

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        __lowerCAmelCase    (       _UpperCamelCase		:  Dict		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  str		,							_UpperCamelCase		:  str	)		->							Union[str, Any]:
 '''simple docstring'''


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

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

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

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

 # Add special tokens to the token vocabulary for downstream tasks
 SCREAMING_SNAKE_CASE								=			AddedToken('<ent>'		,							lstrip=_UpperCamelCase		,							rstrip=_UpperCamelCase	)
 SCREAMING_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:
  SCREAMING_SNAKE_CASE								=			json.load(_UpperCamelCase	)
 SCREAMING_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	)

 SCREAMING_SNAKE_CASE								=			MLukeTokenizer.from_pretrained(_UpperCamelCase	)

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

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

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

 SCREAMING_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'	)
 SCREAMING_SNAKE_CASE								=			OrderedDict()
 for key, value in state_dict.items():
  if not (key.startswith('lm_head'	) or key.startswith('entity_predictions'	)):
   SCREAMING_SNAKE_CASE								=			state_dict[key]
  else:
   SCREAMING_SNAKE_CASE								=			state_dict[key]

 SCREAMING_SNAKE_CASE						,	SCREAMING_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
 SCREAMING_SNAKE_CASE								=			MLukeTokenizer.from_pretrained(_UpperCamelCase		,							task='entity_classification'	)

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

 SCREAMING_SNAKE_CASE								=			model(**_UpperCamelCase	)

 # Verify word hidden states
 if model_size == "large":
  raise NotImplementedError
 else:  # base
  SCREAMING_SNAKE_CASE								=			torch.Size((1, 33, 7_68)	)
  SCREAMING_SNAKE_CASE								=			torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]]	)

 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
  SCREAMING_SNAKE_CASE								=			torch.Size((1, 1, 7_68)	)
  SCREAMING_SNAKE_CASE								=			torch.tensor([[-0.14_82, 0.06_09, 0.03_22]]	)

 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
 SCREAMING_SNAKE_CASE								=			MLukeTokenizer.from_pretrained(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			'Tokyo is the capital of <mask>.'
 SCREAMING_SNAKE_CASE								=			(24, 30)
 SCREAMING_SNAKE_CASE								=			tokenizer(_UpperCamelCase		,							entity_spans=[span]		,							return_tensors='pt'	)

 SCREAMING_SNAKE_CASE								=			model(**_UpperCamelCase	)

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

 SCREAMING_SNAKE_CASE								=			outputs.entity_logits[0][0].argmax().item()
 SCREAMING_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        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]	)		->							Optional[Any]:
 '''simple docstring'''


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

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

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


if __name__ == "__main__":
      a_    :						Union[str, 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."
      )
      a_    :						List[str]    		=      parser.parse_args()
      convert_luke_checkpoint(
          args.checkpoint_path,
          args.metadata_path,
          args.entity_vocab_path,
          args.pytorch_dump_folder_path,
          args.model_size,
      )




import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


a_    :						Optional[int]    		=      logging.get_logger(__name__)


a_    :						Union[str, Any]    		=      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_    :						Any    		=      {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

a_    :						List[Any]    		=      {
    "allenai/led-base-16384": 1_6384,
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =VOCAB_FILES_NAMES
    __UpperCamelCase         =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase         =LEDTokenizer
    __UpperCamelCase         =["input_ids", "attention_mask"]

    def __init__(			self :      Tuple							,    snake_case__ :      List[Any]=None							,    snake_case__ :      List[str]=None							,    snake_case__ :      List[str]=None							,    snake_case__ :      Dict="replace"							,    snake_case__ :      Tuple="<s>"							,    snake_case__ :      Optional[Any]="</s>"							,    snake_case__ :      int="</s>"							,    snake_case__ :      Dict="<s>"							,    snake_case__ :      Union[str, Any]="<unk>"							,    snake_case__ :      Optional[int]="<pad>"							,    snake_case__ :      List[str]="<mask>"							,    snake_case__ :      List[Any]=False							,    snake_case__ :      int=True							,    **snake_case__ :      Dict							,    ):

     """simple docstring"""






     super().__init__(
         snake_case__							,    snake_case__							,    tokenizer_file=snake_case__							,    errors=snake_case__							,    bos_token=snake_case__							,    eos_token=snake_case__							,    sep_token=snake_case__							,    cls_token=snake_case__							,    unk_token=snake_case__							,    pad_token=snake_case__							,    mask_token=snake_case__							,    add_prefix_space=snake_case__							,    trim_offsets=snake_case__							,    **snake_case__							,    )

     SCREAMING_SNAKE_CASE								=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
     if pre_tok_state.get('add_prefix_space'							,    snake_case__    ) != add_prefix_space:
      SCREAMING_SNAKE_CASE								=			getattr(snake_case__							,    pre_tok_state.pop('type'    )    )
      SCREAMING_SNAKE_CASE								=			add_prefix_space
      SCREAMING_SNAKE_CASE								=			pre_tok_class(**snake_case__    )

     SCREAMING_SNAKE_CASE								=			add_prefix_space

     # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
     SCREAMING_SNAKE_CASE								=			'post_processor'
     SCREAMING_SNAKE_CASE								=			getattr(self.backend_tokenizer							,    snake_case__							,    snake_case__    )
     if tokenizer_component_instance:
      SCREAMING_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:
       SCREAMING_SNAKE_CASE								=			tuple(state['sep']    )
      if "cls" in state:
       SCREAMING_SNAKE_CASE								=			tuple(state['cls']    )

      SCREAMING_SNAKE_CASE								=			False

      if state.get('add_prefix_space'							,    snake_case__    ) != add_prefix_space:
       SCREAMING_SNAKE_CASE								=			add_prefix_space
       SCREAMING_SNAKE_CASE								=			True

      if state.get('trim_offsets'							,    snake_case__    ) != trim_offsets:
       SCREAMING_SNAKE_CASE								=			trim_offsets
       SCREAMING_SNAKE_CASE								=			True

      if changes_to_apply:
       SCREAMING_SNAKE_CASE								=			getattr(snake_case__							,    state.pop('type'    )    )
       SCREAMING_SNAKE_CASE								=			component_class(**snake_case__    )
       setattr(self.backend_tokenizer							,    snake_case__							,    snake_case__    )

    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     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 UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			AddedToken(snake_case__							,    lstrip=snake_case__							,    rstrip=snake_case__    ) if isinstance(snake_case__							,    snake_case__    ) else value
     SCREAMING_SNAKE_CASE								=			value

    def UpperCamelCase			(			self :      Dict							,    *snake_case__ :      Optional[Any]							,    **snake_case__ :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			kwargs.get('is_split_into_words'							,    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(*snake_case__							,    **snake_case__    )

    def UpperCamelCase			(			self :      List[str]							,    *snake_case__ :      List[Any]							,    **snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			kwargs.get('is_split_into_words'							,    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(*snake_case__							,    **snake_case__    )

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      str							,    snake_case__ :      Optional[str] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self._tokenizer.model.save(snake_case__							,    name=snake_case__    )
     return tuple(snake_case__    )

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      int							,    snake_case__ :      Tuple=None    ):

     """simple docstring"""






     SCREAMING_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 UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.sep_token_id]
     SCREAMING_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]




    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      Union[Dict[str, EncodedInput], BatchEncoding]							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      Optional[bool] = None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			super()._pad(
         encoded_inputs=snake_case__							,    max_length=snake_case__							,    padding_strategy=snake_case__							,    pad_to_multiple_of=snake_case__							,    return_attention_mask=snake_case__							,    )

     # Load from model defaults
     if return_attention_mask is None:
      SCREAMING_SNAKE_CASE								=			'attention_mask' in self.model_input_names

     if return_attention_mask and "global_attention_mask" in encoded_inputs:
      SCREAMING_SNAKE_CASE								=			encoded_inputs[self.model_input_names[0]]
      # `global_attention_mask` need to have the same length as other (sequential) inputs.
      SCREAMING_SNAKE_CASE								=			len(encoded_inputs['global_attention_mask']    ) != len(snake_case__    )

      if needs_to_be_padded:
       SCREAMING_SNAKE_CASE								=			len(snake_case__    ) - len(encoded_inputs['global_attention_mask']    )

       if self.padding_side == "right":
        # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
        SCREAMING_SNAKE_CASE								=			(
            encoded_inputs['global_attention_mask'] + [-1] * difference
        )
       elif self.padding_side == "left":
        SCREAMING_SNAKE_CASE								=			[-1] * difference + encoded_inputs[
            'global_attention_mask'
        ]
       else:
        raise ValueError('Invalid padding strategy:' + str(self.padding_side    )    )

     return encoded_inputs

