code: 
  
  
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  A_														=      logging.get_logger(__name__)
  
  class 						snake_case			(       lowerCAmelCase__	):
  			'''simple docstring'''
  
  			UpperCAmelCase       :       Any       = """mt5"""
  			UpperCAmelCase       :       Optional[int]       = ["""past_key_values"""]
  
  			def __init__(	self     :     Optional[int]							,							lowerCAmelCase_     :     List[str]=250_112							,							lowerCAmelCase_     :     Dict=512							,							lowerCAmelCase_     :     List[str]=64							,							lowerCAmelCase_     :     Any=1_024							,							lowerCAmelCase_     :     Optional[int]=8							,							lowerCAmelCase_     :     Dict=None							,							lowerCAmelCase_     :     Dict=6							,							lowerCAmelCase_     :     Any=32							,							lowerCAmelCase_     :     Dict=128							,							lowerCAmelCase_     :     Tuple=0.1							,							lowerCAmelCase_     :     str=1e-6							,							lowerCAmelCase_     :     List[str]=1.0							,							lowerCAmelCase_     :     Optional[int]="gated-gelu"							,							lowerCAmelCase_     :     Union[str, Any]=True							,							lowerCAmelCase_     :     List[Any]=True							,							lowerCAmelCase_     :     str="T5Tokenizer"							,							lowerCAmelCase_     :     str=False							,							lowerCAmelCase_     :     List[str]=0							,							lowerCAmelCase_     :     Optional[int]=1							,							lowerCAmelCase_     :     List[str]=0							,							**lowerCAmelCase_     :     List[str]							,							)					->					Optional[int]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								super().__init__(
  								    is_encoder_decoder=lowerCAmelCase_							,							tokenizer_class=lowerCAmelCase_							,							tie_word_embeddings=lowerCAmelCase_							,							pad_token_id=lowerCAmelCase_							,							eos_token_id=lowerCAmelCase_							,							decoder_start_token_id=lowerCAmelCase_							,							**lowerCAmelCase_							,							)
  								SCREAMING_SNAKE_CASE_	      =       vocab_size
  								SCREAMING_SNAKE_CASE_	      =       d_model
  								SCREAMING_SNAKE_CASE_	      =       d_kv
  								SCREAMING_SNAKE_CASE_	      =       d_ff
  								SCREAMING_SNAKE_CASE_	      =       num_layers
  								SCREAMING_SNAKE_CASE_	      =       (
  								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
  								)  # default = symmetry
  								SCREAMING_SNAKE_CASE_	      =       num_heads
  								SCREAMING_SNAKE_CASE_	      =       relative_attention_num_buckets
  								SCREAMING_SNAKE_CASE_	      =       relative_attention_max_distance
  								SCREAMING_SNAKE_CASE_	      =       dropout_rate
  								SCREAMING_SNAKE_CASE_	      =       layer_norm_epsilon
  								SCREAMING_SNAKE_CASE_	      =       initializer_factor
  								SCREAMING_SNAKE_CASE_	      =       feed_forward_proj
  								SCREAMING_SNAKE_CASE_	      =       use_cache
  
  								SCREAMING_SNAKE_CASE_	      =       self.feed_forward_proj.split('''-'''					)
  								SCREAMING_SNAKE_CASE_	      =       act_info[-1]
  								SCREAMING_SNAKE_CASE_	      =       act_info[0] == '''gated'''
  
  								if len(lowerCAmelCase_					) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_					) > 2:
  													raise ValueError(
  													    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
  													    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
  													    '''\'gated-gelu\' or \'relu\''''					)
  
  								# for backwards compatibility
  								if feed_forward_proj == "gated-gelu":
  													SCREAMING_SNAKE_CASE_	      =       '''gelu_new'''
  
  			@property
  			def _lowercase (	self     :     List[str]					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.d_model
  
  			@property
  			def _lowercase (	self     :     Union[str, Any]					)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.num_heads
  
  
  
  
  			@property
  			def _lowercase (	self     :     str					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.num_layers
  
  class 						snake_case			(       lowerCAmelCase__	):
  			'''simple docstring'''
  
  			@property
  			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
  			def _lowercase (	self     :     str					)					->					Mapping[str, Mapping[int, str]]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       {
  								    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
  								    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
  								}
  								if self.use_past:
  													SCREAMING_SNAKE_CASE_	      =       '''past_encoder_sequence + sequence'''
  													SCREAMING_SNAKE_CASE_	      =       {0: '''batch'''}
  													SCREAMING_SNAKE_CASE_	      =       {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
  								else:
  													SCREAMING_SNAKE_CASE_	      =       {0: '''batch''', 1: '''decoder_sequence'''}
  													SCREAMING_SNAKE_CASE_	      =       {0: '''batch''', 1: '''decoder_sequence'''}
  
  								if self.use_past:
  													self.fill_with_past_key_values_(lowerCAmelCase_							,							direction='''inputs'''					)
  
  								return common_inputs
  
  			@property
  			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
  			def _lowercase (	self     :     Optional[int]					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return 13
  
  
  
  
  			@property
  			def _lowercase (	self     :     int					)					->					float:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return 5e-4
  
  
  
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,
  )
  
  
  A_														=      {
      "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["MarianTokenizer"]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      [
  		    "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "MarianForCausalLM",
  		    "MarianModel",
  		    "MarianMTModel",
  		    "MarianPreTrainedModel",
  		]
  
  try:
  		if not is_tf_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]
  
  if TYPE_CHECKING:
  		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig
  
  		try:
  				if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_marian import MarianTokenizer
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_marian import (
  				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    MarianForCausalLM,
  				    MarianModel,
  				    MarianMTModel,
  				    MarianPreTrainedModel,
  				)
  
  		try:
  				if not is_tf_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel
  
  		try:
  				if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel
  
  else:
  		import sys
  
  		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  