

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					:			Union[str, Any]  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			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"),
    ]
)

_lowerCAmelCase					:			List[Any]  				=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






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

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

			for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
						if getattr(_snake_case       ,     '__name__'       ,     _snake_case	) == 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(_snake_case       ,     _snake_case	):
						return getattr(_snake_case       ,     _snake_case	)

			return None






def        UpperCamelCase_(							_snake_case   :  Union[str, os.PathLike]       ,     _snake_case   :  Optional[Union[str, os.PathLike]] = None       ,     _snake_case   :  bool = False       ,     _snake_case   :  bool = False       ,     _snake_case   :  Optional[Dict[str, str]] = None       ,     _snake_case   :  Optional[Union[bool, str]] = None       ,     _snake_case   :  Optional[str] = None       ,     _snake_case   :  bool = False       ,     **_snake_case   :  List[str]       ,     ):




			"""simple docstring"""






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






class 	__magic_name__    :





			def __init__(		self					)   -> 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(__snake_case					)
			def 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> str:




						'''simple docstring'''
						__a        =kwargs.pop('config'					,  __snake_case					)
						__a        =kwargs.pop('trust_remote_code'					,  __snake_case					)
						__a        =True

						__a      ,    __a        =ImageProcessingMixin.get_image_processor_dict(__snake_case					,  **__snake_case					)
						__a        =config_dict.get('image_processor_type'					,  __snake_case					)
						__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'					,  __snake_case					)
									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(__snake_case					,  __snake_case					):
												__a        =AutoConfig.from_pretrained(__snake_case					,  **__snake_case					)
									# It could be in `config.image_processor_type``
									__a        =getattr(__snake_case					,  'image_processor_type'					,  __snake_case					)
									if hasattr(__snake_case					,  '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(__snake_case					)

						__a        =image_processor_auto_map is not None
						__a        =image_processor_class is not None or type(__snake_case					) in IMAGE_PROCESSOR_MAPPING
						__a        =resolve_trust_remote_code(
						    __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					)

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

						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 		__magic_name__    (		__snake_case					,  __snake_case					)   -> Any:




						'''simple docstring'''
						IMAGE_PROCESSOR_MAPPING.register(__snake_case					,  __snake_case					)









from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
_lowerCAmelCase					:			Optional[Any]  				=			Lock()






def        UpperCamelCase_(							_snake_case   :  List[str]       ,     _snake_case   :  Optional[int]       ,     _snake_case   :  Dict       ,     _snake_case   :  Any       ,     _snake_case   :  List[str]       ,     _snake_case   :  Tuple       ,     _snake_case   :  List[str]	):




			"""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       ,     10	):
						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(_snake_case	)
									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(_snake_case       ,     _snake_case	)
						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(_snake_case	)
									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(_snake_case       ,     _snake_case	)
    # after all swaps are performed, send the values back to main
			result_pipe[1].send(_snake_case	)






def        UpperCamelCase_(							_snake_case   :  List[str]	):




			"""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=_snake_case       ,     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(_snake_case	) - 1	):
						__a        =Pipe()
						__a        =Pipe()
						process_array_.append(
						    Process(
						        target=_snake_case       ,     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=_snake_case       ,     args=(
			            len(_snake_case	) - 1,
			            arr[len(_snake_case	) - 1],
			            temp_ls,
			            None,
			            temp_lr,
			            None,
			            result_pipe[len(_snake_case	) - 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(_snake_case	)	):
						__a        =result_pipe[p][0].recv()
						process_array_[p].join()
			return arr






def        UpperCamelCase_(							):




			"""simple docstring"""






			__a        =list(range(10       ,     0       ,     -1	)	)
			print('Initial List'	)
			print(*_snake_case	)
			__a        =odd_even_transposition(_snake_case	)
			print('Sorted List\n'	)
			print(*_snake_case	)


if __name__ == "__main__":
						main()






