


'''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       lowercase					(_A ,    _A ,    _A ,    _A ,    _A       ):







       """simple docstring"""







       with open(_A       ) as metadata_file:
              _lowerCAmelCase     :				Optional[int]       =							json.load(_A       )
       _lowerCAmelCase     :				Tuple       =							LukeConfig(use_entity_aware_attention=_A ,    **metadata['model_config']       )

       # Load in the weights from the checkpoint_path
       _lowerCAmelCase     :				Optional[int]       =							torch.load(_A ,    map_location='cpu'       )

       # Load the entity vocab file
       _lowerCAmelCase     :				List[Any]       =							load_entity_vocab(_A       )

       _lowerCAmelCase     :				Any       =							RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']       )

       # Add special tokens to the token vocabulary for downstream tasks
       _lowerCAmelCase     :				Optional[int]       =							AddedToken('<ent>' ,    lstrip=_A ,    rstrip=_A       )
       _lowerCAmelCase     :				Optional[int]       =							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       )

       _lowerCAmelCase     :				List[Any]       =							LukeTokenizer.from_pretrained(_A       )

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

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

       _lowerCAmelCase     :				Any       =							LukeModel(config=_A       ).eval()

       _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, 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
       _lowerCAmelCase     :				Any       =							LukeTokenizer.from_pretrained(_A ,    task='entity_classification'       )

       _lowerCAmelCase     :				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 .'
       )
       _lowerCAmelCase     :				Dict       =							(3_9, 4_2)
       _lowerCAmelCase     :				str       =							tokenizer(_A ,    entity_spans=[span] ,    add_prefix_space=_A ,    return_tensors='pt'       )

       _lowerCAmelCase     :				int       =							model(**_A       )

       # Verify word hidden states
       if model_size == "large":
              _lowerCAmelCase     :				Any       =							torch.Size((1, 4_2, 1_0_2_4)       )
              _lowerCAmelCase     :				List[str]       =							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     :				List[str]       =							torch.Size((1, 4_2, 7_6_8)       )
              _lowerCAmelCase     :				Tuple       =							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] ,    _A ,    atol=1E-4       ):
              raise ValueError

       # Verify entity hidden states
       if model_size == "large":
              _lowerCAmelCase     :				Optional[Any]       =							torch.Size((1, 1, 1_0_2_4)       )
              _lowerCAmelCase     :				Optional[int]       =							torch.tensor([[0.0_466, -0.0_106, -0.0_179]]       )
       else:  # base
              _lowerCAmelCase     :				Any       =							torch.Size((1, 1, 7_6_8)       )
              _lowerCAmelCase     :				Any       =							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] ,    _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       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							{}
       with open(_A ,    'r' ,    encoding='utf-8'       ) as f:
              for index, line in enumerate(_A       ):
                     _lowerCAmelCase      ,  _lowerCAmelCase     :				Tuple       =							line.rstrip().split('\t'       )
                     _lowerCAmelCase     :				int       =							index

       return entity_vocab


if __name__ == "__main__":
       lowerCAmelCase     :  Optional[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."""
       )
       lowerCAmelCase     :  Tuple											=    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 unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     RoFormerTokenizer
     __magic_name__           =     RoFormerTokenizerFast
     __magic_name__           =     True
     __magic_name__           =     True







     def a						(			self ):

            '''simple docstring'''

            super().setUp()







     def a						(			self   ,						**snake_case__ ):

            '''simple docstring'''

            return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'   ,						**snake_case__ )







     def a						(			self   ,						**snake_case__ ):

            '''simple docstring'''

            return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'   ,						**snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							'永和服装饰品有限公司,今天天气非常好'
            _lowerCAmelCase     :				str       =							'永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
            return input_text, output_text







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.get_tokenizer()
            _lowerCAmelCase      ,  _lowerCAmelCase     :				int       =							self.get_chinese_input_output_texts()
            _lowerCAmelCase     :				List[Any]       =							tokenizer.tokenize(snake_case__ )

            self.assertListEqual(snake_case__   ,						output_text.split() )

            _lowerCAmelCase     :				Optional[Any]       =							tokens + [tokenizer.unk_token]
            _lowerCAmelCase     :				List[str]       =							[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							self.get_rust_tokenizer()
            _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							self.get_chinese_input_output_texts()
            _lowerCAmelCase     :				Optional[int]       =							tokenizer.tokenize(snake_case__ )
            self.assertListEqual(snake_case__   ,						output_text.split() )
            _lowerCAmelCase     :				Tuple       =							tokens + [tokenizer.unk_token]
            _lowerCAmelCase     :				List[Any]       =							[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

            pass

