import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Union[str, Any]      =			['image_processor', 'tokenizer']
   __UpperCAmelCase : Dict      =			'CLIPImageProcessor'
   __UpperCAmelCase : Any      =			('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						str=None       ,  __UpperCAmelCase     :						List[Any]=None       ,  **__UpperCAmelCase     :						Dict		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  None
         if "feature_extractor" in kwargs:
               warnings.warn(
                   "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                   " instead."       ,  __UpperCAmelCase       ,  )
               UpperCAmelCase__								=  kwargs.pop("feature_extractor"		)

         UpperCAmelCase__								=  image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
               raise ValueError("You need to specify an `image_processor`."		)
         if tokenizer is None:
               raise ValueError("You need to specify a `tokenizer`."		)

         super().__init__(__UpperCAmelCase       ,  __UpperCAmelCase		)


   def __call__(self     :						Optional[int]       ,  __UpperCAmelCase     :						Optional[Any]=None       ,  __UpperCAmelCase     :						str=None       ,  __UpperCAmelCase     :						Optional[Any]=None       ,  **__UpperCAmelCase     :						Dict		)						->				Any:
         """simple docstring"""



         if text is None and images is None:
               raise ValueError("You have to specify either text or images. Both cannot be none."		)

         if text is not None:
               UpperCAmelCase__								=  self.tokenizer(__UpperCAmelCase       ,  return_tensors=__UpperCAmelCase       ,  **__UpperCAmelCase		)

         if images is not None:
               UpperCAmelCase__								=  self.image_processor(__UpperCAmelCase       ,  return_tensors=__UpperCAmelCase       ,  **__UpperCAmelCase		)

         if text is not None and images is not None:
               UpperCAmelCase__								=  image_features.pixel_values
               return encoding
         elif text is not None:
               return encoding
         else:
               return BatchEncoding(data=dict(**__UpperCAmelCase		)       ,  tensor_type=__UpperCAmelCase		)


   def 			lowercase_      (self     :						Any       ,  *__UpperCAmelCase     :						Union[str, Any]       ,  **__UpperCAmelCase     :						Dict		)						->				int:
         """simple docstring"""



         return self.tokenizer.batch_decode(*__UpperCAmelCase       ,  **__UpperCAmelCase		)


   def 			lowercase_      (self     :						Any       ,  *__UpperCAmelCase     :						List[Any]       ,  **__UpperCAmelCase     :						Dict		)						->				Optional[int]:
         """simple docstring"""



         return self.tokenizer.decode(*__UpperCAmelCase       ,  **__UpperCAmelCase		)





   @property
   def 			lowercase_      (self     :						str		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  self.tokenizer.model_input_names
         UpperCAmelCase__								=  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)




import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def    lowerCAmelCase_ (     __A							)     ->				Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  [
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "_float_tensor",
          "decoder.output_projection.weight",
      ]
      for k in ignore_keys:
            state_dict.pop(__A,  __A							)




def    lowerCAmelCase_ (     __A							)     ->				Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__		,      UpperCAmelCase__								=  emb.weight.shape
      UpperCAmelCase__								=  nn.Linear(__A,  __A,  bias=__A							)
      UpperCAmelCase__								=  emb.weight.data
      return lin_layer




def    lowerCAmelCase_ (     __A,  __A="facebook/mbart-large-en-ro",  __A=False,  __A=False							)     ->				Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  torch.load(__A,  map_location="cpu"							)["model"]
      remove_ignore_keys_(__A							)
      UpperCAmelCase__								=  state_dict["encoder.embed_tokens.weight"].shape[0]

      UpperCAmelCase__								=  MBartConfig.from_pretrained(__A,  vocab_size=__A							)
      if mbart_aa and finetuned:
            UpperCAmelCase__								=  "relu"

      UpperCAmelCase__								=  state_dict["decoder.embed_tokens.weight"]
      UpperCAmelCase__								=  MBartForConditionalGeneration(__A							)
      model.model.load_state_dict(__A							)

      if finetuned:
            UpperCAmelCase__								=  make_linear_from_emb(model.model.shared							)

      return model


if __name__ == "__main__":
      UpperCamelCase__											=					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')
      UpperCamelCase__											=					parser.parse_args()
      UpperCamelCase__											=					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)



