto_id in collection schema
        method: specify auto_id True in collection schema
        expected: verify schema auto_id and collection schema
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        int_field = cf.gen_int64_field(is_primary=True)
        vec_field = cf.gen_float_vec_field(name='vec')
        schema, _ = self.collection_schema_wrap.init_collection_schema([int_field, vec_field], auto_id=auto_id)
        assert schema.auto_id == auto_id
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.check_collection_property,
                                             check_items={exp_name: c_name, exp_schema: schema})

    @pytest.mark.tags(CaseLabel.L0)
    def test_collection_auto_id_non_primary_field(self):
        """
        target: test collection set auto_id in non-primary field
        method: set auto_id=True in non-primary field
        expected: raise exception
        """
        self._connect()
        error = {ct.err_code: 0, ct.err_msg: "auto_id can only be specified on the primary key field"}
        self.field_schema_wrap.init_field_schema(name=ct.default_int64_field_name, dtype=DataType.INT64, auto_id=True,
                                                 check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_auto_id_false_non_primary(self):
        """
        target: test collection set auto_id in non-primary field
        method: set auto_id=True in non-primary field
        expected: verify schema auto_id is False
        """
        self._connect()
        int_field_one = cf.gen_int64_field(is_primary=True)
        int_field_two = cf.gen_int64_field(name='int2', auto_id=False)
        fields = [int_field_one, int_field_two, cf.gen_float_vec_field()]
        schema, _ = self.collection_schema_wrap.init_collection_schema(fields)
        assert not schema.auto_id

    @pytest.mark.tags(CaseLabel.L0)
    def test_collection_auto_id_inconsistent(self):
        """
        target: test collection auto_id with both collection schema and field schema
        method: 1.set primary field auto_id=True in field schema 2.set auto_id=False in collection schema
        expected: raise exception
        """
        self._connect()
        int_field = cf.gen_int64_field(is_primary=True, auto_id=True)
        vec_field = cf.gen_float_vec_field(name='vec')
        error = {ct.err_code: 0, ct.err_msg: "The auto_id of the collection is inconsistent with "
                                             "the auto_id of the primary key field"}
        self.collection_schema_wrap.init_collection_schema([int_field, vec_field], auto_id=False,
                                                           check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("auto_id", [True, False])
    def test_collection_auto_id_consistent(self, auto_id):
        """
        target: test collection auto_id with both collection schema and field schema
        method: set auto_id=True/False both field and schema
        expected: verify auto_id
        """
        self._connect()
        int_field = cf.gen_int64_field(is_primary=True, auto_id=auto_id)
        vec_field = cf.gen_float_vec_field(name='vec')
        schema, _ = self.collection_schema_wrap.init_collection_schema([int_field, vec_field], auto_id=auto_id)
        assert schema.auto_id == auto_id

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_auto_id_none_in_field(self):
        """
        target: test collection with auto_id is None
        method: set auto_id=None
        expected: raise exception
        """
        self._connect()
        error = {ct.err_code: 0, ct.err_msg: "Param auto_id must be bool type"}
        self.field_schema_wrap.init_field_schema(name=ct.default_int64_field_name, dtype=DataType.INT64,
                                                 is_primary=True,
                                                 auto_id=None, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("auto_id", ct.get_invalid_strs)
    def test_collection_invalid_auto_id(self, auto_id):
        """
        target: test collection with invalid auto_id
        method: define field with auto_id=non-bool
        expected: raise exception
        """
        self._connect()
        int_field = cf.gen_int64_field(is_primary=True)
        vec_field = cf.gen_float_vec_field(name='vec')
        error = {ct.err_code: 0, ct.err_msg: "Param auto_id must be bool type"}
        self.collection_schema_wrap.init_collection_schema([int_field, vec_field], auto_id=auto_id,
                                                           check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_multi_fields_auto_id(self):
        """
        target: test collection auto_id with multi fields
        method: specify auto_id=True for multi int64 fields
        expected: todo raise exception
        """
        self._connect()
        error = {ct.err_code: 0, ct.err_msg: "auto_id can only be specified on the primary key field"}
        cf.gen_int64_field(is_primary=True, auto_id=True)
        self.field_schema_wrap.init_field_schema(name="int", dtype=DataType.INT64, auto_id=True,
                                                 check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L0)
    @pytest.mark.parametrize("dtype", [DataType.FLOAT_VECTOR, DataType.BINARY_VECTOR])
    def test_collection_vector_without_dim(self, dtype):
        """
        target: test collection without dimension
        method: define vector field without dim
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        float_vec_field, _ = self.field_schema_wrap.init_field_schema(name="vec", dtype=dtype)
        schema = cf.gen_collection_schema(fields=[cf.gen_int64_field(is_primary=True), float_vec_field])
        error = {ct.err_code: 0, ct.err_msg: "dimension is not defined in field type params"}
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.xfail(reason="#5950")
    def test_collection_vector_invalid_dim(self, get_invalid_dim):
        """
        target: test collection with invalid dimension
        method: define float-vec field with invalid dimension
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        float_vec_field = cf.gen_float_vec_field(dim=get_invalid_dim)
        schema = cf.gen_collection_schema(fields=[cf.gen_int64_field(is_primary=True), float_vec_field])
        error = {ct.err_code: 0, ct.err_msg: "dim must be of int"}
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("dim", [-1, 32769])
    def test_collection_vector_out_bounds_dim(self, dim):
        """
        target: test collection with out of bounds dim
        method: invalid dim -1 and 32759
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        float_vec_field = cf.gen_float_vec_field(dim=dim)
        schema = cf.gen_collection_schema(fields=[cf.gen_int64_field(is_primary=True), float_vec_field])
        error = {ct.err_code: 0, ct.err_msg: "invalid dimension: {}. should be in range 1 ~ 32768".format(dim)}
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.skip(reason="waiting for primary field")
    def test_collection_non_vector_field_dim(self):
        """
        target: test collection with dim for non-vector field
        method: define int64 field with dim
        expected: no exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        int_field, _ = self.field_schema_wrap.init_field_schema(name="int", dtype=DataType.INT64, dim=ct.default_dim)
        float_vec_field = cf.gen_float_vec_field()
        schema = cf.gen_collection_schema(fields=[int_field, float_vec_field])
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.check_collection_property,
                                             check_items={exp_name: c_name, exp_schema: schema})

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_desc(self):
        """
        target: test collection with description
        method: create with description
        expected: assert default description
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        schema = cf.gen_default_collection_schema(description=ct.collection_desc)
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.check_collection_property,
                                             check_items={exp_name: c_name, exp_schema: schema})

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_none_desc(self):
        """
        target: test collection with none description
        method: create with none description
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        schema = cf.gen_default_collection_schema(description=None)
        error = {ct.err_code: 0, ct.err_msg: "expected one of: bytes, unicode"}
        self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_long_desc(self):
        """
        target: test collection with long desc
        method: create with long desc
        expected:
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        desc = "a".join("a" for _ in range(256))
        schema = cf.gen_default_collection_schema(description=desc)
        self.collection_wrap.init_collection(c_name, schema=schema,
                                             check_task=CheckTasks.check_collection_property,
                                             check_items={exp_name: c_name, exp_schema: schema})

    @pytest.mark.tags(CaseLabel.L0)
    def test_collection_binary(self):
        """
        target: test collection with binary-vec
        method: create collection with binary field
        expected: assert binary field
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        self.collection_wrap.init_collection(c_name, schema=default_binary_schema,
                                             check_task=CheckTasks.check_collection_property,
                                             check_items={exp_name: c_name, exp_schema: default_binary_schema})
        assert c_name, _ in self.utility_wrap.list_collections()


class TestCollectionOperation(TestcaseBase):
    """
    ******************************************************************
      The following cases are used to test collection interface operations
    ******************************************************************
    """

    # def teardown_method(self):
    #     if self.self.collection_wrap is not None and self.self.collection_wrap.collection is not None:
    #         self.self.collection_wrap.drop()

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_without_connection(self):
        """
        target: test collection without connection
        method: 1.create collection after connection removed
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        self.connection_wrap.remove_connection(ct.default_alias)
        res_list, _ = self.connection_wrap.list_connections()
        assert ct.default_alias not in res_list
        error = {ct.err_code: 0, ct.err_msg: 'should create connect first'}
        self.collection_wrap.init_collection(c_name, schema=default_schema,
                                             check_task=CheckTasks.err_res, check_items=error)
        assert self.collection_wrap.collection is None

    @pytest.mark.tags(CaseLabel.L2)
    def test_collection_multi_create_drop(self):
        """
        target: test cycle creation and deletion of multiple collections
        method: in a loop, collections are created and deleted sequentially
        expected: no exception
        """
        self._connect()
        c_num = 20
        for _ in range(c_num):
            c_name = cf.gen_unique_str(prefix)
            self.collection_wrap.init_collection(c_name, schema=default_schema,
                                                 check_task=CheckTasks.check_collection_property,
                                                 check_items={exp_name: c_name, exp_schema: default_schema})
            self.collection_wrap.drop()
            assert c_name, _ not in self.utility_wrap.list_collections()

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_dup_name_drop(self):
        """
        target: test collection with dup name, and drop
        method: 1. two dup name collection object
                2. one object drop collection
        expected: collection dropped
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        collection_w = self.init_collection_wrap(name=c_name, check_task=CheckTasks.check_collection_property,
                                                 check_items={exp_name: c_name, exp_schema: default_schema})
        self.collection_wrap.init_collection(c_name, check_task=CheckTasks.check_collection_property,
                                             check_items={exp_name: c_name, exp_schema: default_schema})
        self.collection_wrap.drop()
        assert not self.utility_wrap.has_collection(c_name)[0]
        error = {ct.err_code: 0, ct.err_msg: "can't find collection"}
        collection_w.has_partition("p", check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_collection_after_drop(self):
        """
        target: test create collection after create and drop
        method: 1. create a 2. drop a 3, re-create a
        expected: no exception
        """
        c_name = cf.gen_unique_str(prefix)
        collection_w = self.init_collection_wrap(name=c_name, check_task=CheckTasks.check_collection_property,
                                                 check_items={exp_name: c_name, exp_schema: default_schema})
        collection_w.drop()
        assert not self.utility_wrap.has_collection(collection_w.name)[0]
        self.init_collection_wrap(name=c_name, check_task=CheckTasks.check_collection_property,
                                  check_items={exp_name: c_name, exp_schema: default_schema})
        assert self.utility_wrap.has_collection(c_name)[0]


class TestCollectionDataframe(TestcaseBase):
    """
    ******************************************************************
      The following cases are used to test construct_from_dataframe
    ******************************************************************
    """

    @pytest.fixture(scope="function", params=ct.get_invalid_strs)
    def get_non_df(self, request):
        if request.param is None:
            pytest.skip("skip None")
        yield request.param

    @pytest.mark.tags(CaseLabel.L0)
    def test_construct_from_dataframe(self):
        """
        target: test collection with dataframe data
        method: create collection and insert with dataframe
        expected: collection num entities equal to nb
        """
        conn = self._connect()
        c_name = cf.gen_unique_str(prefix)
        df = cf.gen_default_dataframe_data(ct.default_nb)
        self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name,
                                                      check_task=CheckTasks.check_collection_property,
                                                      check_items={exp_name: c_name, exp_schema: default_schema})
        conn.flush([c_name])
        assert self.collection_wrap.num_entities == ct.default_nb

    @pytest.mark.tags(CaseLabel.L0)
    def test_construct_from_binary_dataframe(self):
        """
        target: test binary collection with dataframe
        method: create binary collection with dataframe
        expected: collection num entities equal to nb
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        df, _ = cf.gen_default_binary_dataframe_data(nb=ct.default_nb)
        self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name,
                                                      check_task=CheckTasks.check_collection_property,
                                                      check_items={exp_name: c_name, exp_schema: default_binary_schema})
        assert self.collection_wrap.num_entities == ct.default_nb

    @pytest.mark.tags(CaseLabel.L1)
    def test_construct_from_none_dataframe(self):
        """
        target: test create collection by empty dataframe
        method: invalid dataframe type create collection
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        error = {ct.err_code: 0, ct.err_msg: "Dataframe can not be None!"}
        self.collection_wrap.construct_from_dataframe(c_name, None, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_construct_from_dataframe_only_column(self):
        """
        target: test collection with dataframe only columns
        method: dataframe only has columns
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        df = pd.DataFrame(columns=[ct.default_int64_field_name, ct.default_float_vec_field_name])
        error = {ct.err_code: 0, ct.err_msg: "Cannot infer schema from empty dataframe"}
        self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name,
                                                      check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_construct_from_inconsistent_dataframe(self):
        """
        target: test collection with data inconsistent
        method: create and insert with inconsistent data
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        # one field different type df
        mix_data = [(1, 2., [0.1, 0.2]), (2, 3., 4)]
        df = pd.DataFrame(data=mix_data, columns=list("ABC"))
        error = {ct.err_code: 0, ct.err_msg: "The data in the same column must be of the same type"}
        self.collection_wrap.construct_from_dataframe(c_name, df, primary_field='A', check_task=CheckTasks.err_res,
                                                      check_items=error)

    @pytest.mark.tags(CaseLabel.L0)
    def test_construct_from_non_dataframe(self, get_non_df):
        """
        target: test create collection by invalid dataframe
        method: non-dataframe type create collection
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        error = {ct.err_code: 0, ct.err_msg: "Data type must be pandas.DataFrame!"}
        df = get_non_df
        self.collection_wrap.construct_from_dataframe(c_name, df, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_construct_from_data_type_dataframe(self):
        """
        target: test collection with invalid dataframe
        method: create with invalid dataframe
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        df = pd.DataFrame({"date": pd.date_range('20210101', periods=3), ct.default_int64_field_name: [1, 2, 3]})
        error = {ct.err_code: 0, ct.err_msg: "Cannot infer schema from dataframe"}
        self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name,
                                                      check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_construct_from_invalid_field_name(self):
        """
        target: test collection with invalid field name
        method: create with invalid field name dataframe
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        df = pd.DataFrame({'%$#': cf.gen_vectors(3, 2), ct.default_int64_field_name: [1, 2, 3]})
        error = {ct.err_code: 1, ct.err_msg: "Invalid field name"}
        self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name,
                                                      check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_construct_none_primary_field(self):
        """
        target: test collection with none primary field
        method: primary_field is none
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        df = cf.gen_default_dataframe_data(ct.default_nb)
        error = {ct.err_code: 0, ct.err_msg: "Schema must have a primary key field!"}
        self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=None,
                                                      check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_construct_not_existed_primary_field(self):
        """
        target: test collection with not existed primary field
        method: primary field not existed
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        df = cf.gen_default_dataframe_data(ct.default_nb)
        error = {ct.err_code: 0, ct.err_msg: "Must be have a primary key field"}
        self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=c_name,
                                                      check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.xfail(reason="issue #6077")
    def test_construct_with_none_auto_id(self):
        """
        target: test construct with non-int64 as primary field
        method: non-int64 as primary field
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        df = cf.gen_default_dataframe_data(ct.default_nb)
        error = {ct.err_code: 0, ct.err_msg: "Param auto_id must be bool type"}
        self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name,
                                                      auto_id=None, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L0)
    def test_construct_auto_id_true_insert(self):
        """
        target: test construct with true auto_id
        method: auto_id=True and insert values
        expected: raise exception
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        df = cf.gen_default_dataframe_data(nb=100)
        error = {ct.err_code: 0, ct.err_msg: "Auto_id is True, but get the data of primary key field"}
        self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name,
                                                      auto_id=True, check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L0)
    @pytest.mark.xfail(reason="#5967")
    def test_construct_auto_id_true_no_insert(self):
        """
        target: test construct with true auto_id
        method: auto_id=True and not insert ids
        expected: verify num entities
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        df = cf.gen_default_dataframe_data(ct.default_nb)
        df.drop(ct.default_int64_field_name, axis=1, inplace=True)
        log.debug(df.head(3))
        self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name,
                                                      auto_id=True)
        assert self.collection_wrap.num_entities == ct.default_nb

    @pytest.mark.tags(CaseLabel.L2)
    def test_construct_none_value_auto_id_true(self):
        """
        target: test construct with none value, auto_id
        method: df primary field with none value, auto_id=true
        expected: todo
        """
        self._connect()
        nb = 100
        df = cf.gen_default_dataframe_data(nb)
        df.iloc[:, 0] = numpy.NaN
        res, _ = self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
                                                               primary_field=ct.default_int64_field_name, auto_id=True)
        mutation_res = res[1]
        assert cf._check_primary_keys(mutation_res.primary_keys, 100)
        assert self.collection_wrap.num_entities == nb

    @pytest.mark.tags(CaseLabel.L0)
    def test_construct_auto_id_false(self):
        """
        target: test construct with false auto_id
        method: auto_id=False, primary_field correct
        expected: verify auto_id
        """
        self._connect()
        c_name = cf.gen_unique_str(prefix)
        df = cf.gen_default_dataframe_data(ct.default_nb)
        self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name,
                                                      auto_id=False)
        assert not self.collection_wrap.schema.auto_id
        assert self.collection_wrap.num_entities == ct.default_nb

    @pytest.mark.tags(CaseLabel.L1)
    def test_construct_none_value_auto_id_false(self):
        """
        target: test construct with none value, auto_id
        method: df primary field with none value, auto_id=false
        expected: raise exception
        """
        self._connect()
        nb = 100
        df = cf.gen_default_dataframe_data(nb)
        df.iloc[:, 0] = numpy.NaN
        error = {ct.err_code: 0, ct.err_msg: "Primary key type must be DataType.INT64"}
        self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
                                                      primary_field=ct.default_int64_field_name, auto_id=False,
                                                      check_task=CheckTasks.err_res, check_items=error)

    @pytest.mark.tags(CaseLabel.L1)
    def test_construct_auto_id_false_same_values(self):
        """
        target: test construct with false auto_id and same value
        method: auto_id=False, primary field same values
        expected: verify num entities
        """
        self._connect()
        nb = 100
        df = cf.gen_default_dataframe_data(nb)
        df.iloc[1:, 0] = 1
        res, _ = self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
                                                               primary_field=ct.default_int64_field_name, auto_id=False)
        collection_w = res[0]
        assert collection_w.num_entities == nb
        mutation_res = res[1]
        assert mutation_res.primary_keys == df[ct.default_int64_field_name].values.tolist()

    @pytest.mark.tags(CaseLabel.L1)
    def test_construct_auto_id_false_negative_values(self):
        """
        target: test construct with negative values
        method: auto_id=False, primary field values is negative
        expected: verify num entities
        """
        self._connect()
        nb = 100
        df = cf.gen_default_dataframe_data(nb)
        new_values = pd.Series(data=[i for i in range(0, -nb, -1)])
        df[ct.default_int64_field_name] = new_values
        self.collection_wrap.construct_from_dataframe(cf.gen_unique_str(prefix), df,
                                                      primary_field=ct.default_int64_field_name, auto_id=False)
        assert self.collection_wrap.num_entities == nb

    @pytest.mark.tags(CaseLabel.L1)
    def test_construct_from_dataframe_dup_name(self):
        """
        target: test collection with dup name and insert dataframe
        method: create collection with dup name, none schema, dataframe
        expected: two collection object is correct
        """
        conn = self._connect()
        c_name = cf.gen_unique_str(prefix)
        collection_w = self.init_collection_wrap(name=c_name, primary_field=ct.default_int64_field_name,
                                                 check_task=CheckTasks.check_collection_property,
                                                 check_items={exp_name: c_name, exp_schema: default_schema})
        df = cf.gen_default_dataframe_data(ct.default_nb)
        self.collection_wrap.construct_from_dataframe(c_name, df, primary_field=ct.default_int64_field_name,
                                                      check_task=CheckTasks.check_collection_property,
                                                      check_items={exp_name: c_name, exp_schema: default_schema})
        conn.flush([collection_w.name])
        assert collection_w.num_entities == ct.default_nb
        assert collection_w.num_entities == self.collection_wrap.num_entities