





"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       int										=  [
      'encoder.version',
      'decoder.version',
      'model.encoder.version',
      'model.decoder.version',
      '_float_tensor',
      'decoder.output_projection.weight',
  ]
  for k in ignore_keys:
    state_dict.pop(_UpperCAmelCase ,					_UpperCAmelCase					)





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_						,       A_  :       Union[str, Any]										=  emb.weight.shape
  A_  :       Dict										=  nn.Linear(_UpperCAmelCase ,					_UpperCAmelCase ,					bias=_UpperCAmelCase					)
  A_  :       str										=  emb.weight.data
  return lin_layer





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase="facebook/mbart-large-en-ro" ,					_UpperCAmelCase=False ,					_UpperCAmelCase=False					):






  """simple docstring"""





  A_  :       Tuple										=  torch.load(_UpperCAmelCase ,					map_location='cpu'					)['model']
  remove_ignore_keys_(_UpperCAmelCase					)
  A_  :       Optional[int]										=  state_dict['encoder.embed_tokens.weight'].shape[0]

  A_  :       Optional[Any]										=  MBartConfig.from_pretrained(_UpperCAmelCase ,					vocab_size=_UpperCAmelCase					)
  if mbart_aa and finetuned:
    A_  :       str										=  'relu'

  A_  :       Optional[Any]										=  state_dict['decoder.embed_tokens.weight']
  A_  :       Dict										=  MBartForConditionalGeneration(_UpperCAmelCase					)
  model.model.load_state_dict(_UpperCAmelCase					)

  if finetuned:
    A_  :       int										=  make_linear_from_emb(model.model.shared					)

  return model


if __name__ == "__main__":
   lowerCamelCase_     :						List[Any]						=					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
   )
   parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   parser.add_argument(
       '--hf_config',
       default='facebook/mbart-large-cc25',
       type=str,
       help='Which huggingface architecture to use: mbart-large',
   )
   parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
   parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
   lowerCamelCase_     :						Any						=					parser.parse_args()
   lowerCamelCase_     :						Tuple						=					convert_fairseq_mbart_checkpoint_from_disk(
       args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
   )
   model.save_pretrained(args.pytorch_dump_folder_path)






"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_     :						str						=					logging.get_logger(__name__)

lowerCamelCase_     :						Any						=					{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Tuple	    =   """xmod"""

      def __init__(     self					,      snake_case_=3_0_5_2_2					,      snake_case_=7_6_8					,      snake_case_=1_2					,      snake_case_=1_2					,      snake_case_=3_0_7_2					,      snake_case_="gelu"					,      snake_case_=0.1					,      snake_case_=0.1					,      snake_case_=5_1_2					,      snake_case_=2					,      snake_case_=0.02					,      snake_case_=1E-12					,      snake_case_=1					,      snake_case_=0					,      snake_case_=2					,      snake_case_="absolute"					,      snake_case_=True					,      snake_case_=None					,      snake_case_=False					,      snake_case_=2					,      snake_case_=False					,      snake_case_=True					,      snake_case_=True					,      snake_case_=("en_XX",)					,      snake_case_=None					,      **snake_case_					,      ):

        """simple docstring"""

        super().__init__(pad_token_id=snake_case_					,      bos_token_id=snake_case_					,      eos_token_id=snake_case_					,      **snake_case_  )

        A_  :       Union[str, Any]										=  vocab_size
        A_  :       Any										=  hidden_size
        A_  :       List[str]										=  num_hidden_layers
        A_  :       Tuple										=  num_attention_heads
        A_  :       int										=  hidden_act
        A_  :       Any										=  intermediate_size
        A_  :       Any										=  hidden_dropout_prob
        A_  :       Dict										=  attention_probs_dropout_prob
        A_  :       Union[str, Any]										=  max_position_embeddings
        A_  :       List[Any]										=  type_vocab_size
        A_  :       List[str]										=  initializer_range
        A_  :       Any										=  layer_norm_eps
        A_  :       Optional[Any]										=  position_embedding_type
        A_  :       int										=  use_cache
        A_  :       Dict										=  classifier_dropout
        A_  :       int										=  pre_norm
        A_  :       Optional[Any]										=  adapter_reduction_factor
        A_  :       List[Any]										=  adapter_layer_norm
        A_  :       int										=  adapter_reuse_layer_norm
        A_  :       Dict										=  ln_before_adapter
        A_  :       List[str]										=  list(snake_case_  )
        A_  :       Union[str, Any]										=  default_language


class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        if self.task == "multiple-choice":
          A_  :       Dict										=  {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
          A_  :       int										=  {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]  )