





'''simple docstring'''


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	 =							logging.get_logger(__name__)

class    lowerCAmelCase_		(		__a		):
 __lowerCamelCase						:							Any         =					['''pixel_values''']




 def __init__(	self			,     _lowerCAmelCase = True			,     _lowerCAmelCase = None			,     _lowerCAmelCase = PILImageResampling.BILINEAR			,     _lowerCAmelCase = True			,     _lowerCAmelCase = 1 / 255			,     _lowerCAmelCase = True			,     _lowerCAmelCase = None			,     _lowerCAmelCase = True			,     **_lowerCAmelCase			,     )    ->							Dict:
     super().__init__(**snake_case__		)
     _lowerCAmelCase				=      size if size is not None else {"shortest_edge": 224}
     _lowerCAmelCase				=      get_size_dict(snake_case__			,     default_to_square=snake_case__		)
     _lowerCAmelCase				=      crop_size if crop_size is not None else {"height": 256, "width": 256}
     _lowerCAmelCase				=      get_size_dict(snake_case__			,     param_name="crop_size"		)

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




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase = PIL.Image.BILINEAR			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							List[str]:
     _lowerCAmelCase				=      get_size_dict(snake_case__			,     default_to_square=snake_case__		)
     if "shortest_edge" not in size:
         raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''		)
     _lowerCAmelCase				=      get_resize_output_image_size(snake_case__			,     size=size["shortest_edge"]			,     default_to_square=snake_case__		)
     return resize(snake_case__			,     size=snake_case__			,     resample=snake_case__			,     data_format=snake_case__			,     **snake_case__		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							Dict:
     _lowerCAmelCase				=      get_size_dict(snake_case__		)
     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(snake_case__			,     size=(size["height"], size["width"])			,     data_format=snake_case__			,     **snake_case__		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							Dict:
     return rescale(snake_case__			,     scale=snake_case__			,     data_format=snake_case__			,     **snake_case__		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None		)    ->							int:
     return flip_channel_order(snake_case__			,     data_format=snake_case__		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = ChannelDimension.FIRST			,     **_lowerCAmelCase			,     )    ->							int:
     _lowerCAmelCase				=      do_resize if do_resize is not None else self.do_resize
     _lowerCAmelCase				=      resample if resample is not None else self.resample
     _lowerCAmelCase				=      do_rescale if do_rescale is not None else self.do_rescale
     _lowerCAmelCase				=      rescale_factor if rescale_factor is not None else self.rescale_factor
     _lowerCAmelCase				=      do_center_crop if do_center_crop is not None else self.do_center_crop
     _lowerCAmelCase				=      (
         do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
     )

     _lowerCAmelCase				=      size if size is not None else self.size
     _lowerCAmelCase				=      get_size_dict(snake_case__			,     default_to_square=snake_case__		)
     _lowerCAmelCase				=      crop_size if crop_size is not None else self.crop_size
     _lowerCAmelCase				=      get_size_dict(snake_case__			,     param_name="crop_size"		)

     _lowerCAmelCase				=      make_list_of_images(snake_case__		)

     if not valid_images(snake_case__		):
         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.
     _lowerCAmelCase				=      [to_numpy_array(snake_case__		) for image in images]

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

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

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

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

     _lowerCAmelCase				=      [to_channel_dimension_format(snake_case__			,     snake_case__		) for image in images]

     _lowerCAmelCase				=      {"pixel_values": images}
     return BatchFeature(data=snake_case__			,     tensor_type=snake_case__		)





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None		)    ->							Tuple:
     _lowerCAmelCase				=      outputs.logits

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

         if is_torch_tensor(snake_case__		):
             _lowerCAmelCase				=      target_sizes.numpy()

         _lowerCAmelCase				=      []

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

     return semantic_segmentation











'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        raise TypeError("Input value must be an 'int' type"    )

    _lowerCAmelCase				=      0
    while number:
        position += 1
        number >>= 1

    return position


if __name__ == "__main__":
       import doctest

       doctest.testmod()


