



'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      snake_case__  :    List[Any]    						=      [
          """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(_lowerCAmelCase     ,   _lowerCAmelCase		)





def 						__snake_case(				_lowerCAmelCase		)		-> int:
      snake_case__						,				snake_case__  :    List[str]    						=      emb.weight.shape
      snake_case__  :    List[Any]    						=      nn.Linear(_lowerCAmelCase     ,   _lowerCAmelCase     ,   bias=_lowerCAmelCase		)
      snake_case__  :    str    						=      emb.weight.data
      return lin_layer





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    Optional[Any]    						=      torch.load(_lowerCAmelCase     ,   map_location="""cpu"""		)
      snake_case__  :    Any    						=      mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
      snake_case__  :    List[str]    						=      mam_aaa["""model"""]
      remove_ignore_keys_(_lowerCAmelCase		)
      snake_case__  :    Tuple    						=      state_dict["""encoder.embed_tokens.weight"""].shape[0]

      snake_case__  :    int    						=      MaMaaaConfig(
          vocab_size=_lowerCAmelCase     ,   max_position_embeddings=1_024     ,   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"""     ,   )

      snake_case__  :    int    						=      state_dict["""decoder.embed_tokens.weight"""]
      snake_case__  :    int    						=      MaMaaaForConditionalGeneration(_lowerCAmelCase		)
      model.model.load_state_dict(_lowerCAmelCase     ,   strict=_lowerCAmelCase		)
      snake_case__  :    Tuple    						=      make_linear_from_emb(model.model.shared		)

      return model


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

'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):

 def update_area_of_max_square(UpperCAmelCase		,     UpperCAmelCase     ) -> int:
  # BASE CASE
  if row >= rows or col >= cols:
   return 0

  lowercase__				:    int  =					update_area_of_max_square(UpperCAmelCase		,     col + 1     )
  lowercase__				:    Any  =					update_area_of_max_square(row + 1		,     col + 1     )
  lowercase__				:    str  =					update_area_of_max_square(row + 1		,     UpperCAmelCase     )

  if mat[row][col]:
   lowercase__				:    List[Any]  =					1 + min([right, diagonal, down]     )
   lowercase__				:    List[Any]  =					max(largest_square_area[0]		,     UpperCAmelCase     )
   return sub_problem_sol
  else:
   return 0

 lowercase__				:    Dict  =					[0]
 update_area_of_max_square(0		,     0     )
 return largest_square_area[0]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):

 def update_area_of_max_square_using_dp_array(
     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ) -> int:
  if row >= rows or col >= cols:
   return 0
  if dp_array[row][col] != -1:
   return dp_array[row][col]

  lowercase__				:    int  =					update_area_of_max_square_using_dp_array(UpperCAmelCase		,     col + 1		,     UpperCAmelCase     )
  lowercase__				:    Optional[int]  =					update_area_of_max_square_using_dp_array(row + 1		,     col + 1		,     UpperCAmelCase     )
  lowercase__				:    Any  =					update_area_of_max_square_using_dp_array(row + 1		,     UpperCAmelCase		,     UpperCAmelCase     )

  if mat[row][col]:
   lowercase__				:    Optional[int]  =					1 + min([right, diagonal, down]     )
   lowercase__				:    Any  =					max(largest_square_area[0]		,     UpperCAmelCase     )
   lowercase__				:    int  =					sub_problem_sol
   return sub_problem_sol
  else:
   return 0

 lowercase__				:    Any  =					[0]
 lowercase__				:    List[Any]  =					[[-1] * cols for _ in range(UpperCAmelCase     )]
 update_area_of_max_square_using_dp_array(0		,     0		,     UpperCAmelCase     )

 return largest_square_area[0]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Optional[int]  =					[[0] * (cols + 1) for _ in range(rows + 1     )]
 lowercase__				:    str  =					0
 for row in range(rows - 1		,     -1		,     -1     ):
  for col in range(cols - 1		,     -1		,     -1     ):
   lowercase__				:    str  =					dp_array[row][col + 1]
   lowercase__				:    Optional[Any]  =					dp_array[row + 1][col + 1]
   lowercase__				:    str  =					dp_array[row + 1][col]

   if mat[row][col] == 1:
    lowercase__				:    Dict  =					1 + min(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
    lowercase__				:    str  =					max(dp_array[row][col]		,     UpperCAmelCase     )
   else:
    lowercase__				:    Any  =					0

 return largest_square_area
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    List[str]  =					[0] * (cols + 1)
 lowercase__				:    str  =					[0] * (cols + 1)
 lowercase__				:    Tuple  =					0
 for row in range(rows - 1		,     -1		,     -1     ):
  for col in range(cols - 1		,     -1		,     -1     ):
   lowercase__				:    List[Any]  =					current_row[col + 1]
   lowercase__				:    Any  =					next_row[col + 1]
   lowercase__				:    Optional[Any]  =					next_row[col]

   if mat[row][col] == 1:
    lowercase__				:    List[str]  =					1 + min(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
    lowercase__				:    List[str]  =					max(current_row[col]		,     UpperCAmelCase     )
   else:
    lowercase__				:    int  =					0
  lowercase__				:    int  =					current_row

 return largest_square_area


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))

