



import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def      _A				(UpperCamelCase		:      Any )  ->Any:



   '''simple docstring'''







   lowerCamelCase__ :      List[str]								=		[
       """decoder.version""",
       """decoder.output_projection.weight""",
       """_float_tensor""",
       """decoder.embed_positions._float_tensor""",
   ]
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase      ,			UpperCamelCase )






def      _A				(UpperCamelCase		:      List[Any] )  ->int:



   '''simple docstring'''







   lowerCamelCase__ :      Tuple								=		emb.weight.shape
   lowerCamelCase__ :      int								=		nn.Linear(UpperCamelCase      ,			UpperCamelCase      ,			bias=UpperCamelCase )
   lowerCamelCase__ :      str								=		emb.weight.data
   return lin_layer






def      _A				(UpperCamelCase		:      Union[str, Any] )  ->List[Any]:



   '''simple docstring'''







   lowerCamelCase__ :      Any								=		torch.load(UpperCamelCase      ,			map_location="""cpu""" )
   lowerCamelCase__ :      Tuple								=		Namespace(**checkpoint["""cfg"""]["""model"""] )
   lowerCamelCase__ :      Optional[Any]								=		checkpoint["""model"""]
   remove_ignore_keys_(UpperCamelCase )
   lowerCamelCase__ :      Dict								=		state_dict["""decoder.embed_tokens.weight"""].shape[0]

   lowerCamelCase__ :      Union[str, Any]								=		{key.replace("""decoder"""      ,			"""model""" ): val for key, val in state_dict.items()}

   lowerCamelCase__ :      int								=		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      ,			)

   lowerCamelCase__ :      List[str]								=		XGLMForCausalLM(UpperCamelCase )
   lowerCamelCase__ :      Optional[int]								=		model.load_state_dict(UpperCamelCase      ,			strict=UpperCamelCase )
   print(UpperCamelCase )
   lowerCamelCase__ :      Optional[int]								=		make_linear_from_emb(model.model.embed_tokens )

   return model


if __name__ == "__main__":
    _lowercase     							=						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.''')
    _lowercase     							=						parser.parse_args()
    _lowercase     							=						convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
    model.save_pretrained(args.pytorch_dump_folder_path)








import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=A_      )
class 				__A					( A_      ):
			UpperCamelCase					:str     =			field(default='''automatic-speech-recognition'''					, metadata={'''include_in_asdict_even_if_is_default''': True}      )
			UpperCamelCase					:ClassVar[Features]     =			Features({'''audio''': Audio()}      )
			UpperCamelCase					:ClassVar[Features]     =			Features({'''transcription''': Value('''string'''      )}      )
			UpperCamelCase					:str     =			"audio"
			UpperCamelCase					:str     =			"transcription"





			def        _snake_case    (self							,    __magic_name__				):
						if self.audio_column not in features:
									raise ValueError(f"Column {self.audio_column} is not present in features."				)
						if not isinstance(features[self.audio_column]							,    __magic_name__				):
									raise ValueError(f"Column {self.audio_column} is not an Audio type."				)
						lowerCamelCase__ :      int								=		copy.deepcopy(self				)
						lowerCamelCase__ :      Tuple								=		self.input_schema.copy()
						lowerCamelCase__ :      Tuple								=		features[self.audio_column]
						lowerCamelCase__ :      List[str]								=		input_schema
						return task_template





			@property
			def        _snake_case    (self				):
						return {self.audio_column: "audio", self.transcription_column: "transcription"}



