



from typing import TYPE_CHECKING

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


__A       =						{
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       =						['''SqueezeBertTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       =						[
     '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''SqueezeBertForMaskedLM''',
     '''SqueezeBertForMultipleChoice''',
     '''SqueezeBertForQuestionAnswering''',
     '''SqueezeBertForSequenceClassification''',
     '''SqueezeBertForTokenClassification''',
     '''SqueezeBertModel''',
     '''SqueezeBertModule''',
     '''SqueezeBertPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_squeezebert import (
     SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SqueezeBertConfig,
     SqueezeBertOnnxConfig,
 )
 from .tokenization_squeezebert import SqueezeBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_squeezebert import (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
      SqueezeBertModule,
      SqueezeBertPreTrainedModel,
  )

else:
 import sys

 __A       =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









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						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						int						,  __UpperCAmelCase     :						pyspark.sql.DataFrame						,  __UpperCAmelCase     :						Optional[NamedSplit] = None						,  __UpperCAmelCase     :						Optional[Features] = None						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						str = "arrow"						,  **__UpperCAmelCase     :						str						,  )      ->			Dict:
							super().__init__(
							    split=__UpperCAmelCase						,  features=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  keep_in_memory=__UpperCAmelCase						,  streaming=__UpperCAmelCase						,  **__UpperCAmelCase						,  )
							UpperCAmelCase_=       load_from_cache_file
							UpperCAmelCase_=       file_format
							UpperCAmelCase_=       Spark(
							    df=__UpperCAmelCase						,  features=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  working_dir=__UpperCAmelCase						,  **__UpperCAmelCase						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Dict:
							if self.streaming:
													return self.builder.as_streaming_dataset(split=self.split					)
							UpperCAmelCase_=       None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
							self.builder.download_and_prepare(
							    download_mode=__UpperCAmelCase						,  file_format=self._file_format						,  )
							return self.builder.as_dataset(split=self.split					)




