





'''simple docstring'''



import tempfile
import unittest

from transformers import DPRConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
	from transformers.models.dpr.modeling_dpr import (
	    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
	    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
	    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
	)






class 							__lowercase				:







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase   ,_lowerCamelCase=13   ,_lowerCamelCase=7   ,_lowerCamelCase=False   ,_lowerCamelCase=True   ,_lowerCamelCase=True   ,_lowerCamelCase=True   ,_lowerCamelCase=99   ,_lowerCamelCase=32   ,_lowerCamelCase=5   ,_lowerCamelCase=4   ,_lowerCamelCase=37   ,_lowerCamelCase="gelu"   ,_lowerCamelCase=0.1   ,_lowerCamelCase=0.1   ,_lowerCamelCase=512   ,_lowerCamelCase=16   ,_lowerCamelCase=2   ,_lowerCamelCase=0.0_2   ,_lowerCamelCase=3   ,_lowerCamelCase=4   ,_lowerCamelCase=None   ,_lowerCamelCase=0   ,)							->							List[Any]:







				'''simple docstring'''





				__lowercase   =							parent
				__lowercase   =							batch_size
				__lowercase   =							seq_length
				__lowercase   =							is_training
				__lowercase   =							use_input_mask
				__lowercase   =							use_token_type_ids
				__lowercase   =							use_labels
				__lowercase   =							vocab_size
				__lowercase   =							hidden_size
				__lowercase   =							num_hidden_layers
				__lowercase   =							num_attention_heads
				__lowercase   =							intermediate_size
				__lowercase   =							hidden_act
				__lowercase   =							hidden_dropout_prob
				__lowercase   =							attention_probs_dropout_prob
				__lowercase   =							max_position_embeddings
				__lowercase   =							type_vocab_size
				__lowercase   =							type_sequence_label_size
				__lowercase   =							initializer_range
				__lowercase   =							num_labels
				__lowercase   =							num_choices
				__lowercase   =							scope
				__lowercase   =							projection_dim


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







				'''simple docstring'''





				__lowercase   =							ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size       )

				__lowercase   =							None
				if self.use_input_mask:
							__lowercase   =							random_attention_mask([self.batch_size, self.seq_length]       )

				__lowercase   =							None
				if self.use_token_type_ids:
							__lowercase   =							ids_tensor([self.batch_size, self.seq_length]   ,self.type_vocab_size       )

				__lowercase   =							None
				__lowercase   =							None
				__lowercase   =							None
				if self.use_labels:
							__lowercase   =							ids_tensor([self.batch_size]   ,self.type_sequence_label_size       )
							__lowercase   =							ids_tensor([self.batch_size, self.seq_length]   ,self.num_labels       )
							__lowercase   =							ids_tensor([self.batch_size]   ,self.num_choices       )

				__lowercase   =							self.get_config()

				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


	def   _UpperCAmelCase   (self       )							->							int:







				'''simple docstring'''





				return DPRConfig(
				    projection_dim=self.projection_dim   ,vocab_size=self.vocab_size   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_size=self.intermediate_size   ,hidden_act=self.hidden_act   ,hidden_dropout_prob=self.hidden_dropout_prob   ,attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,max_position_embeddings=self.max_position_embeddings   ,type_vocab_size=self.type_vocab_size   ,initializer_range=self.initializer_range   ,)


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase       )							->							Tuple:







				'''simple docstring'''





				__lowercase   =							DPRContextEncoder(config=_lowerCamelCase       )
				model.to(_lowerCamelCase       )
				model.eval()
				__lowercase   =							model(_lowerCamelCase   ,attention_mask=_lowerCamelCase   ,token_type_ids=_lowerCamelCase       )
				__lowercase   =							model(_lowerCamelCase   ,token_type_ids=_lowerCamelCase       )
				__lowercase   =							model(_lowerCamelCase       )
				self.parent.assertEqual(result.pooler_output.shape   ,(self.batch_size, self.projection_dim or self.hidden_size)       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase       )							->							Any:







				'''simple docstring'''





				__lowercase   =							DPRQuestionEncoder(config=_lowerCamelCase       )
				model.to(_lowerCamelCase       )
				model.eval()
				__lowercase   =							model(_lowerCamelCase   ,attention_mask=_lowerCamelCase   ,token_type_ids=_lowerCamelCase       )
				__lowercase   =							model(_lowerCamelCase   ,token_type_ids=_lowerCamelCase       )
				__lowercase   =							model(_lowerCamelCase       )
				self.parent.assertEqual(result.pooler_output.shape   ,(self.batch_size, self.projection_dim or self.hidden_size)       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase       )							->							List[str]:







				'''simple docstring'''





				__lowercase   =							DPRReader(config=_lowerCamelCase       )
				model.to(_lowerCamelCase       )
				model.eval()
				__lowercase   =							model(
				    _lowerCamelCase   ,attention_mask=_lowerCamelCase   ,)

				self.parent.assertEqual(result.start_logits.shape   ,(self.batch_size, self.seq_length)       )
				self.parent.assertEqual(result.end_logits.shape   ,(self.batch_size, self.seq_length)       )
				self.parent.assertEqual(result.relevance_logits.shape   ,(self.batch_size,)       )


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







				'''simple docstring'''





				__lowercase   =							self.prepare_config_and_inputs()
				(
				    (
				    __lowercase
				)  ,						(
				    __lowercase
				)  ,						(
				    __lowercase
				)  ,						(
				    __lowercase
				)  ,						(
				    __lowercase
				)  ,						(
				    __lowercase
				)  ,						(
				    __lowercase
				)  ,						
				)   =							config_and_inputs
				__lowercase   =							{'''input_ids''': input_ids}
				return config, inputs_dict






@require_torch
class 							__lowercase				(					lowerCAmelCase__       ,					lowerCAmelCase__       ,					unittest.TestCase  ):







	'''simple docstring'''

	a			: Union[str, Any]      =				(
	    (
	        DPRContextEncoder,
	        DPRQuestionEncoder,
	        DPRReader,
	    )
	    if is_torch_available()
	    else ()
	)
	a			: Optional[Any]      =				{"feature-extraction": DPRQuestionEncoder} if is_torch_available() else {}

	a			: int      =				False
	a			: Dict      =				False  # why?
	a			: Union[str, Any]      =				False
	a			: Tuple      =				False


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







				'''simple docstring'''





				__lowercase   =							DPRModelTester(self       )
				__lowercase   =							ConfigTester(self   ,config_class=_lowerCamelCase   ,hidden_size=37       )


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







				'''simple docstring'''





				self.config_tester.run_common_tests()


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







				'''simple docstring'''





				__lowercase   =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_context_encoder(*_lowerCamelCase       )


	def   _UpperCAmelCase   (self       )							->							Tuple:







				'''simple docstring'''





				__lowercase   =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_question_encoder(*_lowerCamelCase       )


	def   _UpperCAmelCase   (self       )							->							Tuple:







				'''simple docstring'''





				__lowercase   =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_reader(*_lowerCamelCase       )


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				__lowercase   =							self.model_tester.prepare_config_and_inputs()[0]

				__lowercase   =							DPRQuestionEncoder(config=_lowerCamelCase       )
				model.to(_lowerCamelCase       )
				model.eval()

				with tempfile.TemporaryDirectory() as tmp_dirname:
							model.save_pretrained(_lowerCamelCase       )
							__lowercase   =							DPRQuestionEncoder.from_pretrained(_lowerCamelCase   ,projection_dim=512       )

				self.assertIsNotNone(_lowerCamelCase       )


	@slow
	def   _UpperCAmelCase   (self       )							->							List[Any]:







				'''simple docstring'''





				for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__lowercase   =							DPRContextEncoder.from_pretrained(_lowerCamelCase       )
							self.assertIsNotNone(_lowerCamelCase       )

				for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__lowercase   =							DPRContextEncoder.from_pretrained(_lowerCamelCase       )
							self.assertIsNotNone(_lowerCamelCase       )

				for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__lowercase   =							DPRQuestionEncoder.from_pretrained(_lowerCamelCase       )
							self.assertIsNotNone(_lowerCamelCase       )

				for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__lowercase   =							DPRReader.from_pretrained(_lowerCamelCase       )
							self.assertIsNotNone(_lowerCamelCase       )






@require_torch
class 							__lowercase				(					unittest.TestCase  ):







	'''simple docstring'''

	@slow
	def   _UpperCAmelCase   (self       )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase   =							DPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''   ,return_dict=_lowerCamelCase       )
				model.to(_lowerCamelCase       )

				__lowercase   =							torch.tensor(
				    [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]]   ,dtype=torch.long   ,device=_lowerCamelCase       )  # [CLS] hello, is my dog cute? [SEP]
				__lowercase   =							model(_lowerCamelCase       )[0]  # embedding shape = (1, 768)
				# compare the actual values for a slice.
				__lowercase   =							torch.tensor(
				    [
				        [
				            0.0_3_2_3_6_2_5_3,
				            0.1_2_7_5_3_3_3_5,
				            0.1_6_8_1_8_5_0_9,
				            0.0_0_2_7_9_7_8_6,
				            0.3_8_9_6_9_3_3,
				            0.2_4_2_6_4_9_4_5,
				            0.2_1_7_8_9_7_1,
				            -0.0_2_3_3_5_2_2_7,
				            -0.0_8_4_8_1_9_5_9,
				            -0.1_4_3_2_4_1_1_7,
				        ]
				    ]   ,dtype=torch.float   ,device=_lowerCamelCase   ,)
				self.assertTrue(torch.allclose(output[:, :10]   ,_lowerCamelCase   ,atol=1E-4       )       )


	@slow
	def   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





				__lowercase   =							DPRReaderTokenizer.from_pretrained('''facebook/dpr-reader-single-nq-base'''       )
				__lowercase   =							DPRReader.from_pretrained('''facebook/dpr-reader-single-nq-base'''       )
				model.to(_lowerCamelCase       )

				__lowercase   =							tokenizer(
				    questions='''What is love ?'''   ,titles='''Haddaway'''   ,texts='''What Is Love is a song recorded by the artist Haddaway'''   ,padding=_lowerCamelCase   ,return_tensors='''pt'''   ,)
				encoded_inputs.to(_lowerCamelCase       )

				__lowercase   =							model(**_lowerCamelCase       )

				# compare the actual values for a slice.
				__lowercase   =							torch.tensor(
				    [[-1_0.3_0_0_5, -1_0.7_7_6_5, -1_1.4_8_7_2, -1_1.6_8_4_1, -1_1.9_3_1_2, -1_0.3_0_0_2, -9.8_5_4_4, -1_1.7_3_7_8, -1_2.0_8_2_1, -1_0.2_9_7_5]]   ,dtype=torch.float   ,device=_lowerCamelCase   ,)

				__lowercase   =							torch.tensor(
				    [[-1_1.0_6_8_4, -1_1.7_0_4_1, -1_1.5_3_9_7, -1_0.3_4_6_5, -1_0.8_7_9_1, -6.8_4_4_3, -1_1.9_9_5_9, -1_1.0_3_6_4, -1_0.0_0_9_6, -6.8_4_0_5]]   ,dtype=torch.float   ,device=_lowerCamelCase   ,)
				self.assertTrue(torch.allclose(outputs.start_logits[:, :10]   ,_lowerCamelCase   ,atol=1E-4       )       )
				self.assertTrue(torch.allclose(outputs.end_logits[:, :10]   ,_lowerCamelCase   ,atol=1E-4       )       )












'''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       )





