



"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification


def   lowercase__     (			_UpperCAmelCase				)     ->			Union[str, Any]:






						'''simple docstring'''


						lowercase  :      Union[str, Any]					=   [
						    'encoder.version',
						    'decoder.version',
						    'model.encoder.version',
						    'model.decoder.version',
						    '_float_tensor',
						    'decoder.output_projection.weight',
						]
						for k in ignore_keys:
												state_dict.pop(lowercase__	,      lowercase__				)


def   lowercase__     (			_UpperCAmelCase				)     ->			List[Any]:






						'''simple docstring'''


						lowercase	,    lowercase  :      Dict					=   emb.weight.shape
						lowercase  :      List[Any]					=   nn.Linear(lowercase__	,      lowercase__	,      bias=lowercase__				)
						lowercase  :      List[Any]					=   emb.weight.data
						return lin_layer


def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase="uclanlp/plbart-base"	,      _UpperCAmelCase=False	,      _UpperCAmelCase=False				)     ->			Optional[Any]:






						'''simple docstring'''


						lowercase  :      Tuple					=   torch.load(lowercase__	,      map_location='cpu'				)['model']
						remove_ignore_keys_(lowercase__				)
						lowercase  :      Tuple					=   state_dict['encoder.embed_tokens.weight'].shape[0]

						lowercase  :      int					=   PLBartConfig.from_pretrained(lowercase__	,      vocab_size=lowercase__				)

						lowercase  :      Optional[int]					=   state_dict['decoder.embed_tokens.weight']
						if not classification:
												lowercase  :      List[Any]					=   PLBartForConditionalGeneration(lowercase__				)
												model.model.load_state_dict(lowercase__				)
												if finetuned:
																		lowercase  :      int					=   make_linear_from_emb(model.model.shared				)

						else:
												lowercase  :      Tuple					=   {}
												for key, value in state_dict.copy().items():
																		if key.startswith('classification_heads.sentence_classification_head'				):
																								lowercase  :      Union[str, Any]					=   value
																								state_dict.pop(lowercase__				)
												lowercase  :      Union[str, Any]					=   PLBartForSequenceClassification(lowercase__				)
												model.model.load_state_dict(lowercase__				)
												model.classification_head.load_state_dict(lowercase__				)

						return model


if __name__ == "__main__":
					_UpperCamelCase:				str    							=  argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('fairseq_path', type=str, help='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='uclanlp/plbart-base',
					    type=str,
					    help='Which huggingface architecture to use: plbart-base',
					)
					parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
					parser.add_argument(
					    '--classification', action='store_true', help='whether the model is a classification checkpoint'
					)
					_UpperCamelCase:				Any    							=  parser.parse_args()
					_UpperCamelCase:				List[Any]    							=  convert_fairseq_plbart_checkpoint_from_disk(
					    args.fairseq_path,
					    hf_config_path=args.hf_config,
					    finetuned=args.finetuned,
					    classification=args.classification,
					)
					model.save_pretrained(args.pytorch_dump_folder_path)





"""simple docstring"""




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				'AutoFeatureExtractor'
   _lowerCamelCase												=				'Speech2Text2Tokenizer'


   def __init__(							self      :							str,     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Optional[Any]			)    ->       Dict:
         super().__init__(lowerCAmelCase,     lowerCAmelCase			)
         lowercase  :      Tuple					=   self.feature_extractor
         lowercase  :      Any					=   False


   def __call__(							self      :							Tuple,     *lowerCAmelCase      :							Tuple,     **lowerCAmelCase      :							Tuple			)    ->       Dict:
         # For backward compatibility
         if self._in_target_context_manager:
               return self.current_processor(*lowerCAmelCase,     **lowerCAmelCase			)

         if "raw_speech" in kwargs:
               warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
               lowercase  :      str					=   kwargs.pop('raw_speech'			)
         else:
               lowercase  :      str					=   kwargs.pop('audio',     lowerCAmelCase			)
         lowercase  :      Dict					=   kwargs.pop('sampling_rate',     lowerCAmelCase			)
         lowercase  :      Optional[Any]					=   kwargs.pop('text',     lowerCAmelCase			)
         if len(lowerCAmelCase			) > 0:
               lowercase  :      Any					=   args[0]
               lowercase  :      int					=   args[1:]

         if audio is None and text is None:
               raise ValueError('You need to specify either an `audio` or `text` input to process.'			)

         if audio is not None:
               lowercase  :      List[Any]					=   self.feature_extractor(lowerCAmelCase,     *lowerCAmelCase,     sampling_rate=lowerCAmelCase,     **lowerCAmelCase			)
         if text is not None:
               lowercase  :      str					=   self.tokenizer(lowerCAmelCase,     **lowerCAmelCase			)

         if text is None:
               return inputs
         elif audio is None:
               return encodings
         else:
               lowercase  :      Tuple					=   encodings['input_ids']
               return inputs


   def        lowercase				(							self      :							str,     *lowerCAmelCase      :							List[str],     **lowerCAmelCase      :							Union[str, Any]			)    ->       str:
         return self.tokenizer.batch_decode(*lowerCAmelCase,     **lowerCAmelCase			)


   def        lowercase				(							self      :							Tuple,     *lowerCAmelCase      :							Union[str, Any],     **lowerCAmelCase      :							int			)    ->       Dict:
         return self.tokenizer.decode(*lowerCAmelCase,     **lowerCAmelCase			)






   @contextmanager
   def        lowercase				(							self      :							str			)    ->       Any:
         warnings.warn(
             '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
             'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
             'your audio inputs, or in a separate call.'			)
         lowercase  :      Optional[Any]					=   True
         lowercase  :      Any					=   self.tokenizer
         yield
         lowercase  :      Any					=   self.feature_extractor
         lowercase  :      Any					=   False
