




'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							int			):
  return int((input_a, input_a).count(0			) != 0			)


def 						SCREAMING_SNAKE_CASE       (      ):
  assert nand_gate(0				,					0			) == 1
  assert nand_gate(0				,					1			) == 1
  assert nand_gate(1				,					0			) == 1
  assert nand_gate(1				,					1			) == 0


if __name__ == "__main__":
  print(nand_gate(0, 0))
  print(nand_gate(0, 1))
  print(nand_gate(1, 0))
  print(nand_gate(1, 1))









'''simple docstring'''


from typing import Dict, List, Optional, 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_vision_available, logging


if is_vision_available():
  import PIL


UpperCAmelCase_ 		=      logging.get_logger(__name__)





class    __lowercase						(  __magic_name__ ):
   _a							=      ["""pixel_values"""]







   def __init__(	self      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = PILImageResampling.BILINEAR      ,							UpperCamelCase = True      ,							UpperCamelCase = 1 / 255      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    None:
     super().__init__(**UpperCamelCase      )
     __a			=					size if size is not None else {'shortest_edge': 384}
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )

     __a			=					do_resize
     __a			=					size
     # Default value set here for backwards compatibility where the value in config is None
     __a			=					crop_pct if crop_pct is not None else 224 / 256
     __a			=					resample
     __a			=					do_rescale
     __a			=					rescale_factor
     __a			=					do_normalize
     __a			=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     __a			=					image_std if image_std is not None else IMAGENET_STANDARD_STD







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = PILImageResampling.BICUBIC      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    np.ndarray:
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )
     if "shortest_edge" not in size:
       raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"      )
     __a			=					size['shortest_edge']

     if shortest_edge < 384:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       __a			=					int(shortest_edge / crop_pct      )
       __a			=					get_resize_output_image_size(UpperCamelCase      ,							size=UpperCamelCase      ,							default_to_square=UpperCamelCase      )
       __a			=					resize(image=UpperCamelCase      ,							size=UpperCamelCase      ,							resample=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=UpperCamelCase      ,							size=(shortest_edge, shortest_edge)      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )
     else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           UpperCamelCase      ,							size=(shortest_edge, shortest_edge)      ,							resample=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    Any:
     return rescale(UpperCamelCase      ,							scale=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    np.ndarray:
     return normalize(UpperCamelCase      ,							mean=UpperCamelCase      ,							std=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = ChannelDimension.FIRST      ,							**UpperCamelCase      ,							)				->    PIL.Image.Image:
     __a			=					do_resize if do_resize is not None else self.do_resize
     __a			=					crop_pct if crop_pct is not None else self.crop_pct
     __a			=					resample if resample is not None else self.resample
     __a			=					do_rescale if do_rescale is not None else self.do_rescale
     __a			=					rescale_factor if rescale_factor is not None else self.rescale_factor
     __a			=					do_normalize if do_normalize is not None else self.do_normalize
     __a			=					image_mean if image_mean is not None else self.image_mean
     __a			=					image_std if image_std is not None else self.image_std

     __a			=					size if size is not None else self.size
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )

     __a			=					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 or resample is None:
       raise ValueError('Size and resample must be specified if do_resize is True.'      )

     if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
       raise ValueError('crop_pct must be specified if size < 384.'      )

     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.
     __a			=					[to_numpy_array(UpperCamelCase      ) for image in images]

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

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

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

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

     __a			=					{'pixel_values': images}
     return BatchFeature(data=UpperCamelCase      ,							tensor_type=UpperCamelCase      )



