


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


SCREAMING_SNAKE_CASE			: int				   =					logging.get_logger(__name__)






class _lowerCamelCase(		_a  ):
       lowercase_					:      Union[str, Any]       					=     ["""pixel_values"""]
       def __init__(   self, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = PILImageResampling.BILINEAR, lowerCamelCase = True, lowerCamelCase = 1 / 2_55, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = True, **lowerCamelCase, )			->  None:




           """simple docstring"""

           super().__init__(**lowerCamelCase)
           _lowercase			:				List[str] 						=						size if size is not None else {'shortest_edge': 2_24}
           _lowercase			:				Dict 						=						get_size_dict(lowerCamelCase, default_to_square=lowerCamelCase)
           _lowercase			:				Dict 						=						crop_size if crop_size is not None else {'height': 2_56, 'width': 2_56}
           _lowercase			:				Tuple 						=						get_size_dict(lowerCamelCase, param_name='crop_size')

           _lowercase			:				Optional[int] 						=						do_resize
           _lowercase			:				List[str] 						=						size
           _lowercase			:				List[str] 						=						resample
           _lowercase			:				Dict 						=						do_rescale
           _lowercase			:				str 						=						rescale_factor
           _lowercase			:				Dict 						=						do_center_crop
           _lowercase			:				Optional[int] 						=						crop_size
           _lowercase			:				Tuple 						=						do_flip_channel_order
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = PIL.Image.BILINEAR, lowerCamelCase = None, **lowerCamelCase, )			->  np.ndarray:




           """simple docstring"""

           _lowercase			:				int 						=						get_size_dict(lowerCamelCase, default_to_square=lowerCamelCase)
           if "shortest_edge" not in size:
               raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}''')
           _lowercase			:				Tuple 						=						get_resize_output_image_size(lowerCamelCase, size=size['shortest_edge'], default_to_square=lowerCamelCase)
           return resize(lowerCamelCase, size=lowerCamelCase, resample=lowerCamelCase, data_format=lowerCamelCase, **lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, **lowerCamelCase, )			->  np.ndarray:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						get_size_dict(lowerCamelCase)
           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(lowerCamelCase, size=(size['height'], size['width']), data_format=lowerCamelCase, **lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, **lowerCamelCase, )			->  List[Any]:




           """simple docstring"""

           return rescale(lowerCamelCase, scale=lowerCamelCase, data_format=lowerCamelCase, **lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  np.ndarray:




           """simple docstring"""

           return flip_channel_order(lowerCamelCase, data_format=lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = ChannelDimension.FIRST, **lowerCamelCase, )			->  PIL.Image.Image:




           """simple docstring"""

           _lowercase			:				str 						=						do_resize if do_resize is not None else self.do_resize
           _lowercase			:				Tuple 						=						resample if resample is not None else self.resample
           _lowercase			:				Optional[int] 						=						do_rescale if do_rescale is not None else self.do_rescale
           _lowercase			:				str 						=						rescale_factor if rescale_factor is not None else self.rescale_factor
           _lowercase			:				Tuple 						=						do_center_crop if do_center_crop is not None else self.do_center_crop
           _lowercase			:				int 						=						(
               do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
           )

           _lowercase			:				List[str] 						=						size if size is not None else self.size
           _lowercase			:				Any 						=						get_size_dict(lowerCamelCase, default_to_square=lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						crop_size if crop_size is not None else self.crop_size
           _lowercase			:				Optional[Any] 						=						get_size_dict(lowerCamelCase, param_name='crop_size')

           _lowercase			:				Union[str, Any] 						=						make_list_of_images(lowerCamelCase)

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

           if do_resize:
               _lowercase			:				Optional[int] 						=						[self.resize(image=lowerCamelCase, size=lowerCamelCase, resample=lowerCamelCase) for image in images]

           if do_center_crop:
               _lowercase			:				List[Any] 						=						[self.center_crop(image=lowerCamelCase, size=lowerCamelCase) for image in images]

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

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

           _lowercase			:				Any 						=						[to_channel_dimension_format(lowerCamelCase, lowerCamelCase) for image in images]

           _lowercase			:				Optional[Any] 						=						{'pixel_values': images}
           return BatchFeature(data=lowerCamelCase, tensor_type=lowerCamelCase)




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  Tuple:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						outputs.logits

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

               if is_torch_tensor(lowerCamelCase):
                   _lowercase			:				List[Any] 						=						target_sizes.numpy()

               _lowercase			:				List[str] 						=						[]

               for idx in range(len(lowerCamelCase)):
                   _lowercase			:				Any 						=						torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode='bilinear', align_corners=lowerCamelCase)
                   _lowercase			:				Union[str, Any] 						=						resized_logits[0].argmax(dim=0)
                   semantic_segmentation.append(lowerCamelCase)
           else:
               _lowercase			:				Dict 						=						logits.argmax(dim=1)
               _lowercase			:				int 						=						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

           return semantic_segmentation









'''simple docstring'''

from string import ascii_uppercase

__lowerCAmelCase				:      Union[str, Any]						      ={str(ord(c) - 55): c for c in ascii_uppercase}





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int			):
    if isinstance(_lowerCamelCase		,			_lowerCamelCase			):
        raise TypeError("int() can't convert non-string with explicit base"			)
    if num < 0:
        raise ValueError("parameter must be positive int"			)
    if isinstance(_lowerCamelCase		,			_lowerCamelCase			):
        raise TypeError("'str' object cannot be interpreted as an integer"			)
    if isinstance(_lowerCamelCase		,			_lowerCamelCase			):
        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"			)
    A__							=     ""
    A__							=     0
    A__							=     0
    while div != 1:
        A__,      A__							=     divmod(_lowerCamelCase		,			_lowerCamelCase			)
        if base >= 11 and 9 < mod < 36:
            A__							=     ALPHABET_VALUES[str(_lowerCamelCase			)]
        else:
            A__							=     str(_lowerCamelCase			)
        new_value += actual_value
        A__							=     num // base
        A__							=     div
        if div == 0:
            return str(new_value[::-1]			)
        elif div == 1:
            new_value += str(_lowerCamelCase			)
            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(1000):
      assert int(decimal_to_any(num, base), base) == num, (
          num,
          base,
          decimal_to_any(num, base),
          int(decimal_to_any(num, base), base),
      )



