code: 
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast
  from ...utils import logging
  
  
  UpperCAmelCase      :						Union[str, Any]    =		logging.get_logger(__name__)
  
  UpperCAmelCase      :						List[Any]    =		{
      "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
      # See all PLBART models at https://huggingface.co/models?filter=plbart
  }
  
  
  
  
  
  
  
  class 					__lowercase					(       a_     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				Optional[Any] 					=		"plbart"
  		UpperCamelCase    :				List[Any] 					=		["past_key_values"]
  		UpperCamelCase    :				List[str] 					=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
  
  
  
  
  
  
  
  		def __init__(   self		,			A=5_00_05		,			A=10_24		,			A=6		,			A=30_72		,			A=12		,			A=6		,			A=30_72		,			A=12		,			A=0.0		,			A=0.0		,			A=True		,			A=True		,			A="gelu"		,			A=7_68		,			A=0.1		,			A=0.1		,			A=0.0		,			A=0.02		,			A=0.0		,			A=True		,			A=1		,			A=0		,			A=2		,			A=2		,			**A		,			)     ->   Union[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     vocab_size
  					lowerCamelCase								=     max_position_embeddings
  					lowerCamelCase								=     d_model
  					lowerCamelCase								=     encoder_ffn_dim
  					lowerCamelCase								=     encoder_layers
  					lowerCamelCase								=     encoder_attention_heads
  					lowerCamelCase								=     decoder_ffn_dim
  					lowerCamelCase								=     decoder_layers
  					lowerCamelCase								=     decoder_attention_heads
  					lowerCamelCase								=     dropout
  					lowerCamelCase								=     attention_dropout
  					lowerCamelCase								=     activation_dropout
  					lowerCamelCase								=     activation_function
  					lowerCamelCase								=     init_std
  					lowerCamelCase								=     encoder_layerdrop
  					lowerCamelCase								=     decoder_layerdrop
  					lowerCamelCase								=     classifier_dropout
  					lowerCamelCase								=     use_cache
  					lowerCamelCase								=     encoder_layers
  					lowerCamelCase								=     scale_embedding  # scale factor will be sqrt(d_model) if True
  					super().__init__(
  					    pad_token_id=A		,			bos_token_id=A		,			eos_token_id=A		,			is_encoder_decoder=A		,			forced_eos_token_id=A		,			**A		,			)
  
  
  
  
  
  
  
  class 					__lowercase					(       a_     ):
  
  
  
  		"""simple docstring"""
  
  		@property
  		def 				__A      (   self  )     ->   Mapping[str, Mapping[int, str]]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return OrderedDict(
  					    [
  					        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
  					        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
  					    ]  )
  
  
  
  
  
  
  
  		@property
  		def 				__A      (   self  )     ->   Mapping[str, Mapping[int, str]]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if self.use_past:
  								return OrderedDict(
  								    [
  								        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  								        ("""past_keys""", {0: """batch""", 2: """sequence"""}),
  								        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  								    ]  )
  					else:
  								return OrderedDict(
  								    [
  								        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  								        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  								    ]  )
  
  
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__)
  
  