
"""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


_a		       =							logging.get_logger(__name__)







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   def constraint_to_multiple_of(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase=0,		__lowerCamelCase=None  ):
      UpperCAmelCase_					:					Tuple			     =    round(val / multiple  ) * multiple

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

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

      return x

   UpperCAmelCase_					:					List[Any]			     =    (output_size, output_size) if isinstance(__lowerCamelCase,		__lowerCamelCase  ) else output_size

   UpperCAmelCase_     ,   UpperCAmelCase_					:					Union[str, Any]			     =    get_image_size(__lowerCamelCase  )
   UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[int]			     =    output_size

   # determine new height and width
   UpperCAmelCase_					:					Union[str, Any]			     =    output_height / input_height
   UpperCAmelCase_					:					Optional[Any]			     =    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_					:					List[str]			     =    scale_width
      else:
         # fit height
         UpperCAmelCase_					:					List[Any]			     =    scale_height

   UpperCAmelCase_					:					Optional[Any]			     =    constraint_to_multiple_of(scale_height * input_height,		multiple=__lowerCamelCase  )
   UpperCAmelCase_					:					Dict			     =    constraint_to_multiple_of(scale_width * input_width,		multiple=__lowerCamelCase  )

   return (new_height, new_width)


class A_	(lowercase__      ):




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







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

     """simple docstring"""
     super().__init__(**lowercase_  )
     UpperCAmelCase_					:					List[str]			     =    size if size is not None else {"height": 384, "width": 384}
     UpperCAmelCase_					:					List[str]			     =    get_size_dict(lowercase_  )
     UpperCAmelCase_					:					Dict			     =    do_resize
     UpperCAmelCase_					:					int			     =    size
     UpperCAmelCase_					:					Any			     =    keep_aspect_ratio
     UpperCAmelCase_					:					Tuple			     =    ensure_multiple_of
     UpperCAmelCase_					:					Optional[Any]			     =    resample
     UpperCAmelCase_					:					Union[str, Any]			     =    do_rescale
     UpperCAmelCase_					:					List[Any]			     =    rescale_factor
     UpperCAmelCase_					:					Dict			     =    do_normalize
     UpperCAmelCase_					:					Optional[int]			     =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     UpperCAmelCase_					:					Tuple			     =    image_std if image_std is not None else IMAGENET_STANDARD_STD







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_ = False				,   lowercase_ = 1				,   lowercase_ = PILImageResampling.BICUBIC				,   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 dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""  )
     UpperCAmelCase_					:					Dict			     =    get_resize_output_image_size(
         lowercase_				,   output_size=(size["height"], size["width"])				,   keep_aspect_ratio=lowercase_				,   multiple=lowercase_				,   )
     return resize(lowercase_				,   size=lowercase_				,   resample=lowercase_				,   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_ = ChannelDimension.FIRST				,   **lowercase_				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    do_resize if do_resize is not None else self.do_resize
     UpperCAmelCase_					:					Optional[int]			     =    size if size is not None else self.size
     UpperCAmelCase_					:					Union[str, Any]			     =    get_size_dict(lowercase_  )
     UpperCAmelCase_					:					Optional[int]			     =    keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
     UpperCAmelCase_					:					Dict			     =    ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
     UpperCAmelCase_					:					Optional[int]			     =    resample if resample is not None else self.resample
     UpperCAmelCase_					:					List[str]			     =    do_rescale if do_rescale is not None else self.do_rescale
     UpperCAmelCase_					:					str			     =    rescale_factor if rescale_factor is not None else self.rescale_factor
     UpperCAmelCase_					:					List[Any]			     =    do_normalize if do_normalize is not None else self.do_normalize
     UpperCAmelCase_					:					Any			     =    image_mean if image_mean is not None else self.image_mean
     UpperCAmelCase_					:					List[str]			     =    image_std if image_std is not None else self.image_std

     UpperCAmelCase_					:					str			     =    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 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_					:					Optional[int]			     =    [to_numpy_array(lowercase_  ) for image in images]

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

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

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

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

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







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

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    outputs.logits

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

        if is_torch_tensor(lowercase_  ):
           UpperCAmelCase_					:					Union[str, Any]			     =    target_sizes.numpy()

        UpperCAmelCase_					:					int			     =    []

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

     return semantic_segmentation






'''simple docstring'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       str     		=      0
				for ch in input_str:
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      ord(SCREAMING_SNAKE_CASE__     )
								_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      pow(2							,      SCREAMING_SNAKE_CASE__     )

								# If we already turned on bit for current character's unicode
								if bitmap >> ch_unicode & 1 == 1:
												return False
								bitmap |= ch_bit_index_on
				return True


if __name__ == "__main__":
				import doctest

				doctest.testmod()




