






import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   [
				    '''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(snake_case,	snake_case			)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[int]   =   list(s_dict.keys()			)
				for key in keys:
								if "transformer_layers" in key:
												__magic_name__		:Tuple   =   s_dict.pop(snake_case			)
								elif "subsample" in key:
												__magic_name__		:Optional[int]   =   s_dict.pop(snake_case			)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__  ,					__magic_name__		:Optional[int]   =   emb.weight.shape
				__magic_name__		:Optional[Any]   =   nn.Linear(snake_case,	snake_case,	bias=snake_case			)
				__magic_name__		:List[str]   =   emb.weight.data
				return lin_layer

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[Any]   =   torch.load(snake_case,	map_location='''cpu'''			)
				__magic_name__		:List[Any]   =   mam_aaa['''args''']
				__magic_name__		:List[str]   =   mam_aaa['''model''']
				__magic_name__		:Optional[Any]   =   state_dict['''decoder.output_projection.weight''']

				remove_ignore_keys_(snake_case			)
				rename_keys(snake_case			)

				__magic_name__		:List[Any]   =   state_dict['''decoder.embed_tokens.weight'''].shape[0]

				__magic_name__		:Union[str, Any]   =   args.share_decoder_input_output_embed

				__magic_name__		:Any   =   [int(snake_case			) for i in args.conv_kernel_sizes.split(''','''			)]
				__magic_name__		:List[Any]   =   SpeechaTextConfig(
				    vocab_size=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(snake_case			),	conv_channels=args.conv_channels,	conv_kernel_sizes=snake_case,	input_feat_per_channel=args.input_feat_per_channel,	input_channels=args.input_channels,	tie_word_embeddings=snake_case,	num_beams=5,	max_length=2_0_0,	use_cache=snake_case,	decoder_start_token_id=2,	early_stopping=snake_case,	)

				__magic_name__		:str   =   SpeechaTextForConditionalGeneration(snake_case			)
				__magic_name__  ,					__magic_name__		:List[Any]   =   model.model.load_state_dict(snake_case,	strict=snake_case			)
				if len(snake_case			) > 0 and not set(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:
								__magic_name__		:Tuple   =   make_linear_from_emb(model.model.decoder.embed_tokens			)
				else:
								__magic_name__		:Optional[Any]   =   lm_head_weights

				model.save_pretrained(snake_case			)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      Dict				=		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.""")
					SCREAMING_SNAKE_CASE__   :      str				=		parser.parse_args()
					convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)














from __future__ import annotations

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				print(f'''Vertex\tShortest Distance from vertex {src}'''			)
				for i, d in enumerate(snake_case			):
								print(f'''{i}\t\t{d}'''			)

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				for j in range(snake_case			):
								__magic_name__  ,					__magic_name__  ,					__magic_name__		:Tuple   =   (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   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[Any]   =   [float('''inf'''			)] * vertex_count
				__magic_name__		:Tuple   =   0.0

				for _ in range(vertex_count - 1			):
								for j in range(snake_case			):
												__magic_name__  ,					__magic_name__  ,					__magic_name__		:Dict   =   (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

												if distance[u] != float('''inf'''			) and distance[u] + w < distance[v]:
																__magic_name__		:Tuple   =   distance[u] + w

				__magic_name__		:Tuple   =   check_negative_cycle(snake_case,	snake_case,	snake_case			)
				if negative_cycle_exists:
								raise Exception('''Negative cycle found'''			)

				return distance


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					SCREAMING_SNAKE_CASE__   :      Tuple				=		int(input("""Enter number of vertices: """).strip())
					SCREAMING_SNAKE_CASE__   :      Any				=		int(input("""Enter number of edges: """).strip())

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

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

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






