


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Any:
  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(__lowerCAmelCase							,   __lowerCAmelCase				)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				str:
  snake_case__				,				snake_case__						= emb.weight.shape
  snake_case__						= nn.Linear(__lowerCAmelCase							,   __lowerCAmelCase							,   bias=__lowerCAmelCase				)
  snake_case__						= emb.weight.data
  return lin_layer







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Dict:
  snake_case__						= torch.load(__lowerCAmelCase							,   map_location='''cpu'''				)
  snake_case__						= mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
  snake_case__						= mam_aaa['''model''']
  remove_ignore_keys_(__lowerCAmelCase				)
  snake_case__						= state_dict['''encoder.embed_tokens.weight'''].shape[0]

  snake_case__						= MaMaaaConfig(
      vocab_size=__lowerCAmelCase							,   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'''							,   )

  snake_case__						= state_dict['''decoder.embed_tokens.weight''']
  snake_case__						= MaMaaaForConditionalGeneration(__lowerCAmelCase				)
  model.model.load_state_dict(__lowerCAmelCase							,   strict=__lowerCAmelCase				)
  snake_case__						= make_linear_from_emb(model.model.shared				)

  return model


if __name__ == "__main__":
   lowerCamelCase__							:						Optional[int]							     =   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__							:						Optional[int]							     =   parser.parse_args()
   lowerCamelCase__							:						List[str]							     =   convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
   model.save_pretrained(args.pytorch_dump_folder_path)





def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2






def 		_A		(		_lowercase					,  _lowercase=0 )					-> Dict:


     """simple docstring"""


     return sorted(_lowercase					,  key=lambda _lowercase : x[column] )






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=float('inf' ) )					-> List[Any]:


     """simple docstring"""


     for i in range(points_counts - 1 ):
          for j in range(i + 1					,  _lowercase ):
               __UpperCamelCase =			euclidean_distance_sqr(points[i]					,  points[j] )
               if current_dis < min_dis:
                    __UpperCamelCase =			current_dis
     return min_dis






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=float('inf' ) )					-> Tuple:


     """simple docstring"""


     for i in range(min(6					,  points_counts - 1 )					,  _lowercase ):
          for j in range(max(0					,  i - 6 )					,  _lowercase ):
               __UpperCamelCase =			euclidean_distance_sqr(points[i]					,  points[j] )
               if current_dis < min_dis:
                    __UpperCamelCase =			current_dis
     return min_dis






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Optional[Any]:


     """simple docstring"""


     if points_counts <= 3:
          return dis_between_closest_pair(_lowercase					,  _lowercase )

     # recursion
     __UpperCamelCase =			points_counts // 2
     __UpperCamelCase =			closest_pair_of_points_sqr(
         _lowercase					,  points_sorted_on_y[:mid]					,  _lowercase )
     __UpperCamelCase =			closest_pair_of_points_sqr(
         _lowercase					,  points_sorted_on_y[mid:]					,  points_counts - mid )
     __UpperCamelCase =			min(_lowercase					,  _lowercase )
     __UpperCamelCase =			[]
     for point in points_sorted_on_x:
          if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
               cross_strip.append(_lowercase )

     __UpperCamelCase =			dis_between_closest_in_strip(
         _lowercase					,  len(_lowercase )					,  _lowercase )
     return min(_lowercase					,  _lowercase )






def 		_A		(		_lowercase					,  _lowercase )					-> Optional[int]:


     """simple docstring"""


     __UpperCamelCase =			column_based_sort(_lowercase					,  column=0 )
     __UpperCamelCase =			column_based_sort(_lowercase					,  column=1 )
     return (
         closest_pair_of_points_sqr(
             _lowercase					,  _lowercase					,  _lowercase )
     ) ** 0.5


if __name__ == "__main__":
    __snake_case   							=						[(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
    print('''Distance:''', closest_pair_of_points(points, len(points)))


