




import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class     A(		UpperCamelCase				, unittest.TestCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		TransfoXLTokenizer
 UpperCamelCase		  =		False
 UpperCamelCase		  =		False

 def 							a__					(							self   :    Optional[Any]    )  ->      int:

       """simple docstring"""






       super().setUp()

       lowerCamelCase_							=       [
           '<unk>',
           '[CLS]',
           '[SEP]',
           'want',
           'unwanted',
           'wa',
           'un',
           'running',
           ',',
           'low',
           'l',
       ]
       lowerCamelCase_							=       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 							a__					(							self   :    Optional[Any]		,				**A_   :    Tuple    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       True
       return TransfoXLTokenizer.from_pretrained(self.tmpdirname		,				**A_    )

 def 							a__					(							self   :    List[str]		,				A_   :    Dict    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       '<unk> UNwanted , running'
       lowerCamelCase_							=       '<unk> unwanted, running'
       return input_text, output_text

 def 							a__					(							self   :    List[Any]    )  ->      Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=       TransfoXLTokenizer(vocab_file=self.vocab_file		,				lower_case=A_    )

       lowerCamelCase_							=       tokenizer.tokenize('<unk> UNwanted , running'    )
       self.assertListEqual(A_		,				['<unk>', 'unwanted', ',', 'running']    )

       self.assertListEqual(tokenizer.convert_tokens_to_ids(A_    )		,				[0, 4, 8, 7]    )

 def 							a__					(							self   :    Any    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       TransfoXLTokenizer(lower_case=A_    )

       self.assertListEqual(
           tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )		,				['hello', '!', 'how', 'are', 'you', '?']    )

 def 							a__					(							self   :    int    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       TransfoXLTokenizer(lower_case=A_    )

       self.assertListEqual(
           tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )		,				['HeLLo', '!', 'how', 'Are', 'yoU', '?']    )

 def 							a__					(							self   :    List[Any]    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       TransfoXLTokenizer(lower_case=A_    )
       lowerCamelCase_							=       'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
       lowerCamelCase_							=       [
           'Hello',
           '(',
           'bracket',
           ')',
           'and',
           'side',
           '@-@',
           'scrolled',
           '[',
           'and',
           ']',
           'Henry',
           '\'s',
           '$',
           '5',
           '@,@',
           '000',
           'with',
           '3',
           '@.@',
           '34',
           'm',
           '.',
           'What',
           '\'s',
           'up',
           '!',
           '?',
       ]

       self.assertListEqual(tokenizer.tokenize(A_    )		,				A_    )

       self.assertEqual(tokenizer.convert_tokens_to_string(A_    )		,				A_    )





 def 							a__					(							self   :    Optional[int]    )  ->      Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.get_tokenizer()
       lowerCamelCase_							=       len(A_    )

       tokenizer.add_tokens(['new1', 'new2']    )
       tokenizer.move_added_token('new1'		,				1    )

       # Check that moved token is not copied (duplicate)
       self.assertEqual(len(A_    )		,				original_len + 2    )
       # Check that token is moved to specified id
       self.assertEqual(tokenizer.encode('new1'    )		,				[1]    )
       self.assertEqual(tokenizer.decode([1]    )		,				'new1'    )








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      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      with open(UpperCAmelCase__			) as metadata_file:
            lowerCamelCase             =		json.load(UpperCAmelCase__			)
      lowerCamelCase             =		LukeConfig(use_entity_aware_attention=UpperCAmelCase__ , **metadata["model_config"]			)

      # Load in the weights from the checkpoint_path
      lowerCamelCase             =		torch.load(UpperCAmelCase__ , map_location="cpu"			)

      # Load the entity vocab file
      lowerCamelCase             =		load_entity_vocab(UpperCAmelCase__			)

      lowerCamelCase             =		RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]			)

      # Add special tokens to the token vocabulary for downstream tasks
      lowerCamelCase             =		AddedToken("<ent>" , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__			)
      lowerCamelCase             =		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             =		LukeTokenizer.from_pretrained(UpperCAmelCase__			)

      # Initialize the embeddings of the special tokens
      lowerCamelCase             =		state_dict["embeddings.word_embeddings.weight"]
      lowerCamelCase             =		word_emb[tokenizer.convert_tokens_to_ids(["@"]			)[0]].unsqueeze(0			)
      lowerCamelCase             =		word_emb[tokenizer.convert_tokens_to_ids(["#"]			)[0]].unsqueeze(0			)
      lowerCamelCase             =		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             =		F"""encoder.layer.{layer_index}.attention.self."""
                  lowerCamelCase             =		state_dict[prefix + matrix_name]
                  lowerCamelCase             =		state_dict[prefix + matrix_name]
                  lowerCamelCase             =		state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      lowerCamelCase             =		state_dict["entity_embeddings.entity_embeddings.weight"]
      lowerCamelCase             =		entity_emb[entity_vocab["[MASK]"]]

      lowerCamelCase             =		LukeModel(config=UpperCAmelCase__			).eval()

      lowerCamelCase      ,				lowerCamelCase             =		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             =		LukeTokenizer.from_pretrained(UpperCAmelCase__ , task="entity_classification"			)

      lowerCamelCase             =		(
          "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             =		(39, 42)
      lowerCamelCase             =		tokenizer(UpperCAmelCase__ , entity_spans=[span] , add_prefix_space=UpperCAmelCase__ , return_tensors="pt"			)

      lowerCamelCase             =		model(**UpperCAmelCase__			)

      # Verify word hidden states
      if model_size == "large":
            lowerCamelCase             =		torch.Size((1, 42, 1024)			)
            lowerCamelCase             =		torch.tensor(
                [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]			)
      else:  # base
            lowerCamelCase             =		torch.Size((1, 42, 768)			)
            lowerCamelCase             =		torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]			)

      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             =		torch.Size((1, 1, 1024)			)
            lowerCamelCase             =		torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]			)
      else:  # base
            lowerCamelCase             =		torch.Size((1, 1, 768)			)
            lowerCamelCase             =		torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]			)

      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      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		{}
      with open(UpperCAmelCase__ , "r" , encoding="utf-8"			) as f:
            for index, line in enumerate(UpperCAmelCase__			):
                  lowerCamelCase      ,				lowerCamelCase             =		line.rstrip().split("\t"			)
                  lowerCamelCase             =		index

      return entity_vocab


if __name__ == "__main__":
     a_    :  Tuple						  =		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,
     )