
"""simple docstring"""


import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							str:







       """simple docstring"""
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				)
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype



@require_sqlalchemy
@pytest.mark.parametrize("keep_in_memory"		,      [False, True]				)
def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							Dict:







       """simple docstring"""
       UpperCamelCase					 =		tmp_path / "cache"
       UpperCamelCase					 =		{"col_1": "string", "col_2": "int64", "col_3": "float64"}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              UpperCamelCase					 =		SqlDatasetReader(
                  "dataset"		,      "sqlite:///" + sqlite_path		,      cache_dir=UpperCAmelCase_		,      keep_in_memory=UpperCAmelCase_				).read()
       _check_sql_dataset(UpperCAmelCase_		,      UpperCAmelCase_				)



@require_sqlalchemy
@pytest.mark.parametrize(
    "features"		,      [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]		,      )
def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							int:







       """simple docstring"""
       UpperCamelCase					 =		tmp_path / "cache"
       UpperCamelCase					 =		{"col_1": "string", "col_2": "int64", "col_3": "float64"}
       UpperCamelCase					 =		features.copy() if features else default_expected_features
       UpperCamelCase					 =		(
           Features({feature: Value(UpperCAmelCase_				) for feature, dtype in features.items()}				) if features is not None else None
       )
       UpperCamelCase					 =		SqlDatasetReader("dataset"		,      "sqlite:///" + sqlite_path		,      features=UpperCAmelCase_		,      cache_dir=UpperCAmelCase_				).read()
       _check_sql_dataset(UpperCAmelCase_		,      UpperCAmelCase_				)



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							List[Any]:







       """simple docstring"""
       with contextlib.closing(sqlitea.connect(UpperCAmelCase_				)				) as con:
              UpperCamelCase					 =		con.cursor()
              cur.execute("SELECT * FROM dataset"				)
              for row in cur:
                     yield row



@require_sqlalchemy
def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							Optional[int]:







       """simple docstring"""
       UpperCamelCase					 =		tmp_path / "cache"
       UpperCamelCase					 =		os.path.join(UpperCAmelCase_		,      "tmp.sql"				)
       UpperCamelCase					 =		SqlDatasetReader("dataset"		,      "sqlite:///" + sqlite_path		,      cache_dir=UpperCAmelCase_				).read()
       SqlDatasetWriter(UpperCAmelCase_		,      "dataset"		,      "sqlite:///" + output_sqlite_path		,      num_proc=1				).write()

       UpperCamelCase					 =		iter_sql_file(UpperCAmelCase_				)
       UpperCamelCase					 =		iter_sql_file(UpperCAmelCase_				)

       for rowa, rowa in zip(UpperCAmelCase_		,      UpperCAmelCase_				):
              assert rowa == rowa



@require_sqlalchemy
def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							Tuple:







       """simple docstring"""
       UpperCamelCase					 =		tmp_path / "cache"
       UpperCamelCase					 =		os.path.join(UpperCAmelCase_		,      "tmp.sql"				)
       UpperCamelCase					 =		SqlDatasetReader("dataset"		,      "sqlite:///" + sqlite_path		,      cache_dir=UpperCAmelCase_				).read()
       SqlDatasetWriter(UpperCAmelCase_		,      "dataset"		,      "sqlite:///" + output_sqlite_path		,      num_proc=2				).write()

       UpperCamelCase					 =		iter_sql_file(UpperCAmelCase_				)
       UpperCamelCase					 =		iter_sql_file(UpperCAmelCase_				)

       for rowa, rowa in zip(UpperCAmelCase_		,      UpperCAmelCase_				):
              assert rowa == rowa



@require_sqlalchemy
def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							List[Any]:







       """simple docstring"""
       UpperCamelCase					 =		tmp_path / "cache"
       UpperCamelCase					 =		os.path.join(UpperCAmelCase_		,      "tmp.sql"				)
       UpperCamelCase					 =		SqlDatasetReader("dataset"		,      "sqlite:///" + sqlite_path		,      cache_dir=UpperCAmelCase_				).read()
       with pytest.raises(UpperCAmelCase_				):
              SqlDatasetWriter(UpperCAmelCase_		,      "dataset"		,      "sqlite:///" + output_sqlite_path		,      num_proc=0				).write()







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE      =     {
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GraphormerForGraphClassification""",
          """GraphormerModel""",
          """GraphormerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_graphormer import (
                GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                GraphormerForGraphClassification,
                GraphormerModel,
                GraphormerPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE      =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





