
'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """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_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				list(s_dict.keys()						)
   for key in keys:
      if "transformer_layers" in key:
         _SCREAMING_SNAKE_CASE      =				s_dict.pop(SCREAMING_SNAKE_CASE_						)
      elif "subsample" in key:
         _SCREAMING_SNAKE_CASE      =				s_dict.pop(SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """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_    ,      SCREAMING_SNAKE_CASE_						)     ->     Union[str, Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				torch.load(SCREAMING_SNAKE_CASE_    ,      map_location="""cpu"""						)
   _SCREAMING_SNAKE_CASE      =				mam_aaa["""args"""]
   _SCREAMING_SNAKE_CASE      =				mam_aaa["""model"""]
   _SCREAMING_SNAKE_CASE      =				state_dict["""decoder.output_projection.weight"""]

   remove_ignore_keys_(SCREAMING_SNAKE_CASE_						)
   rename_keys(SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				state_dict["""decoder.embed_tokens.weight"""].shape[0]

   _SCREAMING_SNAKE_CASE      =				args.share_decoder_input_output_embed

   _SCREAMING_SNAKE_CASE      =				[int(SCREAMING_SNAKE_CASE_						) for i in args.conv_kernel_sizes.split(""","""						)]
   _SCREAMING_SNAKE_CASE      =				SpeechaTextConfig(
       vocab_size=SCREAMING_SNAKE_CASE_    ,      max_source_positions=args.max_source_positions    ,      max_target_positions=args.max_target_positions    ,      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    ,      dropout=args.dropout    ,      attention_dropout=args.attention_dropout    ,      activation_dropout=args.activation_dropout    ,      activation_function="""relu"""    ,      num_conv_layers=len(SCREAMING_SNAKE_CASE_						)    ,      conv_channels=args.conv_channels    ,      conv_kernel_sizes=SCREAMING_SNAKE_CASE_    ,      input_feat_per_channel=args.input_feat_per_channel    ,      input_channels=args.input_channels    ,      tie_word_embeddings=SCREAMING_SNAKE_CASE_    ,      num_beams=5    ,      max_length=2_00    ,      use_cache=SCREAMING_SNAKE_CASE_    ,      decoder_start_token_id=2    ,      early_stopping=SCREAMING_SNAKE_CASE_    ,      )

   _SCREAMING_SNAKE_CASE      =				SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				model.model.load_state_dict(SCREAMING_SNAKE_CASE_    ,      strict=SCREAMING_SNAKE_CASE_						)
   if len(SCREAMING_SNAKE_CASE_						) > 0 and not set(SCREAMING_SNAKE_CASE_						) <= {
       "encoder.embed_positions.weights",
       "decoder.embed_positions.weights",
   }:
      raise ValueError(
          """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
          F" but all the following weights are missing {missing}"						)

   if tie_embeds:
      _SCREAMING_SNAKE_CASE      =				make_linear_from_emb(model.model.decoder.embed_tokens						)
   else:
      _SCREAMING_SNAKE_CASE      =				lm_head_weights

   model.save_pretrained(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
     UpperCamelCase__      :		List[str]       							=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
     parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
     UpperCamelCase__      :		Dict       							=  parser.parse_args()
     convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)








'''simple docstring'''



from __future__ import annotations
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Tuple:




   """simple docstring"""
   print(F"Vertex\tShortest Distance from vertex {src}"						)
   for i, d in enumerate(SCREAMING_SNAKE_CASE_						):
      print(F"{i}\t\t{d}"						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   for j in range(SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				(graph[j][k] for k in ["""src""", """dst""", """weight"""])
      if distance[u] != float("""inf"""						) and distance[u] + w < distance[v]:
         return True
   return False
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     list[float]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				[float("""inf"""						)] * vertex_count
   _SCREAMING_SNAKE_CASE      =				0.0

   for _ in range(vertex_count - 1						):
      for j in range(SCREAMING_SNAKE_CASE_						):
         _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				(graph[j][k] for k in ["""src""", """dst""", """weight"""])

         if distance[u] != float("""inf"""						) and distance[u] + w < distance[v]:
            _SCREAMING_SNAKE_CASE      =				distance[u] + w

   _SCREAMING_SNAKE_CASE      =				check_negative_cycle(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   if negative_cycle_exists:
      raise Exception("""Negative cycle found"""						)

   return distance


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     UpperCamelCase__      :		int       							=  int(input("Enter number of vertices: ").strip())
     UpperCamelCase__      :		int       							=  int(input("Enter number of edges: ").strip())

     UpperCamelCase__      :		list[dict[str, int]]       							=  [{} for _ in range(E)]

     for i in range(E):
          print("Edge ", i + 1)
          UpperCamelCase__							,      UpperCamelCase__							,      UpperCamelCase__      :		Dict       							=  (
              int(x)
              for x in input("Enter source, destination, weight: ").strip().split(" ")
          )
          UpperCamelCase__      :		Optional[Any]       							=  {"src": src, "dst": dest, "weight": weight}

     UpperCamelCase__      :		Optional[Any]       							=  int(input("\nEnter shortest path source:").strip())
     UpperCamelCase__      :		Any       							=  bellman_ford(graph, V, E, source)
     print_distance(shortest_distance, 0)






