






'''simple docstring'''






import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  ,		A  :       str  ,		A  :       Any  ,		A  :       Union[str, Any]  ,		A  :       int  )    -> List[Any]:







   """simple docstring"""
   # Load configuration defined in the metadata file
   with open(a_  ) as metadata_file:
      __snake_case				:      Union[str, Any]     							= json.load(a_  )
   __snake_case				:      Union[str, Any]     							= LukeConfig(use_entity_aware_attention=a_  ,		**metadata['model_config']  )

   # Load in the weights from the checkpoint_path
   __snake_case				:      List[str]     							= torch.load(a_  ,		map_location='cpu'  )

   # Load the entity vocab file
   __snake_case				:      List[str]     							= load_entity_vocab(a_  )

   __snake_case				:      Optional[int]     							= RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']  )

   # Add special tokens to the token vocabulary for downstream tasks
   __snake_case				:      int     							= AddedToken('<ent>'  ,		lstrip=a_  ,		rstrip=a_  )
   __snake_case				:      Optional[Any]     							= AddedToken('<ent2>'  ,		lstrip=a_  ,		rstrip=a_  )
   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(a_  )
   with open(os.path.join(a_  ,		LukeTokenizer.vocab_files_names['entity_vocab_file']  )  ,		'w'  ) as f:
      json.dump(a_  ,		a_  )

   __snake_case				:      str     							= LukeTokenizer.from_pretrained(a_  )

   # Initialize the embeddings of the special tokens
   __snake_case				:      Union[str, Any]     							= state_dict['''embeddings.word_embeddings.weight''']
   __snake_case				:      Any     							= word_emb[tokenizer.convert_tokens_to_ids(['@']  )[0]].unsqueeze(0  )
   __snake_case				:      List[Any]     							= word_emb[tokenizer.convert_tokens_to_ids(['#']  )[0]].unsqueeze(0  )
   __snake_case				:      Dict     							= torch.cat([word_emb, ent_emb, enta_emb]  )

   # 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				:      Tuple     							= F"""encoder.layer.{layer_index}.attention.self."""
         __snake_case				:      List[Any]     							= state_dict[prefix + matrix_name]
         __snake_case				:      Any     							= state_dict[prefix + matrix_name]
         __snake_case				:      str     							= state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
   __snake_case				:      Any     							= state_dict['''entity_embeddings.entity_embeddings.weight''']
   __snake_case				:      List[Any]     							= entity_emb[entity_vocab['''[MASK]''']]

   __snake_case				:      int     							= LukeModel(config=a_  ).eval()

   __snake_case				:      List[Any]     							= model.load_state_dict(a_  ,		strict=a_  )
   if not (len(a_  ) == 1 and missing_keys[0] == "embeddings.position_ids"):
      raise ValueError(F"""Missing keys {", ".join(a_  )}. Expected only missing embeddings.position_ids"""  )
   if not (all(key.startswith('entity_predictions'  ) or key.startswith('lm_head'  ) for key in unexpected_keys  )):
      raise ValueError(
          'Unexpected keys'
          F""" {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"  ) or key.startswith("lm_head"  ))]  )}"""  )

   # Check outputs
   __snake_case				:      Tuple     							= LukeTokenizer.from_pretrained(a_  ,		task='entity_classification'  )

   __snake_case				:      int     							= (
       '''Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'''
       ''' new world number one avoid a humiliating second- round exit at Wimbledon .'''
   )
   __snake_case				:      int     							= (39, 42)
   __snake_case				:      Optional[Any]     							= tokenizer(a_  ,		entity_spans=[span]  ,		add_prefix_space=a_  ,		return_tensors='pt'  )

   __snake_case				:      List[str]     							= model(**a_  )

   # Verify word hidden states
   if model_size == "large":
      __snake_case				:      List[Any]     							= torch.Size((1, 42, 10_24)  )
      __snake_case				:      Union[str, Any]     							= torch.tensor(
          [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]  )
   else:  # base
      __snake_case				:      Optional[Any]     							= torch.Size((1, 42, 7_68)  )
      __snake_case				:      List[Any]     							= torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]  )

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

   # Verify entity hidden states
   if model_size == "large":
      __snake_case				:      List[Any]     							= torch.Size((1, 1, 10_24)  )
      __snake_case				:      int     							= torch.tensor([[0.0466, -0.0106, -0.0179]]  )
   else:  # base
      __snake_case				:      Optional[int]     							= torch.Size((1, 1, 7_68)  )
      __snake_case				:      List[str]     							= torch.tensor([[0.1457, 0.1044, 0.0174]]  )

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

   # Finally, save our PyTorch model and tokenizer
   print('Saving PyTorch model to {}'.format(a_  )  )
   model.save_pretrained(a_  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  )    -> Dict:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= {}
   with open(a_  ,		'r'  ,		encoding='utf-8'  ) as f:
      for index, line in enumerate(a_  ):
         __snake_case				:      Tuple     							= line.rstrip().split('\t'  )
         __snake_case				:      str     							= index

   return entity_vocab


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'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A    	=   {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BioGptForCausalLM''',
          '''BioGptForTokenClassification''',
          '''BioGptForSequenceClassification''',
          '''BioGptModel''',
          '''BioGptPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

      __A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)