


"""simple docstring"""


import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def  __snake_case       (   UpperCamelCase__	)      ->		List[Any]:







  """simple docstring"""





  A					       = [
      'decoder.version',
      'decoder.output_projection.weight',
      '_float_tensor',
      'decoder.embed_positions._float_tensor',
  ]
  for k in ignore_keys:
    state_dict.pop(UpperCamelCase__ ,			UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		int:







  """simple docstring"""





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







def  __snake_case       (   UpperCamelCase__	)      ->		str:







  """simple docstring"""





  A					       = torch.load(UpperCamelCase__ ,			map_location='cpu'	)
  A					       = Namespace(**checkpoint['cfg']['model']	)
  A					       = checkpoint['model']
  remove_ignore_keys_(UpperCamelCase__	)
  A					       = state_dict['decoder.embed_tokens.weight'].shape[0]

  A					       = {key.replace('decoder' ,			'model'	): val for key, val in state_dict.items()}

  A					       = XGLMConfig(
      vocab_size=UpperCamelCase__ ,			max_position_embeddings=args.max_target_positions ,			num_layers=args.decoder_layers ,			attention_heads=args.decoder_attention_heads ,			ffn_dim=args.decoder_ffn_embed_dim ,			d_model=args.decoder_embed_dim ,			layerdrop=args.decoder_layerdrop ,			dropout=args.dropout ,			attention_dropout=args.attention_dropout ,			activation_dropout=args.activation_dropout ,			activation_function='gelu' ,			scale_embedding=not args.no_scale_embedding ,			tie_word_embeddings=args.share_decoder_input_output_embed ,			)

  A					       = XGLMForCausalLM(UpperCamelCase__	)
  A					       = model.load_state_dict(UpperCamelCase__ ,			strict=UpperCamelCase__	)
  print(UpperCamelCase__	)
  A					       = make_linear_from_emb(model.model.embed_tokens	)

  return model


if __name__ == "__main__":
 UpperCamelCase						:				Any             =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument("fairseq_path", type=str, help="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.")
 UpperCamelCase						:				Optional[Any]             =      parser.parse_args()
 UpperCamelCase						:				str             =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
 model.save_pretrained(args.pytorch_dump_folder_path)









"""simple docstring"""


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



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """ClapFeatureExtractor"""
						lowerCAmelCase       =     ("""RobertaTokenizer""", """RobertaTokenizerFast""")







						def __init__(							self   :    Dict			,			_lowercase   :    List[str]			,			_lowercase   :    Optional[int]		):
								super().__init__(_lowercase			,			_lowercase		)







						def __call__(							self   :    Optional[int]			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Dict=None			,			_lowercase   :    str=None			,			**_lowercase   :    Union[str, Any]		):
								A					       = kwargs.pop('sampling_rate'			,			_lowercase		)

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

								if text is not None:
										A					       = self.tokenizer(_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if audios is not None:
										A					       = self.feature_extractor(
										    _lowercase			,			sampling_rate=_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if text is not None and audios is not None:
										A					       = audio_features.input_features
										return encoding
								elif text is not None:
										return encoding
								else:
										return BatchEncoding(data=dict(**_lowercase		)			,			tensor_type=_lowercase		)







						def __a						(							self   :    List[Any]			,			*_lowercase   :    Dict			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.batch_decode(*_lowercase			,			**_lowercase		)







						def __a						(							self   :    List[str]			,			*_lowercase   :    str			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.decode(*_lowercase			,			**_lowercase		)







						@property
						def __a						(							self   :    Tuple		):
								A					       = self.tokenizer.model_input_names
								A					       = self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names		)		)





