


'''simple docstring'''
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,
)


lowerCAmelCase   : Tuple             =				logging.get_logger(__name__)

lowerCAmelCase   : str             =				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"""),
    ]
)

lowerCAmelCase   : List[str]             =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)




def 	_A  (       A							)  ->				Dict:
	for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
		if class_name in extractors:
			lowercase						:		int   					=		model_type_to_module_name(A							)

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

	for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
		if getattr(A							,"__name__"							,A							) == 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.
	lowercase						:		List[str]   					=		importlib.import_module("transformers"							)
	if hasattr(A							,A							):
		return getattr(A							,A							)

	return None




def 	_A  (       A							,A = None							,A = False							,A = False							,A = None							,A = None							,A = None							,A = False							,**A							,)  ->				Optional[int]:
	lowercase						:		Any   					=		get_file_from_repo(
	    A							,A							,cache_dir=A							,force_download=A							,resume_download=A							,proxies=A							,use_auth_token=A							,revision=A							,local_files_only=A							,)
	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(A							,encoding="utf-8"							) as reader:
		return json.load(A							)






class        _UpperCamelCase	:


				'''simple docstring'''


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

					lowercase					,		lowercase						:		List[str]   					=		ImageProcessingMixin.get_image_processor_dict(a_							,      **a_							)
					lowercase						:		Tuple   					=		config_dict.get("image_processor_type"							,      a_							)
					lowercase						:		List[str]   					=		None
					if "AutoImageProcessor" in config_dict.get("auto_map"							,      {}							):
						lowercase						:		List[Any]   					=		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:
						lowercase						:		List[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."							)
							lowercase						:		Optional[Any]   					=		feature_extractor_class.replace("FeatureExtractor"							,      "ImageProcessor"							)
						if "AutoFeatureExtractor" in config_dict.get("auto_map"							,      {}							):
							lowercase						:		Optional[Any]   					=		config_dict["auto_map"]["AutoFeatureExtractor"]
							lowercase						:		Dict   					=		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_							):
							lowercase						:		Dict   					=		AutoConfig.from_pretrained(a_							,      **a_							)
						# It could be in `config.image_processor_type``
						lowercase						:		List[str]   					=		getattr(a_							,      "image_processor_type"							,      a_							)
						if hasattr(a_							,      "auto_map"							) and "AutoImageProcessor" in config.auto_map:
							lowercase						:		List[str]   					=		config.auto_map["AutoImageProcessor"]

					if image_processor_class is not None:
						lowercase						:		Any   					=		image_processor_class_from_name(a_							)

					lowercase						:		Union[str, Any]   					=		image_processor_auto_map is not None
					lowercase						:		Dict   					=		image_processor_class is not None or type(a_							) in IMAGE_PROCESSOR_MAPPING
					lowercase						:		Dict   					=		resolve_trust_remote_code(
					    a_							,      a_							,      a_							,      a_							)

					if has_remote_code and trust_remote_code:
						lowercase						:		Tuple   					=		get_class_from_dynamic_module(
						    a_							,      a_							,      **a_							)
						lowercase						:		List[str]   					=		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:
						lowercase						:		List[Any]   					=		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 	a__		(     a_							,      a_							)    ->  Any:
					IMAGE_PROCESSOR_MAPPING.register(a_							,      a_							)






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

	if len(A							) <= 1:
		return [tuple(A							)]

	lowercase						:		Dict   					=		[]

	def generate(A							,A							):
		lowercase						:		List[Any]   					=		[0] * n
		res.append(tuple(A							)							)

		lowercase						:		Tuple   					=		0
		while i < n:
			if c[i] < i:
				if i % 2 == 0:
					lowercase					,		lowercase						:		List[Any]   					=		arr[i], arr[0]
				else:
					lowercase					,		lowercase						:		Optional[Any]   					=		arr[i], arr[c[i]]
				res.append(tuple(A							)							)
				c[i] += 1
				lowercase						:		Optional[Any]   					=		0
			else:
				lowercase						:		Any   					=		0
				i += 1

	generate(len(A							)							,A							)
	return res


if __name__ == "__main__":
					lowerCAmelCase   : Any             =				input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase   : List[str]             =				[int(item) for item in user_input.split(""",""")]
					print(heaps(arr))


