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, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  lowercase__						=			logging.get_logger(__name__)
  
  lowercase__						=			{
      "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json",
      # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
  }
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	"""m2m_100"""
  		lowerCamelCase           =	["""past_key_values"""]
  		lowerCamelCase           =	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  
  
  
  		def __init__(			self							: List[Any]     ,  UpperCamelCase__							: List[Any]=12_8112     ,  UpperCamelCase__							: int=1024     ,  UpperCamelCase__							: List[str]=12     ,  UpperCamelCase__							: str=4096     ,  UpperCamelCase__							: List[Any]=16     ,  UpperCamelCase__							: str=12     ,  UpperCamelCase__							: str=4096     ,  UpperCamelCase__							: Union[str, Any]=16     ,  UpperCamelCase__							: Optional[int]=0.05     ,  UpperCamelCase__							: Dict=0.05     ,  UpperCamelCase__							: int=True     ,  UpperCamelCase__							: Dict=True     ,  UpperCamelCase__							: str="relu"     ,  UpperCamelCase__							: Optional[Any]=1024     ,  UpperCamelCase__							: str=0.1     ,  UpperCamelCase__							: Tuple=0.1     ,  UpperCamelCase__							: Optional[int]=0.0     ,  UpperCamelCase__							: Dict=0.02     ,  UpperCamelCase__							: int=2     ,  UpperCamelCase__							: List[Any]=True     ,  UpperCamelCase__							: Dict=1     ,  UpperCamelCase__							: List[Any]=0     ,  UpperCamelCase__							: str=2     ,  **UpperCamelCase__							: List[Any]     ,  )	-> Any:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	vocab_size
  				snake_case     :					List[str]     =	max_position_embeddings
  				snake_case     :					List[Any]     =	d_model
  				snake_case     :					List[Any]     =	encoder_ffn_dim
  				snake_case     :					Optional[Any]     =	encoder_layers
  				snake_case     :					List[str]     =	encoder_attention_heads
  				snake_case     :					str     =	decoder_ffn_dim
  				snake_case     :					List[Any]     =	decoder_layers
  				snake_case     :					Any     =	decoder_attention_heads
  				snake_case     :					Dict     =	dropout
  				snake_case     :					Union[str, Any]     =	attention_dropout
  				snake_case     :					Tuple     =	activation_dropout
  				snake_case     :					Tuple     =	activation_function
  				snake_case     :					List[str]     =	init_std
  				snake_case     :					Optional[int]     =	encoder_layerdrop
  				snake_case     :					List[Any]     =	decoder_layerdrop
  				snake_case     :					Optional[Any]     =	use_cache
  				snake_case     :					Any     =	encoder_layers
  				snake_case     :					List[Any]     =	scale_embedding  # scale factor will be sqrt(d_model) if True
  
  				super().__init__(
  				    pad_token_id=UpperCamelCase__     ,  bos_token_id=UpperCamelCase__     ,  eos_token_id=UpperCamelCase__     ,  is_encoder_decoder=UpperCamelCase__     ,  decoder_start_token_id=UpperCamelCase__     ,  **UpperCamelCase__     ,  )
  
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		@property
  		def 			lowerCAmelCase							(			self							: Union[str, Any]			)	-> Mapping[str, Mapping[int, str]]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	OrderedDict(
  				    [
  				        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
  				        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
  				    ]			)
  
  				if self.use_past:
  						snake_case     :					Union[str, Any]     =	{0: '''batch'''}
  						snake_case     :					str     =	{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
  				else:
  						snake_case     :					Tuple     =	{0: '''batch''', 1: '''decoder_sequence'''}
  						snake_case     :					Optional[Any]     =	{0: '''batch''', 1: '''decoder_sequence'''}
  
  				if self.use_past:
  						self.fill_with_past_key_values_(UpperCamelCase__     ,  direction='''inputs'''			)
  				return common_inputs
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict     ,  UpperCamelCase__							: PreTrainedTokenizer     ,  UpperCamelCase__							: int = -1     ,  UpperCamelCase__							: int = -1     ,  UpperCamelCase__							: bool = False     ,  UpperCamelCase__							: Optional[TensorType] = None     ,  )	-> Mapping[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					str     =	compute_effective_axis_dimension(
  				    UpperCamelCase__     ,  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
  				snake_case     :					Union[str, Any]     =	tokenizer.num_special_tokens_to_add(UpperCamelCase__			)
  				snake_case     :					Optional[int]     =	compute_effective_axis_dimension(
  				    UpperCamelCase__     ,  fixed_dimension=OnnxConfig.default_fixed_sequence     ,  num_token_to_add=UpperCamelCase__			)
  
  				# Generate dummy inputs according to compute batch and sequence
  				snake_case     :					Any     =	[''' '''.join([tokenizer.unk_token]			) * seq_length] * batch_size
  				snake_case     :					Union[str, Any]     =	dict(tokenizer(UpperCamelCase__     ,  return_tensors=UpperCamelCase__			)			)
  				return common_inputs
  
  
  
  		def 			lowerCAmelCase							(			self							: Optional[Any]     ,  UpperCamelCase__							: PreTrainedTokenizer     ,  UpperCamelCase__							: int = -1     ,  UpperCamelCase__							: int = -1     ,  UpperCamelCase__							: bool = False     ,  UpperCamelCase__							: Optional[TensorType] = None     ,  )	-> Mapping[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  				    UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__			)
  
  				# Generate decoder inputs
  				snake_case     :					int     =	seq_length if not self.use_past else 1
  				snake_case     :					Any     =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  				    UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__			)
  				snake_case     :					List[str]     =	{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
  				snake_case     :					Union[str, Any]     =	dict(**UpperCamelCase__     ,  **UpperCamelCase__			)
  
  				if self.use_past:
  						if not is_torch_available():
  								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''			)
  						else:
  								import torch
  						snake_case ,snake_case     :					str     =	common_inputs['''input_ids'''].shape
  						snake_case     :					Union[str, Any]     =	common_inputs['''decoder_input_ids'''].shape[1]
  						snake_case ,snake_case     :					List[str]     =	self.num_attention_heads
  						snake_case     :					str     =	(
  						    batch,
  						    num_encoder_attention_heads,
  						    encoder_seq_length,
  						    self._config.hidden_size // num_encoder_attention_heads,
  						)
  						snake_case     :					Optional[Any]     =	decoder_seq_length + 3
  						snake_case     :					Tuple     =	(
  						    batch,
  						    num_decoder_attention_heads,
  						    decoder_past_length,
  						    self._config.hidden_size // num_decoder_attention_heads,
  						)
  
  						snake_case     :					str     =	torch.cat(
  						    [common_inputs['''decoder_attention_mask'''], torch.ones(UpperCamelCase__     ,  UpperCamelCase__			)]     ,  dim=1			)
  
  						snake_case     :					Union[str, Any]     =	[]
  						# If the number of encoder and decoder layers are present in the model configuration, both are considered
  						snake_case ,snake_case     :					Dict     =	self.num_layers
  						snake_case     :					Dict     =	min(UpperCamelCase__     ,  UpperCamelCase__			)
  						snake_case     :					int     =	max(UpperCamelCase__     ,  UpperCamelCase__			) - min_num_layers
  						snake_case     :					Optional[int]     =	'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
  
  						for _ in range(UpperCamelCase__			):
  								common_inputs["past_key_values"].append(
  								    (
  								        torch.zeros(UpperCamelCase__			),
  								        torch.zeros(UpperCamelCase__			),
  								        torch.zeros(UpperCamelCase__			),
  								        torch.zeros(UpperCamelCase__			),
  								    )			)
  						# TODO: test this.
  						snake_case     :					int     =	encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
  						for _ in range(UpperCamelCase__     ,  UpperCamelCase__			):
  								common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase__			), torch.zeros(UpperCamelCase__			))			)
  				return common_inputs
  
  
  
  		lowerCamelCase           =	_generate_dummy_inputs_for_default_and_seqaseq_lm
  
  
  
style_context: 
  '''simple docstring'''
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[Any]     =	0
  		for ele in arr:
  				total_sum += product_sum(SCREAMING_SNAKE_CASE__   ,					depth + 1 ) if isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ) else ele
  		return total_sum * depth
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		return product_sum(SCREAMING_SNAKE_CASE__   ,					1 )
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  
  
  