code: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  _UpperCAmelCase   :      int 						=  {"""configuration_levit""": ["""LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LevitConfig""", """LevitOnnxConfig"""]}
  
  try:
  	if not is_vision_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	_UpperCAmelCase   :      Dict 						=  ["""LevitFeatureExtractor"""]
  	_UpperCAmelCase   :      Tuple 						=  ["""LevitImageProcessor"""]
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	_UpperCAmelCase   :      Union[str, Any] 						=  [
  	    """LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
  	    """LevitForImageClassification""",
  	    """LevitForImageClassificationWithTeacher""",
  	    """LevitModel""",
  	    """LevitPreTrainedModel""",
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig
  
  	try:
  		if not is_vision_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .feature_extraction_levit import LevitFeatureExtractor
  		from .image_processing_levit import LevitImageProcessor
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_levit import (
  		    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    LevitForImageClassification,
  		    LevitForImageClassificationWithTeacher,
  		    LevitModel,
  		    LevitPreTrainedModel,
  		)
  else:
  	import sys
  
  	_UpperCAmelCase   :      List[str] 						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  _UpperCAmelCase   :      Optional[int] 						=  logging.get_logger(__name__)
  
  _UpperCAmelCase   :      Any 						=  {
      """google/bigbird-pegasus-large-arxiv""": (
          """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
      ),
      """google/bigbird-pegasus-large-pubmed""": (
          """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
      ),
      """google/bigbird-pegasus-large-bigpatent""": (
          """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
      ),
      # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
  }
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		A__  :   List[str]          =    'bigbird_pegasus'
  		A__  :   Union[str, Any]          =    ['past_key_values']
  		A__  :   Optional[int]          =    {
  		    'num_attention_heads': 'encoder_attention_heads',
  		    'hidden_size': 'd_model',
  		    'attention_probs_dropout_prob': 'attention_dropout',
  		}
  		def __init__(    self  ,					_snake_case=96103  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=16  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=0.0  ,					_snake_case=0.0  ,					_snake_case=True  ,					_snake_case=True  ,					_snake_case="gelu_new"  ,					_snake_case=1024  ,					_snake_case=0.1  ,					_snake_case=0.0  ,					_snake_case=0.0  ,					_snake_case=0.02  ,					_snake_case=2  ,					_snake_case=0.0  ,					_snake_case=True  ,					_snake_case=0  ,					_snake_case=2  ,					_snake_case=1  ,					_snake_case="block_sparse"  ,					_snake_case=64  ,					_snake_case=3  ,					_snake_case=False  ,					**_snake_case  ,					)				->    Optional[Any]:
  				_UpperCamelCase       : str    =      vocab_size
  				_UpperCamelCase       : Tuple    =      max_position_embeddings
  				_UpperCamelCase       : int    =      d_model
  				_UpperCamelCase       : List[str]    =      encoder_ffn_dim
  				_UpperCamelCase       : str    =      encoder_layers
  				_UpperCamelCase       : Dict    =      encoder_attention_heads
  				_UpperCamelCase       : str    =      decoder_ffn_dim
  				_UpperCamelCase       : List[str]    =      decoder_layers
  				_UpperCamelCase       : Any    =      decoder_attention_heads
  				_UpperCamelCase       : str    =      dropout
  				_UpperCamelCase       : int    =      attention_dropout
  				_UpperCamelCase       : Optional[int]    =      activation_dropout
  				_UpperCamelCase       : Optional[Any]    =      activation_function
  				_UpperCamelCase       : Tuple    =      init_std
  				_UpperCamelCase       : Any    =      encoder_layerdrop
  				_UpperCamelCase       : List[str]    =      decoder_layerdrop
  				_UpperCamelCase       : Any    =      classifier_dropout
  				_UpperCamelCase       : int    =      use_cache
  				_UpperCamelCase       : Union[str, Any]    =      encoder_layers
  				_UpperCamelCase       : str    =      scale_embedding  # scale factor will be sqrt(d_model) if True
  
  				# extra config
  				_UpperCamelCase       : str    =      attention_type
  				_UpperCamelCase       : Tuple    =      block_size
  				_UpperCamelCase       : Optional[Any]    =      num_random_blocks
  				_UpperCamelCase       : Dict    =      use_bias
  
  				super().__init__(
  				    pad_token_id=_snake_case  ,					bos_token_id=_snake_case  ,					eos_token_id=_snake_case  ,					is_encoder_decoder=_snake_case  ,					decoder_start_token_id=_snake_case  ,					**_snake_case  ,					)
  
  
  
  
  
  
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		@property
  		def _lowercase  (    self     )				->    Mapping[str, Mapping[int, str]]:
  				if self.task in ["default", "seq2seq-lm"]:
  						_UpperCamelCase       : str    =      OrderedDict(
  						    [
  						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						    ]     )
  
  						if self.use_past:
  								_UpperCamelCase       : List[Any]    =      {0: '''batch'''}
  								_UpperCamelCase       : Tuple    =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
  						else:
  								_UpperCamelCase       : Union[str, Any]    =      {0: '''batch''', 1: '''decoder_sequence'''}
  								_UpperCamelCase       : Union[str, Any]    =      {0: '''batch''', 1: '''decoder_sequence'''}
  
  						if self.use_past:
  								self.fill_with_past_key_values_(_snake_case  ,					direction='''inputs'''     )
  				elif self.task == "causal-lm":
  						# TODO: figure this case out.
  						_UpperCamelCase       : Optional[int]    =      OrderedDict(
  						    [
  						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						    ]     )
  						if self.use_past:
  								_UpperCamelCase,							_UpperCamelCase       : List[str]    =      self.num_layers
  								for i in range(_snake_case     ):
  										_UpperCamelCase       : Optional[int]    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
  										_UpperCamelCase       : Dict    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
  				else:
  						_UpperCamelCase       : Optional[int]    =      OrderedDict(
  						    [
  						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
  						        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
  						    ]     )
  
  				return common_inputs
  		@property
  		def _lowercase  (    self     )				->    Mapping[str, Mapping[int, str]]:
  				if self.task in ["default", "seq2seq-lm"]:
  						_UpperCamelCase       : int    =      super().outputs
  				else:
  						_UpperCamelCase       : Optional[int]    =      super(_snake_case  ,					self     ).outputs
  						if self.use_past:
  								_UpperCamelCase,							_UpperCamelCase       : Tuple    =      self.num_layers
  								for i in range(_snake_case     ):
  										_UpperCamelCase       : int    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
  										_UpperCamelCase       : Optional[int]    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
  				return common_outputs
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
  				_UpperCamelCase       : List[str]    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
  
  				# Generate decoder inputs
  				_UpperCamelCase       : Optional[int]    =      seq_length if not self.use_past else 1
  				_UpperCamelCase       : Optional[int]    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
  				_UpperCamelCase       : Optional[int]    =      {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
  				_UpperCamelCase       : Any    =      dict(**_snake_case  ,					**_snake_case     )
  
  				if self.use_past:
  						if not is_torch_available():
  								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''     )
  						else:
  								import torch
  						_UpperCamelCase,							_UpperCamelCase       : Dict    =      common_inputs['''input_ids'''].shape
  						_UpperCamelCase       : Dict    =      common_inputs['''decoder_input_ids'''].shape[1]
  						_UpperCamelCase,							_UpperCamelCase       : List[Any]    =      self.num_attention_heads
  						_UpperCamelCase       : List[Any]    =      (
  						    batch,
  						    num_encoder_attention_heads,
  						    encoder_seq_length,
  						    self._config.hidden_size // num_encoder_attention_heads,
  						)
  						_UpperCamelCase       : int    =      decoder_seq_length + 3
  						_UpperCamelCase       : Union[str, Any]    =      (
  						    batch,
  						    num_decoder_attention_heads,
  						    decoder_past_length,
  						    self._config.hidden_size // num_decoder_attention_heads,
  						)
  
  						_UpperCamelCase       : Any    =      torch.cat(
  						    [common_inputs['''decoder_attention_mask'''], torch.ones(_snake_case  ,					_snake_case     )]  ,					dim=1     )
  
  						_UpperCamelCase       : Any    =      []
  						# If the number of encoder and decoder layers are present in the model configuration, both are considered
  						_UpperCamelCase,							_UpperCamelCase       : int    =      self.num_layers
  						_UpperCamelCase       : Union[str, Any]    =      min(_snake_case  ,					_snake_case     )
  						_UpperCamelCase       : Union[str, Any]    =      max(_snake_case  ,					_snake_case     ) - min_num_layers
  						_UpperCamelCase       : Optional[int]    =      '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
  
  						for _ in range(_snake_case     ):
  								common_inputs["past_key_values"].append(
  								    (
  								        torch.zeros(_snake_case     ),
  								        torch.zeros(_snake_case     ),
  								        torch.zeros(_snake_case     ),
  								        torch.zeros(_snake_case     ),
  								    )     )
  						# TODO: test this.
  						_UpperCamelCase       : Optional[Any]    =      encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
  						for _ in range(_snake_case  ,					_snake_case     ):
  								common_inputs["past_key_values"].append((torch.zeros(_snake_case     ), torch.zeros(_snake_case     ))     )
  				return common_inputs
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
  				_UpperCamelCase       : Dict    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
  
  				if self.use_past:
  						if not is_torch_available():
  								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''     )
  						else:
  								import torch
  						_UpperCamelCase,							_UpperCamelCase       : Union[str, Any]    =      common_inputs['''input_ids'''].shape
  						# Not using the same length for past_key_values
  						_UpperCamelCase       : str    =      seqlen + 2
  						_UpperCamelCase,							_UpperCamelCase       : str    =      self.num_layers
  						_UpperCamelCase,							_UpperCamelCase       : str    =      self.num_attention_heads
  						_UpperCamelCase       : List[str]    =      (
  						    batch,
  						    num_encoder_attention_heads,
  						    past_key_values_length,
  						    self._config.hidden_size // num_encoder_attention_heads,
  						)
  
  						_UpperCamelCase       : Optional[int]    =      common_inputs['''attention_mask'''].dtype
  						_UpperCamelCase       : Union[str, Any]    =      torch.cat(
  						    [common_inputs['''attention_mask'''], torch.ones(_snake_case  ,					_snake_case  ,					dtype=_snake_case     )]  ,					dim=1     )
  						_UpperCamelCase       : int    =      [
  						    (torch.zeros(_snake_case     ), torch.zeros(_snake_case     )) for _ in range(_snake_case     )
  						]
  				return common_inputs
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
  				# Copied from OnnxConfig.generate_dummy_inputs
  				# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
  				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
  				_UpperCamelCase       : Union[str, Any]    =      compute_effective_axis_dimension(
  				    _snake_case  ,					fixed_dimension=OnnxConfig.default_fixed_batch  ,					num_token_to_add=0     )
  
  				# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
  				_UpperCamelCase       : List[Any]    =      tokenizer.num_special_tokens_to_add(_snake_case     )
  				_UpperCamelCase       : List[str]    =      compute_effective_axis_dimension(
  				    _snake_case  ,					fixed_dimension=OnnxConfig.default_fixed_sequence  ,					num_token_to_add=_snake_case     )
  
  				# Generate dummy inputs according to compute batch and sequence
  				_UpperCamelCase       : Optional[Any]    =      [''' '''.join([tokenizer.unk_token]     ) * seq_length] * batch_size
  				_UpperCamelCase       : Any    =      dict(tokenizer(_snake_case  ,					return_tensors=_snake_case     )     )
  				return common_inputs
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
  				if self.task in ["default", "seq2seq-lm"]:
  						_UpperCamelCase       : List[Any]    =      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
  						    _snake_case  ,					batch_size=_snake_case  ,					seq_length=_snake_case  ,					is_pair=_snake_case  ,					framework=_snake_case     )
  
  				elif self.task == "causal-lm":
  						_UpperCamelCase       : List[Any]    =      self._generate_dummy_inputs_for_causal_lm(
  						    _snake_case  ,					batch_size=_snake_case  ,					seq_length=_snake_case  ,					is_pair=_snake_case  ,					framework=_snake_case     )
  				else:
  						_UpperCamelCase       : List[Any]    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  						    _snake_case  ,					batch_size=_snake_case  ,					seq_length=_snake_case  ,					is_pair=_snake_case  ,					framework=_snake_case     )
  
  				return common_inputs
  
  
  
  
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    Tuple:
  				if self.task in ["default", "seq2seq-lm"]:
  						_UpperCamelCase       : List[Any]    =      super()._flatten_past_key_values_(_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
  				else:
  						_UpperCamelCase       : int    =      super(_snake_case  ,					self     )._flatten_past_key_values_(
  						    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
  
  
  
  
  
  