
'''simple docstring'''



from __future__ import annotations

import numpy as np
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     tuple[np.ndarray, np.ndarray]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				np.shape(SCREAMING_SNAKE_CASE_						)
   if rows != columns:
      _SCREAMING_SNAKE_CASE      =				(
          """'table' has to be of square shaped array but got a """
          F"{rows}x{columns} array:\n{table}"
      )
      raise ValueError(SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				np.zeros((rows, columns)						)
   _SCREAMING_SNAKE_CASE      =				np.zeros((rows, columns)						)
   for i in range(SCREAMING_SNAKE_CASE_						):
      for j in range(SCREAMING_SNAKE_CASE_						):
         _SCREAMING_SNAKE_CASE      =				sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE_						)						)
         if upper[j][j] == 0:
            raise ArithmeticError("""No LU decomposition exists"""						)
         _SCREAMING_SNAKE_CASE      =				(table[i][j] - total) / upper[j][j]
      _SCREAMING_SNAKE_CASE      =				1
      for j in range(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
         _SCREAMING_SNAKE_CASE      =				sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE_						)						)
         _SCREAMING_SNAKE_CASE      =				table[i][j] - total
   return lower, upper


if __name__ == "__main__":
     import doctest

     doctest.testmod()








'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				[
       """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(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				emb.weight.shape
   _SCREAMING_SNAKE_CASE      =				nn.Linear(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      bias=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				emb.weight.data
   return lin_layer
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				torch.load(SCREAMING_SNAKE_CASE_    ,      map_location="""cpu"""						)
   _SCREAMING_SNAKE_CASE      =				mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
   _SCREAMING_SNAKE_CASE      =				mam_aaa["""model"""]
   remove_ignore_keys_(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				state_dict["""encoder.embed_tokens.weight"""].shape[0]

   _SCREAMING_SNAKE_CASE      =				MaMaaaConfig(
       vocab_size=SCREAMING_SNAKE_CASE_    ,      max_position_embeddings=10_24    ,      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"""    ,      )

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

   return model


if __name__ == "__main__":
     UpperCamelCase__      :		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.")
     UpperCamelCase__      :		List[str]       							=  parser.parse_args()
     UpperCamelCase__      :		Any       							=  convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)






