






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


UpperCAmelCase__      			=						logging.get_logger(__name__)




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      ["""pixel_values"""]



						def __init__(self:					Optional[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:					List[str] ,					)    ->     None:




											'''simple docstring'''



											super().__init__(**__UpperCAmelCase   )
											__a					:						Optional[Any]      =					size if size is not None else {"shortest_edge": 224}
											__a					:						Tuple      =					get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase   )
											__a					:						Dict      =					crop_size if crop_size is not None else {"height": 224, "width": 224}
											__a					:						str      =					get_size_dict(__UpperCAmelCase ,					param_name="crop_size"   )

											__a					:						Optional[Any]      =					do_resize
											__a					:						Dict      =					size
											__a					:						Tuple      =					crop_pct
											__a					:						Union[str, Any]      =					resample
											__a					:						int      =					do_center_crop
											__a					:						Optional[int]      =					crop_size
											__a					:						int      =					do_rescale
											__a					:						List[Any]      =					rescale_factor
											__a					:						Optional[int]      =					do_normalize
											__a					:						Any      =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
											__a					:						int      =					image_std if image_std is not None else IMAGENET_DEFAULT_STD



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




											'''simple docstring'''



											__a					:						Optional[Any]      =					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:
																					__a					:						str      =					int(size["shortest_edge"] / crop_pct   )
																elif "height" in size and "width" in size:
																					if size["height"] == size["width"]:
																										__a					:						Optional[int]      =					int(size["height"] / crop_pct   )
																					else:
																										__a					:						Optional[int]      =					(int(size["height"] / crop_pct   ), int(size["width"] / crop_pct   ))
																else:
																					raise ValueError("Invalid size for resize: {}".format(__UpperCAmelCase   )   )

																__a					:						Any      =					get_resize_output_image_size(__UpperCAmelCase ,					size=__UpperCAmelCase ,					default_to_square=__UpperCAmelCase   )
											else:
																if "shortest_edge" in size:
																					__a					:						List[Any]      =					get_resize_output_image_size(__UpperCAmelCase ,					size=size["shortest_edge"] ,					default_to_square=__UpperCAmelCase   )
																elif "height" in size and "width" in size:
																					__a					:						List[str]      =					(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        UpperCAmelCase__					(self:					Optional[Any] ,					__UpperCAmelCase:					np.ndarray ,					__UpperCAmelCase:					Dict[str, int] ,					__UpperCAmelCase:					Optional[Union[str, ChannelDimension]] = None ,					**__UpperCAmelCase:					Tuple ,					)    ->     np.ndarray:




											'''simple docstring'''



											__a					:						Dict      =					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        UpperCAmelCase__					(self:					List[Any] ,					__UpperCAmelCase:					np.ndarray ,					__UpperCAmelCase:					Union[int, float] ,					__UpperCAmelCase:					Optional[Union[str, ChannelDimension]] = None ,					**__UpperCAmelCase:					int ,					)    ->     Any:




											'''simple docstring'''



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



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




											'''simple docstring'''



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



						def        UpperCAmelCase__					(self:					Tuple ,					__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:					Union[str, Any] ,					)    ->     PIL.Image.Image:




											'''simple docstring'''



											__a					:						int      =					do_resize if do_resize is not None else self.do_resize
											__a					:						Union[str, Any]      =					crop_pct if crop_pct is not None else self.crop_pct
											__a					:						str      =					resample if resample is not None else self.resample
											__a					:						str      =					do_center_crop if do_center_crop is not None else self.do_center_crop
											__a					:						str      =					do_rescale if do_rescale is not None else self.do_rescale
											__a					:						Dict      =					rescale_factor if rescale_factor is not None else self.rescale_factor
											__a					:						str      =					do_normalize if do_normalize is not None else self.do_normalize
											__a					:						List[str]      =					image_mean if image_mean is not None else self.image_mean
											__a					:						Optional[Any]      =					image_std if image_std is not None else self.image_std

											__a					:						List[Any]      =					size if size is not None else self.size
											__a					:						Dict      =					get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase   )
											__a					:						Union[str, Any]      =					crop_size if crop_size is not None else self.crop_size
											__a					:						str      =					get_size_dict(__UpperCAmelCase ,					param_name="crop_size"   )

											__a					:						Optional[Any]      =					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.
											__a					:						Optional[int]      =					[to_numpy_array(__UpperCAmelCase   ) for image in images]

											if do_resize:
																__a					:						str      =					[self.resize(image=__UpperCAmelCase ,					size=__UpperCAmelCase ,					crop_pct=__UpperCAmelCase ,					resample=__UpperCAmelCase   ) for image in images]

											if do_center_crop:
																__a					:						Optional[Any]      =					[self.center_crop(image=__UpperCAmelCase ,					size=__UpperCAmelCase   ) for image in images]

											if do_rescale:
																__a					:						Tuple      =					[self.rescale(image=__UpperCAmelCase ,					scale=__UpperCAmelCase   ) for image in images]

											if do_normalize:
																__a					:						Optional[int]      =					[self.normalize(image=__UpperCAmelCase ,					mean=__UpperCAmelCase ,					std=__UpperCAmelCase   ) for image in images]

											__a					:						Dict      =					[to_channel_dimension_format(__UpperCAmelCase ,					__UpperCAmelCase   ) for image in images]

											__a					:						Union[str, Any]      =					{"pixel_values": images}
											return BatchFeature(data=__UpperCAmelCase ,					tensor_type=__UpperCAmelCase   )













from __future__ import annotations







def     a_   (__A      )				->						int:



					"""simple docstring"""
					__a					:						List[Any]      =					len(__A      ) // 2

					# choose the middle 3 elements
					__a					:						Any      =					lst[m - 1 : m + 2]

					# if middle element is peak
					if three[1] > three[0] and three[1] > three[2]:
										return three[1]

					# if increasing, recurse on right
					elif three[0] < three[2]:
										if len(lst[:m]      ) == 2:
															m -= 1
										return peak(lst[m:]      )

					# decreasing
					else:
										if len(lst[:m]      ) == 2:
															m += 1
										return peak(lst[:m]      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





