


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


__lowerCAmelCase	: str  				=get_tests_dir('fixtures/dummy_feature_extractor_config.json')
__lowerCAmelCase	: Optional[int]  				=get_tests_dir('fixtures/vocab.json')
__lowerCAmelCase	: Dict  				=get_tests_dir('fixtures')
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



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


				def 		__magic_name__(       self			:Optional[Any]	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		0


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


				def 		__magic_name__(       self			:Any	)		->			Optional[int]:
										with tempfile.TemporaryDirectory() as tmpdirname:
																__SCREAMING_SNAKE_CASE						:     Dict					=		WavaVecaConfig()
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''	)

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

																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		AutoProcessor.from_pretrained(lowercase_	)

										self.assertIsInstance(lowercase_			,			lowercase_	)


				def 		__magic_name__(       self			:Dict	)		->			str:
										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						:     Dict					=		AutoProcessor.from_pretrained(lowercase_	)

										self.assertIsInstance(lowercase_			,			lowercase_	)


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

																__SCREAMING_SNAKE_CASE						:     List[str]					=		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						:     Optional[int]					=		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						:     List[Any]					=		AutoProcessor.from_pretrained(lowercase_	)

										self.assertIsInstance(lowercase_			,			lowercase_	)


				def 		__magic_name__(       self			:Optional[Any]	)		->			List[Any]:
										with tempfile.TemporaryDirectory() as tmpdirname:
																__SCREAMING_SNAKE_CASE						:     str					=		WavaVecaFeatureExtractor()
																__SCREAMING_SNAKE_CASE						:     Dict					=		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 feature extractor
																with open(os.path.join(lowercase_			,			lowercase_	)			,			'''r'''	) as f:
																						__SCREAMING_SNAKE_CASE						:     Dict					=		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						:     List[Any]					=		AutoProcessor.from_pretrained(lowercase_	)

										self.assertIsInstance(lowercase_			,			lowercase_	)


				def 		__magic_name__(       self			:Dict	)		->			List[str]:
										with tempfile.TemporaryDirectory() as tmpdirname:
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		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						:     List[str]					=		AutoProcessor.from_pretrained(lowercase_	)

										self.assertIsInstance(lowercase_			,			lowercase_	)


				def 		__magic_name__(       self			:Union[str, Any]	)		->			Any:
										# 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						:     Dict					=		AutoProcessor.from_pretrained(
																    '''hf-internal-testing/test_dynamic_processor'''			,			trust_remote_code=lowercase_	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		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						:     List[Any]					=		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						:     Optional[Any]					=		AutoProcessor.from_pretrained(
																    '''hf-internal-testing/test_dynamic_processor'''			,			trust_remote_code=lowercase_			,			use_fast=lowercase_	)
																__SCREAMING_SNAKE_CASE						:     Dict					=		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			:List[Any]	)		->			List[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						:     Tuple					=		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						:     Any					=		CustomProcessor(lowercase_			,			lowercase_	)

																with tempfile.TemporaryDirectory() as tmp_dir:
																						processor.save_pretrained(lowercase_	)
																						__SCREAMING_SNAKE_CASE						:     Union[str, 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			:int	)		->			int:
										class        _lowercase      (			lowercase__       ):






														'''simple docstring'''



														SCREAMING_SNAKE_CASE__				:			Dict						=		False




										class        _lowercase      (			lowercase__       ):






														'''simple docstring'''



														SCREAMING_SNAKE_CASE__				:			Optional[Any]						=		False




										class        _lowercase      (			lowercase__       ):






														'''simple docstring'''



														SCREAMING_SNAKE_CASE__				:			List[str]						=		"""AutoFeatureExtractor"""
														SCREAMING_SNAKE_CASE__				:			List[str]						=		"""AutoTokenizer"""
														SCREAMING_SNAKE_CASE__				:			Any						=		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						:     Dict					=		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						:     Optional[Any]					=		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]	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     int					=		AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''	)
										self.assertEqual(processor.__class__.__name__			,			'''BertTokenizerFast'''	)



				def 		__magic_name__(       self			:Any	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Dict					=		AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''	)
										self.assertEqual(processor.__class__.__name__			,			'''ConvNextImageProcessor'''	)




@is_staging_test
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



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


				@classmethod
				def 		__magic_name__(       cls			:str	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		TOKEN
										HfFolder.save_token(lowercase_	)


				@classmethod
				def 		__magic_name__(       cls			:Optional[int]	)		->			int:
										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			:List[str]	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Dict					=		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						:     Optional[Any]					=		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			:Any	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		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						:     Union[str, 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			:Optional[Any]	)		->			int:
										CustomFeatureExtractor.register_for_auto_class()
										CustomTokenizer.register_for_auto_class()
										CustomProcessor.register_for_auto_class()

										__SCREAMING_SNAKE_CASE						:     Union[str, 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						:     Union[str, Any]					=		CustomTokenizer(lowercase_	)

										__SCREAMING_SNAKE_CASE						:     Any					=		CustomProcessor(lowercase_			,			lowercase_	)

										with tempfile.TemporaryDirectory() as tmp_dir:
																create_repo(f'''{USER}/test-dynamic-processor'''			,			token=self._token	)
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		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						:     Dict					=		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						:     List[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 os
import re


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

# Matches is_xxx_available()
_a		       =							re.compile(R'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
_a		       =							re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


_a		       =							'\n{0} = None\n'

_a		       =							'\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


_a		       =							'\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    _re_backend.findall(__lowerCamelCase  )
   if len(__lowerCamelCase  ) == 0:
      return None

   return "_and_".join(__lowerCamelCase  )







def 	__a     (		):
   with open(os.path.join(__lowerCamelCase,		"__init__.py"  ),		"r",		encoding="utf-8",		newline="\n"  ) as f:
      UpperCAmelCase_					:					Optional[int]			     =    f.readlines()

   # Get to the point we do the actual imports for type checking
   UpperCAmelCase_					:					Union[str, Any]			     =    0
   UpperCAmelCase_					:					Optional[int]			     =    {}
   # Go through the end of the file
   while line_index < len(__lowerCamelCase  ):
      # If the line contains is_backend_available, we grab all objects associated with the `else` block
      UpperCAmelCase_					:					Union[str, Any]			     =    find_backend(lines[line_index]  )
      if backend is not None:
         while not lines[line_index].startswith("else:"  ):
            line_index += 1
         line_index += 1
         UpperCAmelCase_					:					List[str]			     =    []
         # Until we unindent, add backend objects to the list
         while line_index < len(__lowerCamelCase  ) and len(lines[line_index]  ) > 1:
            UpperCAmelCase_					:					Union[str, Any]			     =    lines[line_index]
            UpperCAmelCase_					:					Optional[Any]			     =    _re_single_line_import.search(__lowerCamelCase  )
            if single_line_import_search is not None:
               objects.extend(single_line_import_search.groups()[0].split(", "  )  )
            elif line.startswith(" " * 8  ):
               objects.append(line[8:-2]  )
            line_index += 1

         if len(__lowerCamelCase  ) > 0:
            UpperCAmelCase_					:					Optional[int]			     =    objects
      else:
         line_index += 1

   return backend_specific_objects







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   if name.isupper():
      return DUMMY_CONSTANT.format(__lowerCamelCase  )
   elif name.islower():
      return DUMMY_FUNCTION.format(__lowerCamelCase,		__lowerCamelCase  )
   else:
      return DUMMY_CLASS.format(__lowerCamelCase,		__lowerCamelCase  )







def 	__a     (		__lowerCamelCase=None  ):
   if backend_specific_objects is None:
      UpperCAmelCase_					:					Tuple			     =    read_init()
   # For special correspondence backend to module name as used in the function requires_modulename
   UpperCAmelCase_					:					str			     =    {}

   for backend, objects in backend_specific_objects.items():
      UpperCAmelCase_					:					int			     =    "[" + ", ".join(f"""\"{b}\"""" for b in backend.split("_and_"  )  ) + "]"
      UpperCAmelCase_					:					Dict			     =    "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
      dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
      dummy_file += "\n".join([create_dummy_object(__lowerCamelCase,		__lowerCamelCase  ) for o in objects]  )
      UpperCAmelCase_					:					int			     =    dummy_file

   return dummy_files







def 	__a     (		__lowerCamelCase=False  ):
   UpperCAmelCase_					:					Optional[Any]			     =    create_dummy_files()
   # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
   UpperCAmelCase_					:					Union[str, Any]			     =    {"torch": "pt"}

   # Locate actual dummy modules and read their content.
   UpperCAmelCase_					:					List[str]			     =    os.path.join(__lowerCamelCase,		"utils"  )
   UpperCAmelCase_					:					Optional[int]			     =    {
       backend: os.path.join(__lowerCamelCase,		f"""dummy_{short_names.get(__lowerCamelCase,		__lowerCamelCase  )}_objects.py"""  )
       for backend in dummy_files.keys()
   }

   UpperCAmelCase_					:					Any			     =    {}
   for backend, file_path in dummy_file_paths.items():
      if os.path.isfile(__lowerCamelCase  ):
         with open(__lowerCamelCase,		"r",		encoding="utf-8",		newline="\n"  ) as f:
            UpperCAmelCase_					:					Optional[int]			     =    f.read()
      else:
         UpperCAmelCase_					:					Any			     =    ""

   for backend in dummy_files.keys():
      if dummy_files[backend] != actual_dummies[backend]:
         if overwrite:
            print(
                f"""Updating diffusers.utils.dummy_{short_names.get(__lowerCamelCase,		__lowerCamelCase  )}_objects.py as the main """
                "__init__ has new objects."  )
            with open(dummy_file_paths[backend],		"w",		encoding="utf-8",		newline="\n"  ) as f:
               f.write(dummy_files[backend]  )
         else:
            raise ValueError(
                "The main __init__ has objects that are not present in "
                f"""diffusers.utils.dummy_{short_names.get(__lowerCamelCase,		__lowerCamelCase  )}_objects.py. Run `make fix-copies` """
                "to fix this."  )


if __name__ == "__main__":
    _a		       =							argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    _a		       =							parser.parse_args()

    check_dummies(args.fix_and_overwrite)


