





'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import MobileNetVaImageProcessor






class 							__lowercase				(					unittest.TestCase  ):







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase   ,_lowerCamelCase=7   ,_lowerCamelCase=3   ,_lowerCamelCase=18   ,_lowerCamelCase=30   ,_lowerCamelCase=400   ,_lowerCamelCase=True   ,_lowerCamelCase=None   ,_lowerCamelCase=True   ,_lowerCamelCase=None   ,)							->							Tuple:







				'''simple docstring'''





				__lowercase   =							size if size is not None else {'''shortest_edge''': 20}
				__lowercase   =							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
				__lowercase   =							parent
				__lowercase   =							batch_size
				__lowercase   =							num_channels
				__lowercase   =							image_size
				__lowercase   =							min_resolution
				__lowercase   =							max_resolution
				__lowercase   =							do_resize
				__lowercase   =							size
				__lowercase   =							do_center_crop
				__lowercase   =							crop_size


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







				'''simple docstring'''





				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_center_crop": self.do_center_crop,
				    "crop_size": self.crop_size,
				}






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







	'''simple docstring'''

	a			: Optional[int]      =				MobileNetVaImageProcessor if is_vision_available() else None


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







				'''simple docstring'''





				__lowercase   =							MobileNetVaImageProcessingTester(self       )


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







				'''simple docstring'''





				return self.image_processor_tester.prepare_image_processor_dict()


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







				'''simple docstring'''





				__lowercase   =							self.image_processing_class(**self.image_processor_dict       )
				self.assertTrue(hasattr(_lowerCamelCase   ,'''do_resize'''       )       )
				self.assertTrue(hasattr(_lowerCamelCase   ,'''size'''       )       )
				self.assertTrue(hasattr(_lowerCamelCase   ,'''do_center_crop'''       )       )
				self.assertTrue(hasattr(_lowerCamelCase   ,'''center_crop'''       )       )


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







				'''simple docstring'''





				__lowercase   =							self.image_processing_class.from_dict(self.image_processor_dict       )
				self.assertEqual(image_processor.size   ,{'''shortest_edge''': 20}       )
				self.assertEqual(image_processor.crop_size   ,{'''height''': 18, '''width''': 18}       )

				__lowercase   =							self.image_processing_class.from_dict(self.image_processor_dict   ,size=42   ,crop_size=84       )
				self.assertEqual(image_processor.size   ,{'''shortest_edge''': 42}       )
				self.assertEqual(image_processor.crop_size   ,{'''height''': 84, '''width''': 84}       )


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







				'''simple docstring'''





				pass


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







				'''simple docstring'''





				__lowercase   =							self.image_processing_class(**self.image_processor_dict       )
				# create random PIL images
				__lowercase   =							prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_lowerCamelCase       )
				for image in image_inputs:
							self.assertIsInstance(_lowerCamelCase   ,Image.Image       )

				# Test not batched input
				__lowercase   =							image_processing(image_inputs[0]   ,return_tensors='''pt'''       ).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,)

				# Test batched
				__lowercase   =							image_processing(_lowerCamelCase   ,return_tensors='''pt'''       ).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,)


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				__lowercase   =							self.image_processing_class(**self.image_processor_dict       )
				# create random numpy tensors
				__lowercase   =							prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_lowerCamelCase   ,numpify=_lowerCamelCase       )
				for image in image_inputs:
							self.assertIsInstance(_lowerCamelCase   ,np.ndarray       )

				# Test not batched input
				__lowercase   =							image_processing(image_inputs[0]   ,return_tensors='''pt'''       ).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,)

				# Test batched
				__lowercase   =							image_processing(_lowerCamelCase   ,return_tensors='''pt'''       ).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,)


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







				'''simple docstring'''





				__lowercase   =							self.image_processing_class(**self.image_processor_dict       )
				# create random PyTorch tensors
				__lowercase   =							prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_lowerCamelCase   ,torchify=_lowerCamelCase       )
				for image in image_inputs:
							self.assertIsInstance(_lowerCamelCase   ,torch.Tensor       )

				# Test not batched input
				__lowercase   =							image_processing(image_inputs[0]   ,return_tensors='''pt'''       ).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,)

				# Test batched
				__lowercase   =							image_processing(_lowerCamelCase   ,return_tensors='''pt'''       ).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )   ,)












'''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       )





