



"""simple docstring"""





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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
  import torch

if is_vision_available():
  import PIL


lowerCamelCase  			=		logging.get_logger(__name__)
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      def constraint_to_multiple_of(lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__=0      ,     lowerCAmelCase__=None ):
            UpperCAmelCase_     					=				round(val / multiple ) * multiple

            if max_val is not None and x > max_val:
                  UpperCAmelCase_     					=				math.floor(val / multiple ) * multiple

            if x < min_val:
                  UpperCAmelCase_     					=				math.ceil(val / multiple ) * multiple

            return x

      UpperCAmelCase_     					=				(output_size, output_size) if isinstance(lowerCAmelCase__      ,     lowerCAmelCase__ ) else output_size

      UpperCAmelCase_					,       UpperCAmelCase_     					=				get_image_size(lowerCAmelCase__ )
      UpperCAmelCase_					,       UpperCAmelCase_     					=				output_size

      # determine new height and width
      UpperCAmelCase_     					=				output_height / input_height
      UpperCAmelCase_     					=				output_width / input_width

      if keep_aspect_ratio:
            # scale as little as possible
            if abs(1 - scale_width ) < abs(1 - scale_height ):
                  # fit width
                  UpperCAmelCase_     					=				scale_width
            else:
                  # fit height
                  UpperCAmelCase_     					=				scale_height

      UpperCAmelCase_     					=				constraint_to_multiple_of(scale_height * input_height      ,     multiple=lowerCAmelCase__ )
      UpperCAmelCase_     					=				constraint_to_multiple_of(scale_width * input_width      ,     multiple=lowerCAmelCase__ )

      return (new_height, new_width)


class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	['''pixel_values''']



       def __init__(				self     :    Optional[int]						,							_UpperCAmelCase     :    bool = True						,							_UpperCAmelCase     :    Dict[str, int] = None						,							_UpperCAmelCase     :    PILImageResampling = PILImageResampling.BILINEAR						,							_UpperCAmelCase     :    bool = False						,							_UpperCAmelCase     :    int = 1						,							_UpperCAmelCase     :    bool = True						,							_UpperCAmelCase     :    Union[int, float] = 1 / 255						,							_UpperCAmelCase     :    bool = True						,							_UpperCAmelCase     :    Optional[Union[float, List[float]]] = None						,							_UpperCAmelCase     :    Optional[Union[float, List[float]]] = None						,							**_UpperCAmelCase     :    int						,							)					->		None:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase       )
             UpperCAmelCase_     					=				size if size is not None else {"height": 384, "width": 384}
             UpperCAmelCase_     					=				get_size_dict(_UpperCAmelCase       )
             UpperCAmelCase_     					=				do_resize
             UpperCAmelCase_     					=				size
             UpperCAmelCase_     					=				keep_aspect_ratio
             UpperCAmelCase_     					=				ensure_multiple_of
             UpperCAmelCase_     					=				resample
             UpperCAmelCase_     					=				do_rescale
             UpperCAmelCase_     					=				rescale_factor
             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



       def 	lowercase__							(				self     :    List[Any]						,							_UpperCAmelCase     :    np.ndarray						,							_UpperCAmelCase     :    Dict[str, int]						,							_UpperCAmelCase     :    bool = False						,							_UpperCAmelCase     :    int = 1						,							_UpperCAmelCase     :    PILImageResampling = PILImageResampling.BICUBIC						,							_UpperCAmelCase     :    Optional[Union[str, ChannelDimension]] = None						,							**_UpperCAmelCase     :    List[Any]						,							)					->		np.ndarray:


             '''simple docstring'''




             UpperCAmelCase_     					=				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()}"""       )
             UpperCAmelCase_     					=				get_resize_output_image_size(
                 _UpperCAmelCase						,							output_size=(size["height"], size["width"])						,							keep_aspect_ratio=_UpperCAmelCase						,							multiple=_UpperCAmelCase						,							)
             return resize(_UpperCAmelCase						,							size=_UpperCAmelCase						,							resample=_UpperCAmelCase						,							data_format=_UpperCAmelCase						,							**_UpperCAmelCase       )



       def 	lowercase__							(				self     :    List[str]						,							_UpperCAmelCase     :    np.ndarray						,							_UpperCAmelCase     :    Union[int, float]						,							_UpperCAmelCase     :    Optional[Union[str, ChannelDimension]] = None						,							**_UpperCAmelCase     :    Union[str, Any]						,							)					->		Tuple:


             '''simple docstring'''




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



       def 	lowercase__							(				self     :    Union[str, Any]						,							_UpperCAmelCase     :    np.ndarray						,							_UpperCAmelCase     :    Union[float, List[float]]						,							_UpperCAmelCase     :    Union[float, List[float]]						,							_UpperCAmelCase     :    Optional[Union[str, ChannelDimension]] = None						,							**_UpperCAmelCase     :    Any						,							)					->		np.ndarray:


             '''simple docstring'''




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



       def 	lowercase__							(				self     :    int						,							_UpperCAmelCase     :    ImageInput						,							_UpperCAmelCase     :    bool = None						,							_UpperCAmelCase     :    int = None						,							_UpperCAmelCase     :    bool = None						,							_UpperCAmelCase     :    int = None						,							_UpperCAmelCase     :    PILImageResampling = None						,							_UpperCAmelCase     :    bool = None						,							_UpperCAmelCase     :    float = None						,							_UpperCAmelCase     :    bool = None						,							_UpperCAmelCase     :    Optional[Union[float, List[float]]] = None						,							_UpperCAmelCase     :    Optional[Union[float, List[float]]] = None						,							_UpperCAmelCase     :    Optional[Union[str, TensorType]] = None						,							_UpperCAmelCase     :    ChannelDimension = ChannelDimension.FIRST						,							**_UpperCAmelCase     :    Any						,							)					->		PIL.Image.Image:


             '''simple docstring'''




             UpperCAmelCase_     					=				do_resize if do_resize is not None else self.do_resize
             UpperCAmelCase_     					=				size if size is not None else self.size
             UpperCAmelCase_     					=				get_size_dict(_UpperCAmelCase       )
             UpperCAmelCase_     					=				keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
             UpperCAmelCase_     					=				ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
             UpperCAmelCase_     					=				resample if resample is not None else self.resample
             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_     					=				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_     					=				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 or resample is None:
                   raise ValueError("Size and resample 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_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(_UpperCAmelCase       ) for image in images]

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

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

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

             UpperCAmelCase_     					=				[to_channel_dimension_format(_UpperCAmelCase						,							_UpperCAmelCase       ) for image in images]

             UpperCAmelCase_     					=				{"pixel_values": images}
             return BatchFeature(data=_UpperCAmelCase						,							tensor_type=_UpperCAmelCase       )







       def 	lowercase__							(				self     :    Union[str, Any]						,							_UpperCAmelCase     :    Optional[int]						,							_UpperCAmelCase     :    List[Tuple] = None       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				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       ):
                         UpperCAmelCase_     					=				target_sizes.numpy()

                   UpperCAmelCase_     					=				[]

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

             return semantic_segmentation










from string import ascii_uppercase

__A								=       {str(ord(c) - 55): c for c in ascii_uppercase}







def      lowerCamelCase_						(			UpperCamelCase__							: int					,	UpperCamelCase__							: int )	->			str:





				"""simple docstring"""


				if isinstance(UpperCamelCase__					,	UpperCamelCase__ ):
								raise TypeError('int() can\'t convert non-string with explicit base' )
				if num < 0:
								raise ValueError('parameter must be positive int' )
				if isinstance(UpperCamelCase__					,	UpperCamelCase__ ):
								raise TypeError('\'str\' object cannot be interpreted as an integer' )
				if isinstance(UpperCamelCase__					,	UpperCamelCase__ ):
								raise TypeError('\'float\' object cannot be interpreted as an integer' )
				if base in (0, 1):
								raise ValueError('base must be >= 2' )
				if base > 36:
								raise ValueError('base must be <= 36' )
				__lowerCamelCase									=       ''
				__lowerCamelCase									=       0
				__lowerCamelCase									=       0
				while div != 1:
								__lowerCamelCase			,		__lowerCamelCase									=       divmod(UpperCamelCase__					,	UpperCamelCase__ )
								if base >= 11 and 9 < mod < 36:
												__lowerCamelCase									=       ALPHABET_VALUES[str(UpperCamelCase__ )]
								else:
												__lowerCamelCase									=       str(UpperCamelCase__ )
								new_value += actual_value
								__lowerCamelCase									=       num // base
								__lowerCamelCase									=       div
								if div == 0:
												return str(new_value[::-1] )
								elif div == 1:
												new_value += str(UpperCamelCase__ )
												return str(new_value[::-1] )

				return new_value[::-1]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					for base in range(2, 37):
										for num in range(10_00):
															assert int(decimal_to_any(num, base), base) == num, (
															    num,
															    base,
															    decimal_to_any(num, base),
															    int(decimal_to_any(num, base), base),
															)


