




import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken

@torch.no_grad()
def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]			,							lowercase			: List[Any]			,							lowercase			: int			,							lowercase			: Dict			,							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      with open(lowercase  ) as metadata_file:
            lowerCamelCase_	  =					json.load(lowercase  )
      lowerCamelCase_	  =					LukeConfig(use_entity_aware_attention=lowercase			,							**metadata['model_config']  )

      # Load in the weights from the checkpoint_path
      lowerCamelCase_	  =					torch.load(lowercase			,							map_location='cpu'  )['module']

      # Load the entity vocab file
      lowerCamelCase_	  =					load_original_entity_vocab(lowercase  )
      # add an entry for [MASK2]
      lowerCamelCase_	  =					max(entity_vocab.values()  ) + 1
      config.entity_vocab_size += 1

      lowerCamelCase_	  =					XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']  )

      # Add special tokens to the token vocabulary for downstream tasks
      lowerCamelCase_	  =					AddedToken('<ent>'			,							lstrip=lowercase			,							rstrip=lowercase  )
      lowerCamelCase_	  =					AddedToken('<ent2>'			,							lstrip=lowercase			,							rstrip=lowercase  )
      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(lowercase  )
      with open(os.path.join(lowercase			,							'tokenizer_config.json'  )			,							'r'  ) as f:
            lowerCamelCase_	  =					json.load(lowercase  )
      lowerCamelCase_	  =					'MLukeTokenizer'
      with open(os.path.join(lowercase			,							'tokenizer_config.json'  )			,							'w'  ) as f:
            json.dump(lowercase			,							lowercase  )

      with open(os.path.join(lowercase			,							MLukeTokenizer.vocab_files_names['entity_vocab_file']  )			,							'w'  ) as f:
            json.dump(lowercase			,							lowercase  )

      lowerCamelCase_	  =					MLukeTokenizer.from_pretrained(lowercase  )

      # Initialize the embeddings of the special tokens
      lowerCamelCase_	  =					tokenizer.convert_tokens_to_ids(['@']  )[0]
      lowerCamelCase_	  =					tokenizer.convert_tokens_to_ids(['#']  )[0]

      lowerCamelCase_	  =					state_dict['embeddings.word_embeddings.weight']
      lowerCamelCase_	  =					word_emb[ent_init_index].unsqueeze(0  )
      lowerCamelCase_	  =					word_emb[enta_init_index].unsqueeze(0  )
      lowerCamelCase_	  =					torch.cat([word_emb, ent_emb, enta_emb]  )
      # add special tokens for 'entity_predictions.bias'
      for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
            lowerCamelCase_	  =					state_dict[bias_name]
            lowerCamelCase_	  =					decoder_bias[ent_init_index].unsqueeze(0  )
            lowerCamelCase_	  =					decoder_bias[enta_init_index].unsqueeze(0  )
            lowerCamelCase_	  =					torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]  )

      # 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"]:
                  lowerCamelCase_	  =					f"""encoder.layer.{layer_index}.attention.self."""
                  lowerCamelCase_	  =					state_dict[prefix + matrix_name]
                  lowerCamelCase_	  =					state_dict[prefix + matrix_name]
                  lowerCamelCase_	  =					state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      lowerCamelCase_	  =					state_dict['entity_embeddings.entity_embeddings.weight']
      lowerCamelCase_	  =					entity_emb[entity_vocab['[MASK]']].unsqueeze(0  )
      lowerCamelCase_	  =					torch.cat([entity_emb, entity_mask_emb]  )
      # add [MASK2] for 'entity_predictions.bias'
      lowerCamelCase_	  =					state_dict['entity_predictions.bias']
      lowerCamelCase_	  =					entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0  )
      lowerCamelCase_	  =					torch.cat([entity_prediction_bias, entity_mask_bias]  )

      lowerCamelCase_	  =					LukeForMaskedLM(config=lowercase  ).eval()

      state_dict.pop('entity_predictions.decoder.weight'  )
      state_dict.pop('lm_head.decoder.weight'  )
      state_dict.pop('lm_head.decoder.bias'  )
      lowerCamelCase_	  =					OrderedDict()
      for key, value in state_dict.items():
            if not (key.startswith('lm_head'  ) or key.startswith('entity_predictions'  )):
                  lowerCamelCase_	  =					state_dict[key]
            else:
                  lowerCamelCase_	  =					state_dict[key]

      lowerCamelCase_  ,       lowerCamelCase_	  =					model.load_state_dict(lowercase			,							strict=lowercase  )

      if set(lowercase  ) != {"luke.embeddings.position_ids"}:
            raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}"""  )
      if set(lowercase  ) != {
          "lm_head.decoder.weight",
          "lm_head.decoder.bias",
          "entity_predictions.decoder.weight",
      }:
            raise ValueError(f"""Unexpected missing_keys: {missing_keys}"""  )

      model.tie_weights()
      assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
      assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

      # Check outputs
      lowerCamelCase_	  =					MLukeTokenizer.from_pretrained(lowercase			,							task='entity_classification'  )

      lowerCamelCase_	  =					'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
      lowerCamelCase_	  =					(0, 9)
      lowerCamelCase_	  =					tokenizer(lowercase			,							entity_spans=[span]			,							return_tensors='pt'  )

      lowerCamelCase_	  =					model(**lowercase  )

      # Verify word hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            lowerCamelCase_	  =					torch.Size((1, 33, 7_68)  )
            lowerCamelCase_	  =					torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]  )

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

      # Verify entity hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            lowerCamelCase_	  =					torch.Size((1, 1, 7_68)  )
            lowerCamelCase_	  =					torch.tensor([[-0.1482, 0.0609, 0.0322]]  )

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

      # Verify masked word/entity prediction
      lowerCamelCase_	  =					MLukeTokenizer.from_pretrained(lowercase  )
      lowerCamelCase_	  =					'Tokyo is the capital of <mask>.'
      lowerCamelCase_	  =					(24, 30)
      lowerCamelCase_	  =					tokenizer(lowercase			,							entity_spans=[span]			,							return_tensors='pt'  )

      lowerCamelCase_	  =					model(**lowercase  )

      lowerCamelCase_	  =					encoding['input_ids'][0].tolist()
      lowerCamelCase_	  =					input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'  )  )
      lowerCamelCase_	  =					outputs.logits[0][mask_position_id].argmax(dim=-1  )
      assert "Japan" == tokenizer.decode(lowercase  )

      lowerCamelCase_	  =					outputs.entity_logits[0][0].argmax().item()
      lowerCamelCase_	  =					[
          entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
      ]
      assert [e for e in multilingual_predicted_entities if e.startswith('en:'  )][0] == "en:Japan"

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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					['[MASK]', '[PAD]', '[UNK]']

      lowerCamelCase_	  =					[json.loads(lowercase  ) for line in open(lowercase  )]

      lowerCamelCase_	  =					{}
      for entry in data:
            lowerCamelCase_	  =					entry['id']
            for entity_name, language in entry["entities"]:
                  if entity_name in SPECIAL_TOKENS:
                        lowerCamelCase_	  =					entity_id
                        break
                  lowerCamelCase_	  =					f"""{language}:{entity_name}"""
                  lowerCamelCase_	  =					entity_id
      return new_mapping


if __name__ == "__main__":
 lowerCamelCase       :	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."
 )
 lowerCamelCase       :	Optional[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,
 )









lowerCamelCase       :	Dict									=     "Alexander Joslin"

import operator as op

from .stack import Stack

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      lowerCamelCase_	  =					{'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

      lowerCamelCase_	  =					Stack()
      lowerCamelCase_	  =					Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(lowercase  )  )
            elif i in operators:
                  # RULE 2
                  operator_stack.push(lowercase  )
            elif i == ")":
                  # RULE 4
                  lowerCamelCase_	  =					operator_stack.peek()
                  operator_stack.pop()
                  lowerCamelCase_	  =					operand_stack.peek()
                  operand_stack.pop()
                  lowerCamelCase_	  =					operand_stack.peek()
                  operand_stack.pop()

                  lowerCamelCase_	  =					operators[opr](lowercase			,							lowercase  )
                  operand_stack.push(lowercase  )

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
 lowerCamelCase       :	Any									=     "(5 + ((4 * 2) * (2 + 3)))"
 # answer = 45
 print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



