


"""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    _lowerCamelCase   (     lowerCamelCase__	:							Dict       ,		lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							Dict       ,		lowerCamelCase__	:							List[Any]  ):
 # Load configuration defined in the metadata file
 with open(lowerCamelCase__  ) as metadata_file:
  lowercase__       :       int				=						json.load(lowerCamelCase__  )
 lowercase__       :       List[str]				=						LukeConfig(use_entity_aware_attention=lowerCamelCase__       ,		**metadata["""model_config"""]  )

 # Load in the weights from the checkpoint_path
 lowercase__       :       int				=						torch.load(lowerCamelCase__       ,		map_location="""cpu"""  )

 # Load the entity vocab file
 lowercase__       :       str				=						load_entity_vocab(lowerCamelCase__  )

 lowercase__       :       Dict				=						RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]  )

 # Add special tokens to the token vocabulary for downstream tasks
 lowercase__       :       Dict				=						AddedToken("""<ent>"""       ,		lstrip=lowerCamelCase__       ,		rstrip=lowerCamelCase__  )
 lowercase__       :       List[Any]				=						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__       ,		LukeTokenizer.vocab_files_names["""entity_vocab_file"""]  )       ,		"""w"""  ) as f:
  json.dump(lowerCamelCase__       ,		lowerCamelCase__  )

 lowercase__       :       Dict				=						LukeTokenizer.from_pretrained(lowerCamelCase__  )

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

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
 lowercase__       :       Union[str, Any]				=						state_dict["""entity_embeddings.entity_embeddings.weight"""]
 lowercase__       :       Union[str, Any]				=						entity_emb[entity_vocab["""[MASK]"""]]

 lowercase__       :       Optional[int]				=						LukeModel(config=lowerCamelCase__  ).eval()

 lowercase__	,       lowercase__       :       Optional[Any]				=						model.load_state_dict(lowerCamelCase__       ,		strict=lowerCamelCase__  )
 if not (len(lowerCamelCase__  ) == 1 and missing_keys[0] == "embeddings.position_ids"):
  raise ValueError(f'''Missing keys {', '.join(lowerCamelCase__  )}. 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
 lowercase__       :       Tuple				=						LukeTokenizer.from_pretrained(lowerCamelCase__       ,		task="""entity_classification"""  )

 lowercase__       :       Tuple				=						(
     """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 ."""
 )
 lowercase__       :       Any				=						(39, 42)
 lowercase__       :       int				=						tokenizer(lowerCamelCase__       ,		entity_spans=[span]       ,		add_prefix_space=lowerCamelCase__       ,		return_tensors="""pt"""  )

 lowercase__       :       Any				=						model(**lowerCamelCase__  )

 # Verify word hidden states
 if model_size == "large":
  lowercase__       :       List[Any]				=						torch.Size((1, 42, 10_24)  )
  lowercase__       :       Optional[int]				=						torch.tensor(
      [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]  )
 else:  # base
  lowercase__       :       Union[str, Any]				=						torch.Size((1, 42, 7_68)  )
  lowercase__       :       Union[str, 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]       ,		lowerCamelCase__       ,		atol=1E-4  ):
  raise ValueError

 # Verify entity hidden states
 if model_size == "large":
  lowercase__       :       str				=						torch.Size((1, 1, 10_24)  )
  lowercase__       :       int				=						torch.tensor([[0.0466, -0.0106, -0.0179]]  )
 else:  # base
  lowercase__       :       Optional[int]				=						torch.Size((1, 1, 7_68)  )
  lowercase__       :       Union[str, Any]				=						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]       ,		lowerCamelCase__       ,		atol=1E-4  ):
  raise ValueError

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


def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]  ):
 lowercase__       :       List[str]				=						{}
 with open(lowerCamelCase__       ,		"""r"""       ,		encoding="""utf-8"""  ) as f:
  for index, line in enumerate(lowerCamelCase__  ):
   lowercase__	,       lowercase__       :       List[Any]				=						line.rstrip().split("""\t"""  )
   lowercase__       :       List[str]				=						index

 return entity_vocab


if __name__ == "__main__":
   __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.'
   )
   __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,
   )



"""simple docstring"""


import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       Optional[int]									=    LayoutLMTokenizer
   _a   :       Dict									=    LayoutLMTokenizerFast
   _a   :       Dict									=    True
   _a   :       Any									=    True


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    super().setUp()

    lowercase__       :       int				=						[
        """[UNK]""",
        """[CLS]""",
        """[SEP]""",
        """want""",
        """##want""",
        """##ed""",
        """wa""",
        """un""",
        """runn""",
        """##ing""",
        """,""",
        """low""",
        """lowest""",
    ]
    lowercase__       :       Dict				=						os.path.join(self.tmpdirname ,     VOCAB_FILES_NAMES["""vocab_file"""]						)
    with open(self.vocab_file ,     """w""" ,     encoding="""utf-8"""						) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							Optional[int]:
    return LayoutLMTokenizer.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[Any]:
    lowercase__       :       List[str]				=						"""UNwant\u00E9d,running"""
    lowercase__       :       Optional[int]				=						"""unwanted, running"""
    return input_text, output_text


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       str				=						self.tokenizer_class(self.vocab_file						)

    lowercase__       :       Optional[Any]				=						tokenizer.tokenize("""UNwant\u00E9d,running"""						)
    self.assertListEqual(lowerCamelCase__ ,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]						)
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__						) ,     [7, 4, 5, 10, 8, 9]						)







   def 					UpperCAmelCase__(						self						)      ->							Any:
    pass