

from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
						import PIL

if is_torch_available():
						import torch


A__						=							logging.get_logger(__name__)

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[Any]       				=  ["pixel_values"]







	def __init__(   self: Optional[int]  , __UpperCamelCase: bool = True  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: PILImageResampling = PILImageResampling.BILINEAR  , __UpperCamelCase: bool = True  , __UpperCamelCase: Union[int, float] = 1 / 2_55  , __UpperCamelCase: bool = True  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: bool = True  , **__UpperCamelCase: Union[str, Any]  , ):

						'''simple docstring'''





						super().__init__(**__UpperCamelCase      )
						__magic_name__							=    size if size is not None else {'shortest_edge': 2_24}
						__magic_name__							=    get_size_dict(__UpperCamelCase  , default_to_square=__UpperCamelCase      )
						__magic_name__							=    crop_size if crop_size is not None else {'height': 2_56, 'width': 2_56}
						__magic_name__							=    get_size_dict(__UpperCamelCase  , param_name='crop_size'      )

						__magic_name__							=    do_resize
						__magic_name__							=    size
						__magic_name__							=    resample
						__magic_name__							=    do_rescale
						__magic_name__							=    rescale_factor
						__magic_name__							=    do_center_crop
						__magic_name__							=    crop_size
						__magic_name__							=    do_flip_channel_order







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Dict[str, int]  , __UpperCamelCase: PILImageResampling = PIL.Image.BILINEAR  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: Optional[int]  , ):

						'''simple docstring'''





						__magic_name__							=    get_size_dict(__UpperCamelCase  , default_to_square=__UpperCamelCase      )
						if "shortest_edge" not in size:
											raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'      )
						__magic_name__							=    get_resize_output_image_size(__UpperCamelCase  , size=size['shortest_edge']  , default_to_square=__UpperCamelCase      )
						return resize(__UpperCamelCase  , size=__UpperCamelCase  , resample=__UpperCamelCase  , data_format=__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Dict[str, int]  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: Optional[Any]  , ):

						'''simple docstring'''





						__magic_name__							=    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 center_crop(__UpperCamelCase  , size=(size['height'], size['width'])  , data_format=__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Union[int, float]  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: Tuple  , ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None      ):

						'''simple docstring'''





						return flip_channel_order(__UpperCamelCase  , data_format=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: ImageInput  , __UpperCamelCase: bool = None  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: PILImageResampling = None  , __UpperCamelCase: bool = None  , __UpperCamelCase: float = None  , __UpperCamelCase: bool = None  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: bool = None  , __UpperCamelCase: Optional[Union[str, TensorType]] = None  , __UpperCamelCase: ChannelDimension = ChannelDimension.FIRST  , **__UpperCamelCase: Dict  , ):

						'''simple docstring'''





						__magic_name__							=    do_resize if do_resize is not None else self.do_resize
						__magic_name__							=    resample if resample is not None else self.resample
						__magic_name__							=    do_rescale if do_rescale is not None else self.do_rescale
						__magic_name__							=    rescale_factor if rescale_factor is not None else self.rescale_factor
						__magic_name__							=    do_center_crop if do_center_crop is not None else self.do_center_crop
						__magic_name__							=    (
						    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
						)

						__magic_name__							=    size if size is not None else self.size
						__magic_name__							=    get_size_dict(__UpperCamelCase  , default_to_square=__UpperCamelCase      )
						__magic_name__							=    crop_size if crop_size is not None else self.crop_size
						__magic_name__							=    get_size_dict(__UpperCamelCase  , param_name='crop_size'      )

						__magic_name__							=    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.'      )

						if do_center_crop and crop_size is None:
											raise ValueError('Crop size must be specified if do_center_crop is True.'      )

						# All transformations expect numpy arrays.
						__magic_name__							=    [to_numpy_array(__UpperCamelCase      ) for image in images]

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

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

						if do_rescale:
											__magic_name__							=    [self.rescale(image=__UpperCamelCase  , scale=__UpperCamelCase      ) for image in images]

						# the pretrained checkpoints assume images are BGR, not RGB
						if do_flip_channel_order:
											__magic_name__							=    [self.flip_channel_order(image=__UpperCamelCase      ) for image in images]

						__magic_name__							=    [to_channel_dimension_format(__UpperCamelCase  , __UpperCamelCase      ) for image in images]

						__magic_name__							=    {'pixel_values': images}
						return BatchFeature(data=__UpperCamelCase  , tensor_type=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: List[Tuple] = None      ):

						'''simple docstring'''





						__magic_name__							=    outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
											if len(__UpperCamelCase      ) != len(__UpperCamelCase      ):
																raise ValueError(
																    'Make sure that you pass in as many target sizes as the batch dimension of the logits'      )

											if is_torch_tensor(__UpperCamelCase      ):
																__magic_name__							=    target_sizes.numpy()

											__magic_name__							=    []

											for idx in range(len(__UpperCamelCase      )      ):
																__magic_name__							=    torch.nn.functional.interpolate(
																    logits[idx].unsqueeze(dim=0      )  , size=target_sizes[idx]  , mode='bilinear'  , align_corners=__UpperCamelCase      )
																__magic_name__							=    resized_logits[0].argmax(dim=0      )
																semantic_segmentation.append(__UpperCamelCase      )
						else:
											__magic_name__							=    logits.argmax(dim=1      )
											__magic_name__							=    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

						return semantic_segmentation







import os

A__						=							{"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}




def 					_lowercase     (				a_				:					str				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					__magic_name__							=    0
					while index < len(a_				) - 1:
										__magic_name__							=    SYMBOLS[numerals[index]]
										__magic_name__							=    SYMBOLS[numerals[index + 1]]
										if current_value < next_value:
															total_value -= current_value
										else:
															total_value += current_value
										index += 1
					total_value += SYMBOLS[numerals[index]]

					return total_value




def 					_lowercase     (				a_				:					int				)				->	str:




					'''simple docstring'''







					__magic_name__							=    ''

					__magic_name__							=    num // 1_0_0_0
					numerals += m_count * "M"
					num %= 1_0_0_0

					__magic_name__							=    num // 1_0_0
					if c_count == 9:
										numerals += "CM"
										c_count -= 9
					elif c_count == 4:
										numerals += "CD"
										c_count -= 4
					if c_count >= 5:
										numerals += "D"
										c_count -= 5
					numerals += c_count * "C"
					num %= 1_0_0

					__magic_name__							=    num // 1_0
					if x_count == 9:
										numerals += "XC"
										x_count -= 9
					elif x_count == 4:
										numerals += "XL"
										x_count -= 4
					if x_count >= 5:
										numerals += "L"
										x_count -= 5
					numerals += x_count * "X"
					num %= 1_0

					if num == 9:
										numerals += "IX"
										num -= 9
					elif num == 4:
										numerals += "IV"
										num -= 4
					if num >= 5:
										numerals += "V"
										num -= 5
					numerals += num * "I"

					return numerals




def 					_lowercase     (				a_				:					str = "/p089_roman.txt"				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					with open(os.path.dirname(a_				) + roman_numerals_filename				) as filea:
										__magic_name__							=    filea.readlines()

					for line in lines:
										__magic_name__							=    line.strip()
										__magic_name__							=    parse_roman_numerals(a_				)
										__magic_name__							=    generate_roman_numerals(a_				)
										savings += len(a_				) - len(a_				)

					return savings


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




