





"""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  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Optional[int]:





  """simple docstring"""


  with open(_SCREAMING_SNAKE_CASE     ) as metadata_file:
    lowerCAmelCase__     :Union[str, Any]											=				json.load(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :List[str]											=				LukeConfig(use_entity_aware_attention=_SCREAMING_SNAKE_CASE	,  **metadata['model_config']     )

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

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

  lowerCAmelCase__     :List[str]											=				XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']     )

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

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

  lowerCAmelCase__     :Union[str, Any]											=				MLukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE     )

  # Initialize the embeddings of the special tokens
  lowerCAmelCase__     :str											=				tokenizer.convert_tokens_to_ids(['@']     )[0]
  lowerCAmelCase__     :int											=				tokenizer.convert_tokens_to_ids(['#']     )[0]

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

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

  lowerCAmelCase__     :int											=				LukeForMaskedLM(config=_SCREAMING_SNAKE_CASE     ).eval()

  state_dict.pop('entity_predictions.decoder.weight'     )
  state_dict.pop('lm_head.decoder.weight'     )
  state_dict.pop('lm_head.decoder.bias'     )
  lowerCAmelCase__     :Tuple											=				OrderedDict()
  for key, value in state_dict.items():
    if not (key.startswith('lm_head'     ) or key.startswith('entity_predictions'     )):
      lowerCAmelCase__     :List[str]											=				state_dict[key]
    else:
      lowerCAmelCase__     :Union[str, Any]											=				state_dict[key]

  lowerCAmelCase__				,							lowerCAmelCase__     :int											=				model.load_state_dict(_SCREAMING_SNAKE_CASE	,  strict=_SCREAMING_SNAKE_CASE     )

  if set(_SCREAMING_SNAKE_CASE     ) != {"luke.embeddings.position_ids"}:
    raise ValueError(F"Unexpected unexpected_keys: {unexpected_keys}"     )
  if set(_SCREAMING_SNAKE_CASE     ) != {
      "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__     :List[Any]											=				MLukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE	,  task='entity_classification'     )

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

  lowerCAmelCase__     :List[str]											=				model(**_SCREAMING_SNAKE_CASE     )

  # Verify word hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    lowerCAmelCase__     :Union[str, Any]											=				torch.Size((1, 33, 768)     )
    lowerCAmelCase__     :Optional[int]											=				torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]     )

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

  # Verify entity hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    lowerCAmelCase__     :Optional[Any]											=				torch.Size((1, 1, 768)     )
    lowerCAmelCase__     :Any											=				torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]     )

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

  # Verify masked word/entity prediction
  lowerCAmelCase__     :str											=				MLukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Tuple											=				'Tokyo is the capital of <mask>.'
  lowerCAmelCase__     :Optional[int]											=				(24, 30)
  lowerCAmelCase__     :Any											=				tokenizer(_SCREAMING_SNAKE_CASE	,  entity_spans=[span]	,  return_tensors='pt'     )

  lowerCAmelCase__     :int											=				model(**_SCREAMING_SNAKE_CASE     )

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

  lowerCAmelCase__     :Optional[int]											=				outputs.entity_logits[0][0].argmax().item()
  lowerCAmelCase__     :Dict											=				[
      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(_SCREAMING_SNAKE_CASE     )     )
  model.save_pretrained(_SCREAMING_SNAKE_CASE     )
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				['[MASK]', '[PAD]', '[UNK]']

  lowerCAmelCase__     :Any											=				[json.loads(_SCREAMING_SNAKE_CASE     ) for line in open(_SCREAMING_SNAKE_CASE     )]

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


if __name__ == "__main__":
  __A											=						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											=						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 math

import tensorflow as tf
from packaging import version







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    str      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    int      							=0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0					)				,							x.dtype					)					))

			return x * cdf







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    Optional[int]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Tuple      							=tf.cast(math.pi				,							x.dtype					)
			snake_case:    Dict      							=tf.cast(0.04_4715				,							x.dtype					)
			snake_case:    List[str]      							=0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi					) * (x + coeff * tf.pow(__UpperCAmelCase				,							3					))					))

			return x * cdf







def a_     (    __UpperCAmelCase					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Optional[int]      							=tf.convert_to_tensor(__UpperCAmelCase					)

			return x * tf.tanh(tf.math.softplus(__UpperCAmelCase					)					)







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    List[str]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Dict      							=tf.cast(0.04_4715				,							x.dtype					)
			snake_case:    List[str]      							=tf.cast(0.79_7884_5608				,							x.dtype					)

			return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)					))







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Tuple      							=tf.cast(1.702				,							x.dtype					)
			return x * tf.math.sigmoid(coeff * x					)







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			return tf.clip_by_value(_gelu(__UpperCAmelCase					)				,							-10				,							10					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=-1					)				->					List[str]:





			"""simple docstring"""







			snake_case							,  snake_case:    int      							=tf.split(__UpperCAmelCase				,							2				,							axis=__UpperCAmelCase					)
			return a * tf.math.sigmoid(__UpperCAmelCase					)


if version.parse(tf.version.VERSION) >= version.parse('2.4'):







	def a_     (    __UpperCAmelCase					)				->					int:





				"""simple docstring"""







				return tf.keras.activations.gelu(__UpperCAmelCase				,							approximate=__UpperCAmelCase					)

	a							   =				tf.keras.activations.gelu







	a							   =				approximate_gelu_wrap
else:
	a							   =				_gelu
	a							   =				_gelu_new


a							   =				{
    'gelu': gelu,
    'gelu_10': gelu_aa,
    'gelu_fast': gelu_fast,
    'gelu_new': gelu_new,
    'glu': glu,
    'mish': mish,
    'quick_gelu': quick_gelu,
    'relu': tf.keras.activations.relu,
    'sigmoid': tf.keras.activations.sigmoid,
    'silu': tf.keras.activations.swish,
    'swish': tf.keras.activations.swish,
    'tanh': tf.keras.activations.tanh,
}







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			if activation_string in ACTaFN:
						return ACTaFN[activation_string]
			else:
						raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()					)}'''					)




