'''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


_lowerCAmelCase  :Dict  				=    logging.get_logger(__name__)


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_       ,      a_		)      ->	Tuple:





					'''simple docstring'''





					def constraint_to_multiple_of(a_       ,      a_       ,      a_=0       ,      a_=None		):
										SCREAMING_SNAKE_CASE					:			Tuple    	=       round(val / multiple		) * multiple

										if max_val is not None and x > max_val:
															SCREAMING_SNAKE_CASE					:			List[str]    	=       math.floor(val / multiple		) * multiple

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

										return x

					SCREAMING_SNAKE_CASE					:			int    	=       (output_size, output_size) if isinstance(snake_case__       ,      snake_case__		) else output_size

					SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       get_image_size(snake_case__		)
					SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			int    	=       output_size

					# determine new height and width
					SCREAMING_SNAKE_CASE					:			Dict    	=       output_height / input_height
					SCREAMING_SNAKE_CASE					:			Tuple    	=       output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width		) < abs(1 - scale_height		):
															# fit width
															SCREAMING_SNAKE_CASE					:			Any    	=       scale_width
										else:
															# fit height
															SCREAMING_SNAKE_CASE					:			int    	=       scale_height

					SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       constraint_to_multiple_of(scale_height * input_height       ,      multiple=snake_case__		)
					SCREAMING_SNAKE_CASE					:			List[str]    	=       constraint_to_multiple_of(scale_width * input_width       ,      multiple=snake_case__		)

					return (new_height, new_width)






class 				UpperCAmelCase (       _lowercase ):




				'''simple docstring'''







				snake_case__							:   str							=						['''pixel_values''']






				def __init__(       self    ,							lowercase__ = True    ,							lowercase__ = None    ,							lowercase__ = PILImageResampling.BILINEAR    ,							lowercase__ = False    ,							lowercase__ = 1    ,							lowercase__ = True    ,							lowercase__ = 1 / 255    ,							lowercase__ = True    ,							lowercase__ = None    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> None:
									super().__init__(**A_ )
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       size if size is not None else {'height': 384, 'width': 384}
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       get_size_dict(A_ )
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       do_resize
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       size
									SCREAMING_SNAKE_CASE					:			int    	=       keep_aspect_ratio
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       ensure_multiple_of
									SCREAMING_SNAKE_CASE					:			Tuple    	=       resample
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       do_rescale
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       rescale_factor
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       do_normalize
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									SCREAMING_SNAKE_CASE					:			Dict    	=       image_std if image_std is not None else IMAGENET_STANDARD_STD






				def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = False    ,							lowercase__ = 1    ,							lowercase__ = PILImageResampling.BICUBIC    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> np.ndarray:
									SCREAMING_SNAKE_CASE					:			str    	=       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()}""" )
									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       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    ,							lowercase__    ,							lowercase__    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> Tuple:
									return rescale(A_    ,							scale=A_    ,							data_format=A_    ,							**A_ )






				def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> np.ndarray:
									return normalize(A_    ,							mean=A_    ,							std=A_    ,							data_format=A_    ,							**A_ )






				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__    ,							)							-> PIL.Image.Image:
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       size if size is not None else self.size
									SCREAMING_SNAKE_CASE					:			Any    	=       get_size_dict(A_ )
									SCREAMING_SNAKE_CASE					:			Tuple    	=       keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
									SCREAMING_SNAKE_CASE					:			Tuple    	=       resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE					:			List[Any]    	=       do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE					:			Any    	=       rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE					:			List[str]    	=       do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE					:			List[str]    	=       image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE					:			int    	=       image_std if image_std is not None else self.image_std

									SCREAMING_SNAKE_CASE					:			List[str]    	=       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.
									SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [to_numpy_array(A_ ) for image in images]

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

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

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

									SCREAMING_SNAKE_CASE					:			Any    	=       [to_channel_dimension_format(A_    ,							A_ ) for image in images]

									SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {'pixel_values': images}
									return BatchFeature(data=A_    ,							tensor_type=A_ )






				def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None )							-> Tuple:
									SCREAMING_SNAKE_CASE					:			List[str]    	=       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_ ):
																			SCREAMING_SNAKE_CASE					:			List[str]    	=       target_sizes.numpy()

														SCREAMING_SNAKE_CASE					:			int    	=       []

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

									return semantic_segmentation




'''simple docstring'''





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_lowerCAmelCase  :List[Any]  				=    False






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    pass






@nightly
@require_torch_gpu
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Any:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Dict    	=       VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'A painting of a squirrel eating a burger '
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy' ).images

         with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(lowercase__ )
              SCREAMING_SNAKE_CASE					:			int    	=       VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase__ )
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       generator.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy' ).images

         assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       VersatileDiffusionTextToImagePipeline.from_pretrained(
             'shi-labs/versatile-diffusion'    ,							torch_dtype=torch.floataa )
         pipe.to(lowercase__ )
         pipe.set_progress_bar_config(disable=lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       'A painting of a squirrel eating a burger '
         SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       pipe(
             prompt=lowercase__    ,							generator=lowercase__    ,							guidance_scale=7.5    ,							num_inference_steps=50    ,							output_type='numpy' ).images

         SCREAMING_SNAKE_CASE					:			Dict    	=       image[0, 253:256, 253:256, -1]

         assert image.shape == (1, 512, 512, 3)
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



