






'''simple docstring'''
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


a_					= get_tests_dir('fixtures/dummy_feature_extractor_config.json')
a_					= get_tests_dir('fixtures/vocab.json')
a_					= get_tests_dir('fixtures')






class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):
 snake_case_  							=							["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]




 def      __magic_name__	(      self	:							Tuple      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=0




 def      __magic_name__	(      self	:							List[str]      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''      )
       self.assertIsInstance(__lowercase					,      __lowercase      )




 def      __magic_name__	(      self	:							str      )	->						Tuple:
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__     :				int									=WavaVecaConfig()
             SCREAMING_SNAKE_CASE__     :				Tuple									=AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''      )

             # save in new folder
             model_config.save_pretrained(__lowercase      )
             processor.save_pretrained(__lowercase      )

             SCREAMING_SNAKE_CASE__     :				str									=AutoProcessor.from_pretrained(__lowercase      )

       self.assertIsInstance(__lowercase					,      __lowercase      )




 def      __magic_name__	(      self	:							Optional[int]      )	->						Dict:
       with tempfile.TemporaryDirectory() as tmpdirname:
             # copy relevant files
             copyfile(__lowercase					,      os.path.join(__lowercase					,      __lowercase      )      )
             copyfile(__lowercase					,      os.path.join(__lowercase					,      '''vocab.json'''      )      )

             SCREAMING_SNAKE_CASE__     :				List[str]									=AutoProcessor.from_pretrained(__lowercase      )

       self.assertIsInstance(__lowercase					,      __lowercase      )




 def      __magic_name__	(      self	:							Any      )	->						Tuple:
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__     :				List[str]									=WavaVecaFeatureExtractor()
             SCREAMING_SNAKE_CASE__     :				str									=AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''      )

             SCREAMING_SNAKE_CASE__     :				List[Any]									=WavaVecaProcessor(__lowercase					,      __lowercase      )

             # save in new folder
             processor.save_pretrained(__lowercase      )

             # drop `processor_class` in tokenizer
             with open(os.path.join(__lowercase					,      __lowercase      )					,      '''r'''      ) as f:
                   SCREAMING_SNAKE_CASE__     :				str									=json.load(__lowercase      )
                   config_dict.pop('''processor_class'''      )

             with open(os.path.join(__lowercase					,      __lowercase      )					,      '''w'''      ) as f:
                   f.write(json.dumps(__lowercase      )      )

             SCREAMING_SNAKE_CASE__     :				Optional[int]									=AutoProcessor.from_pretrained(__lowercase      )

       self.assertIsInstance(__lowercase					,      __lowercase      )




 def      __magic_name__	(      self	:							Tuple      )	->						Optional[Any]:
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__     :				Tuple									=WavaVecaFeatureExtractor()
             SCREAMING_SNAKE_CASE__     :				int									=AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''      )

             SCREAMING_SNAKE_CASE__     :				int									=WavaVecaProcessor(__lowercase					,      __lowercase      )

             # save in new folder
             processor.save_pretrained(__lowercase      )

             # drop `processor_class` in feature extractor
             with open(os.path.join(__lowercase					,      __lowercase      )					,      '''r'''      ) as f:
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=json.load(__lowercase      )
                   config_dict.pop('''processor_class'''      )

             with open(os.path.join(__lowercase					,      __lowercase      )					,      '''w'''      ) as f:
                   f.write(json.dumps(__lowercase      )      )

             SCREAMING_SNAKE_CASE__     :				Tuple									=AutoProcessor.from_pretrained(__lowercase      )

       self.assertIsInstance(__lowercase					,      __lowercase      )




 def      __magic_name__	(      self	:							Optional[Any]      )	->						int:
       with tempfile.TemporaryDirectory() as tmpdirname:
             SCREAMING_SNAKE_CASE__     :				int									=WavaVecaConfig(processor_class='''Wav2Vec2Processor'''      )
             model_config.save_pretrained(__lowercase      )
             # copy relevant files
             copyfile(__lowercase					,      os.path.join(__lowercase					,      '''vocab.json'''      )      )
             # create emtpy sample processor
             with open(os.path.join(__lowercase					,      __lowercase      )					,      '''w'''      ) as f:
                   f.write('''{}'''      )

             SCREAMING_SNAKE_CASE__     :				str									=AutoProcessor.from_pretrained(__lowercase      )

       self.assertIsInstance(__lowercase					,      __lowercase      )




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       # If remote code is not set, we will time out when asking whether to load the model.
       with self.assertRaises(__lowercase      ):
             SCREAMING_SNAKE_CASE__     :				List[str]									=AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''      )
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(__lowercase      ):
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=AutoProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_processor'''					,      trust_remote_code=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''					,      trust_remote_code=__lowercase      )
       self.assertTrue(processor.special_attribute_present      )
       self.assertEqual(processor.__class__.__name__					,      '''NewProcessor'''      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=processor.feature_extractor
       self.assertTrue(feature_extractor.special_attribute_present      )
       self.assertEqual(feature_extractor.__class__.__name__					,      '''NewFeatureExtractor'''      )

       SCREAMING_SNAKE_CASE__     :				Dict									=processor.tokenizer
       self.assertTrue(tokenizer.special_attribute_present      )
       if is_tokenizers_available():
             self.assertEqual(tokenizer.__class__.__name__					,      '''NewTokenizerFast'''      )

             # Test we can also load the slow version
             SCREAMING_SNAKE_CASE__     :				List[str]									=AutoProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_processor'''					,      trust_remote_code=__lowercase					,      use_fast=__lowercase      )
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=new_processor.tokenizer
             self.assertTrue(new_tokenizer.special_attribute_present      )
             self.assertEqual(new_tokenizer.__class__.__name__					,      '''NewTokenizer'''      )
       else:
             self.assertEqual(tokenizer.__class__.__name__					,      '''NewTokenizer'''      )




 def      __magic_name__	(      self	:							str      )	->						Union[str, Any]:
       try:
             AutoConfig.register('''custom'''					,      __lowercase      )
             AutoFeatureExtractor.register(__lowercase					,      __lowercase      )
             AutoTokenizer.register(__lowercase					,      slow_tokenizer_class=__lowercase      )
             AutoProcessor.register(__lowercase					,      __lowercase      )
             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(__lowercase      ):
                   AutoProcessor.register(__lowercase					,      __lowercase      )

             # Now that the config is registered, it can be used as any other config with the auto-API
             SCREAMING_SNAKE_CASE__     :				Any									=CustomFeatureExtractor.from_pretrained(__lowercase      )

             with tempfile.TemporaryDirectory() as tmp_dir:
                   SCREAMING_SNAKE_CASE__     :				Any									=os.path.join(__lowercase					,      '''vocab.txt'''      )
                   with open(__lowercase					,      '''w'''					,      encoding='''utf-8'''      ) as vocab_writer:
                         vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )
                   SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=CustomTokenizer(__lowercase      )

             SCREAMING_SNAKE_CASE__     :				Dict									=CustomProcessor(__lowercase					,      __lowercase      )

             with tempfile.TemporaryDirectory() as tmp_dir:
                   processor.save_pretrained(__lowercase      )
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=AutoProcessor.from_pretrained(__lowercase      )
                   self.assertIsInstance(__lowercase					,      __lowercase      )

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                   del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]
             if CustomConfig in PROCESSOR_MAPPING._extra_content:
                   del PROCESSOR_MAPPING._extra_content[CustomConfig]




 def      __magic_name__	(      self	:							str      )	->						Dict:






       class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
        snake_case_  							=							False






       class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
        snake_case_  							=							False






       class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
        snake_case_  							=							"""AutoFeatureExtractor"""
        snake_case_  							=							"""AutoTokenizer"""
        snake_case_  							=							False

       try:
             AutoConfig.register('''custom'''					,      __lowercase      )
             AutoFeatureExtractor.register(__lowercase					,      __lowercase      )
             AutoTokenizer.register(__lowercase					,      slow_tokenizer_class=__lowercase      )
             AutoProcessor.register(__lowercase					,      __lowercase      )
             # If remote code is not set, the default is to use local classes.
             SCREAMING_SNAKE_CASE__     :				Tuple									=AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''      )
             self.assertEqual(processor.__class__.__name__					,      '''NewProcessor'''      )
             self.assertFalse(processor.special_attribute_present      )
             self.assertFalse(processor.feature_extractor.special_attribute_present      )
             self.assertFalse(processor.tokenizer.special_attribute_present      )

             # If remote code is disabled, we load the local ones.
             SCREAMING_SNAKE_CASE__     :				Any									=AutoProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_processor'''					,      trust_remote_code=__lowercase      )
             self.assertEqual(processor.__class__.__name__					,      '''NewProcessor'''      )
             self.assertFalse(processor.special_attribute_present      )
             self.assertFalse(processor.feature_extractor.special_attribute_present      )
             self.assertFalse(processor.tokenizer.special_attribute_present      )

             # If remote is enabled, we load from the Hub.
             SCREAMING_SNAKE_CASE__     :				str									=AutoProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_processor'''					,      trust_remote_code=__lowercase      )
             self.assertEqual(processor.__class__.__name__					,      '''NewProcessor'''      )
             self.assertTrue(processor.special_attribute_present      )
             self.assertTrue(processor.feature_extractor.special_attribute_present      )
             self.assertTrue(processor.tokenizer.special_attribute_present      )

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                   del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]
             if CustomConfig in PROCESSOR_MAPPING._extra_content:
                   del PROCESSOR_MAPPING._extra_content[CustomConfig]




 def      __magic_name__	(      self	:							List[Any]      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )
       self.assertEqual(processor.__class__.__name__					,      '''BertTokenizerFast'''      )




 def      __magic_name__	(      self	:							List[Any]      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				List[str]									=AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''      )
       self.assertEqual(processor.__class__.__name__					,      '''ConvNextImageProcessor'''      )






@is_staging_test
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):
 snake_case_  							=							["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]




 @classmethod
 def      __magic_name__	(      cls	:							str      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=TOKEN
       HfFolder.save_token(__lowercase      )




 @classmethod
 def      __magic_name__	(      cls	:							List[str]      )	->						Optional[Any]:
       try:
             delete_repo(token=cls._token					,      repo_id='''test-processor'''      )
       except HTTPError:
             pass

       try:
             delete_repo(token=cls._token					,      repo_id='''valid_org/test-processor-org'''      )
       except HTTPError:
             pass

       try:
             delete_repo(token=cls._token					,      repo_id='''test-dynamic-processor'''      )
       except HTTPError:
             pass




 def      __magic_name__	(      self	:							int      )	->						str:
       SCREAMING_SNAKE_CASE__     :				Any									=WavaVecaProcessor.from_pretrained(__lowercase      )
       with tempfile.TemporaryDirectory() as tmp_dir:
             processor.save_pretrained(
                 os.path.join(__lowercase					,      '''test-processor'''      )					,      push_to_hub=__lowercase					,      use_auth_token=self._token      )

             SCREAMING_SNAKE_CASE__     :				int									=WavaVecaProcessor.from_pretrained(F"{USER}/test-processor"      )
             for k, v in processor.feature_extractor.__dict__.items():
                   self.assertEqual(__lowercase					,      getattr(new_processor.feature_extractor					,      __lowercase      )      )
             self.assertDictEqual(new_processor.tokenizer.get_vocab()					,      processor.tokenizer.get_vocab()      )




 def      __magic_name__	(      self	:							Tuple      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Any									=WavaVecaProcessor.from_pretrained(__lowercase      )

       with tempfile.TemporaryDirectory() as tmp_dir:
             processor.save_pretrained(
                 os.path.join(__lowercase					,      '''test-processor-org'''      )					,      push_to_hub=__lowercase					,      use_auth_token=self._token					,      organization='''valid_org'''					,      )

             SCREAMING_SNAKE_CASE__     :				Any									=WavaVecaProcessor.from_pretrained('''valid_org/test-processor-org'''      )
             for k, v in processor.feature_extractor.__dict__.items():
                   self.assertEqual(__lowercase					,      getattr(new_processor.feature_extractor					,      __lowercase      )      )
             self.assertDictEqual(new_processor.tokenizer.get_vocab()					,      processor.tokenizer.get_vocab()      )




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Dict:
       CustomFeatureExtractor.register_for_auto_class()
       CustomTokenizer.register_for_auto_class()
       CustomProcessor.register_for_auto_class()

       SCREAMING_SNAKE_CASE__     :				Any									=CustomFeatureExtractor.from_pretrained(__lowercase      )

       with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__     :				List[str]									=os.path.join(__lowercase					,      '''vocab.txt'''      )
             with open(__lowercase					,      '''w'''					,      encoding='''utf-8'''      ) as vocab_writer:
                   vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )
             SCREAMING_SNAKE_CASE__     :				str									=CustomTokenizer(__lowercase      )

       SCREAMING_SNAKE_CASE__     :				int									=CustomProcessor(__lowercase					,      __lowercase      )

       with tempfile.TemporaryDirectory() as tmp_dir:
             create_repo(F"{USER}/test-dynamic-processor"					,      token=self._token      )
             SCREAMING_SNAKE_CASE__     :				List[str]									=Repository(__lowercase					,      clone_from=F"{USER}/test-dynamic-processor"					,      token=self._token      )
             processor.save_pretrained(__lowercase      )

             # This has added the proper auto_map field to the feature extractor config
             self.assertDictEqual(
                 processor.feature_extractor.auto_map					,      {
                     '''AutoFeatureExtractor''': '''custom_feature_extraction.CustomFeatureExtractor''',
                     '''AutoProcessor''': '''custom_processing.CustomProcessor''',
                 }					,      )

             # This has added the proper auto_map field to the tokenizer config
             with open(os.path.join(__lowercase					,      '''tokenizer_config.json'''      )      ) as f:
                   SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=json.load(__lowercase      )
             self.assertDictEqual(
                 tokenizer_config['''auto_map''']					,      {
                     '''AutoTokenizer''': ['''custom_tokenization.CustomTokenizer''', None],
                     '''AutoProcessor''': '''custom_processing.CustomProcessor''',
                 }					,      )

             # The code has been copied from fixtures
             self.assertTrue(os.path.isfile(os.path.join(__lowercase					,      '''custom_feature_extraction.py'''      )      )      )
             self.assertTrue(os.path.isfile(os.path.join(__lowercase					,      '''custom_tokenization.py'''      )      )      )
             self.assertTrue(os.path.isfile(os.path.join(__lowercase					,      '''custom_processing.py'''      )      )      )

             repo.push_to_hub()

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=AutoProcessor.from_pretrained(F"{USER}/test-dynamic-processor"					,      trust_remote_code=__lowercase      )
       # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
       self.assertEqual(new_processor.__class__.__name__					,      '''CustomProcessor'''      )







'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						Any,	UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






      with open(UpperCamelCase__							) as metadata_file:
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=json.load(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=LukeConfig(use_entity_aware_attention=UpperCamelCase__,	**metadata['''model_config''']							)

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

      # Load the entity vocab file
      SCREAMING_SNAKE_CASE__     :				List[str]									=load_original_entity_vocab(UpperCamelCase__							)
      # add an entry for [MASK2]
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=max(entity_vocab.values()							) + 1
      config.entity_vocab_size += 1

      SCREAMING_SNAKE_CASE__     :				Optional[int]									=XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']							)

      # Add special tokens to the token vocabulary for downstream tasks
      SCREAMING_SNAKE_CASE__     :				List[Any]									=AddedToken('''<ent>''',	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Any									=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__,	'''tokenizer_config.json'''							),	'''r'''							) as f:
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=json.load(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				int									='''MLukeTokenizer'''
      with open(os.path.join(UpperCamelCase__,	'''tokenizer_config.json'''							),	'''w'''							) as f:
            json.dump(UpperCamelCase__,	UpperCamelCase__							)

      with open(os.path.join(UpperCamelCase__,	MLukeTokenizer.vocab_files_names['''entity_vocab_file''']							),	'''w'''							) as f:
            json.dump(UpperCamelCase__,	UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=MLukeTokenizer.from_pretrained(UpperCamelCase__							)

      # Initialize the embeddings of the special tokens
      SCREAMING_SNAKE_CASE__     :				str									=tokenizer.convert_tokens_to_ids(['''@''']							)[0]
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=tokenizer.convert_tokens_to_ids(['''#''']							)[0]

      SCREAMING_SNAKE_CASE__     :				Dict									=state_dict['''embeddings.word_embeddings.weight''']
      SCREAMING_SNAKE_CASE__     :				List[str]									=word_emb[ent_init_index].unsqueeze(0							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=word_emb[enta_init_index].unsqueeze(0							)
      SCREAMING_SNAKE_CASE__     :				str									=torch.cat([word_emb, ent_emb, enta_emb]							)
      # add special tokens for 'entity_predictions.bias'
      for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=state_dict[bias_name]
            SCREAMING_SNAKE_CASE__     :				List[Any]									=decoder_bias[ent_init_index].unsqueeze(0							)
            SCREAMING_SNAKE_CASE__     :				str									=decoder_bias[enta_init_index].unsqueeze(0							)
            SCREAMING_SNAKE_CASE__     :				List[str]									=torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]							)

      # 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"]:
                  SCREAMING_SNAKE_CASE__     :				Tuple									=f"encoder.layer.{layer_index}.attention.self."
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=state_dict[prefix + matrix_name]
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=state_dict[prefix + matrix_name]
                  SCREAMING_SNAKE_CASE__     :				List[Any]									=state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      SCREAMING_SNAKE_CASE__     :				Any									=state_dict['''entity_embeddings.entity_embeddings.weight''']
      SCREAMING_SNAKE_CASE__     :				Any									=entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0							)
      SCREAMING_SNAKE_CASE__     :				Any									=torch.cat([entity_emb, entity_mask_emb]							)
      # add [MASK2] for 'entity_predictions.bias'
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=state_dict['''entity_predictions.bias''']
      SCREAMING_SNAKE_CASE__     :				Tuple									=entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0							)
      SCREAMING_SNAKE_CASE__     :				Any									=torch.cat([entity_prediction_bias, entity_mask_bias]							)

      SCREAMING_SNAKE_CASE__     :				int									=LukeForMaskedLM(config=UpperCamelCase__							).eval()

      state_dict.pop('''entity_predictions.decoder.weight'''							)
      state_dict.pop('''lm_head.decoder.weight'''							)
      state_dict.pop('''lm_head.decoder.bias'''							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=OrderedDict()
      for key, value in state_dict.items():
            if not (key.startswith('''lm_head'''							) or key.startswith('''entity_predictions'''							)):
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=state_dict[key]
            else:
                  SCREAMING_SNAKE_CASE__     :				Any									=state_dict[key]

      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[str]									=model.load_state_dict(UpperCamelCase__,	strict=UpperCamelCase__							)

      if set(UpperCamelCase__							) != {"luke.embeddings.position_ids"}:
            raise ValueError(f"Unexpected unexpected_keys: {unexpected_keys}"							)
      if set(UpperCamelCase__							) != {
          "lm_head.decoder.weight",
          "lm_head.decoder.bias",
          "entity_predictions.decoder.weight",
      }:
            raise ValueError(f"Unexpected missing_keys: {missing_keys}"							)

      model.tie_weights()
      assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
      assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

      # Check outputs
      SCREAMING_SNAKE_CASE__     :				Any									=MLukeTokenizer.from_pretrained(UpperCamelCase__,	task='''entity_classification'''							)

      SCREAMING_SNAKE_CASE__     :				str									='''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(0, 9)
      SCREAMING_SNAKE_CASE__     :				str									=tokenizer(UpperCamelCase__,	entity_spans=[span],	return_tensors='''pt'''							)

      SCREAMING_SNAKE_CASE__     :				List[Any]									=model(**UpperCamelCase__							)

      # Verify word hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            SCREAMING_SNAKE_CASE__     :				str									=torch.Size((1, 3_3, 7_6_8)							)
            SCREAMING_SNAKE_CASE__     :				int									=torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]							)

      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":
            raise NotImplementedError
      else:  # base
            SCREAMING_SNAKE_CASE__     :				Any									=torch.Size((1, 1, 7_6_8)							)
            SCREAMING_SNAKE_CASE__     :				int									=torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]							)

      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

      # Verify masked word/entity prediction
      SCREAMING_SNAKE_CASE__     :				str									=MLukeTokenizer.from_pretrained(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									='''Tokyo is the capital of <mask>.'''
      SCREAMING_SNAKE_CASE__     :				Dict									=(2_4, 3_0)
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=tokenizer(UpperCamelCase__,	entity_spans=[span],	return_tensors='''pt'''							)

      SCREAMING_SNAKE_CASE__     :				List[str]									=model(**UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__     :				List[Any]									=encoding['''input_ids'''][0].tolist()
      SCREAMING_SNAKE_CASE__     :				Any									=input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''							)							)
      SCREAMING_SNAKE_CASE__     :				List[Any]									=outputs.logits[0][mask_position_id].argmax(dim=-1							)
      assert "Japan" == tokenizer.decode(UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=outputs.entity_logits[0][0].argmax().item()
      SCREAMING_SNAKE_CASE__     :				Dict									=[
          entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
      ]
      assert [e for e in multilingual_predicted_entities if e.startswith('''en:'''							)][0] == "en:Japan"

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





def  _a(  UpperCamelCase__				:						Optional[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[Any]									=['''[MASK]''', '''[PAD]''', '''[UNK]''']

      SCREAMING_SNAKE_CASE__     :				List[str]									=[json.loads(UpperCamelCase__							) for line in open(UpperCamelCase__							)]

      SCREAMING_SNAKE_CASE__     :				Optional[int]									={}
      for entry in data:
            SCREAMING_SNAKE_CASE__     :				Tuple									=entry['''id''']
            for entity_name, language in entry["entities"]:
                  if entity_name in SPECIAL_TOKENS:
                        SCREAMING_SNAKE_CASE__     :				str									=entity_id
                        break
                  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=f"{language}:{entity_name}"
                  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=entity_id
      return new_mapping


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,
  )