import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int ,	lowerCAmelCase_: List[str]     ):
      assert isinstance(lowerCAmelCase_ ,	lowerCAmelCase_     )
      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






@pytest.mark.parametrize("keep_in_memory" ,	[False, True]     )
def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: Optional[Any] ,	lowerCAmelCase_: Dict     ):
      snake_case_     :			int       =       tmp_path / "cache"
      snake_case_     :			str       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            snake_case_     :			Union[str, Any]       =       CsvDatasetReader(lowerCAmelCase_ ,	cache_dir=lowerCAmelCase_ ,	keep_in_memory=lowerCAmelCase_     ).read()
      _check_csv_dataset(lowerCAmelCase_ ,	lowerCAmelCase_     )






@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 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: List[str] ,	lowerCAmelCase_: Any     ):
      snake_case_     :			List[str]       =       tmp_path / "cache"
      # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
      snake_case_     :			Optional[Any]       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      snake_case_     :			List[str]       =       features.copy() if features else default_expected_features
      snake_case_     :			List[str]       =       (
          Features({feature: Value(lowerCAmelCase_     ) for feature, dtype in features.items()}     ) if features is not None else None
      )
      snake_case_     :			int       =       CsvDatasetReader(lowerCAmelCase_ ,	features=lowerCAmelCase_ ,	cache_dir=lowerCAmelCase_     ).read()
      _check_csv_dataset(lowerCAmelCase_ ,	lowerCAmelCase_     )






@pytest.mark.parametrize("split" ,	[None, NamedSplit("train"     ), "train", "test"]     )
def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Tuple ,	lowerCAmelCase_: List[str] ,	lowerCAmelCase_: Optional[Any]     ):
      snake_case_     :			int       =       tmp_path / "cache"
      snake_case_     :			Tuple       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      snake_case_     :			Dict       =       CsvDatasetReader(lowerCAmelCase_ ,	cache_dir=lowerCAmelCase_ ,	split=lowerCAmelCase_     ).read()
      _check_csv_dataset(lowerCAmelCase_ ,	lowerCAmelCase_     )
      assert dataset.split == split if split else "train"






@pytest.mark.parametrize("path_type" ,	[str, list]     )
def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Optional[Any] ,	lowerCAmelCase_: Dict ,	lowerCAmelCase_: List[Any]     ):
      if issubclass(lowerCAmelCase_ ,	lowerCAmelCase_     ):
            snake_case_     :			Union[str, Any]       =       csv_path
      elif issubclass(lowerCAmelCase_ ,	lowerCAmelCase_     ):
            snake_case_     :			Optional[int]       =       [csv_path]
      snake_case_     :			List[str]       =       tmp_path / "cache"
      snake_case_     :			Optional[int]       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      snake_case_     :			str       =       CsvDatasetReader(lowerCAmelCase_ ,	cache_dir=lowerCAmelCase_     ).read()
      _check_csv_dataset(lowerCAmelCase_ ,	lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: List[str] ,	lowerCAmelCase_: int=("train",)     ):
      assert isinstance(lowerCAmelCase_ ,	lowerCAmelCase_     )
      for split in splits:
            snake_case_     :			Optional[int]       =       dataset_dict[split]
            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






@pytest.mark.parametrize("keep_in_memory" ,	[False, True]     )
def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int ,	lowerCAmelCase_: Tuple ,	lowerCAmelCase_: int     ):
      snake_case_     :			int       =       tmp_path / "cache"
      snake_case_     :			Union[str, Any]       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            snake_case_     :			List[Any]       =       CsvDatasetReader({"train": csv_path} ,	cache_dir=lowerCAmelCase_ ,	keep_in_memory=lowerCAmelCase_     ).read()
      _check_csv_datasetdict(lowerCAmelCase_ ,	lowerCAmelCase_     )






@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 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Dict ,	lowerCAmelCase_: Tuple ,	lowerCAmelCase_: str     ):
      snake_case_     :			List[str]       =       tmp_path / "cache"
      # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
      snake_case_     :			Optional[Any]       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      snake_case_     :			str       =       features.copy() if features else default_expected_features
      snake_case_     :			List[Any]       =       (
          Features({feature: Value(lowerCAmelCase_     ) for feature, dtype in features.items()}     ) if features is not None else None
      )
      snake_case_     :			int       =       CsvDatasetReader({"train": csv_path} ,	features=lowerCAmelCase_ ,	cache_dir=lowerCAmelCase_     ).read()
      _check_csv_datasetdict(lowerCAmelCase_ ,	lowerCAmelCase_     )






@pytest.mark.parametrize("split" ,	[None, NamedSplit("train"     ), "train", "test"]     )
def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Optional[int] ,	lowerCAmelCase_: Optional[int] ,	lowerCAmelCase_: Union[str, Any]     ):
      if split:
            snake_case_     :			Optional[Any]       =       {split: csv_path}
      else:
            snake_case_     :			Tuple       =       {"train": csv_path, "test": csv_path}
      snake_case_     :			Optional[Any]       =       tmp_path / "cache"
      snake_case_     :			List[str]       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      snake_case_     :			Any       =       CsvDatasetReader(lowerCAmelCase_ ,	cache_dir=lowerCAmelCase_     ).read()
      _check_csv_datasetdict(lowerCAmelCase_ ,	lowerCAmelCase_ ,	splits=list(path.keys()     )     )
      assert all(dataset[split].split == split for split in path.keys()     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Union[str, Any]     ):
      with open(lowerCAmelCase_ ,	encoding="utf-8"     ) as csvfile:
            yield from csv.reader(lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Dict ,	lowerCAmelCase_: Optional[Any]     ):
      snake_case_     :			Tuple       =       tmp_path / "cache"
      snake_case_     :			Any       =       os.path.join(lowerCAmelCase_ ,	"tmp.csv"     )
      snake_case_     :			Any       =       CsvDatasetReader({"train": csv_path} ,	cache_dir=lowerCAmelCase_     ).read()
      CsvDatasetWriter(dataset["train"] ,	lowerCAmelCase_ ,	num_proc=1     ).write()

      snake_case_     :			Optional[int]       =       iter_csv_file(lowerCAmelCase_     )
      snake_case_     :			Union[str, Any]       =       iter_csv_file(lowerCAmelCase_     )

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






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Dict ,	lowerCAmelCase_: Dict     ):
      snake_case_     :			str       =       tmp_path / "cache"
      snake_case_     :			List[str]       =       os.path.join(lowerCAmelCase_ ,	"tmp.csv"     )
      snake_case_     :			Optional[int]       =       CsvDatasetReader({"train": csv_path} ,	cache_dir=lowerCAmelCase_     ).read()
      CsvDatasetWriter(dataset["train"] ,	lowerCAmelCase_ ,	num_proc=2     ).write()

      snake_case_     :			int       =       iter_csv_file(lowerCAmelCase_     )
      snake_case_     :			Union[str, Any]       =       iter_csv_file(lowerCAmelCase_     )

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






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Any ,	lowerCAmelCase_: Optional[int]     ):
      snake_case_     :			Dict       =       tmp_path / "cache"
      snake_case_     :			int       =       os.path.join(lowerCAmelCase_ ,	"tmp.csv"     )
      snake_case_     :			Tuple       =       CsvDatasetReader({"train": csv_path} ,	cache_dir=lowerCAmelCase_     ).read()
      with pytest.raises(lowerCAmelCase_     ):
            CsvDatasetWriter(dataset["train"] ,	lowerCAmelCase_ ,	num_proc=0     )




from typing import TYPE_CHECKING

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


UpperCAmelCase							=       {
    "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase							=       [
         "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "NystromformerForMaskedLM",
         "NystromformerForMultipleChoice",
         "NystromformerForQuestionAnswering",
         "NystromformerForSequenceClassification",
         "NystromformerForTokenClassification",
         "NystromformerLayer",
         "NystromformerModel",
         "NystromformerPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nystromformer import (
              NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              NystromformerForMaskedLM,
              NystromformerForMultipleChoice,
              NystromformerForQuestionAnswering,
              NystromformerForSequenceClassification,
              NystromformerForTokenClassification,
              NystromformerLayer,
              NystromformerModel,
              NystromformerPreTrainedModel,
          )


else:
     import sys

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



