



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def A    (							__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     [
			    '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(__UpperCamelCase			,						__UpperCamelCase       )


def A    (							__UpperCamelCase       )    ->   int:
			A__  ,			A__	  =     emb.weight.shape
			A__	  =     nn.Linear(__UpperCamelCase			,						__UpperCamelCase			,						bias=__UpperCamelCase       )
			A__	  =     emb.weight.data
			return lin_layer


def A    (							__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     torch.load(__UpperCamelCase			,						map_location='cpu'       )
			A__	  =     mam_aaa['args'] or mam_aaa['cfg']['model']
			A__	  =     mam_aaa['model']
			remove_ignore_keys_(__UpperCamelCase       )
			A__	  =     state_dict['encoder.embed_tokens.weight'].shape[0]

			A__	  =     MaMaaaConfig(
			    vocab_size=__UpperCamelCase			,						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'			,						)

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

			return model


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   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.''')
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()
							SCREAMING_SNAKE_CASE__   			=   convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
							model.save_pretrained(args.pytorch_dump_folder_path)











def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    print('\nThe shortest path matrix using Floyd Warshall algorithm\n'				)
    for i in range(_SCREAMING_SNAKE_CASE				):
        for j in range(_SCREAMING_SNAKE_CASE				):
            if dist[i][j] != float('inf'				):
                print(int(dist[i][j]				)							,     end='\t'				)
            else:
                print('INF'							,     end='\t'				)
        print()





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    _A     =							[[float('inf'				) for _ in range(_SCREAMING_SNAKE_CASE				)] for _ in range(_SCREAMING_SNAKE_CASE				)]

    for i in range(_SCREAMING_SNAKE_CASE				):
        for j in range(_SCREAMING_SNAKE_CASE				):
            _A     =							graph[i][j]

            # check vertex k against all other vertices (i, j)
    for k in range(_SCREAMING_SNAKE_CASE				):
        # looping through rows of graph array
        for i in range(_SCREAMING_SNAKE_CASE				):
            # looping through columns of graph array
            for j in range(_SCREAMING_SNAKE_CASE				):
                if (
                    dist[i][k] != float('inf'				)
                    and dist[k][j] != float('inf'				)
                    and dist[i][k] + dist[k][j] < dist[i][j]
                ):
                    _A     =							dist[i][k] + dist[k][j]

    _print_dist(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    return dist, v


if __name__ == "__main__":
 __A							:			Dict         =			int(input("Enter number of vertices: "))
 __A							:			Union[str, Any]         =			int(input("Enter number of edges: "))

 __A							:			List[str]         =			[[float("inf") for i in range(v)] for j in range(v)]

 for i in range(v):
  __A							:			List[Any]         =			0.0

  # src and dst are indices that must be within the array size graph[e][v]
  # failure to follow this will result in an error
 for i in range(e):
  print("\nEdge ", i + 1)
  __A							:			Union[str, Any]         =			int(input("Enter source:"))
  __A							:			List[str]         =			int(input("Enter destination:"))
  __A							:			Union[str, Any]         =			float(input("Enter weight:"))
  __A							:			Any         =			weight

 floyd_warshall(graph, v)

 # Example Input
 # Enter number of vertices: 3
 # Enter number of edges: 2

 # # generated graph from vertex and edge inputs
 # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
 # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

 # specify source, destination and weight for edge #1
 # Edge  1
 # Enter source:1
 # Enter destination:2
 # Enter weight:2

 # specify source, destination and weight for edge #2
 # Edge  2
 # Enter source:2
 # Enter destination:1
 # Enter weight:1

 # # Expected Output from the vertice, edge and src, dst, weight inputs!!
 # 0		INF	INF
 # INF	0	2
 # INF	1	0





