'''simple docstring'''





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Any			)							->      List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   [
      """decoder.version""",
      """decoder.output_projection.weight""",
      """_float_tensor""",
      """decoder.embed_positions._float_tensor""",
  ]
  for k in ignore_keys:
    state_dict.pop(__UpperCamelCase				,  __UpperCamelCase			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   emb.weight.shape
  SCREAMING_SNAKE_CASE__    							=   nn.Linear(__UpperCamelCase				,  __UpperCamelCase				,  bias=__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   emb.weight.data
  return lin_layer



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str			)							->      Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   torch.load(__UpperCamelCase				,  map_location="""cpu"""			)
  SCREAMING_SNAKE_CASE__    							=   Namespace(**checkpoint["""cfg"""]["""model"""]			)
  SCREAMING_SNAKE_CASE__    							=   checkpoint["""model"""]
  remove_ignore_keys_(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   state_dict["""decoder.embed_tokens.weight"""].shape[0]

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

  SCREAMING_SNAKE_CASE__    							=   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				,  )

  SCREAMING_SNAKE_CASE__    							=   XGLMForCausalLM(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   model.load_state_dict(__UpperCamelCase				,  strict=__UpperCamelCase			)
  print(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   make_linear_from_emb(model.model.embed_tokens			)

  return model


if __name__ == "__main__":
   __lowerCamelCase    :				Dict							     =	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.''')
   __lowerCamelCase    :				Dict							     =	parser.parse_args()
   __lowerCamelCase    :				Optional[int]							     =	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 ClassLabel, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowerCamelCase_	)
class __snake_case       (		lowerCamelCase_	):
   # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
   lowerCAmelCase_														=							field(default="text-classification"     ,	metadata={"include_in_asdict_even_if_is_default": True}	)
   lowerCAmelCase_														=							Features({"text": Value("string"	)}	)
   lowerCAmelCase_														=							Features({"labels": ClassLabel}	)
   lowerCAmelCase_														=							"text"
   lowerCAmelCase_														=							"labels"




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     if self.label_column not in features:
       raise ValueError(f"""Column {self.label_column} is not present in features."""   )
     if not isinstance(features[self.label_column] ,			_lowercase   ):
       raise ValueError(f"""Column {self.label_column} is not a ClassLabel."""   )
     SCREAMING_SNAKE_CASE__    							=   copy.deepcopy(self   )
     SCREAMING_SNAKE_CASE__    							=   self.label_schema.copy()
     SCREAMING_SNAKE_CASE__    							=   features[self.label_column]
     SCREAMING_SNAKE_CASE__    							=   label_schema
     return task_template




   @property
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     return {
         self.text_column: "text",
         self.label_column: "labels",
     }
