

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__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Tuple[int, int]:
		def constraint_to_multiple_of(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=0 ,			__SCREAMING_SNAKE_CASE=None			):
				__lowerCAmelCase:	str   				=      round(val / multiple			) * multiple

				if max_val is not None and x > max_val:
						__lowerCAmelCase:	int   				=      math.floor(val / multiple			) * multiple

				if x < min_val:
						__lowerCAmelCase:	List[str]   				=      math.ceil(val / multiple			) * multiple

				return x

		__lowerCAmelCase:	Dict   				=      (output_size, output_size) if isinstance(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			) else output_size

		__lowerCAmelCase:	List[str]   				=      get_image_size(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Dict   				=      output_size

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

		__lowerCAmelCase:	List[str]   				=      constraint_to_multiple_of(scale_height * input_height ,			multiple=__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	List[Any]   				=      constraint_to_multiple_of(scale_width * input_width ,			multiple=__SCREAMING_SNAKE_CASE			)

		return (new_height, new_width)





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  ["""pixel_values"""]

				def __init__(     self							:    int					,  UpperCamelCase__							:    bool = True					,  UpperCamelCase__							:    Dict[str, int] = None					,  UpperCamelCase__							:    PILImageResampling = PILImageResampling.BILINEAR					,  UpperCamelCase__							:    bool = False					,  UpperCamelCase__							:    int = 1					,  UpperCamelCase__							:    bool = True					,  UpperCamelCase__							:    Union[int, float] = 1 / 2_5_5					,  UpperCamelCase__							:    bool = True					,  UpperCamelCase__							:    Optional[Union[float, List[float]]] = None					,  UpperCamelCase__							:    Optional[Union[float, List[float]]] = None					,  **UpperCamelCase__							:    Union[str, Any]					,  )->       None:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      size if size is not None else {"height": 3_8_4, "width": 3_8_4}
						__lowerCAmelCase:	Any   				=      get_size_dict(UpperCamelCase__)
						__lowerCAmelCase:	str   				=      do_resize
						__lowerCAmelCase:	List[str]   				=      size
						__lowerCAmelCase:	Optional[int]   				=      keep_aspect_ratio
						__lowerCAmelCase:	Union[str, Any]   				=      ensure_multiple_of
						__lowerCAmelCase:	Union[str, Any]   				=      resample
						__lowerCAmelCase:	List[str]   				=      do_rescale
						__lowerCAmelCase:	Dict   				=      rescale_factor
						__lowerCAmelCase:	List[Any]   				=      do_normalize
						__lowerCAmelCase:	Dict   				=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__lowerCAmelCase:	int   				=      image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    np.ndarray					,  UpperCamelCase__							:    Dict[str, int]					,  UpperCamelCase__							:    bool = False					,  UpperCamelCase__							:    int = 1					,  UpperCamelCase__							:    PILImageResampling = PILImageResampling.BICUBIC					,  UpperCamelCase__							:    Optional[Union[str, ChannelDimension]] = None					,  **UpperCamelCase__							:    Dict					,  )->       np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      get_size_dict(UpperCamelCase__)
						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()}")
						__lowerCAmelCase:	Union[str, Any]   				=      get_resize_output_image_size(
						    UpperCamelCase__					,  output_size=(size["height"], size["width"])					,  keep_aspect_ratio=UpperCamelCase__					,  multiple=UpperCamelCase__					,  )
						return resize(UpperCamelCase__					,  size=UpperCamelCase__					,  resample=UpperCamelCase__					,  data_format=UpperCamelCase__					,  **UpperCamelCase__)

				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    np.ndarray					,  UpperCamelCase__							:    Union[int, float]					,  UpperCamelCase__							:    Optional[Union[str, ChannelDimension]] = None					,  **UpperCamelCase__							:    List[str]					,  )->       int:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    np.ndarray					,  UpperCamelCase__							:    Union[float, List[float]]					,  UpperCamelCase__							:    Union[float, List[float]]					,  UpperCamelCase__							:    Optional[Union[str, ChannelDimension]] = None					,  **UpperCamelCase__							:    List[str]					,  )->       np.ndarray:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    ImageInput					,  UpperCamelCase__							:    bool = None					,  UpperCamelCase__							:    int = None					,  UpperCamelCase__							:    bool = None					,  UpperCamelCase__							:    int = None					,  UpperCamelCase__							:    PILImageResampling = None					,  UpperCamelCase__							:    bool = None					,  UpperCamelCase__							:    float = None					,  UpperCamelCase__							:    bool = None					,  UpperCamelCase__							:    Optional[Union[float, List[float]]] = None					,  UpperCamelCase__							:    Optional[Union[float, List[float]]] = None					,  UpperCamelCase__							:    Optional[Union[str, TensorType]] = None					,  UpperCamelCase__							:    ChannelDimension = ChannelDimension.FIRST					,  **UpperCamelCase__							:    Any					,  )->       PIL.Image.Image:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      do_resize if do_resize is not None else self.do_resize
						__lowerCAmelCase:	Optional[int]   				=      size if size is not None else self.size
						__lowerCAmelCase:	str   				=      get_size_dict(UpperCamelCase__)
						__lowerCAmelCase:	int   				=      keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
						__lowerCAmelCase:	Any   				=      ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
						__lowerCAmelCase:	Optional[int]   				=      resample if resample is not None else self.resample
						__lowerCAmelCase:	Any   				=      do_rescale if do_rescale is not None else self.do_rescale
						__lowerCAmelCase:	Optional[int]   				=      rescale_factor if rescale_factor is not None else self.rescale_factor
						__lowerCAmelCase:	List[str]   				=      do_normalize if do_normalize is not None else self.do_normalize
						__lowerCAmelCase:	Optional[int]   				=      image_mean if image_mean is not None else self.image_mean
						__lowerCAmelCase:	Optional[int]   				=      image_std if image_std is not None else self.image_std

						__lowerCAmelCase:	int   				=      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_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.
						__lowerCAmelCase:	Union[str, Any]   				=      [to_numpy_array(UpperCamelCase__) for image in images]

						if do_resize:
								__lowerCAmelCase:	List[str]   				=      [self.resize(image=UpperCamelCase__					,  size=UpperCamelCase__					,  resample=UpperCamelCase__) for image in images]

						if do_rescale:
								__lowerCAmelCase:	Optional[Any]   				=      [self.rescale(image=UpperCamelCase__					,  scale=UpperCamelCase__) for image in images]

						if do_normalize:
								__lowerCAmelCase:	Optional[int]   				=      [self.normalize(image=UpperCamelCase__					,  mean=UpperCamelCase__					,  std=UpperCamelCase__) for image in images]

						__lowerCAmelCase:	str   				=      [to_channel_dimension_format(UpperCamelCase__					,  UpperCamelCase__) for image in images]

						__lowerCAmelCase:	List[Any]   				=      {"pixel_values": images}
						return BatchFeature(data=UpperCamelCase__					,  tensor_type=UpperCamelCase__)




				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    List[Tuple] = None)->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      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__):
										__lowerCAmelCase:	Optional[Any]   				=      target_sizes.numpy()

								__lowerCAmelCase:	List[Any]   				=      []

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

						return semantic_segmentation





"""simple docstring"""


def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     int:
		return int((input_a, input_a).count(0			) == 0			)






def    a__   (   )     ->     None:
		assert and_gate(0 ,			0			) == 0
		assert and_gate(0 ,			1			) == 0
		assert and_gate(1 ,			0			) == 0
		assert and_gate(1 ,			1			) == 1


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


