






'''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


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Tuple[int, int]:
      def constraint_to_multiple_of(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=0		,						__UpperCamelCase=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(__UpperCamelCase		,						__UpperCamelCase   ) else output_size

      UpperCamelCase  ,UpperCamelCase      = get_image_size(__UpperCamelCase   )
      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=__UpperCamelCase   )
      UpperCamelCase      = constraint_to_multiple_of(scale_width * input_width		,						multiple=__UpperCamelCase   )

      return (new_height, new_width)






class     a_						(      lowerCamelCase    ):
  lowercase							=				["""pixel_values"""]

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



        """simple docstring"""






        super().__init__(**_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = size if size is not None else {"""height""": 384, """width""": 384}
        UpperCamelCase      = get_size_dict(_SCREAMING_SNAKE_CASE				)
        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   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = False      ,		_SCREAMING_SNAKE_CASE = 1      ,		_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				np.ndarray:



        """simple docstring"""






        UpperCamelCase      = get_size_dict(_SCREAMING_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()}"				)
        UpperCamelCase      = get_resize_output_image_size(
            _SCREAMING_SNAKE_CASE      ,		output_size=(size["""height"""], size["""width"""])      ,		keep_aspect_ratio=_SCREAMING_SNAKE_CASE      ,		multiple=_SCREAMING_SNAKE_CASE      ,		)
        return resize(_SCREAMING_SNAKE_CASE      ,		size=_SCREAMING_SNAKE_CASE      ,		resample=_SCREAMING_SNAKE_CASE      ,		data_format=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				str:



        """simple docstring"""






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

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				np.ndarray:



        """simple docstring"""






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

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				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(_SCREAMING_SNAKE_CASE				)
        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(_SCREAMING_SNAKE_CASE				)

        if not valid_images(_SCREAMING_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 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(_SCREAMING_SNAKE_CASE				) for image in images]

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

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

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

        UpperCamelCase      = [to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				) for image in images]

        UpperCamelCase      = {"""pixel_values""": images}
        return BatchFeature(data=_SCREAMING_SNAKE_CASE      ,		tensor_type=_SCREAMING_SNAKE_CASE				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = outputs.logits

        # Resize logits and compute semantic segmentation maps
        if target_sizes is not None:
              if len(_SCREAMING_SNAKE_CASE				) != len(_SCREAMING_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(_SCREAMING_SNAKE_CASE				):
                    UpperCamelCase      = target_sizes.numpy()

              UpperCamelCase      = []

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

        return semantic_segmentation








'''simple docstring'''

from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors







def    lowercase__			(				__UpperCamelCase   )->			int:
      UpperCamelCase      = prime_factors(__UpperCamelCase   )
      if is_square_free(__UpperCamelCase   ):
            return -1 if len(__UpperCamelCase   ) % 2 else 1
      return 0


if __name__ == "__main__":
     import doctest

     doctest.testmod()
