
"""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 							A						(    snake_case				:int    , snake_case				:Optional[int]    , snake_case				:List[str]    , snake_case				:Dict    , snake_case				:Union[str, Any]		)				->     int:
 # Load configuration defined in the metadata file
 with open(snake_case		) as metadata_file:
  __UpperCamelCase      =	json.load(snake_case		)
 __UpperCamelCase      =	LukeConfig(use_entity_aware_attention=snake_case    , **metadata['model_config']		)

 # Load in the weights from the checkpoint_path
 __UpperCamelCase      =	torch.load(snake_case    , map_location='cpu'		)

 # Load the entity vocab file
 __UpperCamelCase      =	load_entity_vocab(snake_case		)

 __UpperCamelCase      =	RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']		)

 # Add special tokens to the token vocabulary for downstream tasks
 __UpperCamelCase      =	AddedToken('<ent>'    , lstrip=snake_case    , rstrip=snake_case		)
 __UpperCamelCase      =	AddedToken('<ent2>'    , lstrip=snake_case    , rstrip=snake_case		)
 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(snake_case		)
 with open(os.path.join(snake_case    , LukeTokenizer.vocab_files_names['entity_vocab_file']		)    , 'w'		) as f:
  json.dump(snake_case    , snake_case		)

 __UpperCamelCase      =	LukeTokenizer.from_pretrained(snake_case		)

 # Initialize the embeddings of the special tokens
 __UpperCamelCase      =	state_dict['embeddings.word_embeddings.weight']
 __UpperCamelCase      =	word_emb[tokenizer.convert_tokens_to_ids(['@']		)[0]].unsqueeze(0		)
 __UpperCamelCase      =	word_emb[tokenizer.convert_tokens_to_ids(['#']		)[0]].unsqueeze(0		)
 __UpperCamelCase      =	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"]:
   __UpperCamelCase      =	f'encoder.layer.{layer_index}.attention.self.'
   __UpperCamelCase      =	state_dict[prefix + matrix_name]
   __UpperCamelCase      =	state_dict[prefix + matrix_name]
   __UpperCamelCase      =	state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
 __UpperCamelCase      =	state_dict['entity_embeddings.entity_embeddings.weight']
 __UpperCamelCase      =	entity_emb[entity_vocab['[MASK]']]

 __UpperCamelCase      =	LukeModel(config=snake_case		).eval()

 __UpperCamelCase  ,     __UpperCamelCase      =	model.load_state_dict(snake_case    , strict=snake_case		)
 if not (len(snake_case		) == 1 and missing_keys[0] == "embeddings.position_ids"):
  raise ValueError(f'Missing keys {", ".join(snake_case		)}. 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
 __UpperCamelCase      =	LukeTokenizer.from_pretrained(snake_case    , task='entity_classification'		)

 __UpperCamelCase      =	(
     '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 .'
 )
 __UpperCamelCase      =	(3_9, 4_2)
 __UpperCamelCase      =	tokenizer(snake_case    , entity_spans=[span]    , add_prefix_space=snake_case    , return_tensors='pt'		)

 __UpperCamelCase      =	model(**snake_case		)

 # Verify word hidden states
 if model_size == "large":
  __UpperCamelCase      =	torch.Size((1, 4_2, 1_0_2_4)		)
  __UpperCamelCase      =	torch.tensor(
      [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]		)
 else:  # base
  __UpperCamelCase      =	torch.Size((1, 4_2, 7_6_8)		)
  __UpperCamelCase      =	torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]		)

 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]    , snake_case    , atol=1e-4		):
  raise ValueError

 # Verify entity hidden states
 if model_size == "large":
  __UpperCamelCase      =	torch.Size((1, 1, 1_0_2_4)		)
  __UpperCamelCase      =	torch.tensor([[0.0_466, -0.0_106, -0.0_179]]		)
 else:  # base
  __UpperCamelCase      =	torch.Size((1, 1, 7_6_8)		)
  __UpperCamelCase      =	torch.tensor([[0.1_457, 0.1_044, 0.0_174]]		)

 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]    , snake_case    , atol=1e-4		):
  raise ValueError

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






def 							A						(    snake_case				:Tuple		)				->     Tuple:
 __UpperCamelCase      =	{}
 with open(snake_case    , 'r'    , encoding='utf-8'		) as f:
  for index, line in enumerate(snake_case		):
   __UpperCamelCase  ,     __UpperCamelCase      =	line.rstrip().split('\t'		)
   __UpperCamelCase      =	index

 return entity_vocab


if __name__ == "__main__":
      UpperCamelCase     :    Optional[int]       =			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     :    Optional[int]       =			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 __future__ import annotations

from typing import Any






def 							A						(    snake_case				:list		)				->     int:
 if not postfix_notation:
  return 0

 __UpperCamelCase      =	{'+', '-', '*', '/'}
 __UpperCamelCase      =	[]

 for token in postfix_notation:
  if token in operations:
   __UpperCamelCase  ,     __UpperCamelCase      =	stack.pop(), stack.pop()
   if token == "+":
    stack.append(a + b		)
   elif token == "-":
    stack.append(a - b		)
   elif token == "*":
    stack.append(a * b		)
   else:
    if a * b < 0 and a % b != 0:
     stack.append(a // b + 1		)
    else:
     stack.append(a // b		)
  else:
   stack.append(int(snake_case		)		)

 return stack.pop()


if __name__ == "__main__":
      import doctest

      doctest.testmod()



