
"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    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


_a		       =							logging.get_logger(__name__)


if is_vision_available():
    import PIL


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Tuple       =		["""pixel_values"""]







  def __init__(		self				,   lowercase_ = True				,   lowercase_ = None				,   lowercase_ = PILImageResampling.BICUBIC				,   lowercase_ = True				,   lowercase_ = None				,   lowercase_ = True				,   lowercase_ = 1 / 255				,   lowercase_ = True				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = True				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(**lowercase_  )
     UpperCAmelCase_					:					Dict			     =    size if size is not None else {"shortest_edge": 224}
     UpperCAmelCase_					:					str			     =    get_size_dict(lowercase_				,   default_to_square=lowercase_  )
     UpperCAmelCase_					:					Dict			     =    crop_size if crop_size is not None else {"height": 224, "width": 224}
     UpperCAmelCase_					:					List[Any]			     =    get_size_dict(lowercase_				,   default_to_square=lowercase_				,   param_name="crop_size"  )

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







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_ = PILImageResampling.BICUBIC				,   lowercase_ = None				,   **lowercase_				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    get_size_dict(lowercase_				,   default_to_square=lowercase_  )
     if "shortest_edge" not in size:
        raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""  )
     UpperCAmelCase_					:					Optional[Any]			     =    get_resize_output_image_size(lowercase_				,   size=size["shortest_edge"]				,   default_to_square=lowercase_  )
     return resize(lowercase_				,   size=lowercase_				,   resample=lowercase_				,   data_format=lowercase_				,   **lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_ = None				,   **lowercase_				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    get_size_dict(lowercase_  )
     if "height" not in size or "width" not in size:
        raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""  )
     return center_crop(lowercase_				,   size=(size["height"], size["width"])				,   data_format=lowercase_				,   **lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_ = None				,   **lowercase_				,   ):

     """simple docstring"""
     return rescale(lowercase_				,   scale=lowercase_				,   data_format=lowercase_				,   **lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_ = None				,   **lowercase_				,   ):

     """simple docstring"""
     return normalize(lowercase_				,   mean=lowercase_				,   std=lowercase_				,   data_format=lowercase_				,   **lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = None				,   lowercase_ = ChannelDimension.FIRST				,   **lowercase_				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    do_resize if do_resize is not None else self.do_resize
     UpperCAmelCase_					:					Optional[Any]			     =    size if size is not None else self.size
     UpperCAmelCase_					:					Any			     =    get_size_dict(lowercase_				,   param_name="size"				,   default_to_square=lowercase_  )
     UpperCAmelCase_					:					str			     =    resample if resample is not None else self.resample
     UpperCAmelCase_					:					List[Any]			     =    do_center_crop if do_center_crop is not None else self.do_center_crop
     UpperCAmelCase_					:					Optional[int]			     =    crop_size if crop_size is not None else self.crop_size
     UpperCAmelCase_					:					Union[str, Any]			     =    get_size_dict(lowercase_				,   param_name="crop_size"				,   default_to_square=lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    do_rescale if do_rescale is not None else self.do_rescale
     UpperCAmelCase_					:					Union[str, Any]			     =    rescale_factor if rescale_factor is not None else self.rescale_factor
     UpperCAmelCase_					:					Any			     =    do_normalize if do_normalize is not None else self.do_normalize
     UpperCAmelCase_					:					Union[str, Any]			     =    image_mean if image_mean is not None else self.image_mean
     UpperCAmelCase_					:					Any			     =    image_std if image_std is not None else self.image_std
     UpperCAmelCase_					:					List[str]			     =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     UpperCAmelCase_					:					Optional[Any]			     =    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:
        raise ValueError("Size 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."  )

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

     # All transformations expect numpy arrays.
     UpperCAmelCase_					:					Tuple			     =    [to_numpy_array(lowercase_  ) for image in images]

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

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

     if do_rescale:
        UpperCAmelCase_					:					Optional[int]			     =    [self.rescale(image=lowercase_				,   scale=lowercase_  ) for image in images]

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

     UpperCAmelCase_					:					Dict			     =    [to_channel_dimension_format(lowercase_				,   lowercase_  ) for image in images]

     UpperCAmelCase_					:					Union[str, Any]			     =    {"pixel_values": images}
     return BatchFeature(data=lowercase_				,   tensor_type=lowercase_  )




"""simple docstring"""





import os

_a		       =							{'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1_000}







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Union[str, Any]			     =    0

   UpperCAmelCase_					:					List[str]			     =    0
   while index < len(__lowerCamelCase  ) - 1:
      UpperCAmelCase_					:					Tuple			     =    SYMBOLS[numerals[index]]
      UpperCAmelCase_					:					List[str]			     =    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 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					List[str]			     =    ""

   UpperCAmelCase_					:					Any			     =    num // 1000
   numerals += m_count * "M"
   num %= 1000

   UpperCAmelCase_					:					Any			     =    num // 100
   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 %= 100

   UpperCAmelCase_					:					str			     =    num // 10
   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 %= 10

   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 	__a     (		__lowerCamelCase = "/p089_roman.txt"  ):
   UpperCAmelCase_					:					int			     =    0

   with open(os.path.dirname(__lowerCamelCase  ) + roman_numerals_filename  ) as filea:
      UpperCAmelCase_					:					Optional[Any]			     =    filea.readlines()

   for line in lines:
      UpperCAmelCase_					:					Tuple			     =    line.strip()
      UpperCAmelCase_					:					Optional[Any]			     =    parse_roman_numerals(__lowerCamelCase  )
      UpperCAmelCase_					:					Tuple			     =    generate_roman_numerals(__lowerCamelCase  )
      savings += len(__lowerCamelCase  ) - len(__lowerCamelCase  )

   return savings


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


