

from queue import PriorityQueue
from typing import Any

import numpy as np



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    ):
					'''simple docstring'''
					for nxt, d in graph[v]:
										if nxt in visited_forward:
															continue
										lowercase					=		cst_fwd.get(__lowerCAmelCase				,    np.inf	)
										lowercase					=		cst_fwd[v] + d
										if new_cost_f < old_cost_f:
															queue.put((new_cost_f, nxt)	)
															lowercase					=		new_cost_f
															lowercase					=		v
										if nxt in visited_backward:
															if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																				lowercase					=		cst_fwd[v] + d + cst_bwd[nxt]
					return shortest_distance



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		-1

					lowercase					=		set()
					lowercase					=		set()
					lowercase					=		{source: 0}
					lowercase					=		{destination: 0}
					lowercase					=		{source: None}
					lowercase					=		{destination: None}
					lowercase					=		PriorityQueue()
					lowercase					=		PriorityQueue()

					lowercase					=		np.inf

					queue_forward.put((0, source)	)
					queue_backward.put((0, destination)	)

					if source == destination:
										return 0

					while not queue_forward.empty() and not queue_backward.empty():
										lowercase			,		lowercase					=		queue_forward.get()
										visited_forward.add(__lowerCAmelCase	)

										lowercase			,		lowercase					=		queue_backward.get()
										visited_backward.add(__lowerCAmelCase	)

										lowercase					=		pass_and_relaxation(
										    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    )

										lowercase					=		pass_and_relaxation(
										    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    )

										if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
															break

					if shortest_distance != np.inf:
										lowercase					=		shortest_distance
					return shortest_path_distance


lowercase__	:List[str]										=					{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
lowercase__	:Dict										=					{
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
							import doctest

							doctest.testmod()






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		[
					    '''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(lowerCAmelCase__				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		emb.weight.shape
					lowercase					=		nn.Linear(lowerCAmelCase__				,    lowerCAmelCase__				,    bias=lowerCAmelCase__	)
					lowercase					=		emb.weight.data
					return lin_layer



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		torch.load(lowerCAmelCase__				,    map_location='''cpu'''	)
					lowercase					=		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
					lowercase					=		mam_aaa['''model''']
					remove_ignore_keys_(lowerCAmelCase__	)
					lowercase					=		state_dict['''encoder.embed_tokens.weight'''].shape[0]

					lowercase					=		MaMaaaConfig(
					    vocab_size=lowerCAmelCase__				,    max_position_embeddings=1024				,    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'''				,    )

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

					return model


if __name__ == "__main__":
							lowercase__	:Optional[int]										=					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.")
							lowercase__	:Tuple										=					parser.parse_args()
							lowercase__	:int										=					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
							model.save_pretrained(args.pytorch_dump_folder_path)




