
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


_UpperCAmelCase :			Optional[int]              =	logging.get_logger(__name__)


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  Tuple[int, int]:
		'''simple docstring'''

		def constraint_to_multiple_of(lowercase   ,	lowercase   ,	lowercase=0   ,	lowercase=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(lowercase   ,	lowercase			) else output_size

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

		return (new_height, new_width)


class     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	Any     					=    ["pixel_values"]



		def __init__(			self    ,				A_ = True    ,				A_ = None    ,				A_ = PILImageResampling.BILINEAR    ,				A_ = False    ,				A_ = 1    ,				A_ = True    ,				A_ = 1 / 255    ,				A_ = True    ,				A_ = None    ,				A_ = None    ,				**A_    ,				)  -> None:
				"""simple docstring"""
				super().__init__(**A_	)
				UpperCamelCase          =					size if size is not None else {'height': 384, 'width': 384}
				UpperCamelCase          =					get_size_dict(A_	)
				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 __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_ = False    ,				A_ = 1    ,				A_ = PILImageResampling.BICUBIC    ,				A_ = None    ,				**A_    ,				)  -> np.ndarray:
				"""simple docstring"""
				UpperCamelCase          =					get_size_dict(A_	)
				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(
				    A_    ,				output_size=(size['height'], size['width'])    ,				keep_aspect_ratio=A_    ,				multiple=A_    ,				)
				return resize(A_    ,				size=A_    ,				resample=A_    ,				data_format=A_    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_ = None    ,				**A_    ,				)  -> Any:
				"""simple docstring"""
				return rescale(A_    ,				scale=A_    ,				data_format=A_    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_ = None    ,				**A_    ,				)  -> np.ndarray:
				"""simple docstring"""
				return normalize(A_    ,				mean=A_    ,				std=A_    ,				data_format=A_    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = ChannelDimension.FIRST    ,				**A_    ,				)  -> 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(A_	)
				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(A_	)

				if not valid_images(A_	):
						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(A_	) for image in images]

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

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

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

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

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



		def __UpperCamelCase			(			self    ,				A_    ,				A_ = None	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					outputs.logits

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

						if is_torch_tensor(A_	):
								UpperCamelCase          =					target_sizes.numpy()

						UpperCamelCase          =					[]

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

				return semantic_segmentation


import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation


class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('swish'	)

				self.assertIsInstance(A_    ,				nn.SiLU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('silu'	)

				self.assertIsInstance(A_    ,				nn.SiLU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('mish'	)

				self.assertIsInstance(A_    ,				nn.Mish	)

				self.assertEqual(act(torch.tensor(-200    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('gelu'	)

				self.assertIsInstance(A_    ,				nn.GELU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)
