


'''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,
)


lowercase							=logging.get_logger(__name__)

lowercase							=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'),
    ]
)

lowercase							=_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)





def lowerCamelCase__      (						__lowerCamelCase	:							List[Any]							):







			'''simple docstring'''




			for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
						if class_name in extractors:
									_UpperCAmelCase       :       Union[str, Any]							   =model_type_to_module_name(__lowerCamelCase							)

									_UpperCAmelCase       :       int							   =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.
			_UpperCAmelCase       :       Any							   =importlib.import_module('transformers'							)
			if hasattr(__lowerCamelCase	,   __lowerCamelCase							):
						return getattr(__lowerCamelCase	,   __lowerCamelCase							)

			return None





def lowerCamelCase__      (						__lowerCamelCase	:							Dict	,   __lowerCamelCase	:							Optional[int] = None	,   __lowerCamelCase	:							str = False	,   __lowerCamelCase	:							Dict = False	,   __lowerCamelCase	:							Any = None	,   __lowerCamelCase	:							int = None	,   __lowerCamelCase	:							Optional[Any] = None	,   __lowerCamelCase	:							List[str] = False	,   **__lowerCamelCase	:							Union[str, Any]	,   ):







			'''simple docstring'''




			_UpperCAmelCase       :       List[str]							   =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  __magic_name__							:






							def __init__(      self)						->  Tuple:




										'''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(__SCREAMING_SNAKE_CASE)
							def       lowerCAmelCase		(      cls  ,							snake_case  ,							**snake_case)						->  Tuple:




										'''simple docstring'''
										_UpperCAmelCase       :       Union[str, Any]							   =kwargs.pop('config'  ,							__SCREAMING_SNAKE_CASE)
										_UpperCAmelCase       :       List[Any]							   =kwargs.pop('trust_remote_code'  ,							__SCREAMING_SNAKE_CASE)
										_UpperCAmelCase       :       Optional[Any]							   =True

										_UpperCAmelCase   ,	_UpperCAmelCase       :       Dict							   =ImageProcessingMixin.get_image_processor_dict(__SCREAMING_SNAKE_CASE  ,							**__SCREAMING_SNAKE_CASE)
										_UpperCAmelCase       :       Optional[Any]							   =config_dict.get('image_processor_type'  ,							__SCREAMING_SNAKE_CASE)
										_UpperCAmelCase       :       int							   =None
										if "AutoImageProcessor" in config_dict.get('auto_map'  ,							{}):
													_UpperCAmelCase       :       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:
													_UpperCAmelCase       :       Union[str, Any]							   =config_dict.pop('feature_extractor_type'  ,							__SCREAMING_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.')
																_UpperCAmelCase       :       Tuple							   =feature_extractor_class.replace('FeatureExtractor'  ,							'ImageProcessor')
													if "AutoFeatureExtractor" in config_dict.get('auto_map'  ,							{}):
																_UpperCAmelCase       :       Any							   =config_dict['auto_map']['AutoFeatureExtractor']
																_UpperCAmelCase       :       str							   =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(__SCREAMING_SNAKE_CASE  ,							__SCREAMING_SNAKE_CASE):
																_UpperCAmelCase       :       Optional[int]							   =AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE  ,							**__SCREAMING_SNAKE_CASE)
													# It could be in `config.image_processor_type``
													_UpperCAmelCase       :       Dict							   =getattr(__SCREAMING_SNAKE_CASE  ,							'image_processor_type'  ,							__SCREAMING_SNAKE_CASE)
													if hasattr(__SCREAMING_SNAKE_CASE  ,							'auto_map') and "AutoImageProcessor" in config.auto_map:
																_UpperCAmelCase       :       str							   =config.auto_map['AutoImageProcessor']

										if image_processor_class is not None:
													_UpperCAmelCase       :       Tuple							   =image_processor_class_from_name(__SCREAMING_SNAKE_CASE)

										_UpperCAmelCase       :       str							   =image_processor_auto_map is not None
										_UpperCAmelCase       :       Optional[int]							   =image_processor_class is not None or type(__SCREAMING_SNAKE_CASE) in IMAGE_PROCESSOR_MAPPING
										_UpperCAmelCase       :       Optional[int]							   =resolve_trust_remote_code(
										    __SCREAMING_SNAKE_CASE  ,							__SCREAMING_SNAKE_CASE  ,							__SCREAMING_SNAKE_CASE  ,							__SCREAMING_SNAKE_CASE)

										if has_remote_code and trust_remote_code:
													_UpperCAmelCase       :       Dict							   =get_class_from_dynamic_module(
													    __SCREAMING_SNAKE_CASE  ,							__SCREAMING_SNAKE_CASE  ,							**__SCREAMING_SNAKE_CASE)
													_UpperCAmelCase       :       Tuple							   =kwargs.pop('code_revision'  ,							__SCREAMING_SNAKE_CASE)
													if os.path.isdir(__SCREAMING_SNAKE_CASE):
																image_processor_class.register_for_auto_class()
													return image_processor_class.from_dict(__SCREAMING_SNAKE_CASE  ,							**__SCREAMING_SNAKE_CASE)
										elif image_processor_class is not None:
													return image_processor_class.from_dict(__SCREAMING_SNAKE_CASE  ,							**__SCREAMING_SNAKE_CASE)
										# Last try: we use the IMAGE_PROCESSOR_MAPPING.
										elif type(__SCREAMING_SNAKE_CASE) in IMAGE_PROCESSOR_MAPPING:
													_UpperCAmelCase       :       Optional[Any]							   =IMAGE_PROCESSOR_MAPPING[type(__SCREAMING_SNAKE_CASE)]
													return image_processor_class.from_dict(__SCREAMING_SNAKE_CASE  ,							**__SCREAMING_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       lowerCAmelCase		(      snake_case  ,							snake_case)						->  List[str]:




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





'''simple docstring'''






from __future__ import annotations

from random import choice





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[int]							):







   '''simple docstring'''




   return choice(__lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =random_pivot(__lowerCamelCase							)

   # partition based on pivot
   # linear time
   _UpperCAmelCase       :       str							   =[e for e in lst if e < pivot]
   _UpperCAmelCase       :       Dict							   =[e for e in lst if e > pivot]

   # if we get lucky, pivot might be the element we want.
   # we can easily see this:
   # small (elements smaller than k)
   # + pivot (kth element)
   # + big (elements larger than k)
   if len(__lowerCamelCase							) == k - 1:
      return pivot
   # pivot is in elements bigger than k
   elif len(__lowerCamelCase							) < k - 1:
      return kth_number(__lowerCamelCase	,   k - len(__lowerCamelCase							) - 1							)
   # pivot is in elements smaller than k
   else:
      return kth_number(__lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

