


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


lowerCAmelCase	=							logging.get_logger(__name__)


class 	_a							(      UpperCamelCase__      ):






							"""simple docstring"""




							_lowercase     :      Any  				=			['''pixel_values''']

							def __init__(							self:     Any   ,						UpperCamelCase_:     bool = True   ,						UpperCamelCase_:     Dict[str, int] = None   ,						UpperCamelCase_:     int = 0.9   ,						UpperCamelCase_:     PILImageResampling = PILImageResampling.BICUBIC   ,						UpperCamelCase_:     bool = True   ,						UpperCamelCase_:     Dict[str, int] = None   ,						UpperCamelCase_:     Union[int, float] = 1 / 255   ,						UpperCamelCase_:     bool = True   ,						UpperCamelCase_:     bool = True   ,						UpperCamelCase_:     Optional[Union[float, List[float]]] = None   ,						UpperCamelCase_:     Optional[Union[float, List[float]]] = None   ,						**UpperCamelCase_:     Union[str, Any]   ,						)      ->       None:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_							)
									lowercase__						=				size if size is not None else {'''shortest_edge''': 224}
									lowercase__						=				get_size_dict(UpperCamelCase_   ,						default_to_square=UpperCamelCase_							)
									lowercase__						=				crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
									lowercase__						=				get_size_dict(UpperCamelCase_   ,						param_name='''crop_size'''							)

									lowercase__						=				do_resize
									lowercase__						=				size
									lowercase__						=				crop_pct
									lowercase__						=				resample
									lowercase__						=				do_center_crop
									lowercase__						=				crop_size
									lowercase__						=				do_rescale
									lowercase__						=				rescale_factor
									lowercase__						=				do_normalize
									lowercase__						=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									lowercase__						=				image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     np.ndarray   ,						UpperCamelCase_:     Dict[str, int]   ,						UpperCamelCase_:     Optional[float] = None   ,						UpperCamelCase_:     PILImageResampling = PILImageResampling.BICUBIC   ,						UpperCamelCase_:     Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase_:     List[str]   ,						)      ->       np.ndarray:





									"""simple docstring"""







									lowercase__						=				get_size_dict(UpperCamelCase_   ,						default_to_square=UpperCamelCase_							)
									if "shortest_edge" not in size and ("height" not in size or "width" not in size):
											raise ValueError(f'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'							)
									if crop_pct is not None:
											if "shortest_edge" in size:
													lowercase__						=				int(size['''shortest_edge'''] / crop_pct							)
											elif "height" in size and "width" in size:
													if size["height"] == size["width"]:
															lowercase__						=				int(size['''height'''] / crop_pct							)
													else:
															lowercase__						=				(int(size['''height'''] / crop_pct							), int(size['''width'''] / crop_pct							))
											else:
													raise ValueError('''Invalid size for resize: {}'''.format(UpperCamelCase_							)							)

											lowercase__						=				get_resize_output_image_size(UpperCamelCase_   ,						size=UpperCamelCase_   ,						default_to_square=UpperCamelCase_							)
									else:
											if "shortest_edge" in size:
													lowercase__						=				get_resize_output_image_size(UpperCamelCase_   ,						size=size['''shortest_edge''']   ,						default_to_square=UpperCamelCase_							)
											elif "height" in size and "width" in size:
													lowercase__						=				(size['''height'''], size['''width'''])
											else:
													raise ValueError('''Invalid size for resize: {}'''.format(UpperCamelCase_							)							)

									return resize(UpperCamelCase_   ,						size=UpperCamelCase_   ,						resample=UpperCamelCase_   ,						data_format=UpperCamelCase_   ,						**UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     np.ndarray   ,						UpperCamelCase_:     Dict[str, int]   ,						UpperCamelCase_:     Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase_:     Dict   ,						)      ->       np.ndarray:





									"""simple docstring"""







									lowercase__						=				get_size_dict(UpperCamelCase_							)
									if "height" not in size or "width" not in size:
											raise ValueError(f'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'							)
									return center_crop(UpperCamelCase_   ,						size=(size['''height'''], size['''width'''])   ,						data_format=UpperCamelCase_   ,						**UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     np.ndarray   ,						UpperCamelCase_:     Union[int, float]   ,						UpperCamelCase_:     Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase_:     List[str]   ,						)      ->       str:





									"""simple docstring"""







									return rescale(UpperCamelCase_   ,						scale=UpperCamelCase_   ,						data_format=UpperCamelCase_   ,						**UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     np.ndarray   ,						UpperCamelCase_:     Union[float, List[float]]   ,						UpperCamelCase_:     Union[float, List[float]]   ,						UpperCamelCase_:     Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase_:     Any   ,						)      ->       np.ndarray:





									"""simple docstring"""







									return normalize(UpperCamelCase_   ,						mean=UpperCamelCase_   ,						std=UpperCamelCase_   ,						data_format=UpperCamelCase_   ,						**UpperCamelCase_							)


							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     ImageInput   ,						UpperCamelCase_:     bool = None   ,						UpperCamelCase_:     Dict[str, int] = None   ,						UpperCamelCase_:     int = None   ,						UpperCamelCase_:     PILImageResampling = None   ,						UpperCamelCase_:     bool = None   ,						UpperCamelCase_:     Dict[str, int] = None   ,						UpperCamelCase_:     bool = None   ,						UpperCamelCase_:     float = None   ,						UpperCamelCase_:     bool = None   ,						UpperCamelCase_:     Optional[Union[float, List[float]]] = None   ,						UpperCamelCase_:     Optional[Union[float, List[float]]] = None   ,						UpperCamelCase_:     Optional[Union[str, TensorType]] = None   ,						UpperCamelCase_:     ChannelDimension = ChannelDimension.FIRST   ,						**UpperCamelCase_:     Optional[Any]   ,						)      ->       PIL.Image.Image:





									"""simple docstring"""







									lowercase__						=				do_resize if do_resize is not None else self.do_resize
									lowercase__						=				crop_pct if crop_pct is not None else self.crop_pct
									lowercase__						=				resample if resample is not None else self.resample
									lowercase__						=				do_center_crop if do_center_crop is not None else self.do_center_crop
									lowercase__						=				do_rescale if do_rescale is not None else self.do_rescale
									lowercase__						=				rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase__						=				do_normalize if do_normalize is not None else self.do_normalize
									lowercase__						=				image_mean if image_mean is not None else self.image_mean
									lowercase__						=				image_std if image_std is not None else self.image_std

									lowercase__						=				size if size is not None else self.size
									lowercase__						=				get_size_dict(UpperCamelCase_   ,						default_to_square=UpperCamelCase_							)
									lowercase__						=				crop_size if crop_size is not None else self.crop_size
									lowercase__						=				get_size_dict(UpperCamelCase_   ,						param_name='''crop_size'''							)

									lowercase__						=				make_list_of_images(UpperCamelCase_							)

									if not valid_images(UpperCamelCase_							):
											raise ValueError(
											    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
											    '''torch.Tensor, tf.Tensor or jax.ndarray.'''							)

									if do_resize and size is None or resample is None:
											raise ValueError('''Size and resample must be specified if do_resize is True.'''							)

									if do_center_crop and crop_pct is None:
											raise ValueError('''Crop_pct must be specified if do_center_crop is True.'''							)

									if do_rescale and rescale_factor is None:
											raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

									if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('''Image mean and std must be specified if do_normalize is True.'''							)

									# All transformations expect numpy arrays.
									lowercase__						=				[to_numpy_array(UpperCamelCase_							) for image in images]

									if do_resize:
											lowercase__						=				[self.resize(image=UpperCamelCase_   ,						size=UpperCamelCase_   ,						crop_pct=UpperCamelCase_   ,						resample=UpperCamelCase_							) for image in images]

									if do_center_crop:
											lowercase__						=				[self.center_crop(image=UpperCamelCase_   ,						size=UpperCamelCase_							) for image in images]

									if do_rescale:
											lowercase__						=				[self.rescale(image=UpperCamelCase_   ,						scale=UpperCamelCase_							) for image in images]

									if do_normalize:
											lowercase__						=				[self.normalize(image=UpperCamelCase_   ,						mean=UpperCamelCase_   ,						std=UpperCamelCase_							) for image in images]

									lowercase__						=				[to_channel_dimension_format(UpperCamelCase_   ,						UpperCamelCase_							) for image in images]

									lowercase__						=				{'''pixel_values''': images}
									return BatchFeature(data=UpperCamelCase_   ,						tensor_type=UpperCamelCase_							)






def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				len(SCREAMING_SNAKE_CASE					)
		lowercase__						=				[]
		for i in range(len(SCREAMING_SNAKE_CASE					) - pat_len + 1					):
				lowercase__						=				True
				for j in range(SCREAMING_SNAKE_CASE					):
						if s[i + j] != pattern[j]:
								lowercase__						=				False
								break
				if match_found:
						position.append(SCREAMING_SNAKE_CASE					)
		return position


if __name__ == "__main__":
			assert naive_pattern_search('ABCDEFG', 'DE') == [3]
			print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))


