code: 
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  snake_case_    						=				logging.get_logger(__name__)
  
  snake_case_    						=				{}
  
  
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
      __lowerCamelCase		: Any														=  """fsmt_decoder"""
  
  
  
  
  
      def __init__(   self		,     a=0		,     a=0):
           super().__init__()
           lowercase__					:     Any   = vocab_size
           lowercase__					:     Optional[Any]   = bos_token_id
  
  
  
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
      __lowerCamelCase		: Optional[int]														=  """fsmt"""
      __lowerCamelCase		: Any														=  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  
  
  
  
  
      def __init__(   self		,     a=["en", "de"]		,     a=4_2024		,     a=4_2024		,     a="relu"		,     a=1024		,     a=200		,     a=1024		,     a=4096		,     a=12		,     a=16		,     a=0.0		,     a=4096		,     a=12		,     a=16		,     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		,     ):
           lowercase__					:     Optional[int]   = langs
           lowercase__					:     Union[str, Any]   = src_vocab_size
           lowercase__					:     Tuple   = tgt_vocab_size
           lowercase__					:     Optional[Any]   = d_model  # encoder_embed_dim and decoder_embed_dim
  
           lowercase__					:     List[Any]   = encoder_ffn_dim
           lowercase__					:     Union[str, Any]   = encoder_layers
           lowercase__					:     Tuple   = encoder_attention_heads
           lowercase__					:     List[str]   = encoder_layerdrop
           lowercase__					:     Tuple   = decoder_layerdrop
           lowercase__					:     List[Any]   = decoder_ffn_dim
           lowercase__					:     Union[str, Any]   = decoder_layers
           lowercase__					:     str   = decoder_attention_heads
           lowercase__					:     Any   = max_position_embeddings
           lowercase__					:     Dict   = init_std  # Normal(0, this parameter)
           lowercase__					:     Tuple   = activation_function
  
           lowercase__					:     Optional[int]   = DecoderConfig(vocab_size=a		,     bos_token_id=a)
           if "decoder" in common_kwargs:
                del common_kwargs["decoder"]
  
           lowercase__					:     str   = scale_embedding  # scale factor will be sqrt(d_model) if True
  
           # 3 Types of Dropout
           lowercase__					:     List[Any]   = attention_dropout
           lowercase__					:     Dict   = activation_dropout
           lowercase__					:     Optional[int]   = dropout
  
           lowercase__					:     Union[str, Any]   = 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    snake_case_       (   self):
           lowercase__					:     Tuple   = copy.deepcopy(self.__dict__)
           lowercase__					:     Tuple   = self.decoder.to_dict()
           lowercase__					:     int   = self.__class__.model_type
           return output
  
  
style_context: 
  
  # as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.
  
  import os
  import sys
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow
  
  from .utils import load_json
  
  
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      @classmethod
      def    snake_case_       (   cls):
           return cls
  
  
  
  
  
      @slow
      @require_torch_gpu
      def    snake_case_       (   self):
           lowercase__					:     List[Any]   = self.get_auto_remove_tmp_dir()
           lowercase__					:     List[str]   = f"""
              --model_name Helsinki-NLP/opus-mt-en-ro
              --save_dir {output_dir}
              --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
              --num_beams 2
              --task translation
          """.split()
  
           # we want this test to run even if there is only one GPU, but if there are more we use them all
           lowercase__					:     int   = get_gpu_count()
           lowercase__					:     Tuple   = f"""
              -m torch.distributed.launch
              --nproc_per_node={n_gpu}
              {self.test_file_dir}/run_distributed_eval.py
          """.split()
           lowercase__					:     Optional[Any]   = [sys.executable] + distributed_args + args
           execute_subprocess_async(a		,     env=self.get_env())
  
           lowercase__					:     str   = os.path.join(a		,     'test_bleu.json')
           lowercase__					:     List[str]   = load_json(a)
           # print(metrics)
           self.assertGreaterEqual(metrics['bleu']		,     25)
  
  