code: 
  '''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   :      Union[str, Any] 						=  logging.get_logger(__name__)
  
  _UpperCAmelCase   :      Dict 						=  {
      """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
      # See all MBART models at https://huggingface.co/models?filter=mbart
  }
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		A__  :   Union[str, Any]          =    'mbart'
  		A__  :   Any          =    ['past_key_values']
  		A__  :   List[str]          =    {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
  		def __init__(    self  ,					_snake_case=50265  ,					_snake_case=1024  ,					_snake_case=12  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=12  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=0.0  ,					_snake_case=0.0  ,					_snake_case=True  ,					_snake_case=True  ,					_snake_case="gelu"  ,					_snake_case=1024  ,					_snake_case=0.1  ,					_snake_case=0.0  ,					_snake_case=0.0  ,					_snake_case=0.02  ,					_snake_case=0.0  ,					_snake_case=False  ,					_snake_case=1  ,					_snake_case=0  ,					_snake_case=2  ,					_snake_case=2  ,					**_snake_case  ,					)				->    Tuple:
  				_UpperCamelCase       : Tuple    =      vocab_size
  				_UpperCamelCase       : Union[str, Any]    =      max_position_embeddings
  				_UpperCamelCase       : Optional[Any]    =      d_model
  				_UpperCamelCase       : Dict    =      encoder_ffn_dim
  				_UpperCamelCase       : List[str]    =      encoder_layers
  				_UpperCamelCase       : List[str]    =      encoder_attention_heads
  				_UpperCamelCase       : Optional[int]    =      decoder_ffn_dim
  				_UpperCamelCase       : Any    =      decoder_layers
  				_UpperCamelCase       : int    =      decoder_attention_heads
  				_UpperCamelCase       : Optional[Any]    =      dropout
  				_UpperCamelCase       : Union[str, Any]    =      attention_dropout
  				_UpperCamelCase       : Tuple    =      activation_dropout
  				_UpperCamelCase       : str    =      activation_function
  				_UpperCamelCase       : Tuple    =      init_std
  				_UpperCamelCase       : Optional[Any]    =      encoder_layerdrop
  				_UpperCamelCase       : str    =      decoder_layerdrop
  				_UpperCamelCase       : Union[str, Any]    =      classifier_dropout
  				_UpperCamelCase       : List[str]    =      use_cache
  				_UpperCamelCase       : str    =      encoder_layers
  				_UpperCamelCase       : Tuple    =      scale_embedding  # scale factor will be sqrt(d_model) if True
  				super().__init__(
  				    pad_token_id=_snake_case  ,					bos_token_id=_snake_case  ,					eos_token_id=_snake_case  ,					is_encoder_decoder=_snake_case  ,					forced_eos_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       : Any    =      OrderedDict(
  						    [
  						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						    ]     )
  
  						if self.use_past:
  								_UpperCamelCase       : Union[str, Any]    =      {0: '''batch'''}
  								_UpperCamelCase       : Any    =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
  						else:
  								_UpperCamelCase       : int    =      {0: '''batch''', 1: '''decoder_sequence'''}
  								_UpperCamelCase       : List[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       : List[str]    =      OrderedDict(
  						    [
  						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
  						    ]     )
  						if self.use_past:
  								_UpperCamelCase,							_UpperCamelCase       : Tuple    =      self.num_layers
  								for i in range(_snake_case     ):
  										_UpperCamelCase       : Any    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
  										_UpperCamelCase       : Tuple    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
  				else:
  						_UpperCamelCase       : Dict    =      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[Any]    =      super(_snake_case  ,					self     ).outputs
  						if self.use_past:
  								_UpperCamelCase,							_UpperCamelCase       : List[Any]    =      self.num_layers
  								for i in range(_snake_case     ):
  										_UpperCamelCase       : Any    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
  										_UpperCamelCase       : Dict    =      {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       : int    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
  
  				# Generate decoder inputs
  				_UpperCamelCase       : Dict    =      seq_length if not self.use_past else 1
  				_UpperCamelCase       : List[str]    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
  				_UpperCamelCase       : Optional[Any]    =      {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
  				_UpperCamelCase       : int    =      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       : List[str]    =      common_inputs['''input_ids'''].shape
  						_UpperCamelCase       : Optional[Any]    =      common_inputs['''decoder_input_ids'''].shape[1]
  						_UpperCamelCase,							_UpperCamelCase       : Tuple    =      self.num_attention_heads
  						_UpperCamelCase       : Union[str, Any]    =      (
  						    batch,
  						    num_encoder_attention_heads,
  						    encoder_seq_length,
  						    self._config.hidden_size // num_encoder_attention_heads,
  						)
  						_UpperCamelCase       : Union[str, Any]    =      decoder_seq_length + 3
  						_UpperCamelCase       : Optional[int]    =      (
  						    batch,
  						    num_decoder_attention_heads,
  						    decoder_past_length,
  						    self._config.hidden_size // num_decoder_attention_heads,
  						)
  
  						_UpperCamelCase       : int    =      torch.cat(
  						    [common_inputs['''decoder_attention_mask'''], torch.ones(_snake_case  ,					_snake_case     )]  ,					dim=1     )
  
  						_UpperCamelCase       : Optional[int]    =      []
  						# If the number of encoder and decoder layers are present in the model configuration, both are considered
  						_UpperCamelCase,							_UpperCamelCase       : Tuple    =      self.num_layers
  						_UpperCamelCase       : str    =      min(_snake_case  ,					_snake_case     )
  						_UpperCamelCase       : Tuple    =      max(_snake_case  ,					_snake_case     ) - min_num_layers
  						_UpperCamelCase       : 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       : 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       : List[str]    =      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       : List[str]    =      common_inputs['''input_ids'''].shape
  						# Not using the same length for past_key_values
  						_UpperCamelCase       : Optional[int]    =      seqlen + 2
  						_UpperCamelCase,							_UpperCamelCase       : List[Any]    =      self.num_layers
  						_UpperCamelCase,							_UpperCamelCase       : Tuple    =      self.num_attention_heads
  						_UpperCamelCase       : Optional[int]    =      (
  						    batch,
  						    num_encoder_attention_heads,
  						    past_key_values_length,
  						    self._config.hidden_size // num_encoder_attention_heads,
  						)
  
  						_UpperCamelCase       : Union[str, Any]    =      common_inputs['''attention_mask'''].dtype
  						_UpperCamelCase       : Optional[int]    =      torch.cat(
  						    [common_inputs['''attention_mask'''], torch.ones(_snake_case  ,					_snake_case  ,					dtype=_snake_case     )]  ,					dim=1     )
  						_UpperCamelCase       : Tuple    =      [
  						    (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       : 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[str]    =      tokenizer.num_special_tokens_to_add(_snake_case     )
  				_UpperCamelCase       : List[Any]    =      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       : Any    =      [''' '''.join([tokenizer.unk_token]     ) * seq_length] * batch_size
  				_UpperCamelCase       : List[str]    =      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       : Dict    =      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       : Dict    =      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       : Union[str, 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     )				->    Optional[int]:
  				if self.task in ["default", "seq2seq-lm"]:
  						_UpperCamelCase       : Dict    =      super()._flatten_past_key_values_(_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
  				else:
  						_UpperCamelCase       : Optional[int]    =      super(_snake_case  ,					self     )._flatten_past_key_values_(
  						    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  import argparse
  import copy
  import json
  import multiprocessing
  import os
  import time
  
  from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
  from huggingface_hub import ModelFilter, hf_api
  
  import transformers
  from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
  from transformers.image_processing_utils import BaseImageProcessor
  
  
  
  
  
  
  
  def 						snake_case__	(      ) ->      List[Any]:
  		_UpperCamelCase       : Union[str, Any]    =      set()
  		# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
  		for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
  				_UpperCamelCase       : List[Any]    =      getattr(transformers.models.auto			,UpperCamelCase			,UpperCamelCase	)
  				if module is None:
  						continue
  				# all mappings in a single auto modeling file
  				_UpperCamelCase       : Optional[Any]    =      [
  				    x
  				    for x in dir(UpperCamelCase	)
  				    if x.endswith('''_MAPPING_NAMES'''	)
  				    and (x.startswith('''MODEL_'''	) or x.startswith('''TF_MODEL_'''	) or x.startswith('''FLAX_MODEL_'''	))
  				]
  				for name in mapping_names:
  						_UpperCamelCase       : List[str]    =      getattr(UpperCamelCase			,UpperCamelCase	)
  						if mapping is not None:
  								for v in mapping.values():
  										if isinstance(UpperCamelCase			,(list, tuple)	):
  												model_names.update(UpperCamelCase	)
  										elif isinstance(UpperCamelCase			,UpperCamelCase	):
  												model_names.add(UpperCamelCase	)
  
  		return sorted(UpperCamelCase	)
  
  
  
  
  
  
  
  def 						snake_case__	(      ) ->      Any:
  		# All model names defined in auto mappings
  		_UpperCamelCase       : Union[str, Any]    =      set(get_all_model_names()	)
  
  		with open('''tests/utils/tiny_model_summary.json'''	) as fp:
  				_UpperCamelCase       : Optional[Any]    =      json.load(UpperCamelCase	)
  		_UpperCamelCase       : Tuple    =      set()
  		for model_base_name in tiny_model_info:
  				tiny_models_names.update(tiny_model_info[model_base_name]['''model_classes''']	)
  
  		# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
  		_UpperCamelCase       : List[Any]    =      model_names.difference(UpperCamelCase	)
  		for model_name in copy.copy(UpperCamelCase	):
  				if not model_name.startswith('''TF'''	) and f'''TF{model_name}''' in not_on_hub:
  						tiny_models_names.remove(UpperCamelCase	)
  				elif model_name.startswith('''TF'''	) and model_name[2:] in not_on_hub:
  						tiny_models_names.remove(UpperCamelCase	)
  
  		return sorted(UpperCamelCase	)
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase	) ->      List[str]:
  		_UpperCamelCase       : Optional[Any]    =      COMPOSITE_MODELS.values()
  
  		# All tiny model base names on Hub
  		_UpperCamelCase       : Optional[Any]    =      get_all_model_names()
  		_UpperCamelCase       : Union[str, Any]    =      hf_api.list_models(
  		    filter=ModelFilter(
  		        author='''hf-internal-testing'''			,)	)
  		_UpperCamelCase       : Any    =      set()
  		for x in models:
  				_UpperCamelCase       : Optional[int]    =      x.modelId
  				_UpperCamelCase,							_UpperCamelCase       : str    =      model.split('''/'''	)
  				if not model.startswith('''tiny-random-'''	):
  						continue
  				_UpperCamelCase       : Optional[Any]    =      model.replace('''tiny-random-'''			,''''''	)
  				if not model[0].isupper():
  						continue
  				if model not in model_names and model not in special_models:
  						continue
  				_models.add(UpperCamelCase	)
  
  		_UpperCamelCase       : List[Any]    =      sorted(_models	)
  		# All tiny model names on Hub
  		_UpperCamelCase       : int    =      {}
  		for model in models:
  				_UpperCamelCase       : Optional[Any]    =      f'''hf-internal-testing/tiny-random-{model}'''
  				_UpperCamelCase       : Any    =      model.split('''-'''	)[0]
  				try:
  						_UpperCamelCase       : List[str]    =      hf_api.repo_info(UpperCamelCase	)
  						_UpperCamelCase       : List[str]    =      {
  						    '''tokenizer_classes''': set(),
  						    '''processor_classes''': set(),
  						    '''model_classes''': set(),
  						    '''sha''': repo_info.sha,
  						}
  				except Exception:
  						continue
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : Union[str, Any]    =      AutoTokenizer.from_pretrained(UpperCamelCase	)
  						content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__	)
  				except Exception:
  						pass
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : int    =      AutoTokenizer.from_pretrained(UpperCamelCase			,use_fast=UpperCamelCase	)
  						content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__	)
  				except Exception:
  						pass
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : int    =      AutoImageProcessor.from_pretrained(UpperCamelCase	)
  						content["processor_classes"].add(img_p.__class__.__name__	)
  				except Exception:
  						pass
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : List[Any]    =      AutoFeatureExtractor.from_pretrained(UpperCamelCase	)
  						if not isinstance(UpperCamelCase			,UpperCamelCase	):
  								content["processor_classes"].add(feat_p.__class__.__name__	)
  				except Exception:
  						pass
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : Tuple    =      getattr(UpperCamelCase			,UpperCamelCase	)
  						_UpperCamelCase       : Tuple    =      model_class.from_pretrained(UpperCamelCase	)
  						content["model_classes"].add(m.__class__.__name__	)
  				except Exception:
  						pass
  				try:
  						time.sleep(1	)
  						_UpperCamelCase       : Dict    =      getattr(UpperCamelCase			,f'''TF{model}'''	)
  						_UpperCamelCase       : Optional[Any]    =      model_class.from_pretrained(UpperCamelCase	)
  						content["model_classes"].add(m.__class__.__name__	)
  				except Exception:
  						pass
  
  				_UpperCamelCase       : Dict    =      sorted(content['''tokenizer_classes''']	)
  				_UpperCamelCase       : Tuple    =      sorted(content['''processor_classes''']	)
  				_UpperCamelCase       : Union[str, Any]    =      sorted(content['''model_classes''']	)
  
  				_UpperCamelCase       : List[Any]    =      content
  				with open(os.path.join(UpperCamelCase			,'''hub_tiny_model_summary.json'''	)			,'''w'''	) as fp:
  						json.dump(UpperCamelCase			,UpperCamelCase			,ensure_ascii=UpperCamelCase			,indent=4	)
  
  
  if __name__ == "__main__":
  	_UpperCAmelCase   :      List[str] 						=  argparse.ArgumentParser()
  	parser.add_argument("""--num_workers""", default=1, type=int, help="""The number of workers to run.""")
  	_UpperCAmelCase   :      List[Any] 						=  parser.parse_args()
  
  	# This has to be `spawn` to avoid hanging forever!
  	multiprocessing.set_start_method("""spawn""")
  
  	_UpperCAmelCase   :      Optional[Any] 						=  """tiny_models"""
  	_UpperCAmelCase   :      Optional[Any] 						=  True
  	_UpperCAmelCase   :      str 						=  None
  	_UpperCAmelCase   :      int 						=  get_tiny_model_names_from_repo()
  	_UpperCAmelCase   :      Optional[Any] 						=  True
  	_UpperCAmelCase   :      List[str] 						=  True
  	_UpperCAmelCase   :      str 						=  """hf-internal-testing"""
  
  	create_tiny_models(
  	    output_path,
  	    all,
  	    model_types,
  	    models_to_skip,
  	    no_check,
  	    upload,
  	    organization,
  	    token=os.environ.get("""TOKEN""", None),
  	    num_workers=args.num_workers,
  	)
  
  
  
  
  
  