






'''simple docstring'''

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

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate






@dataclass(frozen=lowercase__       )
class        a_		(    lowercase__       ):
  lowercase  	=   field(default="""text-classification"""			, metadata={"""include_in_asdict_even_if_is_default""": True}       )
  lowercase  	=   Features({"""text""": Value("""string"""       )}       )
  lowercase  	=   Features({"""labels""": ClassLabel}       )
  lowercase  	=   """text"""
  lowercase  	=   """labels"""

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Optional[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]    ,	__lowerCamelCase   ):
              raise ValueError(F"Column {self.label_column} is not a ClassLabel."   )
        UpperCamelCase       						=      copy.deepcopy(self   )
        UpperCamelCase       						=      self.label_schema.copy()
        UpperCamelCase       						=      features[self.label_column]
        UpperCamelCase       						=      label_schema
        return task_template


  @property
  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






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













'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 					lowercase__				(   __UpperCamelCase					)->				Any:
      UpperCamelCase       						=      [
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(__UpperCamelCase	,		__UpperCamelCase					)







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase						,UpperCamelCase       						=      emb.weight.shape
      UpperCamelCase       						=      nn.Linear(__UpperCamelCase	,		__UpperCamelCase	,		bias=__UpperCamelCase					)
      UpperCamelCase       						=      emb.weight.data
      return lin_layer







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase       						=      torch.load(__UpperCamelCase	,		map_location="""cpu"""					)
      UpperCamelCase       						=      mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
      UpperCamelCase       						=      mam_aaa["""model"""]
      remove_ignore_keys_(__UpperCamelCase					)
      UpperCamelCase       						=      state_dict["""encoder.embed_tokens.weight"""].shape[0]

      UpperCamelCase       						=      MaMaaaConfig(
          vocab_size=__UpperCamelCase	,		max_position_embeddings=1024	,		encoder_layers=args.encoder_layers	,		decoder_layers=args.decoder_layers	,		encoder_attention_heads=args.encoder_attention_heads	,		decoder_attention_heads=args.decoder_attention_heads	,		encoder_ffn_dim=args.encoder_ffn_embed_dim	,		decoder_ffn_dim=args.decoder_ffn_embed_dim	,		d_model=args.encoder_embed_dim	,		encoder_layerdrop=args.encoder_layerdrop	,		decoder_layerdrop=args.decoder_layerdrop	,		dropout=args.dropout	,		attention_dropout=args.attention_dropout	,		activation_dropout=args.activation_dropout	,		activation_function="""relu"""	,		)

      UpperCamelCase       						=      state_dict["""decoder.embed_tokens.weight"""]
      UpperCamelCase       						=      MaMaaaForConditionalGeneration(__UpperCamelCase					)
      model.model.load_state_dict(__UpperCamelCase	,		strict=__UpperCamelCase					)
      UpperCamelCase       						=      make_linear_from_emb(model.model.shared					)

      return model


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     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.')
     SCREAMING_SNAKE_CASE__											=     parser.parse_args()
     SCREAMING_SNAKE_CASE__											=     convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)
