


'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 			_lowercase       (				UpperCamelCase__       :					Dict			):
 __A  :		int       =			[
     '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 			_lowercase       (				UpperCamelCase__       :					Dict			):
 __A     ,__A  :		List[Any]       =			emb.weight.shape
 __A  :		int       =			nn.Linear(UpperCamelCase__,				UpperCamelCase__,				bias=UpperCamelCase__			)
 __A  :		Dict       =			emb.weight.data
 return lin_layer


def 			_lowercase       (				UpperCamelCase__       :					List[str]			):
 __A  :		List[str]       =			torch.load(UpperCamelCase__,				map_location='cpu'			)
 __A  :		Union[str, Any]       =			mam_aaa['args'] or mam_aaa['cfg']['model']
 __A  :		Any       =			mam_aaa['model']
 remove_ignore_keys_(UpperCamelCase__			)
 __A  :		List[str]       =			state_dict['encoder.embed_tokens.weight'].shape[0]

 __A  :		List[str]       =			MaMaaaConfig(
     vocab_size=UpperCamelCase__,				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',				)

 __A  :		Any       =			state_dict['decoder.embed_tokens.weight']
 __A  :		str       =			MaMaaaForConditionalGeneration(UpperCamelCase__			)
 model.model.load_state_dict(UpperCamelCase__,				strict=UpperCamelCase__			)
 __A  :		Union[str, Any]       =			make_linear_from_emb(model.model.shared			)

 return model


if __name__ == "__main__":
 UpperCAmelCase_ :     Tuple								=      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.')
 UpperCAmelCase_ :     Union[str, Any]								=      parser.parse_args()
 UpperCAmelCase_ :     Tuple								=      convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)










'''simple docstring'''
from heapq import heappop, heappush

import numpy as np


def 			_lowercase       (				UpperCamelCase__       :					np.ndarray,				UpperCamelCase__       :					tuple[int, int],				UpperCamelCase__       :					tuple[int, int],				UpperCamelCase__       :					bool,				):
 __A     ,__A  :		Optional[Any]       =			grid.shape
 __A  :		List[Any]       =			[-1, 1, 0, 0]
 __A  :		Optional[int]       =			[0, 0, -1, 1]
 if allow_diagonal:
  dx += [-1, -1, 1, 1]
  dy += [-1, 1, -1, 1]

 __A     ,__A  :		int       =			[(0, source)], set()
 __A  :		Any       =			np.full((rows, cols),				np.inf			)
 __A  :		int       =			0
 __A  :		Any       =			np.empty((rows, cols),				dtype=UpperCamelCase__			)
 __A  :		List[Any]       =			None

 while queue:
  ((__A)     ,(__A))  :		List[Any]       =			heappop(UpperCamelCase__			)
  if (x, y) in visited:
   continue
  visited.add((x, y)			)

  if (x, y) == destination:
   __A  :		Tuple       =			[]
   while (x, y) != source:
    path.append((x, y)			)
    __A     ,__A  :		int       =			predecessors[x, y]
   path.append(UpperCamelCase__			)  # add the source manually
   path.reverse()
   return matrix[destination], path

  for i in range(len(UpperCamelCase__			)			):
   __A     ,__A  :		List[Any]       =			x + dx[i], y + dy[i]
   if 0 <= nx < rows and 0 <= ny < cols:
    __A  :		str       =			grid[nx][ny]
    if next_node == 1 and matrix[nx, ny] > dist + 1:
     heappush(UpperCamelCase__,				(dist + 1, (nx, ny))			)
     __A  :		int       =			dist + 1
     __A  :		List[str]       =			(x, y)

 return np.inf, []


if __name__ == "__main__":
 import doctest

 doctest.testmod()






