

"""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 (
    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


_UpperCamelCase       =						logging.get_logger(__name__)


if is_vision_available():
    import PIL






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Union[str, Any]          =	['pixel_values']






       def __init__(      self ,				snake_case = True ,				snake_case = None ,				snake_case = PILImageResampling.BILINEAR ,				snake_case = True ,				snake_case = False ,				snake_case = True ,				snake_case = True ,				snake_case = 1 / 255 ,				snake_case = True ,				snake_case = None ,				snake_case = None ,				**snake_case ,				):





        """simple docstring"""

        super().__init__(**snake_case      )

        lowerCAmelCase__      :							int    =							size if size is not None else {"height": 2_560, "width": 1_920}
        if isinstance(snake_case ,				(tuple, list)      ):
         # The previous feature extractor size parameter was in (width, height) format
         lowerCAmelCase__      :							Optional[Any]    =							size[::-1]
        lowerCAmelCase__      :							str    =							get_size_dict(snake_case      )

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case = None      ):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							get_image_size(snake_case      )
        lowerCAmelCase__      :							Tuple    =							size["height"], size["width"]

        if (output_width < output_height and input_width > input_height) or (
            output_width > output_height and input_width < input_height
        ):
         lowerCAmelCase__      :							Tuple    =							np.rotaa(snake_case ,				3      )

        if data_format is not None:
         lowerCAmelCase__      :							List[Any]    =							to_channel_dimension_format(snake_case ,				snake_case      )

        return image






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case = False ,				snake_case = None ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							size["height"], size["width"]
        lowerCAmelCase__      :							Tuple    =							get_image_size(snake_case      )

        lowerCAmelCase__      :							Optional[Any]    =							output_width - input_width
        lowerCAmelCase__      :							List[Any]    =							output_height - input_height

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

        lowerCAmelCase__      :							Any    =							delta_height - pad_top
        lowerCAmelCase__      :							Any    =							delta_width - pad_left

        lowerCAmelCase__      :							Tuple    =							((pad_top, pad_bottom), (pad_left, pad_right))
        return pad(snake_case ,				snake_case ,				data_format=snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				*snake_case ,				**snake_case      ):





        """simple docstring"""

        logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."      )
        return self.pad_image(*snake_case ,				**snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case = PILImageResampling.BICUBIC ,				snake_case = None ,				**snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							get_image_size(snake_case      )
        lowerCAmelCase__      :							Any    =							size["height"], size["width"]

        # We always resize to the smallest of either the input or output size.
        lowerCAmelCase__      :							Optional[Any]    =							min(snake_case ,				snake_case      )
        lowerCAmelCase__      :							Optional[int]    =							min(snake_case ,				snake_case      )

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

        if input_height > input_width:
         lowerCAmelCase__      :							Tuple    =							int(input_width * height / input_height      )
        elif input_width > input_height:
         lowerCAmelCase__      :							Dict    =							int(input_height * width / input_width      )

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case = PILImageResampling.BICUBIC ,				snake_case = None ,				**snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							get_size_dict(snake_case      )
        lowerCAmelCase__      :							Any    =							min(size["height"] ,				size["width"]      )
        lowerCAmelCase__      :							Union[str, Any]    =							get_resize_output_image_size(snake_case ,				size=snake_case ,				default_to_square=snake_case      )
        lowerCAmelCase__      :							Tuple    =							resize(snake_case ,				size=snake_case ,				resample=snake_case ,				data_format=snake_case ,				**snake_case      )
        return resized_image






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case = None ,				**snake_case ,				):





        """simple docstring"""

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case = None ,				**snake_case ,				):





        """simple docstring"""

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = False ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = ChannelDimension.FIRST ,				**snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase__      :							Optional[Any]    =							size if size is not None else self.size
        if isinstance(snake_case ,				(tuple, list)      ):
         # Previous feature extractor had size in (width, height) format
         lowerCAmelCase__      :							List[str]    =							size[::-1]
        lowerCAmelCase__      :							List[Any]    =							get_size_dict(snake_case      )
        lowerCAmelCase__      :							str    =							resample if resample is not None else self.resample
        lowerCAmelCase__      :							Any    =							do_thumbnail if do_thumbnail is not None else self.do_thumbnail
        lowerCAmelCase__      :							Any    =							do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
        lowerCAmelCase__      :							Optional[int]    =							do_pad if do_pad is not None else self.do_pad
        lowerCAmelCase__      :							Union[str, Any]    =							do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase__      :							str    =							rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase__      :							List[str]    =							do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase__      :							Optional[int]    =							image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase__      :							List[str]    =							image_std if image_std is not None else self.image_std

        lowerCAmelCase__      :							Optional[Any]    =							make_list_of_images(snake_case      )

        if not valid_images(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:
         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.
        lowerCAmelCase__      :							Tuple    =							[to_numpy_array(snake_case      ) for image in images]

        if do_align_long_axis:
         lowerCAmelCase__      :							int    =							[self.align_long_axis(snake_case ,				size=snake_case      ) for image in images]

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

        if do_thumbnail:
         lowerCAmelCase__      :							Any    =							[self.thumbnail(image=snake_case ,				size=snake_case      ) for image in images]

        if do_pad:
         lowerCAmelCase__      :							Tuple    =							[self.pad_image(image=snake_case ,				size=snake_case ,				random_padding=snake_case      ) for image in images]

        if do_rescale:
         lowerCAmelCase__      :							Union[str, Any]    =							[self.rescale(image=snake_case ,				scale=snake_case      ) for image in images]

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

        lowerCAmelCase__      :							Optional[int]    =							[to_channel_dimension_format(snake_case ,				snake_case      ) for image in images]

        lowerCAmelCase__      :							Dict    =							{"pixel_values": images}
        return BatchFeature(data=snake_case ,				tensor_type=snake_case      )









"""simple docstring"""

from itertools import product



def 			SCREAMING_SNAKE_CASE       (   lowercase__ = 1_5      ) ->	int:
 lowerCAmelCase__      :							float    =							0.0
 lowerCAmelCase__      :							float
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							tuple[int, ...]

 for series in product(range(2      )				,				repeat=lowercase__      ):
  lowerCAmelCase__      :							Optional[int]    =							series.count(1      )
  lowerCAmelCase__      :							Dict    =							num_turns - num_blue
  if num_red >= num_blue:
   continue
  lowerCAmelCase__      :							Dict    =							1.0
  for ind, col in enumerate(lowercase__				,				2      ):
   if col == 0:
    prob *= (ind - 1) / ind
   else:
    prob *= 1 / ind

  total_prob += prob

 return int(1 / total_prob      )


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






