code: 
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  UpperCAmelCase      :						Optional[int]    =		logging.get_logger(__name__)
  
  UpperCAmelCase      :						Dict    =		{
      "google/reformer-crime-and-punishment": (
          "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"
      ),
      "google/reformer-enwik8": "https://huggingface.co/google/reformer-enwik8/resolve/main/config.json",
  }
  
  
  
  
  
  
  
  class 					__lowercase					(       a_     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				Union[str, Any] 					=		"reformer"
  		UpperCamelCase    :				List[Any] 					=		["past_buckets_states"]
  		UpperCamelCase    :				Union[str, Any] 					=		{}
  
  
  
  
  
  
  
  		def __init__(   self		,			A=64		,			A=["local", "lsh", "local", "lsh", "local", "lsh"]		,			A=1.0		,			A=True		,			A=[64, 64]		,			A=[64, 1_92]		,			A=0		,			A=2		,			A=5_12		,			A=None		,			A="relu"		,			A=0.05		,			A=2_56		,			A=0.02		,			A=False		,			A=1e-1_2		,			A=1		,			A=0		,			A=0.05		,			A=64		,			A=64		,			A=0.0		,			A=1		,			A=0		,			A=40_96		,			A=12		,			A=None		,			A=1		,			A=0		,			A=3_20		,			A=False		,			A=True		,			A=None		,			**A		,			)     ->   Union[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     hash_seed
  					lowerCamelCase								=     vocab_size
  					lowerCamelCase								=     attention_head_size
  					lowerCamelCase								=     hidden_size
  					lowerCamelCase								=     num_attention_heads
  					lowerCamelCase								=     num_hashes
  					lowerCamelCase								=     len(A  )
  					lowerCamelCase								=     tuple(A  ) if isinstance(A		,			A  ) else num_buckets
  					lowerCamelCase								=     lsh_attn_chunk_length
  					lowerCamelCase								=     local_attn_chunk_length
  					lowerCamelCase								=     lsh_num_chunks_after
  					lowerCamelCase								=     lsh_num_chunks_before
  					lowerCamelCase								=     local_num_chunks_after
  					lowerCamelCase								=     local_num_chunks_before
  					lowerCamelCase								=     hidden_act
  					lowerCamelCase								=     feed_forward_size
  					lowerCamelCase								=     hidden_dropout_prob
  					lowerCamelCase								=     lsh_attention_probs_dropout_prob
  					lowerCamelCase								=     local_attention_probs_dropout_prob
  					lowerCamelCase								=     max_position_embeddings
  					lowerCamelCase								=     initializer_range
  					lowerCamelCase								=     layer_norm_eps
  					lowerCamelCase								=     axial_pos_embds
  					lowerCamelCase								=     tuple(A  )
  					lowerCamelCase								=     tuple(A  )
  					lowerCamelCase								=     axial_norm_std
  					lowerCamelCase								=     chunk_size_lm_head
  					lowerCamelCase								=     attn_layers
  					lowerCamelCase								=     use_cache
  					lowerCamelCase								=     classifier_dropout
  					super().__init__(
  					    pad_token_id=A		,			eos_token_id=A		,			is_decoder=A		,			tie_word_embeddings=A		,			**A		,			)
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  UpperCAmelCase      :						List[str]    =		{
      "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						str    =		["BigBirdTokenizer"]
  
  try:
  		if not is_tokenizers_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						str    =		["BigBirdTokenizerFast"]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						Dict    =		[
  		    "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "BigBirdForCausalLM",
  		    "BigBirdForMaskedLM",
  		    "BigBirdForMultipleChoice",
  		    "BigBirdForPreTraining",
  		    "BigBirdForQuestionAnswering",
  		    "BigBirdForSequenceClassification",
  		    "BigBirdForTokenClassification",
  		    "BigBirdLayer",
  		    "BigBirdModel",
  		    "BigBirdPreTrainedModel",
  		    "load_tf_weights_in_big_bird",
  		]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						Dict    =		[
  		    "FlaxBigBirdForCausalLM",
  		    "FlaxBigBirdForMaskedLM",
  		    "FlaxBigBirdForMultipleChoice",
  		    "FlaxBigBirdForPreTraining",
  		    "FlaxBigBirdForQuestionAnswering",
  		    "FlaxBigBirdForSequenceClassification",
  		    "FlaxBigBirdForTokenClassification",
  		    "FlaxBigBirdModel",
  		    "FlaxBigBirdPreTrainedModel",
  		]
  
  if TYPE_CHECKING:
  		from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig
  
  		try:
  				if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_big_bird import BigBirdTokenizer
  
  		try:
  				if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_big_bird_fast import BigBirdTokenizerFast
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_big_bird import (
  				    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    BigBirdForCausalLM,
  				    BigBirdForMaskedLM,
  				    BigBirdForMultipleChoice,
  				    BigBirdForPreTraining,
  				    BigBirdForQuestionAnswering,
  				    BigBirdForSequenceClassification,
  				    BigBirdForTokenClassification,
  				    BigBirdLayer,
  				    BigBirdModel,
  				    BigBirdPreTrainedModel,
  				    load_tf_weights_in_big_bird,
  				)
  
  		try:
  				if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_flax_big_bird import (
  				    FlaxBigBirdForCausalLM,
  				    FlaxBigBirdForMaskedLM,
  				    FlaxBigBirdForMultipleChoice,
  				    FlaxBigBirdForPreTraining,
  				    FlaxBigBirdForQuestionAnswering,
  				    FlaxBigBirdForSequenceClassification,
  				    FlaxBigBirdForTokenClassification,
  				    FlaxBigBirdModel,
  				    FlaxBigBirdPreTrainedModel,
  				)
  
  else:
  		import sys
  
  		UpperCAmelCase      :						Dict    =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  