'''simple docstring'''




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Union[str, 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(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_				,lowercase_					:       Optional[Any]     				=					emb.weight.shape
     lowercase_					:       List[str]     				=					nn.Linear(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       bias=SCREAMING_SNAKE_CASE_							)
     lowercase_					:       int     				=					emb.weight.data
     return lin_layer

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Tuple     				=					torch.load(SCREAMING_SNAKE_CASE_  ,       map_location='cpu'							)
     lowercase_					:       List[str]     				=					mam_aaa['args'] or mam_aaa['cfg']['model']
     lowercase_					:       Tuple     				=					mam_aaa['model']
     remove_ignore_keys_(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Optional[int]     				=					state_dict['encoder.embed_tokens.weight'].shape[0]

     lowercase_					:       str     				=					MaMaaaConfig(
         vocab_size=SCREAMING_SNAKE_CASE_  ,       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'  ,       )

     lowercase_					:       Dict     				=					state_dict['decoder.embed_tokens.weight']
     lowercase_					:       int     				=					MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_							)
     model.model.load_state_dict(SCREAMING_SNAKE_CASE_  ,       strict=SCREAMING_SNAKE_CASE_							)
     lowercase_					:       int     				=					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   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[Any]     				=					len(SCREAMING_SNAKE_CASE_							)
     for _ in range(SCREAMING_SNAKE_CASE_							):
          for i in range(_ % 2  ,       arr_size - 1  ,       2							):
               if arr[i + 1] < arr[i]:
                    lowercase_				,lowercase_					:       Optional[Any]     				=					arr[i + 1], arr[i]

     return arr


if __name__ == "__main__":
 _A								=   list(range(1_0, 0, -1))
 print(F"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")




