



"""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 center_crop, 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, is_vision_available, logging


if is_vision_available():
			import PIL


snake_case_         = logging.get_logger(__name__)







class    A_    (  lowerCamelCase_       ):



						"""simple docstring"""
						__UpperCamelCase	       =							["""pixel_values"""]






						def __init__(      self				:Union[str, Any]     ,						lowercase_				:Optional[Any] = True     ,						lowercase_				:Tuple = None     ,						lowercase_				:Any = PIL.Image.NEAREST     ,						lowercase_				:str = False     ,						lowercase_				:Optional[int] = None     ,						lowercase_				:Union[str, Any] = 1 / 2_55     ,						lowercase_				:List[Any] = False     ,						lowercase_				:Optional[Any] = True     ,						lowercase_				:Union[str, Any] = True     ,						lowercase_				:Dict = None     ,						lowercase_				:Union[str, Any] = None     ,						lowercase_				:Dict = True     ,						**lowercase_				:Optional[int]     ,						)       ->    Optional[Any]:
												super().__init__(**lowercase_      )
												UpperCAmelCase    =				size if size is not None else {"height": 3_46, "width": 3_46}
												UpperCAmelCase    =				get_size_dict(lowercase_      )
												UpperCAmelCase    =				crop_size if crop_size is not None else {"height": 2_89, "width": 2_89}
												UpperCAmelCase    =				get_size_dict(lowercase_     ,						param_name='crop_size'      )

												UpperCAmelCase    =				do_resize
												UpperCAmelCase    =				size
												UpperCAmelCase    =				resample
												UpperCAmelCase    =				do_center_crop
												UpperCAmelCase    =				crop_size
												UpperCAmelCase    =				do_rescale
												UpperCAmelCase    =				rescale_factor
												UpperCAmelCase    =				rescale_offset
												UpperCAmelCase    =				do_normalize
												UpperCAmelCase    =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												UpperCAmelCase    =				image_std if image_std is not None else IMAGENET_STANDARD_STD
												UpperCAmelCase    =				include_top






						def 			UpperCAmelCase__  (      self				:Optional[Any]     ,						lowercase_				:Optional[int]     ,						lowercase_				:List[str]     ,						lowercase_				:Tuple = PIL.Image.NEAREST     ,						lowercase_				:List[Any] = None     ,						**lowercase_				:Optional[Any]     ,						)       ->    Union[str, Any]:
												UpperCAmelCase    =				get_size_dict(lowercase_      )
												if "height" not in size or "width" not in size:
																		raise ValueError(f"""The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}"""      )
												return resize(
												    lowercase_     ,						size=(size['height'], size['width'])     ,						resample=lowercase_     ,						data_format=lowercase_     ,						**lowercase_      )






						def 			UpperCAmelCase__  (      self				:Any     ,						lowercase_				:str     ,						lowercase_				:Tuple     ,						lowercase_				:str = None     ,						**lowercase_				:Union[str, Any]     ,						)       ->    Optional[int]:
												UpperCAmelCase    =				get_size_dict(lowercase_      )
												if "height" not in size or "width" not in size:
																		raise ValueError(f"""The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}"""      )
												return center_crop(lowercase_     ,						size=(size['height'], size['width'])     ,						data_format=lowercase_     ,						**lowercase_      )






						def 			UpperCAmelCase__  (      self				:List[str]     ,						lowercase_				:Any     ,						lowercase_				:Dict     ,						lowercase_				:Dict = True     ,						lowercase_				:Optional[Any] = None     ,						**lowercase_				:int     ,						)       ->    Dict:







												if offset:
																		UpperCAmelCase    =				(image - 1_27.5) * scale
																		if data_format is not None:
																								UpperCAmelCase    =				to_channel_dimension_format(lowercase_     ,						lowercase_      )
																		UpperCAmelCase    =				rescaled_image.astype(np.floataa      )
												else:
																		UpperCAmelCase    =				rescale(lowercase_     ,						scale=lowercase_     ,						data_format=lowercase_     ,						**lowercase_      )
												return rescaled_image






						def 			UpperCAmelCase__  (      self				:Optional[Any]     ,						lowercase_				:Optional[Any]     ,						lowercase_				:List[Any]     ,						lowercase_				:str     ,						lowercase_				:Optional[int] = None     ,						**lowercase_				:int     ,						)       ->    List[Any]:
												return normalize(lowercase_     ,						mean=lowercase_     ,						std=lowercase_     ,						data_format=lowercase_     ,						**lowercase_      )






						def 			UpperCAmelCase__  (      self				:List[Any]     ,						lowercase_				:Optional[Any]     ,						lowercase_				:List[str] = None     ,						lowercase_				:Optional[Any] = None     ,						lowercase_				:str=None     ,						lowercase_				:int = None     ,						lowercase_				:Tuple = None     ,						lowercase_				:int = None     ,						lowercase_				:List[Any] = None     ,						lowercase_				:Optional[Any] = None     ,						lowercase_				:Dict = None     ,						lowercase_				:str = None     ,						lowercase_				:Optional[int] = None     ,						lowercase_				:Tuple = None     ,						lowercase_				:Union[str, Any] = None     ,						lowercase_				:List[str] = ChannelDimension.FIRST     ,						**lowercase_				:Any     ,						)       ->    Dict:
												UpperCAmelCase    =				do_resize if do_resize is not None else self.do_resize
												UpperCAmelCase    =				resample if resample is not None else self.resample
												UpperCAmelCase    =				do_center_crop if do_center_crop is not None else self.do_center_crop
												UpperCAmelCase    =				do_rescale if do_rescale is not None else self.do_rescale
												UpperCAmelCase    =				rescale_factor if rescale_factor is not None else self.rescale_factor
												UpperCAmelCase    =				rescale_offset if rescale_offset is not None else self.rescale_offset
												UpperCAmelCase    =				do_normalize if do_normalize is not None else self.do_normalize
												UpperCAmelCase    =				image_mean if image_mean is not None else self.image_mean
												UpperCAmelCase    =				image_std if image_std is not None else self.image_std
												UpperCAmelCase    =				include_top if include_top is not None else self.include_top

												UpperCAmelCase    =				size if size is not None else self.size
												UpperCAmelCase    =				get_size_dict(lowercase_      )
												UpperCAmelCase    =				crop_size if crop_size is not None else self.crop_size
												UpperCAmelCase    =				get_size_dict(lowercase_     ,						param_name='crop_size'      )

												UpperCAmelCase    =				make_list_of_images(lowercase_      )

												if not valid_images(lowercase_      ):
																		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_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.
												UpperCAmelCase    =				[to_numpy_array(lowercase_      ) for image in images]

												if do_resize:
																		UpperCAmelCase    =				[self.resize(image=lowercase_     ,						size=lowercase_     ,						resample=lowercase_      ) for image in images]

												if do_center_crop:
																		UpperCAmelCase    =				[self.center_crop(image=lowercase_     ,						size=lowercase_      ) for image in images]

												if do_rescale:
																		UpperCAmelCase    =				[self.rescale(image=lowercase_     ,						scale=lowercase_     ,						offset=lowercase_      ) for image in images]

												if do_normalize:
																		UpperCAmelCase    =				[self.normalize(image=lowercase_     ,						mean=lowercase_     ,						std=lowercase_      ) for image in images]

												if include_top:
																		UpperCAmelCase    =				[self.normalize(image=lowercase_     ,						mean=[0, 0, 0]     ,						std=lowercase_      ) for image in images]

												UpperCAmelCase    =				[to_channel_dimension_format(lowercase_     ,						lowercase_      ) for image in images]

												UpperCAmelCase    =				{"pixel_values": images}
												return BatchFeature(data=lowercase_     ,						tensor_type=lowercase_      )










"""simple docstring"""


def 				_lowerCAmelCase   (     lowercase_	,			lowercase_		):
						UpperCAmelCase    =				1.0
						for i in range(1	,			r + 1		):
												ret *= (n + 1 - i) / i
						return round(lowercase_		)

def 				_lowerCAmelCase   (     lowercase_		):
						return choose(8 + n	,			lowercase_		) + choose(9 + n	,			lowercase_		) - 10

def 				_lowerCAmelCase   (     lowercase_		):
						return sum(non_bouncy_exact(lowercase_		) for i in range(1	,			n + 1		)		)

def 				_lowerCAmelCase   (     lowercase_ = 100		):
						return non_bouncy_upto(lowercase_		)


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





