
'''simple docstring'''





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 	snake_case_							(				_a  :				Any			,       _a  :				str      ):
      assert isinstance(_a			,       _a      )
      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 	snake_case_							(				_a  :				List[str]			,       _a  :				List[Any]			,       _a  :				str      ):
      snake_case_    							=				tmp_path / "cache"
      snake_case_    							=				{"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_    							=				CsvDatasetReader(_a			,       cache_dir=_a			,       keep_in_memory=_a      ).read()
      _check_csv_dataset(_a			,       _a      )
@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 	snake_case_							(				_a  :				Any			,       _a  :				int			,       _a  :				Optional[int]      ):
      snake_case_    							=				tmp_path / "cache"
      # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
      snake_case_    							=				{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      snake_case_    							=				features.copy() if features else default_expected_features
      snake_case_    							=				(
          Features({feature: Value(_a      ) for feature, dtype in features.items()}      ) if features is not None else None
      )
      snake_case_    							=				CsvDatasetReader(_a			,       features=_a			,       cache_dir=_a      ).read()
      _check_csv_dataset(_a			,       _a      )
@pytest.mark.parametrize("split"			,       [None, NamedSplit("train"      ), "train", "test"]      )
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Dict			,       _a  :				Tuple      ):
      snake_case_    							=				tmp_path / "cache"
      snake_case_    							=				{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      snake_case_    							=				CsvDatasetReader(_a			,       cache_dir=_a			,       split=_a      ).read()
      _check_csv_dataset(_a			,       _a      )
      assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type"			,       [str, list]      )
def 	snake_case_							(				_a  :				int			,       _a  :				Optional[int]			,       _a  :				Dict      ):
      if issubclass(_a			,       _a      ):
            snake_case_    							=				csv_path
      elif issubclass(_a			,       _a      ):
            snake_case_    							=				[csv_path]
      snake_case_    							=				tmp_path / "cache"
      snake_case_    							=				{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      snake_case_    							=				CsvDatasetReader(_a			,       cache_dir=_a      ).read()
      _check_csv_dataset(_a			,       _a      )
def 	snake_case_							(				_a  :				Tuple			,       _a  :				str			,       _a  :				List[str]=("train",)      ):
      assert isinstance(_a			,       _a      )
      for split in splits:
            snake_case_    							=				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 	snake_case_							(				_a  :				Any			,       _a  :				int			,       _a  :				Union[str, Any]      ):
      snake_case_    							=				tmp_path / "cache"
      snake_case_    							=				{"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_    							=				CsvDatasetReader({"train": csv_path}			,       cache_dir=_a			,       keep_in_memory=_a      ).read()
      _check_csv_datasetdict(_a			,       _a      )
@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 	snake_case_							(				_a  :				Optional[int]			,       _a  :				Optional[int]			,       _a  :				List[str]      ):
      snake_case_    							=				tmp_path / "cache"
      # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
      snake_case_    							=				{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      snake_case_    							=				features.copy() if features else default_expected_features
      snake_case_    							=				(
          Features({feature: Value(_a      ) for feature, dtype in features.items()}      ) if features is not None else None
      )
      snake_case_    							=				CsvDatasetReader({"train": csv_path}			,       features=_a			,       cache_dir=_a      ).read()
      _check_csv_datasetdict(_a			,       _a      )
@pytest.mark.parametrize("split"			,       [None, NamedSplit("train"      ), "train", "test"]      )
def 	snake_case_							(				_a  :				str			,       _a  :				List[str]			,       _a  :				List[Any]      ):
      if split:
            snake_case_    							=				{split: csv_path}
      else:
            snake_case_    							=				{"train": csv_path, "test": csv_path}
      snake_case_    							=				tmp_path / "cache"
      snake_case_    							=				{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      snake_case_    							=				CsvDatasetReader(_a			,       cache_dir=_a      ).read()
      _check_csv_datasetdict(_a			,       _a			,       splits=list(path.keys()      )      )
      assert all(dataset[split].split == split for split in path.keys()      )
def 	snake_case_							(				_a  :				List[Any]      ):
      with open(_a			,       encoding="utf-8"      ) as csvfile:
            yield from csv.reader(_a      )
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				int      ):
      snake_case_    							=				tmp_path / "cache"
      snake_case_    							=				os.path.join(_a			,       "tmp.csv"      )
      snake_case_    							=				CsvDatasetReader({"train": csv_path}			,       cache_dir=_a      ).read()
      CsvDatasetWriter(dataset["train"]			,       _a			,       num_proc=1      ).write()

      snake_case_    							=				iter_csv_file(_a      )
      snake_case_    							=				iter_csv_file(_a      )

      for rowa, rowa in zip(_a			,       _a      ):
            assert rowa == rowa
def 	snake_case_							(				_a  :				str			,       _a  :				Dict      ):
      snake_case_    							=				tmp_path / "cache"
      snake_case_    							=				os.path.join(_a			,       "tmp.csv"      )
      snake_case_    							=				CsvDatasetReader({"train": csv_path}			,       cache_dir=_a      ).read()
      CsvDatasetWriter(dataset["train"]			,       _a			,       num_proc=2      ).write()

      snake_case_    							=				iter_csv_file(_a      )
      snake_case_    							=				iter_csv_file(_a      )

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




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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__   :				Any						=	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				List[Any]						=	['CamembertTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Dict						=	['CamembertTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CamembertForCausalLM',
           'CamembertForMaskedLM',
           'CamembertForMultipleChoice',
           'CamembertForQuestionAnswering',
           'CamembertForSequenceClassification',
           'CamembertForTokenClassification',
           'CamembertModel',
           'CamembertPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFCamembertForCausalLM',
           'TFCamembertForMaskedLM',
           'TFCamembertForMultipleChoice',
           'TFCamembertForQuestionAnswering',
           'TFCamembertForSequenceClassification',
           'TFCamembertForTokenClassification',
           'TFCamembertModel',
           'TFCamembertPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert import CamembertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert_fast import CamembertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_camembert import (
                  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CamembertForCausalLM,
                  CamembertForMaskedLM,
                  CamembertForMultipleChoice,
                  CamembertForQuestionAnswering,
                  CamembertForSequenceClassification,
                  CamembertForTokenClassification,
                  CamembertModel,
                  CamembertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_camembert import (
                  TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCamembertForCausalLM,
                  TFCamembertForMaskedLM,
                  TFCamembertForMultipleChoice,
                  TFCamembertForQuestionAnswering,
                  TFCamembertForSequenceClassification,
                  TFCamembertForTokenClassification,
                  TFCamembertModel,
                  TFCamembertPreTrainedModel,
              )

else:
       import sys

       snake_case__   :				Any						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





