





'''simple docstring'''



import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE    =			logging.get_logger(__name__)




def       _lowerCAmelCase  ( lowerCamelCase_	:     int ):
			__lowercase   =							ConvNextVaConfig()

			if "atto" in checkpoint_url:
						__lowercase   =							[2, 2, 6, 2]
						__lowercase   =							[4_0, 8_0, 1_6_0, 3_2_0]
			if "femto" in checkpoint_url:
						__lowercase   =							[2, 2, 6, 2]
						__lowercase   =							[4_8, 9_6, 1_9_2, 3_8_4]
			if "pico" in checkpoint_url:
						__lowercase   =							[2, 2, 6, 2]
						__lowercase   =							[6_4, 1_2_8, 2_5_6, 5_1_2]
			if "nano" in checkpoint_url:
						__lowercase   =							[2, 2, 8, 2]
						__lowercase   =							[8_0, 1_6_0, 3_2_0, 6_4_0]
			if "tiny" in checkpoint_url:
						__lowercase   =							[3, 3, 9, 3]
						__lowercase   =							[9_6, 1_9_2, 3_8_4, 7_6_8]
			if "base" in checkpoint_url:
						__lowercase   =							[3, 3, 2_7, 3]
						__lowercase   =							[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
			if "large" in checkpoint_url:
						__lowercase   =							[3, 3, 2_7, 3]
						__lowercase   =							[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
			if "huge" in checkpoint_url:
						__lowercase   =							[3, 3, 2_7, 3]
						__lowercase   =							[3_5_2, 7_0_4, 1_4_0_8, 2_8_1_6]

			__lowercase   =							1_0_0_0
			__lowercase   =							'''imagenet-1k-id2label.json'''
			__lowercase   =							(1, 1_0_0_0)

			__lowercase   =							'''huggingface/label-files'''
			__lowercase   =							num_labels
			__lowercase   =							json.load(open(hf_hub_download(lowerCamelCase_						,				lowerCamelCase_						,				repo_type='''dataset''' )						,				'''r''' ) )
			__lowercase   =							{int(lowerCamelCase_ ): v for k, v in idalabel.items()}

			__lowercase   =							idalabel
			__lowercase   =							{v: k for k, v in idalabel.items()}
			__lowercase   =							hidden_sizes
			__lowercase   =							depths

			return config, expected_shape




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int] ):
			if "downsample_layers.0.0" in name:
						__lowercase   =							name.replace('''downsample_layers.0.0'''						,				'''embeddings.patch_embeddings''' )
			if "downsample_layers.0.1" in name:
						__lowercase   =							name.replace('''downsample_layers.0.1'''						,				'''embeddings.norm''' )  # we rename to layernorm later on
			if "downsample_layers.1.0" in name:
						__lowercase   =							name.replace('''downsample_layers.1.0'''						,				'''stages.1.downsampling_layer.0''' )
			if "downsample_layers.1.1" in name:
						__lowercase   =							name.replace('''downsample_layers.1.1'''						,				'''stages.1.downsampling_layer.1''' )
			if "downsample_layers.2.0" in name:
						__lowercase   =							name.replace('''downsample_layers.2.0'''						,				'''stages.2.downsampling_layer.0''' )
			if "downsample_layers.2.1" in name:
						__lowercase   =							name.replace('''downsample_layers.2.1'''						,				'''stages.2.downsampling_layer.1''' )
			if "downsample_layers.3.0" in name:
						__lowercase   =							name.replace('''downsample_layers.3.0'''						,				'''stages.3.downsampling_layer.0''' )
			if "downsample_layers.3.1" in name:
						__lowercase   =							name.replace('''downsample_layers.3.1'''						,				'''stages.3.downsampling_layer.1''' )
			if "stages" in name and "downsampling_layer" not in name:
						# stages.0.0. for instance should be renamed to stages.0.layers.0.
						__lowercase   =							name[: len('''stages.0''' )] + '''.layers''' + name[len('''stages.0''' ) :]
			if "gamma" in name:
						__lowercase   =							name.replace('''gamma'''						,				'''weight''' )
			if "beta" in name:
						__lowercase   =							name.replace('''beta'''						,				'''bias''' )
			if "stages" in name:
						__lowercase   =							name.replace('''stages'''						,				'''encoder.stages''' )
			if "norm" in name:
						__lowercase   =							name.replace('''norm'''						,				'''layernorm''' )
			if "head" in name:
						__lowercase   =							name.replace('''head'''						,				'''classifier''' )

			return name




def       _lowerCAmelCase  ( ):
			__lowercase   =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
			__lowercase   =							Image.open(requests.get(lowerCamelCase_						,				stream=lowerCamelCase_ ).raw )
			return im




def       _lowerCAmelCase  ( lowerCamelCase_	:     Dict ):
			if "224" in checkpoint_url:
						__lowercase   =							2_2_4
						__lowercase   =							2_2_4 / 2_5_6
			elif "384" in checkpoint_url:
						__lowercase   =							3_8_4
						__lowercase   =							None
			else:
						__lowercase   =							5_1_2
						__lowercase   =							None

			return ConvNextImageProcessor(
			    size=lowerCamelCase_						,				crop_pct=lowerCamelCase_						,				image_mean=[0.4_85, 0.4_56, 0.4_06]						,				image_std=[0.2_29, 0.2_24, 0.2_25]						,				resample=PILImageResampling.BICUBIC						,				)




@torch.no_grad()
def       _lowerCAmelCase  ( lowerCamelCase_	:     Any						,				lowerCamelCase_	:     List[str]						,				lowerCamelCase_	:     Dict						,				lowerCamelCase_	:     Tuple ):
			print('''Downloading original model from checkpoint...''' )
			# define ConvNeXTV2 configuration based on URL
			__lowercase  ,						__lowercase   =							get_convnextva_config(lowerCamelCase_ )
			# load original state_dict from URL
			__lowercase   =							torch.hub.load_state_dict_from_url(lowerCamelCase_ )['''model''']

			print('''Converting model parameters...''' )
			# rename keys
			for key in state_dict.copy().keys():
						__lowercase   =							state_dict.pop(lowerCamelCase_ )
						__lowercase   =							val
			# add prefix to all keys expect classifier head
			for key in state_dict.copy().keys():
						__lowercase   =							state_dict.pop(lowerCamelCase_ )
						if not key.startswith('''classifier''' ):
									__lowercase   =							'''convnextv2.''' + key
						__lowercase   =							val

			# load HuggingFace model
			__lowercase   =							ConvNextVaForImageClassification(lowerCamelCase_ )
			model.load_state_dict(lowerCamelCase_ )
			model.eval()

			# Check outputs on an image, prepared by ConvNextImageProcessor
			__lowercase   =							convert_preprocessor(lowerCamelCase_ )
			__lowercase   =							preprocessor(images=prepare_img()						,				return_tensors='''pt''' )
			__lowercase   =							model(**lowerCamelCase_ ).logits

			# note: the logits below were obtained without center cropping
			if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
						__lowercase   =							torch.tensor([-0.39_30, 0.17_47, -0.52_46, 0.41_77, 0.42_95] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
						__lowercase   =							torch.tensor([-0.17_27, -0.53_41, -0.78_18, -0.47_45, -0.65_66] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
						__lowercase   =							torch.tensor([-0.03_33, 0.15_63, -0.91_37, 0.10_54, 0.03_81] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
						__lowercase   =							torch.tensor([-0.17_44, -0.15_55, -0.07_13, 0.09_50, -0.14_31] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
						__lowercase   =							torch.tensor([0.99_96, 0.19_66, -0.43_86, -0.34_72, 0.66_61] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
						__lowercase   =							torch.tensor([-0.25_53, -0.67_08, -0.13_59, 0.25_18, -0.24_88] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
						__lowercase   =							torch.tensor([-0.06_73, -0.56_27, -0.37_53, -0.27_22, 0.01_78] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
						__lowercase   =							torch.tensor([-0.63_77, -0.74_58, -0.21_50, 0.11_84, -0.05_97] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
						__lowercase   =							torch.tensor([1.07_99, 0.23_22, -0.88_60, 1.02_19, 0.62_31] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
						__lowercase   =							torch.tensor([0.37_66, 0.49_17, -1.14_26, 0.99_42, 0.60_24] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
						__lowercase   =							torch.tensor([0.42_20, -0.69_19, -0.43_17, -0.28_81, -0.66_09] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
						__lowercase   =							torch.tensor([0.10_82, -0.82_86, -0.50_95, 0.46_81, -0.80_85] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
						__lowercase   =							torch.tensor([-0.24_19, -0.62_21, 0.21_76, -0.09_80, -0.75_27] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
						__lowercase   =							torch.tensor([0.03_91, -0.43_71, 0.37_86, 0.12_51, -0.27_84] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
						__lowercase   =							torch.tensor([-0.05_04, 0.56_36, -0.17_29, -0.65_07, -0.39_49] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
						__lowercase   =							torch.tensor([0.35_60, 0.94_86, 0.31_49, -0.26_67, -0.51_38] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
						__lowercase   =							torch.tensor([-0.24_69, -0.45_50, -0.58_53, -0.08_10, 0.03_09] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
						__lowercase   =							torch.tensor([-0.30_90, 0.08_02, -0.06_82, -0.19_79, -0.28_26] )
			else:
						raise ValueError(f"Unknown URL: {checkpoint_url}" )

			assert torch.allclose(logits[0, :5]						,				lowerCamelCase_						,				atol=1E-3 )
			assert logits.shape == expected_shape
			print('''Model outputs match the original results!''' )

			if save_model:
						print('''Saving model to local...''' )
						# Create folder to save model
						if not os.path.isdir(lowerCamelCase_ ):
									os.mkdir(lowerCamelCase_ )

						model.save_pretrained(lowerCamelCase_ )
						preprocessor.save_pretrained(lowerCamelCase_ )

			__lowercase   =							'''convnextv2'''
			if "atto" in checkpoint_url:
						model_name += "-atto"
			if "femto" in checkpoint_url:
						model_name += "-femto"
			if "pico" in checkpoint_url:
						model_name += "-pico"
			if "nano" in checkpoint_url:
						model_name += "-nano"
			elif "tiny" in checkpoint_url:
						model_name += "-tiny"
			elif "base" in checkpoint_url:
						model_name += "-base"
			elif "large" in checkpoint_url:
						model_name += "-large"
			elif "huge" in checkpoint_url:
						model_name += "-huge"
			if "22k" in checkpoint_url and "1k" not in checkpoint_url:
						model_name += "-22k"
			elif "22k" in checkpoint_url and "1k" in checkpoint_url:
						model_name += "-22k-1k"
			elif "1k" in checkpoint_url:
						model_name += "-1k"
			if "224" in checkpoint_url:
						model_name += "-224"
			elif "384" in checkpoint_url:
						model_name += "-384"
			elif "512" in checkpoint_url:
						model_name += "-512"

			if push_to_hub:
						print(f"Pushing {model_name} to the hub..." )
						model.push_to_hub(lowerCamelCase_ )
						preprocessor.push_to_hub(lowerCamelCase_ )


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE    =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--checkpoint_url''',
	    default='''https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt''',
	    type=str,
	    help='''URL of the original ConvNeXTV2 checkpoint you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''',
	    default='''model''',
	    type=str,
	    help='''Path to the output PyTorch model directory.''',
	)
	parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
	parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image preprocessor to the hub''')

	_SCREAMING_SNAKE_CASE    =			parser.parse_args()
	convert_convnextva_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
	)












'''simple docstring'''



import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


_SCREAMING_SNAKE_CASE    =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    =			OrderedDict(
    [
        ('''align''', '''AlignProcessor'''),
        ('''altclip''', '''AltCLIPProcessor'''),
        ('''bark''', '''BarkProcessor'''),
        ('''blip''', '''BlipProcessor'''),
        ('''blip-2''', '''Blip2Processor'''),
        ('''bridgetower''', '''BridgeTowerProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPProcessor'''),
        ('''clap''', '''ClapProcessor'''),
        ('''clip''', '''CLIPProcessor'''),
        ('''clipseg''', '''CLIPSegProcessor'''),
        ('''flava''', '''FlavaProcessor'''),
        ('''git''', '''GitProcessor'''),
        ('''groupvit''', '''CLIPProcessor'''),
        ('''hubert''', '''Wav2Vec2Processor'''),
        ('''instructblip''', '''InstructBlipProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2Processor'''),
        ('''layoutlmv3''', '''LayoutLMv3Processor'''),
        ('''markuplm''', '''MarkupLMProcessor'''),
        ('''mctct''', '''MCTCTProcessor'''),
        ('''mgp-str''', '''MgpstrProcessor'''),
        ('''oneformer''', '''OneFormerProcessor'''),
        ('''owlvit''', '''OwlViTProcessor'''),
        ('''pix2struct''', '''Pix2StructProcessor'''),
        ('''sam''', '''SamProcessor'''),
        ('''sew''', '''Wav2Vec2Processor'''),
        ('''sew-d''', '''Wav2Vec2Processor'''),
        ('''speech_to_text''', '''Speech2TextProcessor'''),
        ('''speech_to_text_2''', '''Speech2Text2Processor'''),
        ('''speecht5''', '''SpeechT5Processor'''),
        ('''trocr''', '''TrOCRProcessor'''),
        ('''tvlt''', '''TvltProcessor'''),
        ('''unispeech''', '''Wav2Vec2Processor'''),
        ('''unispeech-sat''', '''Wav2Vec2Processor'''),
        ('''vilt''', '''ViltProcessor'''),
        ('''vision-text-dual-encoder''', '''VisionTextDualEncoderProcessor'''),
        ('''wav2vec2''', '''Wav2Vec2Processor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2Processor'''),
        ('''wavlm''', '''Wav2Vec2Processor'''),
        ('''whisper''', '''WhisperProcessor'''),
        ('''xclip''', '''XCLIPProcessor'''),
    ]
)

_SCREAMING_SNAKE_CASE    =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)




def       _lowerCAmelCase  ( lowerCamelCase_	:     str ):
			for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
						if class_name in processors:
									__lowercase   =							model_type_to_module_name(lowerCamelCase_ )

									__lowercase   =							importlib.import_module(f".{module_name}"						,				'''transformers.models''' )
									try:
												return getattr(lowerCamelCase_						,				lowerCamelCase_ )
									except AttributeError:
												continue

			for processor in PROCESSOR_MAPPING._extra_content.values():
						if getattr(lowerCamelCase_						,				'''__name__'''						,				lowerCamelCase_ ) == class_name:
									return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
			__lowercase   =							importlib.import_module('''transformers''' )
			if hasattr(lowerCamelCase_						,				lowerCamelCase_ ):
						return getattr(lowerCamelCase_						,				lowerCamelCase_ )

			return None






class 							__lowercase				:







	'''simple docstring'''

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







				'''simple docstring'''





				raise EnvironmentError(
				    '''AutoProcessor is designed to be instantiated '''
				    '''using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''       )


	@classmethod
	@replace_list_option_in_docstrings(_lowerCamelCase       )
	def   _UpperCAmelCase   (cls   ,_lowerCamelCase   ,**_lowerCamelCase       )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase   =							kwargs.pop('''config'''   ,_lowerCamelCase       )
				__lowercase   =							kwargs.pop('''trust_remote_code'''   ,_lowerCamelCase       )
				__lowercase   =							True

				__lowercase   =							None
				__lowercase   =							None

				# First, let's see if we have a preprocessor config.
				# Filter the kwargs for `get_file_from_repo`.
				__lowercase   =							{
				    key: kwargs[key] for key in inspect.signature(_lowerCamelCase       ).parameters.keys() if key in kwargs
				}
				# Let's start by checking whether the processor class is saved in an image processor
				__lowercase   =							get_file_from_repo(
				    _lowerCamelCase   ,_lowerCamelCase   ,**_lowerCamelCase       )
				if preprocessor_config_file is not None:
							__lowercase  ,						__lowercase   =							ImageProcessingMixin.get_image_processor_dict(_lowerCamelCase   ,**_lowerCamelCase       )
							__lowercase   =							config_dict.get('''processor_class'''   ,_lowerCamelCase       )
							if "AutoProcessor" in config_dict.get('''auto_map'''   ,{}       ):
										__lowercase   =							config_dict['''auto_map''']['''AutoProcessor''']

        # If not found, let's check whether the processor class is saved in a feature extractor config
				if preprocessor_config_file is not None and processor_class is None:
							__lowercase  ,						__lowercase   =							FeatureExtractionMixin.get_feature_extractor_dict(_lowerCamelCase   ,**_lowerCamelCase       )
							__lowercase   =							config_dict.get('''processor_class'''   ,_lowerCamelCase       )
							if "AutoProcessor" in config_dict.get('''auto_map'''   ,{}       ):
										__lowercase   =							config_dict['''auto_map''']['''AutoProcessor''']

				if processor_class is None:
							# Next, let's check whether the processor class is saved in a tokenizer
							__lowercase   =							get_file_from_repo(
							    _lowerCamelCase   ,_lowerCamelCase   ,**_lowerCamelCase       )
							if tokenizer_config_file is not None:
										with open(_lowerCamelCase   ,encoding='''utf-8'''       ) as reader:
													__lowercase   =							json.load(_lowerCamelCase       )

										__lowercase   =							config_dict.get('''processor_class'''   ,_lowerCamelCase       )
										if "AutoProcessor" in config_dict.get('''auto_map'''   ,{}       ):
													__lowercase   =							config_dict['''auto_map''']['''AutoProcessor''']

				if processor_class is None:
							# Otherwise, load config, if it can be loaded.
							if not isinstance(_lowerCamelCase   ,_lowerCamelCase       ):
										__lowercase   =							AutoConfig.from_pretrained(
										    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )

							# And check if the config contains the processor class.
							__lowercase   =							getattr(_lowerCamelCase   ,'''processor_class'''   ,_lowerCamelCase       )
							if hasattr(_lowerCamelCase   ,'''auto_map'''       ) and "AutoProcessor" in config.auto_map:
										__lowercase   =							config.auto_map['''AutoProcessor''']

				if processor_class is not None:
							__lowercase   =							processor_class_from_name(_lowerCamelCase       )

				__lowercase   =							processor_auto_map is not None
				__lowercase   =							processor_class is not None or type(_lowerCamelCase       ) in PROCESSOR_MAPPING
				__lowercase   =							resolve_trust_remote_code(
				    _lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase       )

				if has_remote_code and trust_remote_code:
							__lowercase   =							get_class_from_dynamic_module(
							    _lowerCamelCase   ,_lowerCamelCase   ,**_lowerCamelCase       )
							__lowercase   =							kwargs.pop('''code_revision'''   ,_lowerCamelCase       )
							if os.path.isdir(_lowerCamelCase       ):
										processor_class.register_for_auto_class()
							return processor_class.from_pretrained(
							    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )
				elif processor_class is not None:
							return processor_class.from_pretrained(
							    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )
				# Last try: we use the PROCESSOR_MAPPING.
				elif type(_lowerCamelCase       ) in PROCESSOR_MAPPING:
							return PROCESSOR_MAPPING[type(_lowerCamelCase       )].from_pretrained(_lowerCamelCase   ,**_lowerCamelCase       )

				# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
				# tokenizer.
				try:
							return AutoTokenizer.from_pretrained(
							    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )
				except Exception:
							try:
										return AutoImageProcessor.from_pretrained(
										    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )
							except Exception:
										pass

							try:
										return AutoFeatureExtractor.from_pretrained(
										    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )
							except Exception:
										pass

				raise ValueError(
				    f"Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a "
				    '''tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'''
				    '''the files of at least one of those processing classes.'''       )


	@staticmethod
	def   _UpperCAmelCase   (_lowerCamelCase   ,_lowerCamelCase       )							->							int:







				'''simple docstring'''





				PROCESSOR_MAPPING.register(_lowerCamelCase   ,_lowerCamelCase       )





