
from typing import Dict, Iterable, 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, logging


SCREAMING_SNAKE_CASE							:Optional[Any]							 =				logging.get_logger(__name__)





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     ["pixel_values"]






	def __init__(       self					: Dict							,A					: bool = True							,A					: Dict[str, int] = None							,A					: PILImageResampling = PILImageResampling.BICUBIC							,A					: bool = True							,A					: Dict[str, int] = None							,A					: bool = True							,A					: Union[int, float] = 1 / 2_55							,A					: bool = True							,A					: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN							,A					: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD							,**A					: Dict							,):
					super().__init__(**A )
					__A   			= size if size is not None else {"shortest_edge": 2_24}
					__A   			= get_size_dict(A							,default_to_square=A )
					__A   			= crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
					__A   			= get_size_dict(A							,param_name="crop_size" )

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






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: np.ndarray							,A					: Dict[str, int]							,A					: PILImageResampling = PILImageResampling.BICUBIC							,A					: Optional[Union[str, ChannelDimension]] = None							,**A					: Optional[Any]							,):
					__A   			= get_size_dict(A							,default_to_square=A )
					# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
					if "shortest_edge" in size:
									__A   			= int((2_56 / 2_24) * size["shortest_edge"] )
									__A   			= get_resize_output_image_size(A							,size=A							,default_to_square=A )
									__A   			= {"height": output_size[0], "width": output_size[1]}
					if "height" not in size_dict or "width" not in size_dict:
									raise ValueError(
									    f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}''' )
					return resize(
					    A							,size=(size_dict["height"], size_dict["width"])							,resample=A							,data_format=A							,**A )






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: np.ndarray							,A					: Dict[str, int]							,A					: Optional[Union[str, ChannelDimension]] = None							,**A					: Tuple							,):
					__A   			= get_size_dict(A )
					if "height" not in size or "width" not in size:
									raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}''' )
					return center_crop(A							,size=(size["height"], size["width"])							,data_format=A							,**A )






	def 				UpperCamelCase_  (       self					: Any							,A					: np.ndarray							,A					: Union[int, float]							,A					: Optional[Union[str, ChannelDimension]] = None							,**A					: str							,):
					return rescale(A							,scale=A							,data_format=A							,**A )






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: np.ndarray							,A					: Union[float, List[float]]							,A					: Union[float, List[float]]							,A					: Optional[Union[str, ChannelDimension]] = None							,**A					: str							,):
					return normalize(A							,mean=A							,std=A							,data_format=A							,**A )







	def 				UpperCamelCase_  (       self					: int							,A					: ImageInput							,A					: Optional[bool] = None							,A					: Optional[Dict[str, int]] = None							,A					: PILImageResampling = None							,A					: Optional[bool] = None							,A					: Optional[Dict[str, int]] = None							,A					: Optional[bool] = None							,A					: Optional[float] = None							,A					: Optional[bool] = None							,A					: Optional[Union[float, Iterable[float]]] = None							,A					: Optional[Union[float, Iterable[float]]] = None							,A					: Optional[TensorType] = None							,A					: ChannelDimension = ChannelDimension.FIRST							,**A					: str							,):
					__A   			= do_resize if do_resize is not None else self.do_resize
					__A   			= resample if resample is not None else self.resample
					__A   			= do_center_crop if do_center_crop is not None else self.do_center_crop
					__A   			= do_rescale if do_rescale is not None else self.do_rescale
					__A   			= rescale_factor if rescale_factor is not None else self.rescale_factor
					__A   			= do_normalize if do_normalize is not None else self.do_normalize
					__A   			= image_mean if image_mean is not None else self.image_mean
					__A   			= image_std if image_std is not None else self.image_std

					__A   			= size if size is not None else self.size
					__A   			= get_size_dict(A							,default_to_square=A )
					__A   			= crop_size if crop_size is not None else self.crop_size
					__A   			= get_size_dict(A							,param_name="crop_size" )

					__A   			= make_list_of_images(A )

					if not valid_images(A ):
									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:
									raise ValueError("Size must be specified if do_resize is True." )

					if do_center_crop and crop_size is None:
									raise ValueError("Crop size 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   			= [to_numpy_array(A ) for image in images]

					if do_resize:
									__A   			= [self.resize(A							,A							,A ) for image in images]

					if do_center_crop:
									__A   			= [self.center_crop(A							,A ) for image in images]

					if do_rescale:
									__A   			= [self.rescale(A							,A ) for image in images]

					if do_normalize:
									__A   			= [self.normalize(A							,A							,A ) for image in images]

					__A   			= [to_channel_dimension_format(A							,A ) for image in images]

					__A   			= {"pixel_values": images}
					return BatchFeature(data=A							,tensor_type=A )

import math
def       A	(    _lowercase						):
						return math.sqrt(_lowercase						) * math.sqrt(_lowercase						) == num
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Dict							=							0
						SCREAMING_SNAKE_CASE							:    Tuple							=							n
						while left <= right:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							(left + right) // 2
												if mid**2 == n:
																		return True
												elif mid**2 > n:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							mid - 1
												else:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							mid + 1
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()





