

import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
					import torch

					from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
					from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import MobileNetVaImageProcessor

class 			a					(		UpperCAmelCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      self.config_class(**self.inputs_dict		)
										self.parent.assertTrue(hasattr(A_  ,   "tf_padding"		)		)
										self.parent.assertTrue(hasattr(A_  ,   "depth_multiplier"		)		)







class 			a					:


						def __init__(    self  ,   A_  ,   A_=13  ,   A_=3  ,   A_=32  ,   A_=0.25  ,   A_=8  ,   A_=8  ,   A_=6  ,   A_=32  ,   A_=True  ,   A_=True  ,   A_=True  ,   A_="relu6"  ,   A_=1280  ,   A_=0.1  ,   A_=0.02  ,   A_=True  ,   A_=True  ,   A_=10  ,   A_=None  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      parent
										_UpperCAmelCase    :						Tuple							=      batch_size
										_UpperCAmelCase    :						Dict							=      num_channels
										_UpperCAmelCase    :						int							=      image_size
										_UpperCAmelCase    :						Union[str, Any]							=      depth_multiplier
										_UpperCAmelCase    :						Optional[int]							=      depth_divisible_by
										_UpperCAmelCase    :						Optional[Any]							=      min_depth
										_UpperCAmelCase    :						List[Any]							=      expand_ratio
										_UpperCAmelCase    :						List[Any]							=      tf_padding
										_UpperCAmelCase    :						List[str]							=      output_stride
										_UpperCAmelCase    :						List[Any]							=      first_layer_is_expansion
										_UpperCAmelCase    :						Tuple							=      finegrained_output
										_UpperCAmelCase    :						Any							=      hidden_act
										_UpperCAmelCase    :						List[Any]							=      last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier		)
										_UpperCAmelCase    :						Optional[int]							=      classifier_dropout_prob
										_UpperCAmelCase    :						List[Any]							=      use_labels
										_UpperCAmelCase    :						Tuple							=      is_training
										_UpperCAmelCase    :						List[str]							=      num_labels
										_UpperCAmelCase    :						int							=      initializer_range
										_UpperCAmelCase    :						Optional[Any]							=      scope


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

										_UpperCAmelCase    :						List[str]							=      None
										_UpperCAmelCase    :						Dict							=      None
										if self.use_labels:
														_UpperCAmelCase    :						str							=      ids_tensor([self.batch_size]  ,   self.num_labels		)
														_UpperCAmelCase    :						Tuple							=      ids_tensor([self.batch_size, self.image_size, self.image_size]  ,   self.num_labels		)

										_UpperCAmelCase    :						Any							=      self.get_config()

										return config, pixel_values, labels, pixel_labels


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return MobileNetVaConfig(
										    num_channels=self.num_channels  ,   image_size=self.image_size  ,   depth_multiplier=self.depth_multiplier  ,   depth_divisible_by=self.depth_divisible_by  ,   min_depth=self.min_depth  ,   expand_ratio=self.expand_ratio  ,   output_stride=self.output_stride  ,   first_layer_is_expansion=self.first_layer_is_expansion  ,   finegrained_output=self.finegrained_output  ,   hidden_act=self.hidden_act  ,   tf_padding=self.tf_padding  ,   classifier_dropout_prob=self.classifier_dropout_prob  ,   initializer_range=self.initializer_range  ,   )


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      MobileNetVaModel(config=A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase    :						Optional[int]							=      model(A_		)
										self.parent.assertEqual(
										    result.last_hidden_state.shape  ,   (
										        self.batch_size,
										        self.last_hidden_size,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )  ,   )
										self.parent.assertEqual(
										    result.pooler_output.shape  ,   (self.batch_size, self.last_hidden_size)  ,   )


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.num_labels
										_UpperCAmelCase    :						Optional[Any]							=      MobileNetVaForImageClassification(A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase    :						int							=      model(A_  ,   labels=A_		)
										self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.num_labels)		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.num_labels
										_UpperCAmelCase    :						int							=      MobileNetVaForSemanticSegmentation(A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase    :						Dict							=      model(A_		)
										self.parent.assertEqual(
										    result.logits.shape  ,   (
										        self.batch_size,
										        self.num_labels,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )  ,   )
										_UpperCAmelCase    :						List[str]							=      model(A_  ,   labels=A_		)
										self.parent.assertEqual(
										    result.logits.shape  ,   (
										        self.batch_size,
										        self.num_labels,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )  ,   )





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self.prepare_config_and_inputs()
										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[int]							=      config_and_inputs
										_UpperCAmelCase    :						Union[str, Any]							=      {"pixel_values": pixel_values}
										return config, inputs_dict







@require_torch
class 			a					(		UpperCAmelCase							,	UpperCAmelCase							,	unittest.TestCase			):
						_lowercase        =     (
						    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						_lowercase        =     (
						    {
						        "feature-extraction": MobileNetVaModel,
						        "image-classification": MobileNetVaForImageClassification,
						        "image-segmentation": MobileNetVaForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						_lowercase        =     False
						_lowercase        =     False
						_lowercase        =     False
						_lowercase        =     False


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      MobileNetVaModelTester(self		)
										_UpperCAmelCase    :						int							=      MobileNetVaConfigTester(self  ,   config_class=A_  ,   has_text_modality=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						@unittest.skip(reason="MobileNetV2 does not use inputs_embeds"		)
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						@unittest.skip(reason="MobileNetV2 does not support input and output embeddings"		)
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						@unittest.skip(reason="MobileNetV2 does not output attentions"		)
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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

										for model_class in self.all_model_classes:
														_UpperCAmelCase    :						Any							=      model_class(A_		)
														_UpperCAmelCase    :						Dict							=      inspect.signature(model.forward		)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_UpperCAmelCase    :						List[Any]							=      [*signature.parameters.keys()]

														_UpperCAmelCase    :						List[Any]							=      ["pixel_values"]
														self.assertListEqual(arg_names[:1]  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										def check_hidden_states_output(A_  ,   A_  ,   A_		):
														_UpperCAmelCase    :						Any							=      model_class(A_		)
														model.to(A_		)
														model.eval()

														with torch.no_grad():
																		_UpperCAmelCase    :						Union[str, Any]							=      model(**self._prepare_for_class(A_  ,   A_		)		)

														_UpperCAmelCase    :						str							=      outputs.hidden_states

														_UpperCAmelCase    :						Union[str, Any]							=      16
														self.assertEqual(len(A_		)  ,   A_		)

										_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[Any]							=      self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_UpperCAmelCase    :						str							=      True
														check_hidden_states_output(A_  ,   A_  ,   A_		)

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

														check_hidden_states_output(A_  ,   A_  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*A_		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase    :						Union[str, Any]							=      MobileNetVaModel.from_pretrained(A_		)
														self.assertIsNotNone(A_		)







def 							__SCREAMING_SNAKE_CASE							(			)				->    Dict:
				_UpperCAmelCase    :						Tuple							=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
				return image





@require_torch
@require_vision
class 			a					(		unittest.TestCase			):


						@cached_property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return (
										    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224"		) if is_vision_available() else None
										)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224"		).to(A_		)

										_UpperCAmelCase    :						List[str]							=      self.default_image_processor
										_UpperCAmelCase    :						Dict							=      prepare_img()
										_UpperCAmelCase    :						Optional[Any]							=      image_processor(images=A_  ,   return_tensors="pt"		).to(A_		)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase    :						Any							=      model(**A_		)

										# verify the logits
										_UpperCAmelCase    :						int							=      torch.Size((1, 1001)		)
										self.assertEqual(outputs.logits.shape  ,   A_		)

										_UpperCAmelCase    :						str							=      torch.tensor([0.24_45, -1.19_93, 0.19_05]		).to(A_		)

										self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,   A_  ,   atol=1e-4		)		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"		)
										_UpperCAmelCase    :						List[str]							=      model.to(A_		)

										_UpperCAmelCase    :						int							=      MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"		)

										_UpperCAmelCase    :						str							=      prepare_img()
										_UpperCAmelCase    :						Optional[int]							=      image_processor(images=A_  ,   return_tensors="pt"		).to(A_		)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase    :						int							=      model(**A_		)
										_UpperCAmelCase    :						Tuple							=      outputs.logits

										# verify the logits
										_UpperCAmelCase    :						str							=      torch.Size((1, 21, 65, 65)		)
										self.assertEqual(logits.shape  ,   A_		)

										_UpperCAmelCase    :						List[str]							=      torch.tensor(
										    [
										        [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
										        [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
										        [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
										    ]  ,   device=A_  ,   )

										self.assertTrue(torch.allclose(logits[0, :3, :3, :3]  ,   A_  ,   atol=1e-4		)		)





import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_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,
)


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = OrderedDict(
    [
        ('align', 'EfficientNetImageProcessor'),
        ('beit', 'BeitImageProcessor'),
        ('bit', 'BitImageProcessor'),
        ('blip', 'BlipImageProcessor'),
        ('blip-2', 'BlipImageProcessor'),
        ('bridgetower', 'BridgeTowerImageProcessor'),
        ('chinese_clip', 'ChineseCLIPImageProcessor'),
        ('clip', 'CLIPImageProcessor'),
        ('clipseg', 'ViTImageProcessor'),
        ('conditional_detr', 'ConditionalDetrImageProcessor'),
        ('convnext', 'ConvNextImageProcessor'),
        ('convnextv2', 'ConvNextImageProcessor'),
        ('cvt', 'ConvNextImageProcessor'),
        ('data2vec-vision', 'BeitImageProcessor'),
        ('deformable_detr', 'DeformableDetrImageProcessor'),
        ('deit', 'DeiTImageProcessor'),
        ('deta', 'DetaImageProcessor'),
        ('detr', 'DetrImageProcessor'),
        ('dinat', 'ViTImageProcessor'),
        ('donut-swin', 'DonutImageProcessor'),
        ('dpt', 'DPTImageProcessor'),
        ('efficientformer', 'EfficientFormerImageProcessor'),
        ('efficientnet', 'EfficientNetImageProcessor'),
        ('flava', 'FlavaImageProcessor'),
        ('focalnet', 'BitImageProcessor'),
        ('git', 'CLIPImageProcessor'),
        ('glpn', 'GLPNImageProcessor'),
        ('groupvit', 'CLIPImageProcessor'),
        ('imagegpt', 'ImageGPTImageProcessor'),
        ('instructblip', 'BlipImageProcessor'),
        ('layoutlmv2', 'LayoutLMv2ImageProcessor'),
        ('layoutlmv3', 'LayoutLMv3ImageProcessor'),
        ('levit', 'LevitImageProcessor'),
        ('mask2former', 'Mask2FormerImageProcessor'),
        ('maskformer', 'MaskFormerImageProcessor'),
        ('mgp-str', 'ViTImageProcessor'),
        ('mobilenet_v1', 'MobileNetV1ImageProcessor'),
        ('mobilenet_v2', 'MobileNetV2ImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevitv2', 'MobileViTImageProcessor'),
        ('nat', 'ViTImageProcessor'),
        ('oneformer', 'OneFormerImageProcessor'),
        ('owlvit', 'OwlViTImageProcessor'),
        ('perceiver', 'PerceiverImageProcessor'),
        ('pix2struct', 'Pix2StructImageProcessor'),
        ('poolformer', 'PoolFormerImageProcessor'),
        ('regnet', 'ConvNextImageProcessor'),
        ('resnet', 'ConvNextImageProcessor'),
        ('sam', 'SamImageProcessor'),
        ('segformer', 'SegformerImageProcessor'),
        ('swiftformer', 'ViTImageProcessor'),
        ('swin', 'ViTImageProcessor'),
        ('swin2sr', 'Swin2SRImageProcessor'),
        ('swinv2', 'ViTImageProcessor'),
        ('table-transformer', 'DetrImageProcessor'),
        ('timesformer', 'VideoMAEImageProcessor'),
        ('tvlt', 'TvltImageProcessor'),
        ('upernet', 'SegformerImageProcessor'),
        ('van', 'ConvNextImageProcessor'),
        ('videomae', 'VideoMAEImageProcessor'),
        ('vilt', 'ViltImageProcessor'),
        ('vit', 'ViTImageProcessor'),
        ('vit_hybrid', 'ViTHybridImageProcessor'),
        ('vit_mae', 'ViTImageProcessor'),
        ('vit_msn', 'ViTImageProcessor'),
        ('xclip', 'CLIPImageProcessor'),
        ('yolos', 'YolosImageProcessor'),
    ]
)

SCREAMING_SNAKE_CASE_            = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str	)				->    Tuple:
				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												_UpperCAmelCase    :						Union[str, Any]							=      model_type_to_module_name(lowerCAmelCase	)

												_UpperCAmelCase    :						Optional[int]							=      importlib.import_module(F'.{module_name}'					, "transformers.models"	)
												try:
																return getattr(lowerCAmelCase					, lowerCAmelCase	)
												except AttributeError:
																continue

				for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
								if getattr(lowerCAmelCase					, "__name__"					, lowerCAmelCase	) == class_name:
												return extractor

    # 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.
				_UpperCAmelCase    :						Any							=      importlib.import_module("transformers"	)
				if hasattr(lowerCAmelCase					, lowerCAmelCase	):
								return getattr(lowerCAmelCase					, lowerCAmelCase	)

				return None

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Union[str, os.PathLike]					, lowerCAmelCase:					Optional[Union[str, os.PathLike]] = None					, lowerCAmelCase:					bool = False					, lowerCAmelCase:					bool = False					, lowerCAmelCase:					Optional[Dict[str, str]] = None					, lowerCAmelCase:					Optional[Union[bool, str]] = None					, lowerCAmelCase:					Optional[str] = None					, lowerCAmelCase:					bool = False					, **lowerCAmelCase:					List[Any]					, )				->    Any:
				_UpperCAmelCase    :						List[Any]							=      get_file_from_repo(
				    lowerCAmelCase					, lowerCAmelCase					, cache_dir=lowerCAmelCase					, force_download=lowerCAmelCase					, resume_download=lowerCAmelCase					, proxies=lowerCAmelCase					, use_auth_token=lowerCAmelCase					, revision=lowerCAmelCase					, local_files_only=lowerCAmelCase					, )
				if resolved_config_file is None:
								logger.info(
								    "Could not locate the image processor configuration file, will try to use the model config instead."	)
								return {}

				with open(lowerCAmelCase					, encoding="utf-8"	) as reader:
								return json.load(lowerCAmelCase	)





class 			a					:


						def __init__(    self		):







										'''simple docstring'''


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


						@classmethod
						@replace_list_option_in_docstrings(A_		)
						def     _UpperCAmelCase			(    cls  ,   A_  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      kwargs.pop("config"  ,   A_		)
										_UpperCAmelCase    :						Dict							=      kwargs.pop("trust_remote_code"  ,   A_		)
										_UpperCAmelCase    :						str							=      True

										_UpperCAmelCase						,							_UpperCAmelCase    :						Tuple							=      ImageProcessingMixin.get_image_processor_dict(A_  ,   **A_		)
										_UpperCAmelCase    :						Any							=      config_dict.get("image_processor_type"  ,   A_		)
										_UpperCAmelCase    :						str							=      None
										if "AutoImageProcessor" in config_dict.get("auto_map"  ,   {}		):
														_UpperCAmelCase    :						int							=      config_dict["auto_map"]["AutoImageProcessor"]

										# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
										# and if so, infer the image processor class from there.
										if image_processor_class is None and image_processor_auto_map is None:
														_UpperCAmelCase    :						Any							=      config_dict.pop("feature_extractor_type"  ,   A_		)
														if feature_extractor_class is not None:
																		logger.warning(
																		    "Could not find image processor class in the image processor config or the model config. Loading"
																		    " based on pattern matching with the model's feature extractor configuration."		)
																		_UpperCAmelCase    :						Optional[Any]							=      feature_extractor_class.replace("FeatureExtractor"  ,   "ImageProcessor"		)
														if "AutoFeatureExtractor" in config_dict.get("auto_map"  ,   {}		):
																		_UpperCAmelCase    :						Optional[Any]							=      config_dict["auto_map"]["AutoFeatureExtractor"]
																		_UpperCAmelCase    :						List[Any]							=      feature_extractor_auto_map.replace("FeatureExtractor"  ,   "ImageProcessor"		)
																		logger.warning(
																		    "Could not find image processor auto map in the image processor config or the model config."
																		    " Loading based on pattern matching with the model's feature extractor configuration."		)

        # If we don't find the image processor class in the image processor config, let's try the model config.
										if image_processor_class is None and image_processor_auto_map is None:
														if not isinstance(A_  ,   A_		):
																		_UpperCAmelCase    :						Any							=      AutoConfig.from_pretrained(A_  ,   **A_		)
														# It could be in `config.image_processor_type``
														_UpperCAmelCase    :						Optional[Any]							=      getattr(A_  ,   "image_processor_type"  ,   A_		)
														if hasattr(A_  ,   "auto_map"		) and "AutoImageProcessor" in config.auto_map:
																		_UpperCAmelCase    :						Any							=      config.auto_map["AutoImageProcessor"]

										if image_processor_class is not None:
														_UpperCAmelCase    :						List[str]							=      image_processor_class_from_name(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      image_processor_auto_map is not None
										_UpperCAmelCase    :						Any							=      image_processor_class is not None or type(A_		) in IMAGE_PROCESSOR_MAPPING
										_UpperCAmelCase    :						List[Any]							=      resolve_trust_remote_code(
										    A_  ,   A_  ,   A_  ,   A_		)

										if has_remote_code and trust_remote_code:
														_UpperCAmelCase    :						Optional[int]							=      get_class_from_dynamic_module(
														    A_  ,   A_  ,   **A_		)
														_UpperCAmelCase    :						Optional[int]							=      kwargs.pop("code_revision"  ,   A_		)
														if os.path.isdir(A_		):
																		image_processor_class.register_for_auto_class()
														return image_processor_class.from_dict(A_  ,   **A_		)
										elif image_processor_class is not None:
														return image_processor_class.from_dict(A_  ,   **A_		)
										# Last try: we use the IMAGE_PROCESSOR_MAPPING.
										elif type(A_		) in IMAGE_PROCESSOR_MAPPING:
														_UpperCAmelCase    :						Optional[int]							=      IMAGE_PROCESSOR_MAPPING[type(A_		)]
														return image_processor_class.from_dict(A_  ,   **A_		)

										raise ValueError(
										    f'Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '
										    f'`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '
										    f'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()		)}'		)





						@staticmethod
						def     _UpperCAmelCase			(    A_  ,   A_		):







										'''simple docstring'''


										IMAGE_PROCESSOR_MAPPING.register(A_  ,   A_		)


