




'''simple docstring'''





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       __a      (  _UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Dict   ,		_UpperCamelCase:       int   ,		_UpperCamelCase:       Dict   ,		_UpperCamelCase:       Optional[int]       )		->					Tuple:

     """simple docstring"""




     with open(_UpperCamelCase       ) as metadata_file:
          _snake_case				= json.load(_UpperCamelCase       )
     _snake_case				= LukeConfig(use_entity_aware_attention=_UpperCamelCase   ,		**metadata["model_config"]       )

     # Load in the weights from the checkpoint_path
     _snake_case				= torch.load(_UpperCamelCase   ,		map_location="cpu"       )["module"]

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

     _snake_case				= XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]       )

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

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

     _snake_case				= MLukeTokenizer.from_pretrained(_UpperCamelCase       )

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

     _snake_case				= state_dict["embeddings.word_embeddings.weight"]
     _snake_case				= word_emb[ent_init_index].unsqueeze(0       )
     _snake_case				= word_emb[enta_init_index].unsqueeze(0       )
     _snake_case				= 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"]:
          _snake_case				= state_dict[bias_name]
          _snake_case				= decoder_bias[ent_init_index].unsqueeze(0       )
          _snake_case				= decoder_bias[enta_init_index].unsqueeze(0       )
          _snake_case				= 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"]:
               _snake_case				= F"""encoder.layer.{layer_index}.attention.self."""
               _snake_case				= state_dict[prefix + matrix_name]
               _snake_case				= state_dict[prefix + matrix_name]
               _snake_case				= state_dict[prefix + matrix_name]

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

     _snake_case				= LukeForMaskedLM(config=_UpperCamelCase       ).eval()

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

     _snake_case				,   _snake_case				= model.load_state_dict(_UpperCamelCase   ,		strict=_UpperCamelCase       )

     if set(_UpperCamelCase       ) != {"luke.embeddings.position_ids"}:
          raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}"""       )
     if set(_UpperCamelCase       ) != {
         "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
     _snake_case				= MLukeTokenizer.from_pretrained(_UpperCamelCase   ,		task="entity_classification"       )

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

     _snake_case				= model(**_UpperCamelCase       )

     # Verify word hidden states
     if model_size == "large":
          raise NotImplementedError
     else:  # base
          _snake_case				= torch.Size((1, 33, 768)       )
          _snake_case				= 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]   ,		_UpperCamelCase   ,		atol=1E-4       ):
          raise ValueError

     # Verify entity hidden states
     if model_size == "large":
          raise NotImplementedError
     else:  # base
          _snake_case				= torch.Size((1, 1, 768)       )
          _snake_case				= 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]   ,		_UpperCamelCase   ,		atol=1E-4       ):
          raise ValueError

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

     _snake_case				= model(**_UpperCamelCase       )

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

     _snake_case				= outputs.entity_logits[0][0].argmax().item()
     _snake_case				= [
         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(_UpperCamelCase       )       )
     model.save_pretrained(_UpperCamelCase       )





def       __a      (  _UpperCamelCase:       str       )		->					Tuple:

     """simple docstring"""




     _snake_case				= ["[MASK]", "[PAD]", "[UNK]"]

     _snake_case				= [json.loads(_UpperCamelCase       ) for line in open(_UpperCamelCase       )]

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


if __name__ == "__main__":
   UpperCamelCase_		:     Any          =		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'''





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


UpperCamelCase_		:     Union[str, Any]          =		logging.get_logger(__name__)


UpperCamelCase_		:     Optional[Any]          =		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase_		:     Dict          =		{
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase_		:     Tuple          =		{
    '''facebook/bart-base''': 1024,
    '''facebook/bart-large''': 1024,
    '''facebook/bart-large-mnli''': 1024,
    '''facebook/bart-large-cnn''': 1024,
    '''facebook/bart-large-xsum''': 1024,
    '''yjernite/bart_eli5''': 1024,
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       Union[str, Any]       							=		VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_				:       int       							=		PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_				:       Optional[Any]       							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_				:       Any       							=		["""input_ids""", """attention_mask"""]
     SCREAMING_SNAKE_CASE_				:       str       							=		BartTokenizer






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE="replace"   ,_SCREAMING_SNAKE_CASE="<s>"   ,_SCREAMING_SNAKE_CASE="</s>"   ,_SCREAMING_SNAKE_CASE="</s>"   ,_SCREAMING_SNAKE_CASE="<s>"   ,_SCREAMING_SNAKE_CASE="<unk>"   ,_SCREAMING_SNAKE_CASE="<pad>"   ,_SCREAMING_SNAKE_CASE="<mask>"   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True   ,**_SCREAMING_SNAKE_CASE   ,)				->     List[Any]:
          super().__init__(
              _SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,tokenizer_file=_SCREAMING_SNAKE_CASE   ,errors=_SCREAMING_SNAKE_CASE   ,bos_token=_SCREAMING_SNAKE_CASE   ,eos_token=_SCREAMING_SNAKE_CASE   ,sep_token=_SCREAMING_SNAKE_CASE   ,cls_token=_SCREAMING_SNAKE_CASE   ,unk_token=_SCREAMING_SNAKE_CASE   ,pad_token=_SCREAMING_SNAKE_CASE   ,mask_token=_SCREAMING_SNAKE_CASE   ,add_prefix_space=_SCREAMING_SNAKE_CASE   ,trim_offsets=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE   ,)

          _snake_case				= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
          if pre_tok_state.get("add_prefix_space"   ,_SCREAMING_SNAKE_CASE       ) != add_prefix_space:
               _snake_case				= getattr(_SCREAMING_SNAKE_CASE   ,pre_tok_state.pop("type"       )       )
               _snake_case				= add_prefix_space
               _snake_case				= pre_tok_class(**_SCREAMING_SNAKE_CASE       )

          _snake_case				= add_prefix_space

          # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
          _snake_case				= "post_processor"
          _snake_case				= getattr(self.backend_tokenizer   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
          if tokenizer_component_instance:
               _snake_case				= json.loads(tokenizer_component_instance.__getstate__()       )

               # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
               if "sep" in state:
                    _snake_case				= tuple(state["sep"]       )
               if "cls" in state:
                    _snake_case				= tuple(state["cls"]       )

               _snake_case				= False

               if state.get("add_prefix_space"   ,_SCREAMING_SNAKE_CASE       ) != add_prefix_space:
                    _snake_case				= add_prefix_space
                    _snake_case				= True

               if state.get("trim_offsets"   ,_SCREAMING_SNAKE_CASE       ) != trim_offsets:
                    _snake_case				= trim_offsets
                    _snake_case				= True

               if changes_to_apply:
                    _snake_case				= getattr(_SCREAMING_SNAKE_CASE   ,state.pop("type"       )       )
                    _snake_case				= component_class(**_SCREAMING_SNAKE_CASE       )
                    setattr(self.backend_tokenizer   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )






     @property
     def 							_lowercase      (					self       )				->     str:
          if self._mask_token is None:
               if self.verbose:
                    logger.error("Using mask_token, but it is not set yet."       )
               return None
          return str(self._mask_token       )






     @mask_token.setter
     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     str:
          _snake_case				= AddedToken(_SCREAMING_SNAKE_CASE   ,lstrip=_SCREAMING_SNAKE_CASE   ,rstrip=_SCREAMING_SNAKE_CASE       ) if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ) else value
          _snake_case				= value






     def 							_lowercase      (					self   ,*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     BatchEncoding:
          _snake_case				= kwargs.get("is_split_into_words"   ,_SCREAMING_SNAKE_CASE       )

          if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                   "to use it with pretokenized inputs."       )

          return super()._batch_encode_plus(*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     BatchEncoding:
          _snake_case				= kwargs.get("is_split_into_words"   ,_SCREAMING_SNAKE_CASE       )

          if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                   "to use it with pretokenized inputs."       )

          return super()._encode_plus(*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     Tuple[str]:
          _snake_case				= self._tokenizer.model.save(_SCREAMING_SNAKE_CASE   ,name=_SCREAMING_SNAKE_CASE       )
          return tuple(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=None       )				->     Tuple:
          _snake_case				= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
               return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     List[int]:
          _snake_case				= [self.sep_token_id]
          _snake_case				= [self.cls_token_id]

          if token_ids_a is None:
               return len(cls + token_ids_a + sep       ) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]


