





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    :					List[str] =      logging.get_logger(__name__)

A    :					Optional[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'''),
    ]
)

A    :					Tuple =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)







def    __lowerCamelCase					(							__a   :str    )		->				int:






		"""simple docstring"""

		for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
				if class_name in extractors:
						A__        =   model_type_to_module_name(__a    )

						A__        =   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.
		A__        =   importlib.import_module("""transformers"""    )
		if hasattr(__a    ,     __a    ):
				return getattr(__a    ,     __a    )

		return None







def    __lowerCamelCase					(							__a   :Union[str, os.PathLike]    ,     __a   :Optional[Union[str, os.PathLike]] = None    ,     __a   :bool = False    ,     __a   :bool = False    ,     __a   :Optional[Dict[str, str]] = None    ,     __a   :Optional[Union[bool, str]] = None    ,     __a   :Optional[str] = None    ,     __a   :bool = False    ,     **__a   :Tuple    ,     )		->				List[str]:






		"""simple docstring"""

		A__        =   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   A			:
			'''simple docstring'''




			def __init__(			self   :    int   )	->	Dict:







					"""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(__lowerCAmelCase   )
			def    a_       (			cls   :    str							,	__lowerCAmelCase   :    str							,	**__lowerCAmelCase   :    Dict   )	->	Any:







					"""simple docstring"""






					A__        =   kwargs.pop("""config"""							,	__lowerCAmelCase   )
					A__        =   kwargs.pop("""trust_remote_code"""							,	__lowerCAmelCase   )
					A__        =   True

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

					if image_processor_class is not None:
							A__        =   image_processor_class_from_name(__lowerCAmelCase   )

					A__        =   image_processor_auto_map is not None
					A__        =   image_processor_class is not None or type(__lowerCAmelCase   ) in IMAGE_PROCESSOR_MAPPING
					A__        =   resolve_trust_remote_code(
					    __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )

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

					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_       (			__lowerCAmelCase   :    Tuple							,	__lowerCAmelCase   :    str   )	->	Dict:







					"""simple docstring"""






					IMAGE_PROCESSOR_MAPPING.register(__lowerCAmelCase							,	__lowerCAmelCase   )










from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
A    :					Dict =      Lock()







def    __lowerCamelCase					(							__a   :Dict    ,     __a   :List[str]    ,     __a   :Optional[int]    ,     __a   :Optional[int]    ,     __a   :Optional[Any]    ,     __a   :Optional[int]    ,     __a   :int    )		->				Dict:






		"""simple docstring"""

		global process_lock

		# we perform n swaps since after n swaps we know we are sorted
		# we *could* stop early if we are sorted already, but it takes as long to
		# find out we are sorted as it does to sort the list with this algorithm
		for i in range(0    ,     1_0    ):
				if (i + position) % 2 == 0 and r_send is not None:
						# send your value to your right neighbor
						process_lock.acquire()
						r_send[1].send(__a    )
						process_lock.release()

						# receive your right neighbor's value
						process_lock.acquire()
						A__        =   rr_cv[0].recv()
						process_lock.release()

						# take the lower value since you are on the left
						A__        =   min(__a    ,     __a    )
				elif (i + position) % 2 != 0 and l_send is not None:
						# send your value to your left neighbor
						process_lock.acquire()
						l_send[1].send(__a    )
						process_lock.release()

						# receive your left neighbor's value
						process_lock.acquire()
						A__        =   lr_cv[0].recv()
						process_lock.release()

						# take the higher value since you are on the right
						A__        =   max(__a    ,     __a    )
    # after all swaps are performed, send the values back to main
		result_pipe[1].send(__a    )







def    __lowerCamelCase					(							__a   :List[str]    )		->				int:






		"""simple docstring"""

		A__        =   []
		A__        =   []
		# initialize the list of pipes where the values will be retrieved
		for _ in arr:
				result_pipe.append(Pipe()    )
		# creates the processes
		# the first and last process only have one neighbor so they are made outside
		# of the loop
		A__        =   Pipe()
		A__        =   Pipe()
		process_array_.append(
		    Process(
		        target=__a    ,     args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])    ,     )    )
		A__        =   temp_rs
		A__        =   temp_rr

		for i in range(1    ,     len(__a    ) - 1    ):
				A__        =   Pipe()
				A__        =   Pipe()
				process_array_.append(
				    Process(
				        target=__a    ,     args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])    ,     )    )
				A__        =   temp_rs
				A__        =   temp_rr

		process_array_.append(
		    Process(
		        target=__a    ,     args=(
		            len(__a    ) - 1,
		            arr[len(__a    ) - 1],
		            temp_ls,
		            None,
		            temp_lr,
		            None,
		            result_pipe[len(__a    ) - 1],
		        )    ,     )    )

		# start the processes
		for p in process_array_:
				p.start()

		# wait for the processes to end and write their values to the list
		for p in range(0    ,     len(__a    )    ):
				A__        =   result_pipe[p][0].recv()
				process_array_[p].join()
		return arr







def    __lowerCamelCase					(							)		->				str:






		"""simple docstring"""

		A__        =   list(range(1_0    ,     0    ,     -1    )    )
		print("""Initial List"""    )
		print(*__a    )
		A__        =   odd_even_transposition(__a    )
		print("""Sorted List\n"""    )
		print(*__a    )


if __name__ == "__main__":
					main()



