






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_       (						_A				,		_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




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

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

     # Load the entity vocab file
     SCREAMING_SNAKE_CASE__   =   load_original_entity_vocab(SCREAMING_SNAKE_CASE_    )
     # 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=SCREAMING_SNAKE_CASE_				,		rstrip=SCREAMING_SNAKE_CASE_    )
     SCREAMING_SNAKE_CASE__   =   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:
          SCREAMING_SNAKE_CASE__   =   json.load(SCREAMING_SNAKE_CASE_    )
     SCREAMING_SNAKE_CASE__   =   '''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_    )

     SCREAMING_SNAKE_CASE__   =   MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_    )

     # 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=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'''    )
     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(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
     SCREAMING_SNAKE_CASE__   =   MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_				,		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(SCREAMING_SNAKE_CASE_				,		entity_spans=[span]				,		return_tensors='''pt'''    )

     SCREAMING_SNAKE_CASE__   =   model(**SCREAMING_SNAKE_CASE_    )

     # 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([[0.0_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
          SCREAMING_SNAKE_CASE__   =   torch.Size((1, 1, 7_68)    )
          SCREAMING_SNAKE_CASE__   =   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
     SCREAMING_SNAKE_CASE__   =   MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_    )
     SCREAMING_SNAKE_CASE__   =   '''Tokyo is the capital of <mask>.'''
     SCREAMING_SNAKE_CASE__   =   (24, 30)
     SCREAMING_SNAKE_CASE__   =   tokenizer(SCREAMING_SNAKE_CASE_				,		entity_spans=[span]				,		return_tensors='''pt'''    )

     SCREAMING_SNAKE_CASE__   =   model(**SCREAMING_SNAKE_CASE_    )

     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(SCREAMING_SNAKE_CASE_    )

     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(SCREAMING_SNAKE_CASE_    )    )
     model.save_pretrained(SCREAMING_SNAKE_CASE_    )
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE__   =   [json.loads(SCREAMING_SNAKE_CASE_    ) for line in open(SCREAMING_SNAKE_CASE_    )]

     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__":
       _SCREAMING_SNAKE_CASE					:  Dict             =      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					:  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,
       )





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Tuple             =      {
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "transfo-xl"
  a									= ["mems"]
  a									= {
      "n_token": "vocab_size",
      "hidden_size": "d_model",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }






  def __init__(							self						:		Any     ,       __lowerCamelCase						:		int=26_7735     ,       __lowerCamelCase						:		Any=[2_0000, 4_0000, 20_0000]     ,       __lowerCamelCase						:		Dict=1024     ,       __lowerCamelCase						:		Optional[int]=1024     ,       __lowerCamelCase						:		List[Any]=16     ,       __lowerCamelCase						:		Union[str, Any]=64     ,       __lowerCamelCase						:		Dict=4096     ,       __lowerCamelCase						:		int=4     ,       __lowerCamelCase						:		Dict=False     ,       __lowerCamelCase						:		Tuple=18     ,       __lowerCamelCase						:		Optional[int]=1600     ,       __lowerCamelCase						:		str=1000     ,       __lowerCamelCase						:		Any=True     ,       __lowerCamelCase						:		Dict=True     ,       __lowerCamelCase						:		List[Any]=0     ,       __lowerCamelCase						:		int=-1     ,       __lowerCamelCase						:		Tuple=True     ,       __lowerCamelCase						:		List[Any]=0.1     ,       __lowerCamelCase						:		int=0.0     ,       __lowerCamelCase						:		int=True     ,       __lowerCamelCase						:		str="normal"     ,       __lowerCamelCase						:		List[str]=0.01     ,       __lowerCamelCase						:		Any=0.01     ,       __lowerCamelCase						:		List[str]=0.02     ,       __lowerCamelCase						:		List[str]=1e-5     ,       __lowerCamelCase						:		Union[str, Any]=0     ,       **__lowerCamelCase						:		int     ,       )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   []
       self.cutoffs.extend(__lowerCamelCase )
       if proj_share_all_but_first:
            SCREAMING_SNAKE_CASE__   =   [False] + [True] * len(self.cutoffs )
       else:
            SCREAMING_SNAKE_CASE__   =   [False] + [False] * len(self.cutoffs )
       SCREAMING_SNAKE_CASE__   =   d_model
       SCREAMING_SNAKE_CASE__   =   d_embed
       SCREAMING_SNAKE_CASE__   =   d_head
       SCREAMING_SNAKE_CASE__   =   d_inner
       SCREAMING_SNAKE_CASE__   =   div_val
       SCREAMING_SNAKE_CASE__   =   pre_lnorm
       SCREAMING_SNAKE_CASE__   =   n_layer
       SCREAMING_SNAKE_CASE__   =   n_head
       SCREAMING_SNAKE_CASE__   =   mem_len
       SCREAMING_SNAKE_CASE__   =   same_length
       SCREAMING_SNAKE_CASE__   =   attn_type
       SCREAMING_SNAKE_CASE__   =   clamp_len
       SCREAMING_SNAKE_CASE__   =   sample_softmax
       SCREAMING_SNAKE_CASE__   =   adaptive
       SCREAMING_SNAKE_CASE__   =   dropout
       SCREAMING_SNAKE_CASE__   =   dropatt
       SCREAMING_SNAKE_CASE__   =   untie_r
       SCREAMING_SNAKE_CASE__   =   init
       SCREAMING_SNAKE_CASE__   =   init_range
       SCREAMING_SNAKE_CASE__   =   proj_init_std
       SCREAMING_SNAKE_CASE__   =   init_std
       SCREAMING_SNAKE_CASE__   =   layer_norm_epsilon
       super().__init__(eos_token_id=__lowerCamelCase     ,       **__lowerCamelCase )






  @property
  def 				lowercase_		(							self						:		str )		->							Dict:
       # Message copied from Transformer-XL documentation
       logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
       return -1







  @max_position_embeddings.setter
  def 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		Any )		->							List[Any]:
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )


