'''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 			UpperCamelCase__       (       _lowercase     :					Tuple ,     _lowercase     :					Optional[Any] ,     _lowercase     :					Any ,     _lowercase     :					Tuple ,     _lowercase     :					str  )				->						Dict:
  # Load configuration defined in the metadata file
  with open(lowerCAmelCase__  ) as metadata_file:
    __UpperCAmelCase:       Optional[Any]		=			json.load(lowerCAmelCase__  )
  __UpperCAmelCase:       List[Any]		=			LukeConfig(use_entity_aware_attention=lowerCAmelCase__ ,     **metadata["""model_config"""]  )

  # Load in the weights from the checkpoint_path
  __UpperCAmelCase:       Optional[Any]		=			torch.load(lowerCAmelCase__ ,     map_location="""cpu"""  )['module']

  # Load the entity vocab file
  __UpperCAmelCase:       Optional[Any]		=			load_original_entity_vocab(lowerCAmelCase__  )
  # add an entry for [MASK2]
  __UpperCAmelCase:       List[Any]		=			max(entity_vocab.values()  ) + 1
  config.entity_vocab_size += 1

  __UpperCAmelCase:       Optional[int]		=			XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]  )

  # Add special tokens to the token vocabulary for downstream tasks
  __UpperCAmelCase:       Union[str, Any]		=			AddedToken("""<ent>""" ,     lstrip=lowerCAmelCase__ ,     rstrip=lowerCAmelCase__  )
  __UpperCAmelCase:       Tuple		=			AddedToken("""<ent2>""" ,     lstrip=lowerCAmelCase__ ,     rstrip=lowerCAmelCase__  )
  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(lowerCAmelCase__  )
  with open(os.path.join(lowerCAmelCase__ ,     """tokenizer_config.json"""  ) ,     """r"""  ) as f:
    __UpperCAmelCase:       Dict		=			json.load(lowerCAmelCase__  )
  __UpperCAmelCase:       str		=			'MLukeTokenizer'
  with open(os.path.join(lowerCAmelCase__ ,     """tokenizer_config.json"""  ) ,     """w"""  ) as f:
    json.dump(lowerCAmelCase__ ,     lowerCAmelCase__  )

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

  __UpperCAmelCase:       Tuple		=			MLukeTokenizer.from_pretrained(lowerCAmelCase__  )

  # Initialize the embeddings of the special tokens
  __UpperCAmelCase:       int		=			tokenizer.convert_tokens_to_ids(["""@"""]  )[0]
  __UpperCAmelCase:       Any		=			tokenizer.convert_tokens_to_ids(["""#"""]  )[0]

  __UpperCAmelCase:       int		=			state_dict['embeddings.word_embeddings.weight']
  __UpperCAmelCase:       str		=			word_emb[ent_init_index].unsqueeze(0  )
  __UpperCAmelCase:       Any		=			word_emb[enta_init_index].unsqueeze(0  )
  __UpperCAmelCase:       Any		=			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"]:
    __UpperCAmelCase:       Any		=			state_dict[bias_name]
    __UpperCAmelCase:       Optional[Any]		=			decoder_bias[ent_init_index].unsqueeze(0  )
    __UpperCAmelCase:       List[Any]		=			decoder_bias[enta_init_index].unsqueeze(0  )
    __UpperCAmelCase:       str		=			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"]:
      __UpperCAmelCase:       Union[str, Any]		=			F'''encoder.layer.{layer_index}.attention.self.'''
      __UpperCAmelCase:       Any		=			state_dict[prefix + matrix_name]
      __UpperCAmelCase:       int		=			state_dict[prefix + matrix_name]
      __UpperCAmelCase:       Union[str, Any]		=			state_dict[prefix + matrix_name]

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

  __UpperCAmelCase:       Dict		=			LukeForMaskedLM(config=lowerCAmelCase__  ).eval()

  state_dict.pop("""entity_predictions.decoder.weight"""  )
  state_dict.pop("""lm_head.decoder.weight"""  )
  state_dict.pop("""lm_head.decoder.bias"""  )
  __UpperCAmelCase:       List[str]		=			OrderedDict()
  for key, value in state_dict.items():
    if not (key.startswith("""lm_head"""  ) or key.startswith("""entity_predictions"""  )):
      __UpperCAmelCase:       int		=			state_dict[key]
    else:
      __UpperCAmelCase:       Optional[Any]		=			state_dict[key]

  __UpperCAmelCase:       str		=			model.load_state_dict(lowerCAmelCase__ ,     strict=lowerCAmelCase__  )

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

  __UpperCAmelCase:       Tuple		=			'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
  __UpperCAmelCase:       int		=			(0, 9)
  __UpperCAmelCase:       Optional[Any]		=			tokenizer(lowerCAmelCase__ ,     entity_spans=[span] ,     return_tensors="""pt"""  )

  __UpperCAmelCase:       Dict		=			model(**lowerCAmelCase__  )

  # Verify word hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    __UpperCAmelCase:       Dict		=			torch.Size((1, 3_3, 7_6_8)  )
    __UpperCAmelCase:       int		=			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] ,     lowerCAmelCase__ ,     atol=1E-4  ):
    raise ValueError

  # Verify entity hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    __UpperCAmelCase:       List[str]		=			torch.Size((1, 1, 7_6_8)  )
    __UpperCAmelCase:       str		=			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] ,     lowerCAmelCase__ ,     atol=1E-4  ):
    raise ValueError

  # Verify masked word/entity prediction
  __UpperCAmelCase:       List[Any]		=			MLukeTokenizer.from_pretrained(lowerCAmelCase__  )
  __UpperCAmelCase:       List[Any]		=			'Tokyo is the capital of <mask>.'
  __UpperCAmelCase:       Optional[Any]		=			(2_4, 3_0)
  __UpperCAmelCase:       List[str]		=			tokenizer(lowerCAmelCase__ ,     entity_spans=[span] ,     return_tensors="""pt"""  )

  __UpperCAmelCase:       Optional[int]		=			model(**lowerCAmelCase__  )

  __UpperCAmelCase:       Union[str, Any]		=			encoding['input_ids'][0].tolist()
  __UpperCAmelCase:       Optional[int]		=			input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""  )  )
  __UpperCAmelCase:       Union[str, Any]		=			outputs.logits[0][mask_position_id].argmax(dim=-1  )
  assert "Japan" == tokenizer.decode(lowerCAmelCase__  )

  __UpperCAmelCase:       str		=			outputs.entity_logits[0][0].argmax().item()
  __UpperCAmelCase:       List[Any]		=			[
      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(lowerCAmelCase__  )  )
  model.save_pretrained(lowerCAmelCase__  )






def 			UpperCamelCase__       (       _lowercase     :					List[str]  )				->						Optional[Any]:
  __UpperCAmelCase:       Dict		=			['[MASK]', '[PAD]', '[UNK]']

  __UpperCAmelCase:       Dict		=			[json.loads(lowerCAmelCase__  ) for line in open(lowerCAmelCase__  )]

  __UpperCAmelCase:       Optional[int]		=			{}
  for entry in data:
    __UpperCAmelCase:       int		=			entry['id']
    for entity_name, language in entry["entities"]:
      if entity_name in SPECIAL_TOKENS:
        __UpperCAmelCase:       Tuple		=			entity_id
        break
      __UpperCAmelCase:       Dict		=			F'''{language}:{entity_name}'''
      __UpperCAmelCase:       Union[str, Any]		=			entity_id
  return new_mapping


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_						 =      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.'
    )
    SCREAMING_SNAKE_CASE_						 =      parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )






_lowercase		:  Any			       =[0, 2, 4, 6, 8]
_lowercase		:  List[Any]			       =[1, 3, 5, 7, 9]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		if remaining_length == 0:
				if digits[0] == 0 or digits[-1] == 0:
						return 0

				for i in range(length // 2 - 1							,-1							,-1						):
						remainder += digits[i] + digits[length - i - 1]

						if remainder % 2 == 0:
								return 0

						remainder //= 10

				return 1

		if remaining_length == 1:
				if remainder % 2 == 0:
						return 0

				lowerCamelCase_	:     Dict 						=      0
				for digit in range(10						):
						lowerCamelCase_	:     List[Any] 						=      digit
						result += reversible_numbers(
						    0							,(remainder + 2 * digit) // 10							,lowerCAmelCase__							,lowerCAmelCase__						)
				return result

		lowerCamelCase_	:     List[str] 						=      0
		for digita in range(10						):
				lowerCamelCase_	:     Tuple 						=      digita

				if (remainder + digita) % 2 == 0:
						lowerCamelCase_	:     Dict 						=      ODD_DIGITS
				else:
						lowerCamelCase_	:     str 						=      EVEN_DIGITS

				for digita in other_parity_digits:
						lowerCamelCase_	:     Optional[int] 						=      digita
						result += reversible_numbers(
						    remaining_length - 2							,(remainder + digita + digita) // 10							,lowerCAmelCase__							,lowerCAmelCase__							,)
		return result


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__ = 9						):
		lowerCamelCase_	:     int 						=      0
		for length in range(1							,max_power + 1						):
				result += reversible_numbers(lowerCAmelCase__							,0							,[0] * length							,lowerCAmelCase__						)
		return result


if __name__ == "__main__":
		print(F'''{solution() = }''')
