code: import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  A_:      Tuple        =			logging.get_logger(__name__)
  
  A_:      Dict        =			{}
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					'fsmt_decoder'
  
  
  
  							def __init__(    self     ,   UpperCAmelCase=0     ,   UpperCAmelCase=0  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														super().__init__()
  														_lowercase				=	vocab_size
  														_lowercase				=	bos_token_id
  
  
  
  
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					'fsmt'
  							lowerCAmelCase__       =					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
  
  
  
  							def __init__(    self     ,   UpperCAmelCase=["en", "de"]     ,   UpperCAmelCase=42024     ,   UpperCAmelCase=42024     ,   UpperCAmelCase="relu"     ,   UpperCAmelCase=1024     ,   UpperCAmelCase=200     ,   UpperCAmelCase=1024     ,   UpperCAmelCase=4096     ,   UpperCAmelCase=12     ,   UpperCAmelCase=16     ,   UpperCAmelCase=0.0     ,   UpperCAmelCase=4096     ,   UpperCAmelCase=12     ,   UpperCAmelCase=16     ,   UpperCAmelCase=0.0     ,   UpperCAmelCase=0.0     ,   UpperCAmelCase=0.1     ,   UpperCAmelCase=0.0     ,   UpperCAmelCase=0.02     ,   UpperCAmelCase=2     ,   UpperCAmelCase=True     ,   UpperCAmelCase=True     ,   UpperCAmelCase=False     ,   UpperCAmelCase=5     ,   UpperCAmelCase=1.0     ,   UpperCAmelCase=False     ,   UpperCAmelCase=True     ,   UpperCAmelCase=1     ,   UpperCAmelCase=0     ,   UpperCAmelCase=2     ,   UpperCAmelCase=2     ,   **UpperCAmelCase     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	langs
  														_lowercase				=	src_vocab_size
  														_lowercase				=	tgt_vocab_size
  														_lowercase				=	d_model  # encoder_embed_dim and decoder_embed_dim
  
  														_lowercase				=	encoder_ffn_dim
  														_lowercase				=	_lowercase				=	encoder_layers
  														_lowercase				=	encoder_attention_heads
  														_lowercase				=	encoder_layerdrop
  														_lowercase				=	decoder_layerdrop
  														_lowercase				=	decoder_ffn_dim
  														_lowercase				=	decoder_layers
  														_lowercase				=	decoder_attention_heads
  														_lowercase				=	max_position_embeddings
  														_lowercase				=	init_std  # Normal(0, this parameter)
  														_lowercase				=	activation_function
  
  														_lowercase				=	DecoderConfig(vocab_size=UpperCAmelCase     ,   bos_token_id=UpperCAmelCase  )
  														if "decoder" in common_kwargs:
  																					del common_kwargs["decoder"]
  
  														_lowercase				=	scale_embedding  # scale factor will be sqrt(d_model) if True
  
  														# 3 Types of Dropout
  														_lowercase				=	attention_dropout
  														_lowercase				=	activation_dropout
  														_lowercase				=	dropout
  
  														_lowercase				=	use_cache
  														super().__init__(
  														    pad_token_id=UpperCAmelCase     ,   bos_token_id=UpperCAmelCase     ,   eos_token_id=UpperCAmelCase     ,   decoder_start_token_id=UpperCAmelCase     ,   is_encoder_decoder=UpperCAmelCase     ,   tie_word_embeddings=UpperCAmelCase     ,   forced_eos_token_id=UpperCAmelCase     ,   max_length=UpperCAmelCase     ,   num_beams=UpperCAmelCase     ,   length_penalty=UpperCAmelCase     ,   early_stopping=UpperCAmelCase     ,   **UpperCAmelCase     ,   )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	copy.deepcopy(self.__dict__  )
  														_lowercase				=	self.decoder.to_dict()
  														_lowercase				=	self.__class__.model_type
  														return output
  
  
  
style_context: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_:      int        =			{
      'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
      'tokenization_bart': ['BartTokenizer'],
  }
  
  try:
  				if not is_tokenizers_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      List[str]        =			['BartTokenizerFast']
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      int        =			[
  				    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
  				    'BartForCausalLM',
  				    'BartForConditionalGeneration',
  				    'BartForQuestionAnswering',
  				    'BartForSequenceClassification',
  				    'BartModel',
  				    'BartPretrainedModel',
  				    'PretrainedBartModel',
  				]
  
  try:
  				if not is_tf_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      Any        =			[
  				    'TFBartForConditionalGeneration',
  				    'TFBartForSequenceClassification',
  				    'TFBartModel',
  				    'TFBartPretrainedModel',
  				]
  
  try:
  				if not is_flax_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      str        =			[
  				    'FlaxBartDecoderPreTrainedModel',
  				    'FlaxBartForCausalLM',
  				    'FlaxBartForConditionalGeneration',
  				    'FlaxBartForQuestionAnswering',
  				    'FlaxBartForSequenceClassification',
  				    'FlaxBartModel',
  				    'FlaxBartPreTrainedModel',
  				]
  
  if TYPE_CHECKING:
  				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
  				from .tokenization_bart import BartTokenizer
  
  				try:
  								if not is_tokenizers_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .tokenization_bart_fast import BartTokenizerFast
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_bart import (
  								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    BartForCausalLM,
  								    BartForConditionalGeneration,
  								    BartForQuestionAnswering,
  								    BartForSequenceClassification,
  								    BartModel,
  								    BartPretrainedModel,
  								    PretrainedBartModel,
  								)
  
  				try:
  								if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_tf_bart import (
  								    TFBartForConditionalGeneration,
  								    TFBartForSequenceClassification,
  								    TFBartModel,
  								    TFBartPretrainedModel,
  								)
  
  				try:
  								if not is_flax_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_flax_bart import (
  								    FlaxBartDecoderPreTrainedModel,
  								    FlaxBartForCausalLM,
  								    FlaxBartForConditionalGeneration,
  								    FlaxBartForQuestionAnswering,
  								    FlaxBartForSequenceClassification,
  								    FlaxBartModel,
  								    FlaxBartPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				A_:      Optional[Any]        =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  