
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,
)


_UpperCAmelCase :			str              =	logging.get_logger(__name__)

_UpperCAmelCase :			Union[str, Any]              =	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"),
    ]
)

_UpperCAmelCase :			int              =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)


def 				A	( lowercase			)       ->  Any:
		'''simple docstring'''

		for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
				if class_name in extractors:
						UpperCamelCase          =					model_type_to_module_name(lowercase			)

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

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

		return None


def 				A	( lowercase   ,	lowercase = None   ,	lowercase = False   ,	lowercase = False   ,	lowercase = None   ,	lowercase = None   ,	lowercase = None   ,	lowercase = False   ,	**lowercase   ,	)       ->  List[str]:
		'''simple docstring'''

		UpperCamelCase          =					get_file_from_repo(
		    lowercase   ,	lowercase   ,	cache_dir=lowercase   ,	force_download=lowercase   ,	resume_download=lowercase   ,	proxies=lowercase   ,	use_auth_token=lowercase   ,	revision=lowercase   ,	local_files_only=lowercase   ,	)
		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(lowercase   ,	encoding='utf-8'			) as reader:
				return json.load(lowercase			)


class     lowercase :



		def __init__(			self	)  -> Union[str, Any]:
				"""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_	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					kwargs.pop('config'    ,				A_	)
				UpperCamelCase          =					kwargs.pop('trust_remote_code'    ,				A_	)
				UpperCamelCase          =					True

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

				if image_processor_class is not None:
						UpperCamelCase          =					image_processor_class_from_name(A_	)

				UpperCamelCase          =					image_processor_auto_map is not None
				UpperCamelCase          =					image_processor_class is not None or type(A_	) in IMAGE_PROCESSOR_MAPPING
				UpperCamelCase          =					resolve_trust_remote_code(
				    A_    ,				A_    ,				A_    ,				A_	)

				if has_remote_code and trust_remote_code:
						UpperCamelCase          =					get_class_from_dynamic_module(
						    A_    ,				A_    ,				**A_	)
						UpperCamelCase          =					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          =					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_	)  -> str:
				"""simple docstring"""
				IMAGE_PROCESSOR_MAPPING.register(A_    ,				A_	)


import pprint

import requests

_UpperCAmelCase :			Union[str, Any]              =	"https://zenquotes.io/api"


def 				A	( )       ->  list:
		'''simple docstring'''

		return requests.get(API_ENDPOINT_URL + '/today'			).json()


def 				A	( )       ->  list:
		'''simple docstring'''

		return requests.get(API_ENDPOINT_URL + '/random'			).json()


if __name__ == "__main__":
					_UpperCAmelCase :			str              =	random_quotes()
					pprint.pprint(response)
