


'''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 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				Dict      ,    UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				Any							)     ->		Any:
     # Load configuration defined in the metadata file
     with open(UpperCAmelCase_							) as metadata_file:
          __lowerCamelCase   :	List[Any]								=		json.load(UpperCAmelCase_							)
     __lowerCamelCase   :	Optional[Any]								=		LukeConfig(use_entity_aware_attention=UpperCAmelCase_      ,    **metadata['model_config']							)

     # Load in the weights from the checkpoint_path
     __lowerCamelCase   :	Union[str, Any]								=		torch.load(UpperCAmelCase_      ,    map_location='cpu'							)

     # Load the entity vocab file
     __lowerCamelCase   :	Tuple								=		load_entity_vocab(UpperCAmelCase_							)

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

     # Add special tokens to the token vocabulary for downstream tasks
     __lowerCamelCase   :	List[Any]								=		AddedToken('<ent>'      ,    lstrip=UpperCAmelCase_      ,    rstrip=UpperCAmelCase_							)
     __lowerCamelCase   :	str								=		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_      ,    LukeTokenizer.vocab_files_names['entity_vocab_file']							)      ,    'w'							) as f:
          json.dump(UpperCAmelCase_      ,    UpperCAmelCase_							)

     __lowerCamelCase   :	List[str]								=		LukeTokenizer.from_pretrained(UpperCAmelCase_							)

     # Initialize the embeddings of the special tokens
     __lowerCamelCase   :	Any								=		state_dict['embeddings.word_embeddings.weight']
     __lowerCamelCase   :	Any								=		word_emb[tokenizer.convert_tokens_to_ids(['@']							)[0]].unsqueeze(0							)
     __lowerCamelCase   :	Optional[int]								=		word_emb[tokenizer.convert_tokens_to_ids(['#']							)[0]].unsqueeze(0							)
     __lowerCamelCase   :	Union[str, Any]								=		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"]:
               __lowerCamelCase   :	Tuple								=		F'encoder.layer.{layer_index}.attention.self.'
               __lowerCamelCase   :	Optional[int]								=		state_dict[prefix + matrix_name]
               __lowerCamelCase   :	List[Any]								=		state_dict[prefix + matrix_name]
               __lowerCamelCase   :	List[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   :	str								=		entity_emb[entity_vocab['[MASK]']]

     __lowerCamelCase   :	Any								=		LukeModel(config=UpperCAmelCase_							).eval()

     __lowerCamelCase				,       __lowerCamelCase   :	Optional[int]								=		model.load_state_dict(UpperCAmelCase_      ,    strict=UpperCAmelCase_							)
     if not (len(UpperCAmelCase_							) == 1 and missing_keys[0] == "embeddings.position_ids"):
          raise ValueError(F'Missing keys {", ".join(UpperCAmelCase_							)}. 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
     __lowerCamelCase   :	Union[str, Any]								=		LukeTokenizer.from_pretrained(UpperCAmelCase_      ,    task='entity_classification'							)

     __lowerCamelCase   :	Union[str, Any]								=		(
         '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 .'
     )
     __lowerCamelCase   :	Dict								=		(39, 42)
     __lowerCamelCase   :	Any								=		tokenizer(UpperCAmelCase_      ,    entity_spans=[span]      ,    add_prefix_space=UpperCAmelCase_      ,    return_tensors='pt'							)

     __lowerCamelCase   :	List[Any]								=		model(**UpperCAmelCase_							)

     # Verify word hidden states
     if model_size == "large":
          __lowerCamelCase   :	int								=		torch.Size((1, 42, 10_24)							)
          __lowerCamelCase   :	Union[str, Any]								=		torch.tensor(
              [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]							)
     else:  # base
          __lowerCamelCase   :	Any								=		torch.Size((1, 42, 7_68)							)
          __lowerCamelCase   :	Any								=		torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]							)

     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":
          __lowerCamelCase   :	Dict								=		torch.Size((1, 1, 10_24)							)
          __lowerCamelCase   :	Dict								=		torch.tensor([[0.0_466, -0.0_106, -0.0_179]]							)
     else:  # base
          __lowerCamelCase   :	Union[str, Any]								=		torch.Size((1, 1, 7_68)							)
          __lowerCamelCase   :	int								=		torch.tensor([[0.1_457, 0.1_044, 0.0_174]]							)

     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

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



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[str]							)     ->		List[Any]:
     __lowerCamelCase   :	Tuple								=		{}
     with open(UpperCAmelCase_      ,    'r'      ,    encoding='utf-8'							) as f:
          for index, line in enumerate(UpperCAmelCase_							):
               __lowerCamelCase				,       __lowerCamelCase   :	Optional[int]								=		line.rstrip().split('\t'							)
               __lowerCamelCase   :	Tuple								=		index

     return entity_vocab


if __name__ == "__main__":
      A__		:					List[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__		:					Dict												=  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 importlib import import_module

from .logging import get_logger


A__		:					str												=  get_logger(__name__)





class    UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None	)				->     List[Any]:
         __lowerCamelCase   :	List[str]								=		attrs or []
         if module is not None:
              for key in module.__dict__:
                   if key in attrs or not key.startswith('__'	):
                        setattr(self			,     SCREAMING_SNAKE_CASE_			,     getattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)
         __lowerCamelCase   :	Optional[int]								=		module._original_module if isinstance(SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj	) else module







class    UpperCAmelCase_  :





    """simple docstring"""






    lowerCamelCase  :					Tuple							     =			[]







    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None	)				->     Union[str, Any]:
         __lowerCamelCase   :	Optional[int]								=		obj
         __lowerCamelCase   :	List[Any]								=		target
         __lowerCamelCase   :	Union[str, Any]								=		new
         __lowerCamelCase   :	Union[str, Any]								=		target.split('.'	)[0]
         __lowerCamelCase   :	Dict								=		{}
         __lowerCamelCase   :	Dict								=		attrs or []







    def __enter__(							self	)				->     Optional[int]:
         *__lowerCamelCase				,       __lowerCamelCase   :	int								=		self.target.split('.'	)

         # Patch modules:
         # it's used to patch attributes of submodules like "os.path.join";
         # in this case we need to patch "os" and "os.path"

         for i in range(len(SCREAMING_SNAKE_CASE_	)	):
              try:
                   __lowerCamelCase   :	Optional[int]								=		import_module('.'.join(submodules[: i + 1]	)	)
              except ModuleNotFoundError:
                   continue
              # We iterate over all the globals in self.obj in case we find "os" or "os.path"
              for attr in self.obj.__dir__():
                   __lowerCamelCase   :	List[Any]								=		getattr(self.obj			,     SCREAMING_SNAKE_CASE_	)
                   # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
                   # This allows to patch renamed modules like "from os import path as ospath".
                   if obj_attr is submodule or (
                       (isinstance(SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj	) and obj_attr._original_module is submodule)
                   ):
                        __lowerCamelCase   :	Optional[Any]								=		obj_attr
                        # patch at top level
                        setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj(SCREAMING_SNAKE_CASE_			,     attrs=self.attrs	)	)
                        __lowerCamelCase   :	str								=		getattr(self.obj			,     SCREAMING_SNAKE_CASE_	)
                        # construct lower levels patches
                        for key in submodules[i + 1 :]:
                             setattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj(getattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     attrs=self.attrs	)	)
                             __lowerCamelCase   :	List[Any]								=		getattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
                        # finally set the target attribute
                        setattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     self.new	)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

         if submodules:  # if it's an attribute of a submodule like "os.path.join"
              try:
                   __lowerCamelCase   :	Union[str, Any]								=		getattr(import_module('.'.join(SCREAMING_SNAKE_CASE_	)	)			,     SCREAMING_SNAKE_CASE_	)
              except (AttributeError, ModuleNotFoundError):
                   return
              # We iterate over all the globals in self.obj in case we find "os.path.join"
              for attr in self.obj.__dir__():
                   # We don't check for the name of the global, but rather if its value *is* "os.path.join".
                   # This allows to patch renamed attributes like "from os.path import join as pjoin".
                   if getattr(self.obj			,     SCREAMING_SNAKE_CASE_	) is attr_value:
                        __lowerCamelCase   :	Optional[int]								=		getattr(self.obj			,     SCREAMING_SNAKE_CASE_	)
                        setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     self.new	)
         elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
              __lowerCamelCase   :	List[Any]								=		globals()['__builtins__'][target_attr]
              setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     self.new	)
         else:
              raise RuntimeError(f'Tried to patch attribute {target_attr} instead of a submodule.'	)







    def __exit__(							self			,     *SCREAMING_SNAKE_CASE_	)				->     Optional[Any]:
         for attr in list(self.original	):
              setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     self.original.pop(SCREAMING_SNAKE_CASE_	)	)







    def       lowercase_				(							self	)				->     Optional[int]:
         self.__enter__()
         self._active_patches.append(self	)







    def       lowercase_				(							self	)				->     str:
         try:
              self._active_patches.remove(self	)
         except ValueError:
              # If the patch hasn't been started this will fail
              return None

         return self.__exit__()






