






'''simple docstring'''







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class _lowercase	(				lowercase_		):
							a				      =       """Speech2TextFeatureExtractor"""
							a				      =       """Speech2TextTokenizer"""




							def __init__(							self:				Dict			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				Optional[Any]     ):
														super().__init__(UpperCamelCase__			,							UpperCamelCase__     )
														lowerCamelCase__					:	Optional[int]         =       self.feature_extractor
														lowerCamelCase__					:	str         =       False




							def __call__(							self:				Dict			,							*UpperCamelCase__:				Optional[Any]			,							**UpperCamelCase__:				List[Any]     ):







														if self._in_target_context_manager:
																					return self.current_processor(*UpperCamelCase__			,							**UpperCamelCase__     )

														if "raw_speech" in kwargs:
																					warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""     )
																					lowerCamelCase__					:	int         =       kwargs.pop("""raw_speech"""     )
														else:
																					lowerCamelCase__					:	Dict         =       kwargs.pop("""audio"""			,							UpperCamelCase__     )
														lowerCamelCase__					:	str         =       kwargs.pop("""sampling_rate"""			,							UpperCamelCase__     )
														lowerCamelCase__					:	List[str]         =       kwargs.pop("""text"""			,							UpperCamelCase__     )
														if len(UpperCamelCase__     ) > 0:
																					lowerCamelCase__					:	int         =       args[0]
																					lowerCamelCase__					:	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:
																					lowerCamelCase__					:	Any         =       self.feature_extractor(UpperCamelCase__			,							*UpperCamelCase__			,							sampling_rate=UpperCamelCase__			,							**UpperCamelCase__     )
														if text is not None:
																					lowerCamelCase__					:	int         =       self.tokenizer(UpperCamelCase__			,							**UpperCamelCase__     )

														if text is None:
																					return inputs
														elif audio is None:
																					return encodings
														else:
																					lowerCamelCase__					:	Union[str, Any]         =       encodings["""input_ids"""]
																					return inputs




							def 		lowerCamelCase_				(							self:				str			,							*UpperCamelCase__:				List[Any]			,							**UpperCamelCase__:				Dict     ):
														return self.tokenizer.batch_decode(*UpperCamelCase__			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							*UpperCamelCase__:				Dict			,							**UpperCamelCase__:				List[Any]     ):
														return self.tokenizer.decode(*UpperCamelCase__			,							**UpperCamelCase__     )




							@contextmanager
							def 		lowerCamelCase_				(							self:				Union[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."""     )
														lowerCamelCase__					:	List[str]         =       True
														lowerCamelCase__					:	Optional[int]         =       self.tokenizer
														yield
														lowerCamelCase__					:	List[str]         =       self.feature_extractor
														lowerCamelCase__					:	Optional[int]         =       False





'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= [
           '''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 _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







       lowercase__			,lowercase__      			= emb.weight.shape
       lowercase__      			= nn.Linear(A  , A  , bias=A       )
       lowercase__      			= emb.weight.data
       return lin_layer

def _SCREAMING_SNAKE_CASE      (A  , A="facebook/mbart-large-en-ro"  , A=False  , A=False       )       ->       Union[str, Any]:






       """simple docstring"""







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

       lowercase__      			= MBartConfig.from_pretrained(A  , vocab_size=A       )
       if mbart_aa and finetuned:
              lowercase__      			= '''relu'''

       lowercase__      			= state_dict['''decoder.embed_tokens.weight''']
       lowercase__      			= MBartForConditionalGeneration(A       )
       model.model.load_state_dict(A       )

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

       return model


if __name__ == "__main__":
 lowerCamelCase							:   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							:   List[str]  						=	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)




