


"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowerCAmelCase							:    int					   =	{
    '''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
    '''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase							:    Union[str, Any]					   =	['''BertTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase							:    List[Any]					   =	[
      '''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BertForMaskedLM''',
      '''BertForMultipleChoice''',
      '''BertForNextSentencePrediction''',
      '''BertForPreTraining''',
      '''BertForQuestionAnswering''',
      '''BertForSequenceClassification''',
      '''BertForTokenClassification''',
      '''BertLayer''',
      '''BertLMHeadModel''',
      '''BertModel''',
      '''BertPreTrainedModel''',
      '''load_tf_weights_in_bert''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase							:    List[str]					   =	[
      '''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFBertEmbeddings''',
      '''TFBertForMaskedLM''',
      '''TFBertForMultipleChoice''',
      '''TFBertForNextSentencePrediction''',
      '''TFBertForPreTraining''',
      '''TFBertForQuestionAnswering''',
      '''TFBertForSequenceClassification''',
      '''TFBertForTokenClassification''',
      '''TFBertLMHeadModel''',
      '''TFBertMainLayer''',
      '''TFBertModel''',
      '''TFBertPreTrainedModel''',
  ]
try:
  if not is_tensorflow_text_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase							:    Union[str, Any]					   =	['''TFBertTokenizer''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase							:    Dict					   =	[
      '''FlaxBertForCausalLM''',
      '''FlaxBertForMaskedLM''',
      '''FlaxBertForMultipleChoice''',
      '''FlaxBertForNextSentencePrediction''',
      '''FlaxBertForPreTraining''',
      '''FlaxBertForQuestionAnswering''',
      '''FlaxBertForSequenceClassification''',
      '''FlaxBertForTokenClassification''',
      '''FlaxBertModel''',
      '''FlaxBertPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
  from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_fast import BertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bert import (
        BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BertForMaskedLM,
        BertForMultipleChoice,
        BertForNextSentencePrediction,
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        BertForTokenClassification,
        BertLayer,
        BertLMHeadModel,
        BertModel,
        BertPreTrainedModel,
        load_tf_weights_in_bert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_bert import (
        TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBertEmbeddings,
        TFBertForMaskedLM,
        TFBertForMultipleChoice,
        TFBertForNextSentencePrediction,
        TFBertForPreTraining,
        TFBertForQuestionAnswering,
        TFBertForSequenceClassification,
        TFBertForTokenClassification,
        TFBertLMHeadModel,
        TFBertMainLayer,
        TFBertModel,
        TFBertPreTrainedModel,
    )

  try:
    if not is_tensorflow_text_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_tf import TFBertTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_bert import (
        FlaxBertForCausalLM,
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
        FlaxBertPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase							:    Dict					   =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






"""simple docstring"""


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A = None						,   _A = None						,   _A = True						,   _A = None						,   _A = False						,   _A = None						,   _A = True						,   _A = "arrow"						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(
									    split=_A						,   features=_A						,   cache_dir=_A						,   keep_in_memory=_A						,   streaming=_A						,   **_A						,   )
									UpperCamelCase				:		Optional[Any]				   =			load_from_cache_file
									UpperCamelCase				:		List[str]				   =			file_format
									UpperCamelCase				:		Optional[Any]				   =			Spark(
									    df=_A						,   features=_A						,   cache_dir=_A						,   working_dir=_A						,   **_A						,   )







			def   _a      (      self    ):






									'''simple docstring'''







									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split    )
									UpperCamelCase				:		Dict				   =			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_A						,   file_format=self._file_format						,   )
									return self.builder.as_dataset(split=self.split    )



