

import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCAmelCase_			      =      '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase_			      =      direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCAmelCase_			      =      re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCAmelCase_			      =      re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase_			      =      re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase_			      =      [
    ('''pretraining''', '''MODEL_FOR_PRETRAINING_MAPPING_NAMES''', '''AutoModelForPreTraining'''),
    ('''feature-extraction''', '''MODEL_MAPPING_NAMES''', '''AutoModel'''),
    ('''audio-classification''', '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForAudioClassification'''),
    ('''text-generation''', '''MODEL_FOR_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForCausalLM'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_CTC_MAPPING_NAMES''', '''AutoModelForCTC'''),
    ('''image-classification''', '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForImageClassification'''),
    ('''image-segmentation''', '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES''', '''AutoModelForImageSegmentation'''),
    ('''fill-mask''', '''MODEL_FOR_MASKED_LM_MAPPING_NAMES''', '''AutoModelForMaskedLM'''),
    ('''object-detection''', '''MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES''', '''AutoModelForObjectDetection'''),
    (
        '''zero-shot-object-detection''',
        '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES''',
        '''AutoModelForZeroShotObjectDetection''',
    ),
    ('''question-answering''', '''MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES''', '''AutoModelForQuestionAnswering'''),
    ('''text2text-generation''', '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForSeq2SeqLM'''),
    ('''text-classification''', '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForSequenceClassification'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES''', '''AutoModelForSpeechSeq2Seq'''),
    (
        '''table-question-answering''',
        '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForTableQuestionAnswering''',
    ),
    ('''token-classification''', '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForTokenClassification'''),
    ('''multiple-choice''', '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES''', '''AutoModelForMultipleChoice'''),
    (
        '''next-sentence-prediction''',
        '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES''',
        '''AutoModelForNextSentencePrediction''',
    ),
    (
        '''audio-frame-classification''',
        '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForAudioFrameClassification''',
    ),
    ('''audio-xvector''', '''MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES''', '''AutoModelForAudioXVector'''),
    (
        '''document-question-answering''',
        '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForDocumentQuestionAnswering''',
    ),
    (
        '''visual-question-answering''',
        '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForVisualQuestionAnswering''',
    ),
    ('''image-to-text''', '''MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES''', '''AutoModelForVision2Seq'''),
    (
        '''zero-shot-image-classification''',
        '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForZeroShotImageClassification''',
    ),
    ('''depth-estimation''', '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES''', '''AutoModelForDepthEstimation'''),
    ('''video-classification''', '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForVideoClassification'''),
    ('''mask-generation''', '''MODEL_FOR_MASK_GENERATION_MAPPING_NAMES''', '''AutoModelForMaskGeneration'''),
]


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''    ,						SCREAMING_SNAKE_CASE__			)
						return [m.group(0			) for m in matches]


def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						snake_case_  =      {
						    config.replace('''Config'''    ,						''''''			): model_type for model_type, config in config_maping_names.items()
						}

						# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
						snake_case_  =      collections.defaultdict(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      collections.defaultdict(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      collections.defaultdict(SCREAMING_SNAKE_CASE__			)

						# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
						for attr_name in dir(SCREAMING_SNAKE_CASE__			):
												snake_case_  =      None
												if _re_tf_models.match(SCREAMING_SNAKE_CASE__			) is not None:
																		snake_case_  =      tf_models
																		snake_case_  =      _re_tf_models.match(SCREAMING_SNAKE_CASE__			).groups()[0]
												elif _re_flax_models.match(SCREAMING_SNAKE_CASE__			) is not None:
																		snake_case_  =      flax_models
																		snake_case_  =      _re_flax_models.match(SCREAMING_SNAKE_CASE__			).groups()[0]
												elif _re_pt_models.match(SCREAMING_SNAKE_CASE__			) is not None:
																		snake_case_  =      pt_models
																		snake_case_  =      _re_pt_models.match(SCREAMING_SNAKE_CASE__			).groups()[0]

												if lookup_dict is not None:
																		while len(SCREAMING_SNAKE_CASE__			) > 0:
																								if attr_name in model_prefix_to_model_type:
																														snake_case_  =      True
																														break
																								# Try again after removing the last word in the name
																								snake_case_  =      ''''''.join(camel_case_split(SCREAMING_SNAKE_CASE__			)[:-1]			)

						snake_case_  =      set(list(pt_models.keys()			) + list(tf_models.keys()			) + list(flax_models.keys()			)			)
						snake_case_  =      list(SCREAMING_SNAKE_CASE__			)
						all_models.sort()

						snake_case_  =      {'''model_type''': all_models}
						snake_case_  =      [pt_models[t] for t in all_models]
						snake_case_  =      [tf_models[t] for t in all_models]
						snake_case_  =      [flax_models[t] for t in all_models]

						# Now let's use the auto-mapping names to make sure
						snake_case_  =      {}
						for t in all_models:
												if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
																		snake_case_  =      '''AutoProcessor'''
												elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
																		snake_case_  =      '''AutoTokenizer'''
												elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
																		snake_case_  =      '''AutoFeatureExtractor'''
												else:
																		# Default to AutoTokenizer if a model has nothing, for backward compatibility.
																		snake_case_  =      '''AutoTokenizer'''

						snake_case_  =      [processors[t] for t in all_models]

						return pd.DataFrame(SCREAMING_SNAKE_CASE__			)


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      [
						    transformers_module.models.auto.modeling_auto,
						    transformers_module.models.auto.modeling_tf_auto,
						    transformers_module.models.auto.modeling_flax_auto,
						]
						for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
												snake_case_  =      [model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
												snake_case_  =      [auto_class, F'''TF_{auto_class}''', F'''Flax_{auto_class}''']
												# Loop through all three frameworks
												for module, cls, mapping in zip(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
																		# The type of pipeline may not exist in this framework
																		if not hasattr(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
																								continue
																		# First extract all model_names
																		snake_case_  =      []
																		for name in getattr(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			).values():
																								if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
																														model_names.append(SCREAMING_SNAKE_CASE__			)
																								else:
																														model_names.extend(list(SCREAMING_SNAKE_CASE__			)			)

            # Add pipeline tag and auto model class for those models
																		table.update({model_name: (pipeline_tag, cls) for model_name in model_names}			)

						return table


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      get_frameworks_table()
						snake_case_  =      Dataset.from_pandas(SCREAMING_SNAKE_CASE__			)

						snake_case_  =      hf_hub_download(
						    '''huggingface/transformers-metadata'''    ,						'''pipeline_tags.json'''    ,						repo_type='''dataset'''    ,						token=SCREAMING_SNAKE_CASE__			)
						snake_case_  =      Dataset.from_json(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      {
						    tags_dataset[i]['''model_class''']: (tags_dataset[i]['''pipeline_tag'''], tags_dataset[i]['''auto_class'''])
						    for i in range(len(SCREAMING_SNAKE_CASE__			)			)
						}
						snake_case_  =      update_pipeline_and_auto_class_table(SCREAMING_SNAKE_CASE__			)

						# Sort the model classes to avoid some nondeterministic updates to create false update commits.
						snake_case_  =      sorted(table.keys()			)
						snake_case_  =      pd.DataFrame(
						    {
						        '''model_class''': model_classes,
						        '''pipeline_tag''': [table[m][0] for m in model_classes],
						        '''auto_class''': [table[m][1] for m in model_classes],
						    }			)
						snake_case_  =      Dataset.from_pandas(SCREAMING_SNAKE_CASE__			)

						with tempfile.TemporaryDirectory() as tmp_dir:
												frameworks_dataset.to_json(os.path.join(SCREAMING_SNAKE_CASE__    ,						'''frameworks.json'''			)			)
												tags_dataset.to_json(os.path.join(SCREAMING_SNAKE_CASE__    ,						'''pipeline_tags.json'''			)			)

												if commit_sha is not None:
																		snake_case_  =      (
																		    F'''Update with commit {commit_sha}\n\nSee: '''
																		    F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
																		)
												else:
																		snake_case_  =      '''Update'''

												upload_folder(
												    repo_id='''huggingface/transformers-metadata'''    ,						folder_path=SCREAMING_SNAKE_CASE__    ,						repo_type='''dataset'''    ,						token=SCREAMING_SNAKE_CASE__    ,						commit_message=SCREAMING_SNAKE_CASE__    ,						)


def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
						snake_case_  =      transformers_module.pipelines.SUPPORTED_TASKS
						snake_case_  =      []
						for key in pipeline_tasks:
												if key not in in_table:
																		snake_case_  =      pipeline_tasks[key]['''pt''']
																		if isinstance(SCREAMING_SNAKE_CASE__    ,						(list, tuple)			):
																								snake_case_  =      model[0]
																		snake_case_  =      model.__name__
																		if model not in in_table.values():
																								missing.append(SCREAMING_SNAKE_CASE__			)

						if len(SCREAMING_SNAKE_CASE__			) > 0:
												snake_case_  =      ''', '''.join(SCREAMING_SNAKE_CASE__			)
												raise ValueError(
												    '''The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '''
												    F'''`utils/update_metadata.py`: {msg}. Please add them!'''			)


if __name__ == "__main__":
					lowerCAmelCase_			      =      argparse.ArgumentParser()
					parser.add_argument('''--token''', type=str, help='''The token to use to push to the transformers-metadata dataset.''')
					parser.add_argument('''--commit_sha''', type=str, help='''The sha of the commit going with this update.''')
					parser.add_argument('''--check-only''', action='''store_true''', help='''Activate to just check all pipelines are present.''')
					lowerCAmelCase_			      =      parser.parse_args()

					if args.check_only:
										check_pipeline_tags()
					else:
										update_metadata(args.token, args.commit_sha)






'''simple docstring'''

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402







class __a			( unittest.TestCase      ):





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							0





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""     )
      self.assertIsInstance(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      with tempfile.TemporaryDirectory() as tmpdirname:
         __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """preprocessor_config.json"""
         __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """config.json"""
         json.dump(
             {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}					,open(lowerCamelCase					,"""w"""     )					,)
         json.dump({"""model_type""": """clip"""}					,open(lowerCamelCase					,"""w"""     )     )

         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase     )
         self.assertIsInstance(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      with tempfile.TemporaryDirectory() as tmpdirname:
         __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """preprocessor_config.json"""
         __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """config.json"""
         json.dump(
             {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}					,open(lowerCamelCase					,"""w"""     )					,)
         json.dump({"""model_type""": """clip"""}					,open(lowerCamelCase					,"""w"""     )     )

         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase     )
         self.assertIsInstance(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      with tempfile.TemporaryDirectory() as tmpdirname:
         __SCREAMING_SNAKE_CASE   				=							CLIPConfig()

         # Create a dummy config file with image_proceesor_type
         __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """preprocessor_config.json"""
         __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """config.json"""
         json.dump(
             {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}					,open(lowerCamelCase					,"""w"""     )					,)
         json.dump({"""model_type""": """clip"""}					,open(lowerCamelCase					,"""w"""     )     )

         # remove image_processor_type to make sure config.json alone is enough to load image processor locally
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase     ).to_dict()

         config_dict.pop("""image_processor_type"""     )
         __SCREAMING_SNAKE_CASE   				=							CLIPImageProcessor(**lowerCamelCase     )

         # save in new folder
         model_config.save_pretrained(lowerCamelCase     )
         config.save_pretrained(lowerCamelCase     )

         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase     )

         # make sure private variable is not incorrectly saved
         __SCREAMING_SNAKE_CASE   				=							json.loads(config.to_json_string()     )
         self.assertTrue("""_processor_class""" not in dict_as_saved     )

      self.assertIsInstance(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      with tempfile.TemporaryDirectory() as tmpdirname:
         __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """preprocessor_config.json"""
         json.dump(
             {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}					,open(lowerCamelCase					,"""w"""     )					,)

         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase     )
         self.assertIsInstance(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      with self.assertRaisesRegex(
          lowerCamelCase					,"""clip-base is not a local folder and is not a valid model identifier"""     ):
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained("""clip-base"""     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      with self.assertRaisesRegex(
          lowerCamelCase					,r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""     ):
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase					,revision="""aaaaaa"""     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      with self.assertRaisesRegex(
          lowerCamelCase					,"""hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""					,):
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      with self.assertRaises(lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""     )
      # If remote code is disabled, we can't load this config.
      with self.assertRaises(lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(
             """hf-internal-testing/test_dynamic_image_processor"""					,trust_remote_code=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(
          """hf-internal-testing/test_dynamic_image_processor"""					,trust_remote_code=lowerCamelCase     )
      self.assertEqual(image_processor.__class__.__name__					,"""NewImageProcessor"""     )

      # Test image processor can be reloaded.
      with tempfile.TemporaryDirectory() as tmp_dir:
         image_processor.save_pretrained(lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase					,trust_remote_code=lowerCamelCase     )
      self.assertEqual(reloaded_image_processor.__class__.__name__					,"""NewImageProcessor"""     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      try:
         AutoConfig.register("""custom"""					,lowerCamelCase     )
         AutoImageProcessor.register(lowerCamelCase					,lowerCamelCase     )
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(lowerCamelCase     ):
            AutoImageProcessor.register(lowerCamelCase					,lowerCamelCase     )

         with tempfile.TemporaryDirectory() as tmpdirname:
            __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """preprocessor_config.json"""
            __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """config.json"""
            json.dump(
                {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}					,open(lowerCamelCase					,"""w"""     )					,)
            json.dump({"""model_type""": """clip"""}					,open(lowerCamelCase					,"""w"""     )     )

            __SCREAMING_SNAKE_CASE   				=							CustomImageProcessor.from_pretrained(lowerCamelCase     )

         # Now that the config is registered, it can be used as any other config with the auto-API
         with tempfile.TemporaryDirectory() as tmp_dir:
            image_processor.save_pretrained(lowerCamelCase     )
            __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(lowerCamelCase     )
            self.assertIsInstance(lowerCamelCase					,lowerCamelCase     )

      finally:
         if "custom" in CONFIG_MAPPING._extra_content:
            del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
            del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      class __a			( _snake_case      ):
         __UpperCamelCase :			int						   =						True

      try:
         AutoConfig.register("""custom"""					,lowerCamelCase     )
         AutoImageProcessor.register(lowerCamelCase					,lowerCamelCase     )
         # If remote code is not set, the default is to use local
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""     )
         self.assertEqual(image_processor.__class__.__name__					,"""NewImageProcessor"""     )
         self.assertTrue(image_processor.is_local     )

         # If remote code is disabled, we load the local one.
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(
             """hf-internal-testing/test_dynamic_image_processor"""					,trust_remote_code=lowerCamelCase     )
         self.assertEqual(image_processor.__class__.__name__					,"""NewImageProcessor"""     )
         self.assertTrue(image_processor.is_local     )

         # If remote is enabled, we load from the Hub
         __SCREAMING_SNAKE_CASE   				=							AutoImageProcessor.from_pretrained(
             """hf-internal-testing/test_dynamic_image_processor"""					,trust_remote_code=lowerCamelCase     )
         self.assertEqual(image_processor.__class__.__name__					,"""NewImageProcessor"""     )
         self.assertTrue(not hasattr(lowerCamelCase					,"""is_local"""     )     )

      finally:
         if "custom" in CONFIG_MAPPING._extra_content:
            del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
            del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





