
"""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        lowerCAmelCase_       (		snake_case_		:      str							,   snake_case_		:      Union[str, Any]      )       ->Dict:
			assert isinstance(snake_case_							,   snake_case_      )
			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        lowerCAmelCase_       (		snake_case_		:      str							,   snake_case_		:      Optional[int]							,   snake_case_		:      Union[str, Any]      )       ->Optional[int]:
			lowerCamelCase__     :					Any		=tmp_path / 'cache'
			lowerCamelCase__     :					Dict		={'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						lowerCamelCase__     :					Dict		=CsvDatasetReader(snake_case_							,   cache_dir=snake_case_							,   keep_in_memory=snake_case_      ).read()
			_check_csv_dataset(snake_case_							,   snake_case_      )






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






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






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






def        lowerCAmelCase_       (		snake_case_		:      List[Any]							,   snake_case_		:      Any							,   snake_case_		:      int=("train",)      )       ->str:
			assert isinstance(snake_case_							,   snake_case_      )
			for split in splits:
						lowerCamelCase__     :					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        lowerCAmelCase_       (		snake_case_		:      int							,   snake_case_		:      Optional[Any]							,   snake_case_		:      str      )       ->Any:
			lowerCamelCase__     :					Tuple		=tmp_path / 'cache'
			lowerCamelCase__     :					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():
						lowerCamelCase__     :					Optional[Any]		=CsvDatasetReader({'train': csv_path}							,   cache_dir=snake_case_							,   keep_in_memory=snake_case_      ).read()
			_check_csv_datasetdict(snake_case_							,   snake_case_      )






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






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






def        lowerCAmelCase_       (		snake_case_		:      List[str]      )       ->Dict:
			with open(snake_case_							,   encoding='utf-8'      ) as csvfile:
						yield from csv.reader(snake_case_      )






def        lowerCAmelCase_       (		snake_case_		:      str							,   snake_case_		:      Union[str, Any]      )       ->Optional[int]:
			lowerCamelCase__     :					List[str]		=tmp_path / 'cache'
			lowerCamelCase__     :					List[str]		=os.path.join(snake_case_							,   'tmp.csv'      )
			lowerCamelCase__     :					Optional[int]		=CsvDatasetReader({'train': csv_path}							,   cache_dir=snake_case_      ).read()
			CsvDatasetWriter(dataset['train']							,   snake_case_							,   num_proc=1      ).write()

			lowerCamelCase__     :					Optional[Any]		=iter_csv_file(snake_case_      )
			lowerCamelCase__     :					List[Any]		=iter_csv_file(snake_case_      )

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






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

			lowerCamelCase__     :					int		=iter_csv_file(snake_case_      )
			lowerCamelCase__     :					int		=iter_csv_file(snake_case_      )

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






def        lowerCAmelCase_       (		snake_case_		:      List[str]							,   snake_case_		:      Any      )       ->Optional[Any]:
			lowerCamelCase__     :					Tuple		=tmp_path / 'cache'
			lowerCamelCase__     :					str		=os.path.join(snake_case_							,   'tmp.csv'      )
			lowerCamelCase__     :					List[Any]		=CsvDatasetReader({'train': csv_path}							,   cache_dir=snake_case_      ).read()
			with pytest.raises(snake_case_      ):
						CsvDatasetWriter(dataset['train']							,   snake_case_							,   num_proc=0      )

"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase								=						{
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						["""DebertaV2TokenizerFast"""]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFDebertaV2ForMaskedLM""",
							    """TFDebertaV2ForQuestionAnswering""",
							    """TFDebertaV2ForSequenceClassification""",
							    """TFDebertaV2ForTokenClassification""",
							    """TFDebertaV2Model""",
							    """TFDebertaV2PreTrainedModel""",
							]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DebertaV2ForMaskedLM""",
							    """DebertaV2ForMultipleChoice""",
							    """DebertaV2ForQuestionAnswering""",
							    """DebertaV2ForSequenceClassification""",
							    """DebertaV2ForTokenClassification""",
							    """DebertaV2Model""",
							    """DebertaV2PreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_deberta_va import (
							    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DebertaVaConfig,
							    DebertaVaOnnxConfig,
							)
							from .tokenization_deberta_va import DebertaVaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta_va import (
														    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaVaForMaskedLM,
														    TFDebertaVaForQuestionAnswering,
														    TFDebertaVaForSequenceClassification,
														    TFDebertaVaForTokenClassification,
														    TFDebertaVaModel,
														    TFDebertaVaPreTrainedModel,
														)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta_va import (
														    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaVaForMaskedLM,
														    DebertaVaForMultipleChoice,
														    DebertaVaForQuestionAnswering,
														    DebertaVaForSequenceClassification,
														    DebertaVaForTokenClassification,
														    DebertaVaModel,
														    DebertaVaPreTrainedModel,
														)

else:
							import sys

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