
"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
    import torch


_a		       =							logging.get_logger(__name__)


class A_	(__a      ):




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







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

     """simple docstring"""
     super().__init__(**_UpperCamelCase  )
     UpperCAmelCase_					:					str			     =    size if size is not None else {"shortest_edge": 256}
     UpperCAmelCase_					:					Dict			     =    get_size_dict(_UpperCamelCase				,   default_to_square=_UpperCamelCase  )
     UpperCAmelCase_					:					int			     =    crop_size if crop_size is not None else {"height": 224, "width": 224}
     UpperCAmelCase_					:					int			     =    get_size_dict(_UpperCamelCase				,   param_name="crop_size"  )
     UpperCAmelCase_					:					Union[str, Any]			     =    do_resize
     UpperCAmelCase_					:					Any			     =    size
     UpperCAmelCase_					:					List[str]			     =    resample
     UpperCAmelCase_					:					List[str]			     =    do_center_crop
     UpperCAmelCase_					:					List[str]			     =    crop_size
     UpperCAmelCase_					:					int			     =    do_rescale
     UpperCAmelCase_					:					List[Any]			     =    rescale_factor
     UpperCAmelCase_					:					str			     =    do_normalize
     UpperCAmelCase_					:					int			     =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     UpperCAmelCase_					:					List[str]			     =    image_std if image_std is not None else IMAGENET_STANDARD_STD







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

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







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

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







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

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







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

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







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

     UpperCAmelCase_					:					List[Any]			     =    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:
        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."  )

     # All transformations expect numpy arrays.
     UpperCAmelCase_					:					List[Any]			     =    [to_numpy_array(_UpperCamelCase  ) for image in images]

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

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

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

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

     UpperCAmelCase_					:					str			     =    [to_channel_dimension_format(_UpperCamelCase				,   _UpperCamelCase  ) for image in images]

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







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

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    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_					:					List[Any]			     =    target_sizes.numpy()

        UpperCAmelCase_					:					Union[str, Any]			     =    []

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

     return semantic_segmentation




import math






def    lowerCamelCase__				(				__lowerCAmelCase  :  int				):




			"""simple docstring"""


			lowerCAmelCase_						=	0
			lowerCAmelCase_						=	0
			while num > 0:
						lowerCAmelCase_						=	num % 8
						lowerCAmelCase_						=	octal + (remainder * math.floor(math.pow(10    ,							__lowerCAmelCase				)				))
						counter += 1
						lowerCAmelCase_						=	math.floor(num / 8				)  # basically /= 8 without remainder if any
						# This formatting removes trailing '.0' from `octal`.
			return F"""0o{int(__lowerCAmelCase				)}"""






def    lowerCamelCase__				(				):




			"""simple docstring"""


			print("\n2 in octal is:"				)
			print(decimal_to_octal(2				)				)  # = 2
			print("\n8 in octal is:"				)
			print(decimal_to_octal(8				)				)  # = 10
			print("\n65 in octal is:"				)
			print(decimal_to_octal(65				)				)  # = 101
			print("\n216 in octal is:"				)
			print(decimal_to_octal(216				)				)  # = 330
			print("\n512 in octal is:"				)
			print(decimal_to_octal(512				)				)  # = 1000
			print("\n"				)


if __name__ == "__main__":
			main()



