'''simple docstring'''




from ...configuration_utils import PretrainedConfig

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						"bert-generation"




      def __init__(       self						:				str						,					A						:				str=50358						,					A						:				int=1024						,					A						:				Optional[Any]=24						,					A						:				Optional[int]=16						,					A						:				str=4096						,					A						:				Tuple="gelu"						,					A						:				str=0.1						,					A						:				Dict=0.1						,					A						:				Tuple=512						,					A						:				Tuple=0.02						,					A						:				Optional[int]=1E-12						,					A						:				Union[str, Any]=0						,					A						:				Any=2						,					A						:				Dict=1						,					A						:				Tuple="absolute"						,					A						:				List[Any]=True						,					**A						:				List[Any]						,					):
             super().__init__(pad_token_id=A						,					bos_token_id=A						,					eos_token_id=A						,					**A       )

             _UpperCAmelCase     :  Optional[int]							      =    vocab_size
             _UpperCAmelCase     :  List[str]							      =    hidden_size
             _UpperCAmelCase     :  int							      =    num_hidden_layers
             _UpperCAmelCase     :  List[str]							      =    num_attention_heads
             _UpperCAmelCase     :  Optional[Any]							      =    hidden_act
             _UpperCAmelCase     :  Tuple							      =    intermediate_size
             _UpperCAmelCase     :  Optional[Any]							      =    hidden_dropout_prob
             _UpperCAmelCase     :  Any							      =    attention_probs_dropout_prob
             _UpperCAmelCase     :  List[str]							      =    max_position_embeddings
             _UpperCAmelCase     :  str							      =    initializer_range
             _UpperCAmelCase     :  List[Any]							      =    layer_norm_eps
             _UpperCAmelCase     :  Optional[int]							      =    position_embedding_type
             _UpperCAmelCase     :  int							      =    use_cache


import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   Union[str, Any]	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	)
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		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	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    TextDatasetReader(lowerCAmelCase   , cache_dir=lowerCAmelCase   , keep_in_memory=lowerCAmelCase	).read()
		_check_text_dataset(lowerCAmelCase   , lowerCAmelCase	)




@pytest.mark.parametrize(
    "features"   , [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]   , )
def       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Dict   , lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Any				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    {"text": "string"}
		SCREAMING_SNAKE_CASE_  :		Any				      =    features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    (
		    Features({feature: Value(lowerCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
		)
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    TextDatasetReader(lowerCAmelCase   , features=lowerCAmelCase   , cache_dir=lowerCAmelCase	).read()
		_check_text_dataset(lowerCAmelCase   , lowerCAmelCase	)




@pytest.mark.parametrize("split"   , [None, NamedSplit("train"	), "train", "test"]	)
def       _snake_case	(   lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    {"text": "string"}
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    TextDatasetReader(lowerCAmelCase   , cache_dir=lowerCAmelCase   , split=lowerCAmelCase	).read()
		_check_text_dataset(lowerCAmelCase   , lowerCAmelCase	)
		assert dataset.split == split if split else "train"




@pytest.mark.parametrize("path_type"   , [str, list]	)
def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   int   , lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







		if issubclass(lowerCAmelCase   , lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    text_path
		elif issubclass(lowerCAmelCase   , lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [text_path]
		SCREAMING_SNAKE_CASE_  :		int				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    {"text": "string"}
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    TextDatasetReader(lowerCAmelCase   , cache_dir=lowerCAmelCase	).read()
		_check_text_dataset(lowerCAmelCase   , lowerCAmelCase	)




def       _snake_case	(   lowerCAmelCase							:   Dict   , lowerCAmelCase							:   Dict   , lowerCAmelCase							:   List[str]=("train",)	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	)
		for split in splits:
				SCREAMING_SNAKE_CASE_  :		int				      =    dataset_dict[split]
				assert dataset.num_rows == 4
				assert dataset.num_columns == 1
				assert dataset.column_names == ["text"]
				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	(   lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   str   , lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    TextDatasetReader({"train": text_path}   , cache_dir=lowerCAmelCase   , keep_in_memory=lowerCAmelCase	).read()
		_check_text_datasetdict(lowerCAmelCase   , lowerCAmelCase	)




@pytest.mark.parametrize(
    "features"   , [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]   , )
def       _snake_case	(   lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   List[Any]   , lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    tmp_path / "cache"
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    {"text": "string"}
		SCREAMING_SNAKE_CASE_  :		Any				      =    features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE_  :		Dict				      =    (
		    Features({feature: Value(lowerCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
		)
		SCREAMING_SNAKE_CASE_  :		str				      =    TextDatasetReader({"train": text_path}   , features=lowerCAmelCase   , cache_dir=lowerCAmelCase	).read()
		_check_text_datasetdict(lowerCAmelCase   , lowerCAmelCase	)







@pytest.mark.parametrize("split"   , [None, NamedSplit("train"	), "train", "test"]	)
def       _snake_case	(   lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   Any   , lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		if split:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    {split: text_path}
		else:
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    "train"
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    {"train": text_path, "test": text_path}
		SCREAMING_SNAKE_CASE_  :		Any				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    {"text": "string"}
		SCREAMING_SNAKE_CASE_  :		str				      =    TextDatasetReader(lowerCAmelCase   , cache_dir=lowerCAmelCase	).read()
		_check_text_datasetdict(lowerCAmelCase   , lowerCAmelCase   , splits=list(path.keys()	)	)
		assert all(dataset[split].split == split for split in path.keys()	)
