

"""simple docstring"""


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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A		     =    logging.get_logger(__name__)





class 					snake_case			(       __snake_case						):
				SCREAMING_SNAKE_CASE_     :   Dict        =						["""pixel_values"""]

				def __init__(			self							:       int  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       Optional[Dict[str, int]] = None  ,						UpperCamelCase__							:       PILImageResampling = PILImageResampling.BILINEAR  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       Union[int, float] = 1 / 2_5_5  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						**UpperCamelCase__							:       Optional[Any]  ,						)->     None:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)
						__lowerCAmelCase: Union[str, Any]					   =    size if size is not None else {"height": 2_2_4, "width": 2_2_4}
						__lowerCAmelCase: Optional[Any]					   =    get_size_dict(UpperCamelCase__)
						__lowerCAmelCase: Union[str, Any]					   =    do_resize
						__lowerCAmelCase: List[Any]					   =    do_rescale
						__lowerCAmelCase: Dict					   =    do_normalize
						__lowerCAmelCase: Union[str, Any]					   =    size
						__lowerCAmelCase: Optional[int]					   =    resample
						__lowerCAmelCase: Dict					   =    rescale_factor
						__lowerCAmelCase: int					   =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__lowerCAmelCase: List[Any]					   =    image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 							lowercase_  (			self							:       Optional[Any]  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Dict[str, int]  ,						UpperCamelCase__							:       PILImageResampling = PILImageResampling.BILINEAR  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__							:       Any  ,						)->     np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase: Dict					   =    get_size_dict(UpperCamelCase__)
						if "height" not in size or "width" not in size:
								raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
						return resize(
						    UpperCamelCase__  ,						size=(size["height"], size["width"])  ,						resample=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__)

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



						'''simple docstring'''


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

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



						'''simple docstring'''


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




				def 							lowercase_  (			self							:       str  ,						UpperCamelCase__							:       ImageInput  ,						UpperCamelCase__							:       Optional[bool] = None  ,						UpperCamelCase__							:       Dict[str, int] = None  ,						UpperCamelCase__							:       PILImageResampling = None  ,						UpperCamelCase__							:       Optional[bool] = None  ,						UpperCamelCase__							:       Optional[float] = None  ,						UpperCamelCase__							:       Optional[bool] = None  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__							:       Optional[Union[str, TensorType]] = None  ,						UpperCamelCase__							:       Union[str, ChannelDimension] = ChannelDimension.FIRST  ,						**UpperCamelCase__							:       str  ,						)->     List[str]:



						'''simple docstring'''


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

						__lowerCAmelCase: Optional[Any]					   =    size if size is not None else self.size
						__lowerCAmelCase: List[Any]					   =    get_size_dict(UpperCamelCase__)

						__lowerCAmelCase: Dict					   =    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:
								raise ValueError("Size must be specified if do_resize is True.")

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

						# All transformations expect numpy arrays.
						__lowerCAmelCase: Tuple					   =    [to_numpy_array(UpperCamelCase__) for image in images]

						if do_resize:
								__lowerCAmelCase: Union[str, Any]					   =    [self.resize(image=UpperCamelCase__  ,						size=UpperCamelCase__  ,						resample=UpperCamelCase__) for image in images]

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

						if do_normalize:
								__lowerCAmelCase: Dict					   =    [self.normalize(image=UpperCamelCase__  ,						mean=UpperCamelCase__  ,						std=UpperCamelCase__) for image in images]

						__lowerCAmelCase: Dict					   =    [to_channel_dimension_format(UpperCamelCase__  ,						UpperCamelCase__) for image in images]

						__lowerCAmelCase: Optional[Any]					   =    {"pixel_values": images}
						return BatchFeature(data=UpperCamelCase__  ,						tensor_type=UpperCamelCase__)





"""simple docstring"""


def 					a__					(					__SCREAMING_SNAKE_CASE = 1_0_0_0				)			->			int:
		__lowerCAmelCase: int					   =    0
		__lowerCAmelCase: Optional[int]					   =    0
		while 1:
				num += 3
				if num >= n:
						break
				total += num
				num += 2
				if num >= n:
						break
				total += num
				num += 1
				if num >= n:
						break
				total += num
				num += 3
				if num >= n:
						break
				total += num
				num += 1
				if num >= n:
						break
				total += num
				num += 2
				if num >= n:
						break
				total += num
				num += 3
				if num >= n:
						break
				total += num
		return total


if __name__ == "__main__":
			print(F'''{solution() = }''')


