





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM




def     A_      (	lowercase_	)      ->    Any:
       _snake_case   :				Tuple  					=    [
           '''decoder.version''',
           '''decoder.output_projection.weight''',
           '''_float_tensor''',
           '''decoder.embed_positions._float_tensor''',
       ]
       for k in ignore_keys:
              state_dict.pop(_UpperCAmelCase   ,		_UpperCAmelCase	)




def     A_      (	lowercase_	)      ->    Tuple:
       _snake_case					,     _snake_case   :				Any  					=    emb.weight.shape
       _snake_case   :				Optional[int]  					=    nn.Linear(_UpperCAmelCase   ,		_UpperCAmelCase   ,		bias=_UpperCAmelCase	)
       _snake_case   :				Optional[Any]  					=    emb.weight.data
       return lin_layer




def     A_      (	lowercase_	)      ->    List[str]:
       _snake_case   :				Optional[int]  					=    torch.load(_UpperCAmelCase   ,		map_location='''cpu'''	)
       _snake_case   :				List[str]  					=    Namespace(**checkpoint['''cfg''']['''model''']	)
       _snake_case   :				Optional[Any]  					=    checkpoint['''model''']
       remove_ignore_keys_(_UpperCAmelCase	)
       _snake_case   :				Tuple  					=    state_dict['''decoder.embed_tokens.weight'''].shape[0]

       _snake_case   :				Optional[int]  					=    {key.replace('''decoder'''   ,		'''model'''	): val for key, val in state_dict.items()}

       _snake_case   :				List[Any]  					=    XGLMConfig(
           vocab_size=_UpperCAmelCase   ,		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   ,		)

       _snake_case   :				List[str]  					=    XGLMForCausalLM(_UpperCAmelCase	)
       _snake_case   :				Optional[Any]  					=    model.load_state_dict(_UpperCAmelCase   ,		strict=_UpperCAmelCase	)
       print(_UpperCAmelCase	)
       _snake_case   :				Union[str, Any]  					=    make_linear_from_emb(model.model.embed_tokens	)

       return model


if __name__ == "__main__":
   lowerCAmelCase_				= 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_				= parser.parse_args()
   lowerCAmelCase_				= convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
   model.save_pretrained(args.pytorch_dump_folder_path)













# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

a__      :     Optional[int]         =		TypeVar('T')





class UpperCAmelCase_				( Generic[T] ):


  def __init__(							self ,__snake_case = True ):



       """simple docstring"""





       A_          =       {}  # dictionary of lists
       A_          =       directed


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ):



       """simple docstring"""





       if not self.directed:  # For undirected graphs
            # if both source vertex and destination vertex are both present in the
            # adjacency list, add destination vertex to source vertex list of adjacent
            # vertices and add source vertex to destination vertex list of adjacent
            # vertices.
            if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
                 self.adj_list[destination_vertex].append(__snake_case )
            # if only source vertex is present in adjacency list, add destination vertex
            # to source vertex list of adjacent vertices, then create a new vertex with
            # destination vertex as key and assign a list containing the source vertex
            # as it's first adjacent vertex.
            elif source_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
                 A_          =       [source_vertex]
            # if only destination vertex is present in adjacency list, add source vertex
            # to destination vertex list of adjacent vertices, then create a new vertex
            # with source vertex as key and assign a list containing the source vertex
            # as it's first adjacent vertex.
            elif destination_vertex in self.adj_list:
                 self.adj_list[destination_vertex].append(__snake_case )
                 A_          =       [destination_vertex]
            # if both source vertex and destination vertex are not present in adjacency
            # list, create a new vertex with source vertex as key and assign a list
            # containing the destination vertex as it's first adjacent vertex also
            # create a new vertex with destination vertex as key and assign a list
            # containing the source vertex as it's first adjacent vertex.
            else:
                 A_          =       [destination_vertex]
                 A_          =       [source_vertex]
       else:  # For directed graphs
            # if both source vertex and destination vertex are present in adjacency
            # list, add destination vertex to source vertex list of adjacent vertices.
            if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
            # if only source vertex is present in adjacency list, add destination
            # vertex to source vertex list of adjacent vertices and create a new vertex
            # with destination vertex as key, which has no adjacent vertex
            elif source_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
                 A_          =       []
            # if only destination vertex is present in adjacency list, create a new
            # vertex with source vertex as key and assign a list containing destination
            # vertex as first adjacent vertex
            elif destination_vertex in self.adj_list:
                 A_          =       [destination_vertex]
            # if both source vertex and destination vertex are not present in adjacency
            # list, create a new vertex with source vertex as key and a list containing
            # destination vertex as it's first adjacent vertex. Then create a new vertex
            # with destination vertex as key, which has no adjacent vertex
            else:
                 A_          =       [destination_vertex]
                 A_          =       []

       return self




  def __repr__(							self ):



       """simple docstring"""





       return pformat(self.adj_list )



