




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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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


_snake_case										=       logging.get_logger(__name__)







class 						lowercase							(   lowerCamelCase_		):
     _a							 =						["pixel_values"]

     def __init__(  self		,     _a = True		,     _a = None		,     _a = PILImageResampling.BICUBIC		,     _a = True		,     _a = 1 / 255		,     _a = True		,     _a = None		,     _a = None		,     _a = True		,     **_a		,     )						->							str:
           super().__init__(**__SCREAMING_SNAKE_CASE				)
           _A							:     str			=						size if size is not None else {'''height''': 384, '''width''': 384}
           _A							:     Union[str, Any]			=						get_size_dict(__SCREAMING_SNAKE_CASE		,     default_to_square=__SCREAMING_SNAKE_CASE				)

           _A							:     Optional[Any]			=						do_resize
           _A							:     Dict			=						size
           _A							:     Optional[Any]			=						resample
           _A							:     Tuple			=						do_rescale
           _A							:     str			=						rescale_factor
           _A							:     Union[str, Any]			=						do_normalize
           _A							:     Dict			=						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           _A							:     Union[str, Any]			=						image_std if image_std is not None else OPENAI_CLIP_STD
           _A							:     Dict			=						do_convert_rgb

     def 					a__	(  self		,     _a		,     _a		,     _a = PILImageResampling.BICUBIC		,     _a = None		,     **_a		,     )						->							List[Any]:
           _A							:     List[str]			=						get_size_dict(__SCREAMING_SNAKE_CASE		,     default_to_square=__SCREAMING_SNAKE_CASE				)
           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()}'''				)
           _A							:     Any			=						(size['''height'''], size['''width'''])
           return resize(__SCREAMING_SNAKE_CASE		,     size=__SCREAMING_SNAKE_CASE		,     resample=__SCREAMING_SNAKE_CASE		,     data_format=__SCREAMING_SNAKE_CASE		,     **__SCREAMING_SNAKE_CASE				)

     def 					a__	(  self		,     _a		,     _a		,     _a = None		,     **_a		,     )						->							Optional[int]:
           return rescale(__SCREAMING_SNAKE_CASE		,     scale=__SCREAMING_SNAKE_CASE		,     data_format=__SCREAMING_SNAKE_CASE		,     **__SCREAMING_SNAKE_CASE				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a = None		,     **_a		,     )						->							Optional[int]:
           return normalize(__SCREAMING_SNAKE_CASE		,     mean=__SCREAMING_SNAKE_CASE		,     std=__SCREAMING_SNAKE_CASE		,     data_format=__SCREAMING_SNAKE_CASE		,     **__SCREAMING_SNAKE_CASE				)



     def 					a__	(  self		,     _a		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = ChannelDimension.FIRST		,     **_a		,     )						->							Union[str, Any]:
           _A							:     List[Any]			=						do_resize if do_resize is not None else self.do_resize
           _A							:     Dict			=						resample if resample is not None else self.resample
           _A							:     List[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							:     Union[str, Any]			=						image_mean if image_mean is not None else self.image_mean
           _A							:     List[Any]			=						image_std if image_std is not None else self.image_std
           _A							:     Optional[Any]			=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           _A							:     List[Any]			=						size if size is not None else self.size
           _A							:     Dict			=						get_size_dict(__SCREAMING_SNAKE_CASE		,     default_to_square=__SCREAMING_SNAKE_CASE				)

           _A							:     Optional[int]			=						make_list_of_images(__SCREAMING_SNAKE_CASE				)

           if not valid_images(__SCREAMING_SNAKE_CASE				):
                 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_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."""				)

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                 _A							:     int			=						[convert_to_rgb(__SCREAMING_SNAKE_CASE				) for image in images]

           # All transformations expect numpy arrays.
           _A							:     Union[str, Any]			=						[to_numpy_array(__SCREAMING_SNAKE_CASE				) for image in images]

           if do_resize:
                 _A							:     List[Any]			=						[self.resize(image=__SCREAMING_SNAKE_CASE		,     size=__SCREAMING_SNAKE_CASE		,     resample=__SCREAMING_SNAKE_CASE				) for image in images]

           if do_rescale:
                 _A							:     Any			=						[self.rescale(image=__SCREAMING_SNAKE_CASE		,     scale=__SCREAMING_SNAKE_CASE				) for image in images]

           if do_normalize:
                 _A							:     str			=						[self.normalize(image=__SCREAMING_SNAKE_CASE		,     mean=__SCREAMING_SNAKE_CASE		,     std=__SCREAMING_SNAKE_CASE				) for image in images]

           _A							:     List[str]			=						[to_channel_dimension_format(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE				) for image in images]

           _A							:     Dict			=						BatchFeature(data={"""pixel_values""": images}		,     tensor_type=__SCREAMING_SNAKE_CASE				)

           return encoded_outputs



'''simple docstring'''







_lowercase :  int    							=       [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       0
						while number:
												# Increased Speed Slightly by checking every 5 digits together.
												sum_of_digits_squared += DIGITS_SQUARED[number % 100000]
												number //= 100000

						return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
_lowercase :  list[bool | None]    							=       [None] * 1_0_0_0_0_0_0_0
_lowercase :  List[str]    							=       True
_lowercase :  Optional[int]    							=       False
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						if CHAINS[number - 1] is not None:
												return CHAINS[number - 1]  # type: ignore

						lowercase_	:     Tuple													=       chain(next_number(__SCREAMING_SNAKE_CASE							)							)
						lowercase_	:     Union[str, Any]													=       number_chain

						while number < 10000000:
												lowercase_	:     int													=       number_chain
												number *= 10

						return number_chain
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int = 10000000							):



						"""simple docstring"""





						for i in range(1      ,      __SCREAMING_SNAKE_CASE							):
												if CHAINS[i] is None:
																		chain(i + 1							)

						return CHAINS[:number].count(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(f"""{solution() = }""")



