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


lowercase_   				=       logging.get_logger(__name__)


if is_vision_available():
     import PIL







class 			SCREAMING_SNAKE_CASE__ (   __UpperCamelCase  ):
       A				:     Tuple					    =     ["pixel_values"]

       def __init__(				self :			Union[str, Any]       ,       _lowerCAmelCase :			Union[str, Any] = True       ,       _lowerCAmelCase :			List[str] = None       ,       _lowerCAmelCase :			Tuple = PILImageResampling.BILINEAR       ,       _lowerCAmelCase :			int = True       ,       _lowerCAmelCase :			Tuple = False       ,       _lowerCAmelCase :			Optional[Any] = True       ,       _lowerCAmelCase :			str = True       ,       _lowerCAmelCase :			List[Any] = 1 / 2_55       ,       _lowerCAmelCase :			Tuple = True       ,       _lowerCAmelCase :			Optional[int] = None       ,       _lowerCAmelCase :			Optional[int] = None       ,       **_lowerCAmelCase :			Optional[int]       ,       ):
           super().__init__(**A_       )

           __snake_case    :							str             =  size if size is not None else {"height": 25_60, "width": 19_20}
           if isinstance(A_       ,       (tuple, list)       ):
               # The previous feature extractor size parameter was in (width, height) format
               __snake_case    :							List[Any]             =  size[::-1]
           __snake_case    :							Dict             =  get_size_dict(A_       )

           __snake_case    :							int             =  do_resize
           __snake_case    :							Dict             =  size
           __snake_case    :							List[Any]             =  resample
           __snake_case    :							Union[str, Any]             =  do_thumbnail
           __snake_case    :							Optional[int]             =  do_align_long_axis
           __snake_case    :							Optional[Any]             =  do_pad
           __snake_case    :							int             =  do_rescale
           __snake_case    :							List[Any]             =  rescale_factor
           __snake_case    :							Union[str, Any]             =  do_normalize
           __snake_case    :							Any             =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           __snake_case    :							Dict             =  image_std if image_std is not None else IMAGENET_STANDARD_STD

       def        snake_case__  (				self :			Optional[int]       ,       _lowerCAmelCase :			Dict       ,       _lowerCAmelCase :			List[Any]       ,       _lowerCAmelCase :			str = None       ):
           __snake_case    :							List[str]             =  get_image_size(A_       )
           __snake_case    :							Any             =  size["height"], size["width"]

           if (output_width < output_height and input_width > input_height) or (
               output_width > output_height and input_width < input_height
           ):
               __snake_case    :							Optional[int]             =  np.rotaa(A_       ,       3       )

           if data_format is not None:
               __snake_case    :							List[str]             =  to_channel_dimension_format(A_       ,       A_       )

           return image

       def        snake_case__  (				self :			Union[str, Any]       ,       _lowerCAmelCase :			str       ,       _lowerCAmelCase :			Tuple       ,       _lowerCAmelCase :			Union[str, Any] = False       ,       _lowerCAmelCase :			str = None       ,       ):
           __snake_case    :							Optional[Any]             =  size["height"], size["width"]
           __snake_case    :							List[Any]             =  get_image_size(A_       )

           __snake_case    :							Optional[Any]             =  output_width - input_width
           __snake_case    :							Any             =  output_height - input_height

           if random_padding:
               __snake_case    :							List[str]             =  np.random.randint(low=0       ,       high=delta_height + 1       )
               __snake_case    :							Optional[int]             =  np.random.randint(low=0       ,       high=delta_width + 1       )
           else:
               __snake_case    :							Union[str, Any]             =  delta_height // 2
               __snake_case    :							str             =  delta_width // 2

           __snake_case    :							Optional[Any]             =  delta_height - pad_top
           __snake_case    :							List[Any]             =  delta_width - pad_left

           __snake_case    :							Optional[Any]             =  ((pad_top, pad_bottom), (pad_left, pad_right))
           return pad(A_       ,       A_       ,       data_format=A_       )

       def        snake_case__  (				self :			List[str]       ,       *_lowerCAmelCase :			Optional[Any]       ,       **_lowerCAmelCase :			Optional[Any]       ):
           logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""       )
           return self.pad_image(*A_       ,       **A_       )

       def        snake_case__  (				self :			List[Any]       ,       _lowerCAmelCase :			Optional[int]       ,       _lowerCAmelCase :			Any       ,       _lowerCAmelCase :			List[Any] = PILImageResampling.BICUBIC       ,       _lowerCAmelCase :			Optional[int] = None       ,       **_lowerCAmelCase :			List[str]       ,       ):
           __snake_case    :							Union[str, Any]             =  get_image_size(A_       )
           __snake_case    :							Union[str, Any]             =  size["height"], size["width"]

           # We always resize to the smallest of either the input or output size.
           __snake_case    :							Dict             =  min(A_       ,       A_       )
           __snake_case    :							Tuple             =  min(A_       ,       A_       )

           if height == input_height and width == input_width:
               return image

           if input_height > input_width:
               __snake_case    :							Any             =  int(input_width * height / input_height       )
           elif input_width > input_height:
               __snake_case    :							Any             =  int(input_height * width / input_width       )

           return resize(
               A_       ,       size=(height, width)       ,       resample=A_       ,       reducing_gap=2.0       ,       data_format=A_       ,       **A_       )

       def        snake_case__  (				self :			Tuple       ,       _lowerCAmelCase :			Optional[int]       ,       _lowerCAmelCase :			List[Any]       ,       _lowerCAmelCase :			Dict = PILImageResampling.BICUBIC       ,       _lowerCAmelCase :			Optional[Any] = None       ,       **_lowerCAmelCase :			Optional[Any]       ,       ):
           __snake_case    :							Any             =  get_size_dict(A_       )
           __snake_case    :							List[str]             =  min(size["""height"""]       ,       size["""width"""]       )
           __snake_case    :							Optional[Any]             =  get_resize_output_image_size(A_       ,       size=A_       ,       default_to_square=A_       )
           __snake_case    :							str             =  resize(A_       ,       size=A_       ,       resample=A_       ,       data_format=A_       ,       **A_       )
           return resized_image

       def        snake_case__  (				self :			int       ,       _lowerCAmelCase :			Dict       ,       _lowerCAmelCase :			Optional[Any]       ,       _lowerCAmelCase :			List[Any] = None       ,       **_lowerCAmelCase :			int       ,       ):
           return rescale(A_       ,       scale=A_       ,       data_format=A_       ,       **A_       )

       def        snake_case__  (				self :			List[Any]       ,       _lowerCAmelCase :			Optional[Any]       ,       _lowerCAmelCase :			Union[str, Any]       ,       _lowerCAmelCase :			Any       ,       _lowerCAmelCase :			Optional[int] = None       ,       **_lowerCAmelCase :			str       ,       ):
           return normalize(A_       ,       mean=A_       ,       std=A_       ,       data_format=A_       ,       **A_       )





       def        snake_case__  (				self :			int       ,       _lowerCAmelCase :			List[Any]       ,       _lowerCAmelCase :			Optional[int] = None       ,       _lowerCAmelCase :			List[Any] = None       ,       _lowerCAmelCase :			Any = None       ,       _lowerCAmelCase :			Any = None       ,       _lowerCAmelCase :			Tuple = None       ,       _lowerCAmelCase :			Union[str, Any] = None       ,       _lowerCAmelCase :			List[Any] = False       ,       _lowerCAmelCase :			Tuple = None       ,       _lowerCAmelCase :			Optional[int] = None       ,       _lowerCAmelCase :			int = None       ,       _lowerCAmelCase :			Any = None       ,       _lowerCAmelCase :			Any = None       ,       _lowerCAmelCase :			Optional[Any] = None       ,       _lowerCAmelCase :			List[str] = ChannelDimension.FIRST       ,       **_lowerCAmelCase :			List[Any]       ,       ):
           __snake_case    :							str             =  do_resize if do_resize is not None else self.do_resize
           __snake_case    :							str             =  size if size is not None else self.size
           if isinstance(A_       ,       (tuple, list)       ):
               # Previous feature extractor had size in (width, height) format
               __snake_case    :							List[Any]             =  size[::-1]
           __snake_case    :							Union[str, Any]             =  get_size_dict(A_       )
           __snake_case    :							Dict             =  resample if resample is not None else self.resample
           __snake_case    :							str             =  do_thumbnail if do_thumbnail is not None else self.do_thumbnail
           __snake_case    :							Optional[Any]             =  do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
           __snake_case    :							List[str]             =  do_pad if do_pad is not None else self.do_pad
           __snake_case    :							Optional[Any]             =  do_rescale if do_rescale is not None else self.do_rescale
           __snake_case    :							int             =  rescale_factor if rescale_factor is not None else self.rescale_factor
           __snake_case    :							List[str]             =  do_normalize if do_normalize is not None else self.do_normalize
           __snake_case    :							Any             =  image_mean if image_mean is not None else self.image_mean
           __snake_case    :							List[Any]             =  image_std if image_std is not None else self.image_std

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

           if do_pad and size is None:
               raise ValueError("""Size must be specified if do_pad 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.
           __snake_case    :							Union[str, Any]             =  [to_numpy_array(A_       ) for image in images]

           if do_align_long_axis:
               __snake_case    :							Tuple             =  [self.align_long_axis(A_       ,       size=A_       ) for image in images]

           if do_resize:
               __snake_case    :							Optional[int]             =  [self.resize(image=A_       ,       size=A_       ,       resample=A_       ) for image in images]

           if do_thumbnail:
               __snake_case    :							Optional[Any]             =  [self.thumbnail(image=A_       ,       size=A_       ) for image in images]

           if do_pad:
               __snake_case    :							List[Any]             =  [self.pad_image(image=A_       ,       size=A_       ,       random_padding=A_       ) for image in images]

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

           if do_normalize:
               __snake_case    :							int             =  [self.normalize(image=A_       ,       mean=A_       ,       std=A_       ) for image in images]

           __snake_case    :							str             =  [to_channel_dimension_format(A_       ,       A_       ) for image in images]

           __snake_case    :							Dict             =  {"pixel_values": images}
           return BatchFeature(data=A_       ,       tensor_type=A_       )






import math

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				list:
				if n <= 0:
								raise ValueError("Only positive integers have prime factors"       )
				_UpperCAmelCase				:						Union[str, Any]       						=			[]
				while n % 2 == 0:
								pf.append(2       )
								_UpperCAmelCase				:						Tuple       						=			int(n / 2       )
				for i in range(3     ,					int(math.sqrt(lowerCAmelCase       )       ) + 1     ,					2       ):
								while n % i == 0:
												pf.append(lowerCAmelCase       )
												_UpperCAmelCase				:						Optional[int]       						=			int(n / i       )
				if n > 2:
								pf.append(lowerCAmelCase       )
				return pf

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				int:
				if n <= 0:
								raise ValueError("Only positive numbers are accepted"       )
				_UpperCAmelCase				:						Dict       						=			1
				_UpperCAmelCase				:						str       						=			1
				while n % 2 == 0:
								temp += 1
								_UpperCAmelCase				:						List[str]       						=			int(n / 2       )
				div *= temp
				for i in range(3     ,					int(math.sqrt(lowerCAmelCase       )       ) + 1     ,					2       ):
								_UpperCAmelCase				:						Any       						=			1
								while n % i == 0:
												temp += 1
												_UpperCAmelCase				:						Any       						=			int(n / i       )
								div *= temp
				if n > 1:
								div *= 2
				return div

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				int:
				if n <= 0:
								raise ValueError("Only positive numbers are accepted"       )
				_UpperCAmelCase				:						Optional[Any]       						=			1
				_UpperCAmelCase				:						Union[str, Any]       						=			1
				while n % 2 == 0:
								temp += 1
								_UpperCAmelCase				:						Optional[Any]       						=			int(n / 2       )
				if temp > 1:
								s *= (2**temp - 1) / (2 - 1)
				for i in range(3     ,					int(math.sqrt(lowerCAmelCase       )       ) + 1     ,					2       ):
								_UpperCAmelCase				:						List[str]       						=			1
								while n % i == 0:
												temp += 1
												_UpperCAmelCase				:						List[Any]       						=			int(n / i       )
								if temp > 1:
												s *= (i**temp - 1) / (i - 1)
				return int(lowerCAmelCase       )

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				int:
				_UpperCAmelCase				:						Any       						=			n
				for x in set(prime_factors(lowerCAmelCase       )       ):
								s *= (x - 1) / x
				return int(lowerCAmelCase       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()


