code: 
  
  
  
  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
  
  
  __lowerCamelCase  				=					logging.get_logger(__name__)
  
  __lowerCamelCase  				=					{
      """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 		_snake_case      (			__lowerCamelCase				):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  	lowerCamelCase_          =			'''mbart'''
  	lowerCamelCase_          =			['''past_key_values''']
  	lowerCamelCase_          =			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  	def __init__(						self	,    a=5_0_2_6_5	,    a=1_0_2_4	,    a=1_2	,    a=4_0_9_6	,    a=1_6	,    a=1_2	,    a=4_0_9_6	,    a=1_6	,    a=0.0	,    a=0.0	,    a=True	,    a=True	,    a="gelu"	,    a=1_0_2_4	,    a=0.1	,    a=0.0	,    a=0.0	,    a=0.02	,    a=0.0	,    a=False	,    a=1	,    a=0	,    a=2	,    a=2	,    **a	,    )	->							List[str]:
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			_A           =    vocab_size
  			_A           =    max_position_embeddings
  			_A           =    d_model
  			_A           =    encoder_ffn_dim
  			_A           =    encoder_layers
  			_A           =    encoder_attention_heads
  			_A           =    decoder_ffn_dim
  			_A           =    decoder_layers
  			_A           =    decoder_attention_heads
  			_A           =    dropout
  			_A           =    attention_dropout
  			_A           =    activation_dropout
  			_A           =    activation_function
  			_A           =    init_std
  			_A           =    encoder_layerdrop
  			_A           =    decoder_layerdrop
  			_A           =    classifier_dropout
  			_A           =    use_cache
  			_A           =    encoder_layers
  			_A           =    scale_embedding  # scale factor will be sqrt(d_model) if True
  			super().__init__(
  			    pad_token_id=_lowerCAmelCase	,    bos_token_id=_lowerCAmelCase	,    eos_token_id=_lowerCAmelCase	,    is_encoder_decoder=_lowerCAmelCase	,    forced_eos_token_id=_lowerCAmelCase	,    **_lowerCAmelCase	,    )
  
  
  
  
  
  
  
  class 		_snake_case      (			__lowerCamelCase				):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  	@property
  	def lowercase_		(						self					)	->							List[str]:
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			if self.task in ["default", "seq2seq-lm"]:
  					_A           =    OrderedDict(
  					    [
  					        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
  					        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
  					    ]					)
  
  					if self.use_past:
  							_A           =    {0: """batch"""}
  							_A           =    {0: """batch""", 1: """past_decoder_sequence + sequence"""}
  					else:
  							_A           =    {0: """batch""", 1: """decoder_sequence"""}
  							_A           =    {0: """batch""", 1: """decoder_sequence"""}
  
  					if self.use_past:
  							self.fill_with_past_key_values_(_lowerCAmelCase	,    direction='''inputs'''					)
  			elif self.task == "causal-lm":
  					# TODO: figure this case out.
  					_A           =    OrderedDict(
  					    [
  					        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
  					        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
  					    ]					)
  					if self.use_past:
  							_A           =    self.num_layers
  							for i in range(_lowerCAmelCase					):
  									_A           =    {0: """batch""", 2: """past_sequence + sequence"""}
  									_A           =    {0: """batch""", 2: """past_sequence + sequence"""}
  			else:
  					_A           =    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					)	->							Union[str, Any]:
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			if self.task in ["default", "seq2seq-lm"]:
  					_A           =    super().outputs
  			else:
  					_A           =    super(_lowerCAmelCase	,    self					).outputs
  					if self.use_past:
  							_A           =    self.num_layers
  							for i in range(_lowerCAmelCase					):
  									_A           =    {0: """batch""", 2: """past_sequence + sequence"""}
  									_A           =    {0: """batch""", 2: """past_sequence + sequence"""}
  			return common_outputs
  	def lowercase_		(						self	,    a	,    a = -1	,    a = -1	,    a = False	,    a = None	,    )	->							str:
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			_A           =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  			    _lowerCAmelCase	,    _lowerCAmelCase	,    _lowerCAmelCase	,    _lowerCAmelCase	,    _lowerCAmelCase					)
  
  			# Generate decoder inputs
  			_A           =    seq_length if not self.use_past else 1
  			_A           =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  			    _lowerCAmelCase	,    _lowerCAmelCase	,    _lowerCAmelCase	,    _lowerCAmelCase	,    _lowerCAmelCase					)
  			_A           =    {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
  			_A           =    dict(**_lowerCAmelCase	,    **_lowerCAmelCase					)
  
  			if self.use_past:
  					if not is_torch_available():
  							raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
  					else:
  							import torch
  					_A           =    common_inputs["""input_ids"""].shape
  					_A           =    common_inputs["""decoder_input_ids"""].shape[1]
  					_A           =    self.num_attention_heads
  					_A           =    (
  					    batch,
  					    num_encoder_attention_heads,
  					    encoder_seq_length,
  					    self._config.hidden_size // num_encoder_attention_heads,
  					)
  					_A           =    decoder_seq_length + 3
  					_A           =    (
  					    batch,
  					    num_decoder_attention_heads,
  					    decoder_past_length,
  					    self._config.hidden_size // num_decoder_attention_heads,
  					)
  
  					_A           =    torch.cat(
  					    [common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCAmelCase	,    _lowerCAmelCase					)]	,    dim=1					)
  
  					_A           =    []
  					# If the number of encoder and decoder layers are present in the model configuration, both are considered
  					_A           =    self.num_layers
  					_A           =    min(_lowerCAmelCase	,    _lowerCAmelCase					)
  					_A           =    max(_lowerCAmelCase	,    _lowerCAmelCase					) - min_num_layers
  					_A           =    """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
  
  					for _ in range(_lowerCAmelCase					):
  							common_inputs["past_key_values"].append(
  							    (
  							        torch.zeros(_lowerCAmelCase					),
  							        torch.zeros(_lowerCAmelCase					),
  							        torch.zeros(_lowerCAmelCase					),
  							        torch.zeros(_lowerCAmelCase					),
  							    )					)
  					# TODO: test this.
  					_A           =    encoder_shape if remaining_side_name == """encoder""" else decoder_shape
  					for _ in range(_lowerCAmelCase	,    _lowerCAmelCase					):
  							common_inputs["past_key_values"].append((torch.zeros(_lowerCAmelCase					), torch.zeros(_lowerCAmelCase					))					)
  			return common_inputs
  	def lowercase_		(						self	,    a	,    a = -1	,    a = -1	,    a = False	,    a = None	,    )	->							str:
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			_A           =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  			    _lowerCAmelCase	,    _lowerCAmelCase	,    _lowerCAmelCase	,    _lowerCAmelCase	,    _lowerCAmelCase					)
  
  			if self.use_past:
  					if not is_torch_available():
  							raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
  					else:
  							import torch
  					_A           =    common_inputs["""input_ids"""].shape
  					# Not using the same length for past_key_values
  					_A           =    seqlen + 2
  					_A           =    self.num_layers
  					_A           =    self.num_attention_heads
  					_A           =    (
  					    batch,
  					    num_encoder_attention_heads,
  					    past_key_values_length,
  					    self._config.hidden_size // num_encoder_attention_heads,
  					)
  
  					_A           =    common_inputs["""attention_mask"""].dtype
  					_A           =    torch.cat(
  					    [common_inputs['''attention_mask'''], torch.ones(_lowerCAmelCase	,    _lowerCAmelCase	,    dtype=_lowerCAmelCase					)]	,    dim=1					)
  					_A           =    [
  					    (torch.zeros(_lowerCAmelCase					), torch.zeros(_lowerCAmelCase					)) for _ in range(_lowerCAmelCase					)
  					]
  			return common_inputs
  	def lowercase_		(						self	,    a	,    a = -1	,    a = -1	,    a = False	,    a = None	,    )	->							Any:
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			_A           =    compute_effective_axis_dimension(
  			    _lowerCAmelCase	,    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
  			_A           =    tokenizer.num_special_tokens_to_add(_lowerCAmelCase					)
  			_A           =    compute_effective_axis_dimension(
  			    _lowerCAmelCase	,    fixed_dimension=OnnxConfig.default_fixed_sequence	,    num_token_to_add=_lowerCAmelCase					)
  
  			# Generate dummy inputs according to compute batch and sequence
  			_A           =    [""" """.join([tokenizer.unk_token]					) * seq_length] * batch_size
  			_A           =    dict(tokenizer(_lowerCAmelCase	,    return_tensors=_lowerCAmelCase					)					)
  			return common_inputs
  	def lowercase_		(						self	,    a	,    a = -1	,    a = -1	,    a = False	,    a = None	,    )	->							Any:
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			if self.task in ["default", "seq2seq-lm"]:
  					_A           =    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
  					    _lowerCAmelCase	,    batch_size=_lowerCAmelCase	,    seq_length=_lowerCAmelCase	,    is_pair=_lowerCAmelCase	,    framework=_lowerCAmelCase					)
  
  			elif self.task == "causal-lm":
  					_A           =    self._generate_dummy_inputs_for_causal_lm(
  					    _lowerCAmelCase	,    batch_size=_lowerCAmelCase	,    seq_length=_lowerCAmelCase	,    is_pair=_lowerCAmelCase	,    framework=_lowerCAmelCase					)
  			else:
  					_A           =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  					    _lowerCAmelCase	,    batch_size=_lowerCAmelCase	,    seq_length=_lowerCAmelCase	,    is_pair=_lowerCAmelCase	,    framework=_lowerCAmelCase					)
  
  			return common_inputs
  
  
  
  	def lowercase_		(						self	,    a	,    a	,    a	,    a					)	->							Tuple:
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  
  
  
  			if self.task in ["default", "seq2seq-lm"]:
  					_A           =    super()._flatten_past_key_values_(_lowerCAmelCase	,    _lowerCAmelCase	,    _lowerCAmelCase	,    _lowerCAmelCase					)
  			else:
  					_A           =    super(_lowerCAmelCase	,    self					)._flatten_past_key_values_(
  					    _lowerCAmelCase	,    _lowerCAmelCase	,    _lowerCAmelCase	,    _lowerCAmelCase					)
style_context: 
  
  
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __lowerCamelCase  				=					logging.get_logger(__name__)
  
  __lowerCamelCase  				=					{}
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''fsmt_decoder'''
   def __init__(						self	,    a=0	,    a=0					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     super().__init__()
     _A           =    vocab_size
     _A           =    bos_token_id
  
  
  
  
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''fsmt'''
   lowerCamelCase_          =			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
   def __init__(						self	,    a=["en", "de"]	,    a=4_2_0_2_4	,    a=4_2_0_2_4	,    a="relu"	,    a=1_0_2_4	,    a=2_0_0	,    a=1_0_2_4	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=0.0	,    a=0.1	,    a=0.0	,    a=0.02	,    a=2	,    a=True	,    a=True	,    a=False	,    a=5	,    a=1.0	,    a=False	,    a=True	,    a=1	,    a=0	,    a=2	,    a=2	,    **a	,    )	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    langs
     _A           =    src_vocab_size
     _A           =    tgt_vocab_size
     _A           =    d_model  # encoder_embed_dim and decoder_embed_dim
  
     _A           =    encoder_ffn_dim
     _A           =    _A           =    encoder_layers
     _A           =    encoder_attention_heads
     _A           =    encoder_layerdrop
     _A           =    decoder_layerdrop
     _A           =    decoder_ffn_dim
     _A           =    decoder_layers
     _A           =    decoder_attention_heads
     _A           =    max_position_embeddings
     _A           =    init_std  # Normal(0, this parameter)
     _A           =    activation_function
  
     _A           =    DecoderConfig(vocab_size=a	,    bos_token_id=a					)
     if "decoder" in common_kwargs:
       del common_kwargs["decoder"]
  
     _A           =    scale_embedding  # scale factor will be sqrt(d_model) if True
  
     # 3 Types of Dropout
     _A           =    attention_dropout
     _A           =    activation_dropout
     _A           =    dropout
  
     _A           =    use_cache
     super().__init__(
         pad_token_id=a	,    bos_token_id=a	,    eos_token_id=a	,    decoder_start_token_id=a	,    is_encoder_decoder=a	,    tie_word_embeddings=a	,    forced_eos_token_id=a	,    max_length=a	,    num_beams=a	,    length_penalty=a	,    early_stopping=a	,    **a	,    )
  
  
  
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    copy.deepcopy(self.__dict__					)
     _A           =    self.decoder.to_dict()
     _A           =    self.__class__.model_type
     return output