





"""simple docstring"""




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def 					lowerCAmelCase_			(	__a		)					->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        =[
      "decoder.version",
      "decoder.output_projection.weight",
      "_float_tensor",
      "decoder.embed_positions._float_tensor",
  ]
  for k in ignore_keys:
    state_dict.pop(__a     ,   __a		)





def 					lowerCAmelCase_			(	__a		)					->		Any:







  """simple docstring"""





  lowerCamelCase__:				Optional[Any]        =emb.weight.shape
  lowerCamelCase__:				Dict        =nn.Linear(__a     ,   __a     ,   bias=__a		)
  lowerCamelCase__:				Optional[int]        =emb.weight.data
  return lin_layer





def 					lowerCAmelCase_			(	__a		)					->		Any:







  """simple docstring"""





  lowerCamelCase__:				int        =torch.load(__a     ,   map_location="cpu"		)
  lowerCamelCase__:				str        =Namespace(**checkpoint["cfg"]["model"]		)
  lowerCamelCase__:				Any        =checkpoint["model"]
  remove_ignore_keys_(__a		)
  lowerCamelCase__:				Dict        =state_dict["decoder.embed_tokens.weight"].shape[0]

  lowerCamelCase__:				Union[str, Any]        ={key.replace("decoder"     ,   "model"		): val for key, val in state_dict.items()}

  lowerCamelCase__:				List[str]        =XGLMConfig(
      vocab_size=__a     ,   max_position_embeddings=args.max_target_positions     ,   num_layers=args.decoder_layers     ,   attention_heads=args.decoder_attention_heads     ,   ffn_dim=args.decoder_ffn_embed_dim     ,   d_model=args.decoder_embed_dim     ,   layerdrop=args.decoder_layerdrop     ,   dropout=args.dropout     ,   attention_dropout=args.attention_dropout     ,   activation_dropout=args.activation_dropout     ,   activation_function="gelu"     ,   scale_embedding=not args.no_scale_embedding     ,   tie_word_embeddings=args.share_decoder_input_output_embed     ,   )

  lowerCamelCase__:				Optional[Any]        =XGLMForCausalLM(__a		)
  lowerCamelCase__:				str        =model.load_state_dict(__a     ,   strict=__a		)
  print(__a		)
  lowerCamelCase__:				int        =make_linear_from_emb(model.model.embed_tokens		)

  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_xglm_checkpoint_from_disk(args.fairseq_path)
     model.save_pretrained(args.pytorch_dump_folder_path)








from __future__ import annotations

from typing import Any



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self    :       Any	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   lowerCamelCase__:				int        =num_of_nodes
   lowerCamelCase__:				list[list[int]]        =[]
   lowerCamelCase__:				dict[int, int]        ={}

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   self.m_edges.append([u_node, v_node, weight])

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       int)	->int:



   '''simple docstring'''



   if self.m_component[u_node] == u_node:
     return u_node
   return self.find_component(self.m_component[u_node])

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   if self.m_component[u_node] != u_node:
     for k in self.m_component:
       lowerCamelCase__:				Dict        =self.find_component(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       list[int]	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   if component_size[u_node] <= component_size[v_node]:
     lowerCamelCase__:				Optional[int]        =v_node
     component_size[v_node] += component_size[u_node]
     self.set_component(UpperCAmelCase_)

   elif component_size[u_node] >= component_size[v_node]:
     lowerCamelCase__:				Tuple        =self.find_component(UpperCAmelCase_)
     component_size[u_node] += component_size[v_node]
     self.set_component(UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->None:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =[]
   lowerCamelCase__:				List[str]        =0

   lowerCamelCase__:				list[Any]        =[-1] * self.m_num_of_nodes

   # A list of components (initialized to all of the nodes)
   for node in range(self.m_num_of_nodes):
     self.m_component.update({node: node})
     component_size.append(1)

   lowerCamelCase__:				List[str]        =self.m_num_of_nodes

   while num_of_components > 1:
     for edge in self.m_edges:
       lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Tuple        =edge

       lowerCamelCase__:				List[Any]        =self.m_component[u]
       lowerCamelCase__:				str        =self.m_component[v]

       if u_component != v_component:

         for component in (u_component, v_component):
           if (
               minimum_weight_edge[component] == -1
               or minimum_weight_edge[component][2] > w
           ):
             lowerCamelCase__:				Union[str, Any]        =[u, v, w]

     for edge in minimum_weight_edge:
       if isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
         lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Tuple        =edge

         lowerCamelCase__:				str        =self.m_component[u]
         lowerCamelCase__:				Any        =self.m_component[v]

         if u_component != v_component:
           mst_weight += w
           self.union(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_)
           print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n""")
           num_of_components -= 1

     lowerCamelCase__:				Tuple        =[-1] * self.m_num_of_nodes
   print(F"""The total weight of the minimal spanning tree is: {mst_weight}""")





def 					lowerCAmelCase_			(	)					->		None:







  """simple docstring"""







if __name__ == "__main__":
     import doctest

     doctest.testmod()

