



'''simple docstring'''
from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
					from PIL import Image

					from transformers import ConvNextImageProcessor



class _A	:





		def __init__(  self			,							__UpperCAmelCase			,							__UpperCAmelCase=13			,							__UpperCAmelCase=32			,							__UpperCAmelCase=3			,							__UpperCAmelCase=4			,							__UpperCAmelCase=[10, 20, 30, 40]			,							__UpperCAmelCase=[2, 2, 3, 2]			,							__UpperCAmelCase=True			,							__UpperCAmelCase=True			,							__UpperCAmelCase=37			,							__UpperCAmelCase="gelu"			,							__UpperCAmelCase=10			,							__UpperCAmelCase=0.02			,							__UpperCAmelCase=3			,							__UpperCAmelCase=None			,							)							->  Any:

					'''simple docstring'''
					__UpperCAmelCase :   Optional[int]    					=  parent
					__UpperCAmelCase :   Optional[Any]    					=  batch_size
					__UpperCAmelCase :   List[str]    					=  image_size
					__UpperCAmelCase :   Optional[Any]    					=  num_channels
					__UpperCAmelCase :   int    					=  num_stages
					__UpperCAmelCase :   List[Any]    					=  hidden_sizes
					__UpperCAmelCase :   Optional[Any]    					=  depths
					__UpperCAmelCase :   List[str]    					=  is_training
					__UpperCAmelCase :   Tuple    					=  use_labels
					__UpperCAmelCase :   Optional[Any]    					=  intermediate_size
					__UpperCAmelCase :   Dict    					=  hidden_act
					__UpperCAmelCase :   List[str]    					=  type_sequence_label_size
					__UpperCAmelCase :   Union[str, Any]    					=  initializer_range
					__UpperCAmelCase :   Optional[Any]    					=  scope





		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   List[Any]    					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

					__UpperCAmelCase :   str    					=  None
					if self.use_labels:
								__UpperCAmelCase :   Optional[Any]    					=  ids_tensor([self.batch_size]			,							self.type_sequence_label_size					)

					__UpperCAmelCase :   Optional[int]    					=  self.get_config()

					return config, pixel_values, labels





		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					return ConvNextConfig(
					    num_channels=self.num_channels			,							hidden_sizes=self.hidden_sizes			,							depths=self.depths			,							num_stages=self.num_stages			,							hidden_act=self.hidden_act			,							is_decoder=__UpperCAmelCase			,							initializer_range=self.initializer_range			,							)





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)							->  List[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   List[Any]    					=  TFConvNextModel(config=__UpperCAmelCase					)
					__UpperCAmelCase :   List[Any]    					=  model(__UpperCAmelCase			,							training=__UpperCAmelCase					)
					# expected last hidden states: B, C, H // 32, W // 32
					self.parent.assertEqual(
					    result.last_hidden_state.shape			,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,							)





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)							->  Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase :   Optional[int]    					=  self.type_sequence_label_size
					__UpperCAmelCase :   List[Any]    					=  TFConvNextForImageClassification(__UpperCAmelCase					)
					__UpperCAmelCase :   List[str]    					=  model(__UpperCAmelCase			,							labels=__UpperCAmelCase			,							training=__UpperCAmelCase					)
					self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)					)





		def 				__A							(  self					)							->  Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase :   List[str]    					=  self.prepare_config_and_inputs()
					__UpperCAmelCase					,	__UpperCAmelCase					,	__UpperCAmelCase :   Optional[int]    					=  config_and_inputs
					__UpperCAmelCase :   Union[str, Any]    					=  {"""pixel_values""": pixel_values}
					return config, inputs_dict



@require_tf
class _A	(     __SCREAMING_SNAKE_CASE						,					__SCREAMING_SNAKE_CASE						,					unittest.TestCase     ):
		_SCREAMING_SNAKE_CASE       :     Dict       =      (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
		_SCREAMING_SNAKE_CASE       :     Optional[Any]       =      (
		    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
		    if is_tf_available()
		    else {}
		)

		_SCREAMING_SNAKE_CASE       :     Tuple       =      False
		_SCREAMING_SNAKE_CASE       :     Tuple       =      False
		_SCREAMING_SNAKE_CASE       :     Union[str, Any]       =      False
		_SCREAMING_SNAKE_CASE       :     Optional[Any]       =      False
		_SCREAMING_SNAKE_CASE       :     str       =      False





		def 				__A							(  self					)							->  List[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   Dict    					=  TFConvNextModelTester(self					)
					__UpperCAmelCase :   Optional[int]    					=  ConfigTester(
					    self			,							config_class=__UpperCAmelCase			,							has_text_modality=__UpperCAmelCase			,							hidden_size=37			,							)





		@unittest.skip(reason="""ConvNext does not use inputs_embeds"""					)
		def 				__A							(  self					)							->  str:

					'''simple docstring'''
					pass





		@unittest.skipIf(
		    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""					)					) == 0			,							reason="""TF does not support backprop for grouped convolutions on CPU."""			,							)
		@slow
		def 				__A							(  self					)							->  Optional[int]:

					'''simple docstring'''
					super().test_keras_fit()





		@unittest.skip(reason="""ConvNext does not support input and output embeddings"""					)
		def 				__A							(  self					)							->  Dict:

					'''simple docstring'''
					pass





		def 				__A							(  self					)							->  str:

					'''simple docstring'''
					__UpperCAmelCase					,	__UpperCAmelCase :   List[str]    					=  self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__UpperCAmelCase :   List[Any]    					=  model_class(__UpperCAmelCase					)
								__UpperCAmelCase :   str    					=  inspect.signature(model.call					)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								__UpperCAmelCase :   Tuple    					=  [*signature.parameters.keys()]

								__UpperCAmelCase :   Tuple    					=  ["""pixel_values"""]
								self.assertListEqual(arg_names[:1]			,							__UpperCAmelCase					)





		def 				__A							(  self					)							->  Dict:

					'''simple docstring'''
					__UpperCAmelCase :   Dict    					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__UpperCAmelCase					)





		@unittest.skipIf(
		    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""					)					) == 0			,							reason="""TF does not support backprop for grouped convolutions on CPU."""			,							)
		def 				__A							(  self					)							->  Union[str, Any]:

					'''simple docstring'''
					super().test_dataset_conversion()





		def 				__A							(  self					)							->  Optional[int]:

					'''simple docstring'''
					def check_hidden_states_output(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					):
								__UpperCAmelCase :   Optional[int]    					=  model_class(__UpperCAmelCase					)

								__UpperCAmelCase :   str    					=  model(**self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase					)					)
								__UpperCAmelCase :   List[str]    					=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

								__UpperCAmelCase :   Optional[int]    					=  self.model_tester.num_stages
								self.assertEqual(len(__UpperCAmelCase					)			,							expected_num_stages + 1					)

								# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]					)			,							[self.model_tester.image_size // 4, self.model_tester.image_size // 4]			,							)

					__UpperCAmelCase					,	__UpperCAmelCase :   Union[str, Any]    					=  self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__UpperCAmelCase :   List[str]    					=  True
								check_hidden_states_output(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								__UpperCAmelCase :   Optional[Any]    					=  True

								check_hidden_states_output(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)





		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					__UpperCAmelCase					,	__UpperCAmelCase :   Any    					=  self.model_tester.prepare_config_and_inputs_for_common()

					def check_equivalence(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase={}					):
								__UpperCAmelCase :   Optional[Any]    					=  model(__UpperCAmelCase			,							return_dict=__UpperCAmelCase			,							**__UpperCAmelCase					)
								__UpperCAmelCase :   Optional[int]    					=  model(__UpperCAmelCase			,							return_dict=__UpperCAmelCase			,							**__UpperCAmelCase					).to_tuple()

								def recursive_check(__UpperCAmelCase			,							__UpperCAmelCase					):
											if isinstance(__UpperCAmelCase			,							(List, Tuple)					):
														for tuple_iterable_value, dict_iterable_value in zip(__UpperCAmelCase			,							__UpperCAmelCase					):
																	recursive_check(__UpperCAmelCase			,							__UpperCAmelCase					)
											elif tuple_object is None:
														return
											else:
														self.assertTrue(
														    all(tf.equal(__UpperCAmelCase			,							__UpperCAmelCase					)					)			,							msg=(
														        """Tuple and dict output are not equal. Difference:"""
														        f' {tf.math.reduce_max(tf.abs(tuple_object - dict_object					)					)}'
														    )			,							)

											recursive_check(__UpperCAmelCase			,							__UpperCAmelCase					)

					for model_class in self.all_model_classes:
								__UpperCAmelCase :   Dict    					=  model_class(__UpperCAmelCase					)

								__UpperCAmelCase :   Optional[int]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase					)
								__UpperCAmelCase :   Tuple    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase					)
								check_equivalence(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)

								__UpperCAmelCase :   Union[str, Any]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase			,							return_labels=__UpperCAmelCase					)
								__UpperCAmelCase :   Union[str, Any]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase			,							return_labels=__UpperCAmelCase					)
								check_equivalence(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)

								__UpperCAmelCase :   Optional[Any]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase					)
								__UpperCAmelCase :   List[str]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase					)
								check_equivalence(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase			,							{"""output_hidden_states""": True}					)

								__UpperCAmelCase :   List[Any]    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase			,							return_labels=__UpperCAmelCase					)
								__UpperCAmelCase :   Dict    					=  self._prepare_for_class(__UpperCAmelCase			,							__UpperCAmelCase			,							return_labels=__UpperCAmelCase					)
								check_equivalence(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase			,							{"""output_hidden_states""": True}					)





		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					__UpperCAmelCase :   int    					=  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase					)





		@slow
		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					__UpperCAmelCase :   Union[str, Any]    					=  TFConvNextModel.from_pretrained("""facebook/convnext-tiny-224"""					)
					self.assertIsNotNone(__UpperCAmelCase					)


def 	lowercase_						( ):

			"""simple docstring"""





			__UpperCAmelCase :   str    					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
			return image






@require_tf
@require_vision
class _A	(     unittest.TestCase     ):





		@cached_property
		def 				__A							(  self					)							->  Any:

					'''simple docstring'''
					return ConvNextImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""					) if is_vision_available() else None





		@slow
		def 				__A							(  self					)							->  int:

					'''simple docstring'''
					__UpperCAmelCase :   Tuple    					=  TFConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""					)

					__UpperCAmelCase :   List[Any]    					=  self.default_image_processor
					__UpperCAmelCase :   int    					=  prepare_img()
					__UpperCAmelCase :   Optional[Any]    					=  image_processor(images=__UpperCAmelCase			,							return_tensors="""tf"""					)

					# forward pass
					__UpperCAmelCase :   List[Any]    					=  model(**__UpperCAmelCase					)

					# verify the logits
					__UpperCAmelCase :   List[str]    					=  tf.TensorShape((1, 1_000)					)
					self.assertEqual(outputs.logits.shape			,							__UpperCAmelCase					)

					__UpperCAmelCase :   str    					=  tf.constant([-0.0260, -0.4739, 0.1911]					)

					tf.debugging.assert_near(outputs.logits[0, :3]			,							__UpperCAmelCase			,							atol=1E-4					)









'''simple docstring'''
import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


_UpperCamelCase									=      logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
_UpperCamelCase									=      direct_transformers_import(Path(__file__).parent)


_UpperCamelCase									=      {
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}



class _A	(     __SCREAMING_SNAKE_CASE     ):
		_SCREAMING_SNAKE_CASE       :     str       =      ["feature_extractor", "tokenizer"]
		# Names need to be attr_class for attr in attributes
		_SCREAMING_SNAKE_CASE       :     Optional[int]       =      None
		_SCREAMING_SNAKE_CASE       :     str       =      None
		_SCREAMING_SNAKE_CASE       :     List[str]       =      None





		def __init__(  self			,							*__UpperCAmelCase			,							**__UpperCAmelCase					)							->  Optional[int]:

					'''simple docstring'''
					# Sanitize args and kwargs
					for key in kwargs:
								if key not in self.attributes:
											raise TypeError(f'Unexpected keyword argument {key}.'					)
					for arg, attribute_name in zip(__UpperCAmelCase			,							self.attributes					):
								if attribute_name in kwargs:
											raise TypeError(f'Got multiple values for argument {attribute_name}.'					)
								else:
											__UpperCAmelCase :   int    					=  arg

					if len(__UpperCAmelCase					) != len(self.attributes					):
								raise ValueError(
								    f'This processor requires {len(self.attributes					)} arguments: {", ".join(self.attributes					)}. Got '
								    f'{len(__UpperCAmelCase					)} arguments instead.'					)

					# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
					for attribute_name, arg in kwargs.items():
								__UpperCAmelCase :   int    					=  getattr(self			,							f'{attribute_name}_class'					)
								# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
								__UpperCAmelCase :   str    					=  AUTO_TO_BASE_CLASS_MAPPING.get(__UpperCAmelCase			,							__UpperCAmelCase					)
								if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
											__UpperCAmelCase :   str    					=  tuple(getattr(__UpperCAmelCase			,							__UpperCAmelCase					) for n in class_name if n is not None					)
								else:
											__UpperCAmelCase :   Optional[Any]    					=  getattr(__UpperCAmelCase			,							__UpperCAmelCase					)

								if not isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
											raise ValueError(
											    f'Received a {type(__UpperCAmelCase					).__name__} for argument {attribute_name}, but a {class_name} was expected.'					)

								setattr(self			,							__UpperCAmelCase			,							__UpperCAmelCase					)





		def __repr__(  self					)							->  List[str]:

					'''simple docstring'''
					__UpperCAmelCase :   Any    					=  [f'- {name}: {repr(getattr(self			,							__UpperCAmelCase					)					)}' for name in self.attributes]
					__UpperCAmelCase :   List[Any]    					=  """\n""".join(__UpperCAmelCase					)
					return f'{self.__class__.__name__}:\n{attributes_repr}'





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase = False			,							**__UpperCAmelCase					)							->  Any:

					'''simple docstring'''
					os.makedirs(__UpperCAmelCase			,							exist_ok=__UpperCAmelCase					)

					if push_to_hub:
								__UpperCAmelCase :   Optional[int]    					=  kwargs.pop("""commit_message"""			,							__UpperCAmelCase					)
								__UpperCAmelCase :   List[Any]    					=  kwargs.pop("""repo_id"""			,							save_directory.split(os.path.sep					)[-1]					)
								__UpperCAmelCase :   Any    					=  self._create_repo(__UpperCAmelCase			,							**__UpperCAmelCase					)
								__UpperCAmelCase :   List[str]    					=  self._get_files_timestamps(__UpperCAmelCase					)
					# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
					# loaded from the Hub.
					if self._auto_class is not None:
								__UpperCAmelCase :   Union[str, Any]    					=  [getattr(self			,							__UpperCAmelCase					) for attribute_name in self.attributes]
								__UpperCAmelCase :   Dict    					=  [(a.init_kwargs if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					) else a) for a in attrs]
								custom_object_save(self			,							__UpperCAmelCase			,							config=__UpperCAmelCase					)

					for attribute_name in self.attributes:
								__UpperCAmelCase :   Optional[int]    					=  getattr(self			,							__UpperCAmelCase					)
								# Include the processor class in the attribute config so this processor can then be reloaded with the
								# `AutoProcessor` API.
								if hasattr(__UpperCAmelCase			,							"""_set_processor_class"""					):
											attribute._set_processor_class(self.__class__.__name__					)
								attribute.save_pretrained(__UpperCAmelCase					)

					if self._auto_class is not None:
								# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
								for attribute_name in self.attributes:
											__UpperCAmelCase :   str    					=  getattr(self			,							__UpperCAmelCase					)
											if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
														del attribute.init_kwargs["auto_map"]

					if push_to_hub:
								self._upload_modified_files(
								    __UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase			,							commit_message=__UpperCAmelCase			,							token=kwargs.get("""use_auth_token"""					)			,							)





		@classmethod
		def 				__A							(  cls			,							__UpperCAmelCase			,							__UpperCAmelCase = None			,							__UpperCAmelCase = False			,							__UpperCAmelCase = False			,							__UpperCAmelCase = None			,							__UpperCAmelCase = "main"			,							**__UpperCAmelCase			,							)							->  Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase :   List[str]    					=  cache_dir
					__UpperCAmelCase :   Tuple    					=  force_download
					__UpperCAmelCase :   Optional[int]    					=  local_files_only
					__UpperCAmelCase :   str    					=  revision

					__UpperCAmelCase :   int    					=  kwargs.pop("""use_auth_token"""			,							__UpperCAmelCase					)
					if use_auth_token is not None:
								warnings.warn(
								    """The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."""			,							__UpperCAmelCase					)
								if token is not None:
											raise ValueError(
											    """`token` and `use_auth_token` are both specified. Please set only the argument `token`."""					)
								__UpperCAmelCase :   Any    					=  use_auth_token

					if token is not None:
								# change to `token` in a follow-up PR
								__UpperCAmelCase :   Dict    					=  token

					__UpperCAmelCase :   Optional[int]    					=  cls._get_arguments_from_pretrained(__UpperCAmelCase			,							**__UpperCAmelCase					)
					return cls(*__UpperCAmelCase					)





		@classmethod
		def 				__A							(  cls			,							__UpperCAmelCase="AutoProcessor"					)							->  Dict:

					'''simple docstring'''
					if not isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
								__UpperCAmelCase :   List[Any]    					=  auto_class.__name__

					import transformers.models.auto as auto_module

					if not hasattr(__UpperCAmelCase			,							__UpperCAmelCase					):
								raise ValueError(f'{auto_class} is not a valid auto class.'					)

					__UpperCAmelCase :   Optional[Any]    					=  auto_class





		@classmethod
		def 				__A							(  cls			,							__UpperCAmelCase			,							**__UpperCAmelCase					)							->  List[str]:

					'''simple docstring'''
					__UpperCAmelCase :   Union[str, Any]    					=  []
					for attribute_name in cls.attributes:
								__UpperCAmelCase :   List[Any]    					=  getattr(cls			,							f'{attribute_name}_class'					)
								if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
											__UpperCAmelCase :   str    					=  tuple(getattr(__UpperCAmelCase			,							__UpperCAmelCase					) if n is not None else None for n in class_name					)
											__UpperCAmelCase :   int    					=  kwargs.get("""use_fast"""			,							__UpperCAmelCase					)
											if use_fast and classes[1] is not None:
														__UpperCAmelCase :   int    					=  classes[1]
											else:
														__UpperCAmelCase :   Optional[Any]    					=  classes[0]
								else:
											__UpperCAmelCase :   str    					=  getattr(__UpperCAmelCase			,							__UpperCAmelCase					)

								args.append(attribute_class.from_pretrained(__UpperCAmelCase			,							**__UpperCAmelCase					)					)
					return args





		@property
		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					__UpperCAmelCase :   Optional[Any]    					=  getattr(self			,							self.attributes[0]					)
					return getattr(__UpperCAmelCase			,							"""model_input_names"""			,							__UpperCAmelCase					)


_UpperCamelCase									=      copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
					_UpperCamelCase									=      ProcessorMixin.push_to_hub.__doc__.format(
					    object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
					)




