
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,
)


__A							=logging.get_logger(__name__)

__A							=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'''),
    ]
)

__A							=_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)





def      lowerCamelCase_					(							lowerCamelCase__       ):
				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												lowerCamelCase_         =     model_type_to_module_name(lowerCamelCase__       )

												lowerCamelCase_         =     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.
				lowerCamelCase_         =     importlib.import_module("transformers"       )
				if hasattr(lowerCamelCase__      ,					lowerCamelCase__       ):
								return getattr(lowerCamelCase__      ,					lowerCamelCase__       )

				return None





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__ = None      ,					lowerCamelCase__ = False      ,					lowerCamelCase__ = False      ,					lowerCamelCase__ = None      ,					lowerCamelCase__ = None      ,					lowerCamelCase__ = None      ,					lowerCamelCase__ = False      ,					**lowerCamelCase__      ,					):
				lowerCamelCase_         =     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        _SCREAMING_SNAKE_CASE	:



	def __init__(       self					)							->			Optional[Any]:
					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(lowercase					)
	def        SCREAMING_SNAKE_CASE_(       cls		,    lowercase		,    **lowercase					)							->			List[str]:
					lowerCamelCase_         =     kwargs.pop("config"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("trust_remote_code"		,    lowercase					)
					lowerCamelCase_         =     True

					lowerCamelCase_      ,       lowerCamelCase_         =     ImageProcessingMixin.get_image_processor_dict(lowercase		,    **lowercase					)
					lowerCamelCase_         =     config_dict.get("image_processor_type"		,    lowercase					)
					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"		,    lowercase					)
									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(lowercase		,    lowercase					):
													lowerCamelCase_         =     AutoConfig.from_pretrained(lowercase		,    **lowercase					)
									# It could be in `config.image_processor_type``
									lowerCamelCase_         =     getattr(lowercase		,    "image_processor_type"		,    lowercase					)
									if hasattr(lowercase		,    "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(lowercase					)

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

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

					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        SCREAMING_SNAKE_CASE_(       lowercase		,    lowercase					)							->			str:
					IMAGE_PROCESSOR_MAPPING.register(lowercase		,    lowercase					)




import math





def      lowerCamelCase_					(							lowerCamelCase__       ):

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5      ,					int(math.sqrt(lowerCamelCase__       ) + 1       )      ,					6       ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True





def      lowerCamelCase_					(							lowerCamelCase__ = 1_0_0_0_1       ):

				try:
								lowerCamelCase_         =     int(lowerCamelCase__       )
				except (TypeError, ValueError):
								raise TypeError("Parameter nth must be int or castable to int."       ) from None
				if nth <= 0:
								raise ValueError("Parameter nth must be greater than or equal to one."       )
				lowerCamelCase_         =     []
				lowerCamelCase_         =     2
				while len(lowerCamelCase__       ) < nth:
								if is_prime(lowerCamelCase__       ):
												primes.append(lowerCamelCase__       )
												num += 1
								else:
												num += 1
				return primes[len(lowerCamelCase__       ) - 1]


if __name__ == "__main__":
			print(F"""{solution() = }""")


