




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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Optional[int]:

    """simple docstring"""


    with open(lowerCamelCase_      ) as metadata_file:
        __UpperCamelCase  		=						json.load(lowerCamelCase_      )
    __UpperCamelCase  		=						LukeConfig(use_entity_aware_attention=lowerCamelCase_					,     **metadata['''model_config''']      )

    # Load in the weights from the checkpoint_path
    __UpperCamelCase  		=						torch.load(lowerCamelCase_					,     map_location='''cpu'''      )

    # Load the entity vocab file
    __UpperCamelCase  		=						load_entity_vocab(lowerCamelCase_      )

    __UpperCamelCase  		=						RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']      )

    # Add special tokens to the token vocabulary for downstream tasks
    __UpperCamelCase  		=						AddedToken('''<ent>'''					,     lstrip=lowerCamelCase_					,     rstrip=lowerCamelCase_      )
    __UpperCamelCase  		=						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_      )

    __UpperCamelCase  		=						LukeTokenizer.from_pretrained(lowerCamelCase_      )

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

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

    __UpperCamelCase  		=						LukeModel(config=lowerCamelCase_      ).eval()

    __UpperCamelCase		,      __UpperCamelCase  		=						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
    __UpperCamelCase  		=						LukeTokenizer.from_pretrained(lowerCamelCase_					,     task='''entity_classification'''      )

    __UpperCamelCase  		=						(
        '''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 .'''
    )
    __UpperCamelCase  		=						(39, 42)
    __UpperCamelCase  		=						tokenizer(lowerCamelCase_					,     entity_spans=[span]					,     add_prefix_space=lowerCamelCase_					,     return_tensors='''pt'''      )

    __UpperCamelCase  		=						model(**lowerCamelCase_      )

    # Verify word hidden states
    if model_size == "large":
        __UpperCamelCase  		=						torch.Size((1, 42, 10_24)      )
        __UpperCamelCase  		=						torch.tensor(
            [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]      )
    else:  # base
        __UpperCamelCase  		=						torch.Size((1, 42, 7_68)      )
        __UpperCamelCase  		=						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":
        __UpperCamelCase  		=						torch.Size((1, 1, 10_24)      )
        __UpperCamelCase  		=						torch.tensor([[0.0466, -0.0106, -0.0179]]      )
    else:  # base
        __UpperCamelCase  		=						torch.Size((1, 1, 7_68)      )
        __UpperCamelCase  		=						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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	List[str]:

    """simple docstring"""


    __UpperCamelCase  		=						{}
    with open(lowerCamelCase_					,     '''r'''					,     encoding='''utf-8'''      ) as f:
        for index, line in enumerate(lowerCamelCase_      ):
            __UpperCamelCase		,      __UpperCamelCase  		=						line.rstrip().split('''\t'''      )
            __UpperCamelCase  		=						index

    return entity_vocab


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,
 )












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  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[int]    =					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						0







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''				)
          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: List[Any]				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						WavaVecaConfig()
              __UpperCamelCase  		=						AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''				)

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

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: Any				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              # copy relevant files
              copyfile(snake_case ,       os.path.join(snake_case ,       snake_case				)				)
              copyfile(snake_case ,       os.path.join(snake_case ,       '''vocab.json'''				)				)

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: List[Any]				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						WavaVecaFeatureExtractor()
              __UpperCamelCase  		=						AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''				)

              __UpperCamelCase  		=						WavaVecaProcessor(snake_case ,       snake_case				)

              # save in new folder
              processor.save_pretrained(snake_case				)

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

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

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: Tuple				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						WavaVecaFeatureExtractor()
              __UpperCamelCase  		=						AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''				)

              __UpperCamelCase  		=						WavaVecaProcessor(snake_case ,       snake_case				)

              # save in new folder
              processor.save_pretrained(snake_case				)

              # drop `processor_class` in feature extractor
              with open(os.path.join(snake_case ,       snake_case				) ,       '''r'''				) as f:
                  __UpperCamelCase  		=						json.load(snake_case				)
                  config_dict.pop('''processor_class'''				)

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

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: Dict				):
          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						WavaVecaConfig(processor_class='''Wav2Vec2Processor'''				)
              model_config.save_pretrained(snake_case				)
              # copy relevant files
              copyfile(snake_case ,       os.path.join(snake_case ,       '''vocab.json'''				)				)
              # create emtpy sample processor
              with open(os.path.join(snake_case ,       snake_case				) ,       '''w'''				) as f:
                  f.write('''{}'''				)

              __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)

          self.assertIsInstance(snake_case ,       snake_case				)







      def       snake_case	(     self					: int				):
          # If remote code is not set, we will time out when asking whether to load the model.
          with self.assertRaises(snake_case				):
              __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''				)
          # If remote code is disabled, we can't load this config.
          with self.assertRaises(snake_case				):
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case				)

          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case				)
          self.assertTrue(processor.special_attribute_present				)
          self.assertEqual(processor.__class__.__name__ ,       '''NewProcessor'''				)

          __UpperCamelCase  		=						processor.feature_extractor
          self.assertTrue(feature_extractor.special_attribute_present				)
          self.assertEqual(feature_extractor.__class__.__name__ ,       '''NewFeatureExtractor'''				)

          __UpperCamelCase  		=						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
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case ,       use_fast=snake_case				)
              __UpperCamelCase  		=						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       snake_case	(     self					: List[Any]				):
          try:
              AutoConfig.register('''custom''' ,       snake_case				)
              AutoFeatureExtractor.register(snake_case ,       snake_case				)
              AutoTokenizer.register(snake_case ,       slow_tokenizer_class=snake_case				)
              AutoProcessor.register(snake_case ,       snake_case				)
              # Trying to register something existing in the Transformers library will raise an error
              with self.assertRaises(snake_case				):
                  AutoProcessor.register(snake_case ,       snake_case				)

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

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

              __UpperCamelCase  		=						CustomProcessor(snake_case ,       snake_case				)

              with tempfile.TemporaryDirectory() as tmp_dir:
                  processor.save_pretrained(snake_case				)
                  __UpperCamelCase  		=						AutoProcessor.from_pretrained(snake_case				)
                  self.assertIsInstance(snake_case ,       snake_case				)

          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       snake_case	(     self					: Optional[int]				):

          class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

                """simple docstring"""

                lowerCAmelCase__			: Tuple    =					False







          class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

                """simple docstring"""

                lowerCAmelCase__			: Tuple    =					False







          class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

                """simple docstring"""

                lowerCAmelCase__			: Any    =					"AutoFeatureExtractor"
                lowerCAmelCase__			: str    =					"AutoTokenizer"
                lowerCAmelCase__			: str    =					False

          try:
              AutoConfig.register('''custom''' ,       snake_case				)
              AutoFeatureExtractor.register(snake_case ,       snake_case				)
              AutoTokenizer.register(snake_case ,       slow_tokenizer_class=snake_case				)
              AutoProcessor.register(snake_case ,       snake_case				)
              # If remote code is not set, the default is to use local classes.
              __UpperCamelCase  		=						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.
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case				)
              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.
              __UpperCamelCase  		=						AutoProcessor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_processor''' ,       trust_remote_code=snake_case				)
              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       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''				)
          self.assertEqual(processor.__class__.__name__ ,       '''BertTokenizerFast'''				)







      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''				)
          self.assertEqual(processor.__class__.__name__ ,       '''ConvNextImageProcessor'''				)







@is_staging_test
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: int    =					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]







      @classmethod
      def       snake_case	(     cls					: List[str]				):
          __UpperCamelCase  		=						TOKEN
          HfFolder.save_token(snake_case				)







      @classmethod
      def       snake_case	(     cls					: Union[str, 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       snake_case	(     self					: int				):
          __UpperCamelCase  		=						WavaVecaProcessor.from_pretrained(snake_case				)
          with tempfile.TemporaryDirectory() as tmp_dir:
              processor.save_pretrained(
                  os.path.join(snake_case ,       '''test-processor'''				) ,       push_to_hub=snake_case ,       use_auth_token=self._token				)

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







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						WavaVecaProcessor.from_pretrained(snake_case				)

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

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







      def       snake_case	(     self					: List[str]				):
          CustomFeatureExtractor.register_for_auto_class()
          CustomTokenizer.register_for_auto_class()
          CustomProcessor.register_for_auto_class()

          __UpperCamelCase  		=						CustomFeatureExtractor.from_pretrained(snake_case				)

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

          __UpperCamelCase  		=						CustomProcessor(snake_case ,       snake_case				)

          with tempfile.TemporaryDirectory() as tmp_dir:
              create_repo(F"{USER}/test-dynamic-processor" ,       token=self._token				)
              __UpperCamelCase  		=						Repository(snake_case ,       clone_from=F"{USER}/test-dynamic-processor" ,       token=self._token				)
              processor.save_pretrained(snake_case				)

              # 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(snake_case ,       '''tokenizer_config.json'''				)				) as f:
                  __UpperCamelCase  		=						json.load(snake_case				)
              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(snake_case ,       '''custom_feature_extraction.py'''				)				)				)
              self.assertTrue(os.path.isfile(os.path.join(snake_case ,       '''custom_tokenization.py'''				)				)				)
              self.assertTrue(os.path.isfile(os.path.join(snake_case ,       '''custom_processing.py'''				)				)				)

              repo.push_to_hub()

          __UpperCamelCase  		=						AutoProcessor.from_pretrained(F"{USER}/test-dynamic-processor" ,       trust_remote_code=snake_case				)
          # 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'''				)






