


"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


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


if is_vision_available():
			import PIL


class    __UpperCAmelCase(       lowercase__							):




						"""simple docstring"""




						__lowerCamelCase    	= ["""pixel_values"""]




						def __init__(       self	,  snake_case__ = True	,  snake_case__ = None	,  snake_case__ = PILImageResampling.BICUBIC	,  snake_case__ = True	,  snake_case__ = None	,  snake_case__ = True	,  snake_case__ = 1 / 255	,  snake_case__ = True	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = True	,  **snake_case__	,  ):
												'''simple docstring'''
												super().__init__(**lowercase_      )
												lowercase__    :				List[str]=	size if size is not None else {"shortest_edge": 224}
												lowercase__    :				Tuple=	get_size_dict(lowercase_	,  default_to_square=lowercase_      )
												lowercase__    :				Union[str, Any]=	crop_size if crop_size is not None else {"height": 224, "width": 224}
												lowercase__    :				int=	get_size_dict(lowercase_	,  default_to_square=lowercase_	,  param_name="crop_size"      )

												lowercase__    :				Any=	do_resize
												lowercase__    :				List[Any]=	size
												lowercase__    :				Union[str, Any]=	resample
												lowercase__    :				int=	do_center_crop
												lowercase__    :				str=	crop_size
												lowercase__    :				Optional[Any]=	do_rescale
												lowercase__    :				Tuple=	rescale_factor
												lowercase__    :				Union[str, Any]=	do_normalize
												lowercase__    :				Dict=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
												lowercase__    :				Tuple=	image_std if image_std is not None else OPENAI_CLIP_STD
												lowercase__    :				List[Any]=	do_convert_rgb




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__ = PILImageResampling.BICUBIC	,  snake_case__ = None	,  **snake_case__	,  ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	get_size_dict(lowercase_	,  default_to_square=lowercase_      )
												if "shortest_edge" not in size:
																		raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''      )
												lowercase__    :				str=	get_resize_output_image_size(lowercase_	,  size=size["shortest_edge"]	,  default_to_square=lowercase_      )
												return resize(lowercase_	,  size=lowercase_	,  resample=lowercase_	,  data_format=lowercase_	,  **lowercase_      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__ = None	,  **snake_case__	,  ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	get_size_dict(lowercase_      )
												if "height" not in size or "width" not in size:
																		raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''      )
												return center_crop(lowercase_	,  size=(size["height"], size["width"])	,  data_format=lowercase_	,  **lowercase_      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__ = None	,  **snake_case__	,  ):
												'''simple docstring'''
												return rescale(lowercase_	,  scale=lowercase_	,  data_format=lowercase_	,  **lowercase_      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__ = None	,  **snake_case__	,  ):
												'''simple docstring'''
												return normalize(lowercase_	,  mean=lowercase_	,  std=lowercase_	,  data_format=lowercase_	,  **lowercase_      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = ChannelDimension.FIRST	,  **snake_case__	,  ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	do_resize if do_resize is not None else self.do_resize
												lowercase__    :				List[str]=	size if size is not None else self.size
												lowercase__    :				str=	get_size_dict(lowercase_	,  param_name="size"	,  default_to_square=lowercase_      )
												lowercase__    :				Optional[Any]=	resample if resample is not None else self.resample
												lowercase__    :				Union[str, Any]=	do_center_crop if do_center_crop is not None else self.do_center_crop
												lowercase__    :				Tuple=	crop_size if crop_size is not None else self.crop_size
												lowercase__    :				List[Any]=	get_size_dict(lowercase_	,  param_name="crop_size"	,  default_to_square=lowercase_      )
												lowercase__    :				Union[str, Any]=	do_rescale if do_rescale is not None else self.do_rescale
												lowercase__    :				List[str]=	rescale_factor if rescale_factor is not None else self.rescale_factor
												lowercase__    :				Any=	do_normalize if do_normalize is not None else self.do_normalize
												lowercase__    :				List[Any]=	image_mean if image_mean is not None else self.image_mean
												lowercase__    :				Optional[Any]=	image_std if image_std is not None else self.image_std
												lowercase__    :				str=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

												lowercase__    :				Tuple=	make_list_of_images(lowercase_      )

												if not valid_images(lowercase_      ):
																		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:
																		raise ValueError("Size must be specified if do_resize is True."      )

												if do_center_crop and crop_size is None:
																		raise ValueError("Crop size must be specified if do_center_crop 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."      )

												# PIL RGBA images are converted to RGB
												if do_convert_rgb:
																		lowercase__    :				List[str]=	[convert_to_rgb(lowercase_      ) for image in images]

												# All transformations expect numpy arrays.
												lowercase__    :				Dict=	[to_numpy_array(lowercase_      ) for image in images]

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

												if do_center_crop:
																		lowercase__    :				List[str]=	[self.center_crop(image=lowercase_	,  size=lowercase_      ) for image in images]

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

												if do_normalize:
																		lowercase__    :				List[Any]=	[self.normalize(image=lowercase_	,  mean=lowercase_	,  std=lowercase_      ) for image in images]

												lowercase__    :				str=	[to_channel_dimension_format(lowercase_	,  lowercase_      ) for image in images]

												lowercase__    :				Tuple=	{"pixel_values": images}
												return BatchFeature(data=lowercase_	,  tensor_type=lowercase_      )














"""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


a			:    Union[str, Any]				 =		False


class    __UpperCAmelCase(       unittest.TestCase							):




						"""simple docstring"""




						pass


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




						"""simple docstring"""




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Dict=	VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"      )
												# remove text_unet
												pipe.remove_unused_weights()
												pipe.to(snake_case__      )
												pipe.set_progress_bar_config(disable=snake_case__      )

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

												with tempfile.TemporaryDirectory() as tmpdirname:
																		pipe.save_pretrained(snake_case__      )
																		lowercase__    :				Optional[Any]=	VersatileDiffusionTextToImagePipeline.from_pretrained(snake_case__      )
												pipe.to(snake_case__      )
												pipe.set_progress_bar_config(disable=snake_case__      )

												lowercase__    :				Any=	generator.manual_seed(0      )
												lowercase__    :				Tuple=	pipe(
												    prompt=snake_case__	,  generator=snake_case__	,  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      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	VersatileDiffusionTextToImagePipeline.from_pretrained(
												    "shi-labs/versatile-diffusion"	,  torch_dtype=torch.floataa      )
												pipe.to(snake_case__      )
												pipe.set_progress_bar_config(disable=snake_case__      )

												lowercase__    :				List[str]=	"A painting of a squirrel eating a burger "
												lowercase__    :				Union[str, Any]=	torch.manual_seed(0      )
												lowercase__    :				Optional[Any]=	pipe(
												    prompt=snake_case__	,  generator=snake_case__	,  guidance_scale=7.5	,  num_inference_steps=50	,  output_type="numpy"      ).images

												lowercase__    :				List[str]=	image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												lowercase__    :				Optional[int]=	np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]      )

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






