
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,
)


lowercase        =  logging.get_logger(__name__)

lowercase        =  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'''),
    ]
)

lowercase        =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str       )  ->    Dict:
							for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
														if class_name in extractors:
																					lowerCamelCase_							  =					model_type_to_module_name(UpperCAmelCase__       )

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

							for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
														if getattr(UpperCAmelCase__			,				"""__name__"""			,				UpperCAmelCase__       ) == 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.
							lowerCamelCase_							  =					importlib.import_module("""transformers"""       )
							if hasattr(UpperCAmelCase__			,				UpperCAmelCase__       ):
														return getattr(UpperCAmelCase__			,				UpperCAmelCase__       )

							return None



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, os.PathLike]			,				UpperCAmelCase__				:      Optional[Union[str, os.PathLike]] = None			,				UpperCAmelCase__				:      bool = False			,				UpperCAmelCase__				:      bool = False			,				UpperCAmelCase__				:      Optional[Dict[str, str]] = None			,				UpperCAmelCase__				:      Optional[Union[bool, str]] = None			,				UpperCAmelCase__				:      Optional[str] = None			,				UpperCAmelCase__				:      bool = False			,				**UpperCAmelCase__				:      Dict			,				)  ->    Optional[Any]:
							lowerCamelCase_							  =					get_file_from_repo(
							    UpperCAmelCase__			,				UpperCAmelCase__			,				cache_dir=UpperCAmelCase__			,				force_download=UpperCAmelCase__			,				resume_download=UpperCAmelCase__			,				proxies=UpperCAmelCase__			,				use_auth_token=UpperCAmelCase__			,				revision=UpperCAmelCase__			,				local_files_only=UpperCAmelCase__			,				)
							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(UpperCAmelCase__			,				encoding="""utf-8"""       ) as reader:
														return json.load(UpperCAmelCase__       )





class       __A:

					def __init__(	self					:     List[str]   ):
												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(__UpperCamelCase   )
					def    lowercase__      (	cls					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ,					**__UpperCamelCase					:     Optional[Any]   ):
												lowerCamelCase_							  =					kwargs.pop("""config"""   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					kwargs.pop("""trust_remote_code"""   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					True

												lowerCamelCase_  ,   lowerCamelCase_							  =					ImageProcessingMixin.get_image_processor_dict(__UpperCamelCase   ,					**__UpperCamelCase   )
												lowerCamelCase_							  =					config_dict.get("""image_processor_type"""   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					None
												if "AutoImageProcessor" in config_dict.get("""auto_map"""   ,					{}   ):
																			lowerCamelCase_							  =					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:
																			lowerCamelCase_							  =					config_dict.pop("""feature_extractor_type"""   ,					__UpperCamelCase   )
																			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."""   )
																										lowerCamelCase_							  =					feature_extractor_class.replace("""FeatureExtractor"""   ,					"""ImageProcessor"""   )
																			if "AutoFeatureExtractor" in config_dict.get("""auto_map"""   ,					{}   ):
																										lowerCamelCase_							  =					config_dict["""auto_map"""]["""AutoFeatureExtractor"""]
																										lowerCamelCase_							  =					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(__UpperCamelCase   ,					__UpperCamelCase   ):
																										lowerCamelCase_							  =					AutoConfig.from_pretrained(__UpperCamelCase   ,					**__UpperCamelCase   )
																			# It could be in `config.image_processor_type``
																			lowerCamelCase_							  =					getattr(__UpperCamelCase   ,					"""image_processor_type"""   ,					__UpperCamelCase   )
																			if hasattr(__UpperCamelCase   ,					"""auto_map"""   ) and "AutoImageProcessor" in config.auto_map:
																										lowerCamelCase_							  =					config.auto_map["""AutoImageProcessor"""]

												if image_processor_class is not None:
																			lowerCamelCase_							  =					image_processor_class_from_name(__UpperCamelCase   )

												lowerCamelCase_							  =					image_processor_auto_map is not None
												lowerCamelCase_							  =					image_processor_class is not None or type(__UpperCamelCase   ) in IMAGE_PROCESSOR_MAPPING
												lowerCamelCase_							  =					resolve_trust_remote_code(
												    __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

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

												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    lowercase__      (	__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Tuple   ):
												IMAGE_PROCESSOR_MAPPING.register(__UpperCamelCase   ,					__UpperCamelCase   )







import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:





		class       __A:

							@staticmethod
							def    lowercase__      (	*__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     Dict   ):
														pass



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Image       )  ->    str:
							lowerCamelCase_							  =					hashlib.mda(image.tobytes()       )
							return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       __A(				unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					DepthEstimationPipeline(model=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
												self.assertEqual({"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )}   ,					__UpperCamelCase   )
												import datasets

												lowerCamelCase_							  =					datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,					"""image"""   ,					split="""test"""   )
												lowerCamelCase_							  =					depth_estimator(
												    [
												        Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   ),
												        """http://images.cocodataset.org/val2017/000000039769.jpg""",
												        # RGBA
												        dataset[0]["""file"""],
												        # LA
												        dataset[1]["""file"""],
												        # L
												        dataset[2]["""file"""],
												    ]   )
												self.assertEqual(
												    [
												        {"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )},
												        {"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )},
												        {"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )},
												        {"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )},
												        {"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )},
												    ]   ,					__UpperCamelCase   ,					)

					@require_tf
					@unittest.skip("""Depth estimation is not implemented in TF"""   )
					def    lowercase__      (	self					:     Optional[int]   ):
												pass

					@slow
					@require_torch
					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					"""Intel/dpt-large"""
												lowerCamelCase_							  =					pipeline("""depth-estimation"""   ,					model=__UpperCamelCase   )
												lowerCamelCase_							  =					depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""   )
												lowerCamelCase_							  =					hashimage(outputs["""depth"""]   )

												# This seems flaky.
												# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
												self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item()   )   ,					29.304   )
												self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()   )   ,					2.662   )



					@require_torch
					def    lowercase__      (	self					:     Dict   ):
												# This is highly irregular to have no small tests.
												self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""   )





