


'''simple docstring'''
import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Dict			):
 # Load configuration defined in the metadata file
 with open(UpperCamelCase__			) as metadata_file:
  __A  :		Tuple       =			json.load(UpperCamelCase__			)
 __A  :		List[str]       =			LukeConfig(use_entity_aware_attention=UpperCamelCase__,				**metadata['model_config']			)

 # Load in the weights from the checkpoint_path
 __A  :		Optional[Any]       =			torch.load(UpperCamelCase__,				map_location='cpu'			)

 # Load the entity vocab file
 __A  :		List[Any]       =			load_entity_vocab(UpperCamelCase__			)

 __A  :		Any       =			RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']			)

 # Add special tokens to the token vocabulary for downstream tasks
 __A  :		Optional[Any]       =			AddedToken('<ent>',				lstrip=UpperCamelCase__,				rstrip=UpperCamelCase__			)
 __A  :		Union[str, Any]       =			AddedToken('<ent2>',				lstrip=UpperCamelCase__,				rstrip=UpperCamelCase__			)
 tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]}			)
 config.vocab_size += 2

 print(f"""Saving tokenizer to {pytorch_dump_folder_path}"""			)
 tokenizer.save_pretrained(UpperCamelCase__			)
 with open(os.path.join(UpperCamelCase__,				LukeTokenizer.vocab_files_names['entity_vocab_file']			),				'w'			) as f:
  json.dump(UpperCamelCase__,				UpperCamelCase__			)

 __A  :		Union[str, Any]       =			LukeTokenizer.from_pretrained(UpperCamelCase__			)

 # Initialize the embeddings of the special tokens
 __A  :		int       =			state_dict['embeddings.word_embeddings.weight']
 __A  :		Union[str, Any]       =			word_emb[tokenizer.convert_tokens_to_ids(['@']			)[0]].unsqueeze(0			)
 __A  :		Union[str, Any]       =			word_emb[tokenizer.convert_tokens_to_ids(['#']			)[0]].unsqueeze(0			)
 __A  :		Dict       =			torch.cat([word_emb, ent_emb, enta_emb]			)

 # Initialize the query layers of the entity-aware self-attention mechanism
 for layer_index in range(config.num_hidden_layers			):
  for matrix_name in ["query.weight", "query.bias"]:
   __A  :		Optional[int]       =			f"""encoder.layer.{layer_index}.attention.self."""
   __A  :		Optional[Any]       =			state_dict[prefix + matrix_name]
   __A  :		List[str]       =			state_dict[prefix + matrix_name]
   __A  :		Optional[int]       =			state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
 __A  :		Optional[int]       =			state_dict['entity_embeddings.entity_embeddings.weight']
 __A  :		Optional[Any]       =			entity_emb[entity_vocab['[MASK]']]

 __A  :		Tuple       =			LukeModel(config=UpperCamelCase__			).eval()

 __A  :		Dict       =			model.load_state_dict(UpperCamelCase__,				strict=UpperCamelCase__			)
 if not (len(UpperCamelCase__			) == 1 and missing_keys[0] == "embeddings.position_ids"):
  raise ValueError(f"""Missing keys {', '.join(UpperCamelCase__			)}. Expected only missing embeddings.position_ids"""			)
 if not (all(key.startswith('entity_predictions'			) or key.startswith('lm_head'			) for key in unexpected_keys			)):
  raise ValueError(
      'Unexpected keys'
      f""" {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'			) or key.startswith('lm_head'			))]			)}"""			)

 # Check outputs
 __A  :		str       =			LukeTokenizer.from_pretrained(UpperCamelCase__,				task='entity_classification'			)

 __A  :		str       =			(
     'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
     ' new world number one avoid a humiliating second- round exit at Wimbledon .'
 )
 __A  :		Tuple       =			(39, 42)
 __A  :		int       =			tokenizer(UpperCamelCase__,				entity_spans=[span],				add_prefix_space=UpperCamelCase__,				return_tensors='pt'			)

 __A  :		int       =			model(**UpperCamelCase__			)

 # Verify word hidden states
 if model_size == "large":
  __A  :		Optional[int]       =			torch.Size((1, 42, 1024)			)
  __A  :		Tuple       =			torch.tensor(
      [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]			)
 else:  # base
  __A  :		Dict       =			torch.Size((1, 42, 768)			)
  __A  :		Dict       =			torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]			)

 if not (outputs.last_hidden_state.shape == expected_shape):
  raise ValueError(
      f"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""			)
 if not torch.allclose(outputs.last_hidden_state[0, :3, :3],				UpperCamelCase__,				atol=1E-4			):
  raise ValueError

 # Verify entity hidden states
 if model_size == "large":
  __A  :		Tuple       =			torch.Size((1, 1, 1024)			)
  __A  :		List[str]       =			torch.tensor([[0.0466, -0.0106, -0.0179]]			)
 else:  # base
  __A  :		str       =			torch.Size((1, 1, 768)			)
  __A  :		Optional[Any]       =			torch.tensor([[0.1457, 0.1044, 0.0174]]			)

 if not (outputs.entity_last_hidden_state.shape != expected_shape):
  raise ValueError(
      f"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
      f""" {expected_shape}"""			)
 if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3],				UpperCamelCase__,				atol=1E-4			):
  raise ValueError

 # Finally, save our PyTorch model and tokenizer
 print('Saving PyTorch model to {}'.format(UpperCamelCase__			)			)
 model.save_pretrained(UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					Dict			):
 __A  :		Any       =			{}
 with open(UpperCamelCase__,				'r',				encoding='utf-8'			) as f:
  for index, line in enumerate(UpperCamelCase__			):
   __A  :		Tuple       =			line.rstrip().split('\t'			)
   __A  :		Optional[int]       =			index

 return entity_vocab


if __name__ == "__main__":
 UpperCAmelCase_ :     List[str]								=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
 parser.add_argument(
     '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
 )
 parser.add_argument(
     '--entity_vocab_path',
     default=None,
     type=str,
     help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
 )
 parser.add_argument(
     '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
 )
 UpperCAmelCase_ :     Union[str, Any]								=      parser.parse_args()
 convert_luke_checkpoint(
     args.checkpoint_path,
     args.metadata_path,
     args.entity_vocab_path,
     args.pytorch_dump_folder_path,
     args.model_size,
 )




'''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()






