from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
   import PIL

if is_torch_available():
   import torch


__lowerCamelCase      :			int							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(   self      :    int	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = PILImageResampling.BILINEAR	,     _lowercase      :    bool = True	,     _lowercase      :    Union[int, float] = 1 / 2_55	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = True	,     **_lowercase      :    List[Any]	,     ):



     """simple docstring"""






     super().__init__(**_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				size if size is not None else {"""shortest_edge""": 2_24}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else {"""height""": 2_56, """width""": 2_56}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     param_name="""crop_size"""				)

     SCREAMING_SNAKE_CASE__      							=				do_resize
     SCREAMING_SNAKE_CASE__      							=				size
     SCREAMING_SNAKE_CASE__      							=				resample
     SCREAMING_SNAKE_CASE__      							=				do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_center_crop
     SCREAMING_SNAKE_CASE__      							=				crop_size
     SCREAMING_SNAKE_CASE__      							=				do_flip_channel_order




   def      __a						(   self      :    List[Any]	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    PILImageResampling = PIL.Image.BILINEAR	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    str	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     if "shortest_edge" not in size:
       raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""				)
     SCREAMING_SNAKE_CASE__      							=				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      __a						(   self      :    str	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Any	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase				)
     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()}"""				)
     return center_crop(_lowercase	,     size=(size["""height"""], size["""width"""])	,     data_format=_lowercase	,     **_lowercase				)




   def      __a						(   self      :    Optional[Any]	,     _lowercase      :    np.ndarray	,     _lowercase      :    Union[int, float]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Any	,     ):



     """simple docstring"""






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




   def      __a						(   self      :    Tuple	,     _lowercase      :    np.ndarray	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None				):



     """simple docstring"""






     return flip_channel_order(_lowercase	,     data_format=_lowercase				)




   def      __a						(   self      :    List[str]	,     _lowercase      :    ImageInput	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = None	,     _lowercase      :    bool = None	,     _lowercase      :    float = None	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = None	,     _lowercase      :    Optional[Union[str, TensorType]] = None	,     _lowercase      :    ChannelDimension = ChannelDimension.FIRST	,     **_lowercase      :    int	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__      							=				resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__      							=				do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE__      							=				(
         do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
     )

     SCREAMING_SNAKE_CASE__      							=				size if size is not None else self.size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     default_to_square=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_lowercase	,     param_name="""crop_size"""				)

     SCREAMING_SNAKE_CASE__      							=				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_rescale and rescale_factor is None:
       raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

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

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__      							=				[to_numpy_array(_lowercase				) for image in images]

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

     if do_center_crop:
       SCREAMING_SNAKE_CASE__      							=				[self.center_crop(image=_lowercase	,     size=_lowercase				) for image in images]

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

     # the pretrained checkpoints assume images are BGR, not RGB
     if do_flip_channel_order:
       SCREAMING_SNAKE_CASE__      							=				[self.flip_channel_order(image=_lowercase				) for image in images]

     SCREAMING_SNAKE_CASE__      							=				[to_channel_dimension_format(_lowercase	,     _lowercase				) for image in images]

     SCREAMING_SNAKE_CASE__      							=				{"""pixel_values""": images}
     return BatchFeature(data=_lowercase	,     tensor_type=_lowercase				)




   def      __a						(   self      :    List[Any]	,     _lowercase      :    Dict	,     _lowercase      :    List[Tuple] = None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				outputs.logits

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

       if is_torch_tensor(_lowercase				):
         SCREAMING_SNAKE_CASE__      							=				target_sizes.numpy()

       SCREAMING_SNAKE_CASE__      							=				[]

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

     return semantic_segmentation

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class __snake_case    (						unittest.TestCase		):




   def      __a						(   self      :    str				):



     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




   @property
   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				1
     SCREAMING_SNAKE_CASE__      							=				3
     SCREAMING_SNAKE_CASE__      							=				(32, 32)

     SCREAMING_SNAKE_CASE__      							=				floats_tensor((batch_size, num_channels) + sizes	,     rng=random.Random(0				)				).to(_lowercase				)
     return image




   @property
   def      __a						(   self      :    Dict				):



     """simple docstring"""






     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				UNetaDConditionModel(
         block_out_channels=(32, 32, 64)	,     layers_per_block=2	,     sample_size=32	,     in_channels=7	,     out_channels=4	,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""", """CrossAttnDownBlock2D""")	,     up_block_types=("""CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """UpBlock2D""")	,     cross_attention_dim=32	,     attention_head_dim=8	,     use_linear_projection=_lowercase	,     only_cross_attention=(True, True, False)	,     num_class_embeds=1_00	,     )
     return model




   @property
   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				AutoencoderKL(
         block_out_channels=[32, 32, 64]	,     in_channels=3	,     out_channels=3	,     down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,     up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,     latent_channels=4	,     )
     return model




   @property
   def      __a						(   self      :    Dict				):



     """simple docstring"""






     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				CLIPTextConfig(
         bos_token_id=0	,     eos_token_id=2	,     hidden_size=32	,     intermediate_size=37	,     layer_norm_eps=1E-05	,     num_attention_heads=4	,     num_hidden_layers=5	,     pad_token_id=1	,     vocab_size=10_00	,     hidden_act="""gelu"""	,     projection_dim=5_12	,     )
     return CLIPTextModel(_lowercase				)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__      							=				self.dummy_cond_unet_upscale
     SCREAMING_SNAKE_CASE__      							=				DDPMScheduler()
     SCREAMING_SNAKE_CASE__      							=				DDIMScheduler(prediction_type="""v_prediction"""				)
     SCREAMING_SNAKE_CASE__      							=				self.dummy_vae
     SCREAMING_SNAKE_CASE__      							=				self.dummy_text_encoder
     SCREAMING_SNAKE_CASE__      							=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

     SCREAMING_SNAKE_CASE__      							=				self.dummy_image.cpu().permute(0	,     2	,     3	,     1				)[0]
     SCREAMING_SNAKE_CASE__      							=				Image.fromarray(np.uinta(_lowercase				)				).convert("""RGB"""				).resize((64, 64)				)

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE__      							=				StableDiffusionUpscalePipeline(
         unet=_lowercase	,     low_res_scheduler=_lowercase	,     scheduler=_lowercase	,     vae=_lowercase	,     text_encoder=_lowercase	,     tokenizer=_lowercase	,     max_noise_level=3_50	,     )
     SCREAMING_SNAKE_CASE__      							=				sd_pipe.to(_lowercase				)
     sd_pipe.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				"""A painting of a squirrel eating a burger"""
     SCREAMING_SNAKE_CASE__      							=				torch.Generator(device=_lowercase				).manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				sd_pipe(
         [prompt]	,     image=_lowercase	,     generator=_lowercase	,     guidance_scale=6.0	,     noise_level=20	,     num_inference_steps=2	,     output_type="""np"""	,     )

     SCREAMING_SNAKE_CASE__      							=				output.images

     SCREAMING_SNAKE_CASE__      							=				torch.Generator(device=_lowercase				).manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				sd_pipe(
         [prompt]	,     image=_lowercase	,     generator=_lowercase	,     guidance_scale=6.0	,     noise_level=20	,     num_inference_steps=2	,     output_type="""np"""	,     return_dict=_lowercase	,     )[0]

     SCREAMING_SNAKE_CASE__      							=				image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE__      							=				image_from_tuple[0, -3:, -3:, -1]

     SCREAMING_SNAKE_CASE__      							=				low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     SCREAMING_SNAKE_CASE__      							=				np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61]				)

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




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__      							=				self.dummy_cond_unet_upscale
     SCREAMING_SNAKE_CASE__      							=				DDPMScheduler()
     SCREAMING_SNAKE_CASE__      							=				DDIMScheduler(prediction_type="""v_prediction"""				)
     SCREAMING_SNAKE_CASE__      							=				self.dummy_vae
     SCREAMING_SNAKE_CASE__      							=				self.dummy_text_encoder
     SCREAMING_SNAKE_CASE__      							=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

     SCREAMING_SNAKE_CASE__      							=				self.dummy_image.cpu().permute(0	,     2	,     3	,     1				)[0]
     SCREAMING_SNAKE_CASE__      							=				Image.fromarray(np.uinta(_lowercase				)				).convert("""RGB"""				).resize((64, 64)				)

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE__      							=				StableDiffusionUpscalePipeline(
         unet=_lowercase	,     low_res_scheduler=_lowercase	,     scheduler=_lowercase	,     vae=_lowercase	,     text_encoder=_lowercase	,     tokenizer=_lowercase	,     max_noise_level=3_50	,     )
     SCREAMING_SNAKE_CASE__      							=				sd_pipe.to(_lowercase				)
     sd_pipe.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				"""A painting of a squirrel eating a burger"""
     SCREAMING_SNAKE_CASE__      							=				sd_pipe(
         2 * [prompt]	,     image=2 * [low_res_image]	,     guidance_scale=6.0	,     noise_level=20	,     num_inference_steps=2	,     output_type="""np"""	,     )
     SCREAMING_SNAKE_CASE__      							=				output.images
     assert image.shape[0] == 2

     SCREAMING_SNAKE_CASE__      							=				torch.Generator(device=_lowercase				).manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				sd_pipe(
         [prompt]	,     image=_lowercase	,     generator=_lowercase	,     num_images_per_prompt=2	,     guidance_scale=6.0	,     noise_level=20	,     num_inference_steps=2	,     output_type="""np"""	,     )
     SCREAMING_SNAKE_CASE__      							=				output.images
     assert image.shape[0] == 2




   @unittest.skipIf(torch_device != """cuda"""	,     """This test requires a GPU"""				)
   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.dummy_cond_unet_upscale
     SCREAMING_SNAKE_CASE__      							=				DDPMScheduler()
     SCREAMING_SNAKE_CASE__      							=				DDIMScheduler(prediction_type="""v_prediction"""				)
     SCREAMING_SNAKE_CASE__      							=				self.dummy_vae
     SCREAMING_SNAKE_CASE__      							=				self.dummy_text_encoder
     SCREAMING_SNAKE_CASE__      							=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

     SCREAMING_SNAKE_CASE__      							=				self.dummy_image.cpu().permute(0	,     2	,     3	,     1				)[0]
     SCREAMING_SNAKE_CASE__      							=				Image.fromarray(np.uinta(_lowercase				)				).convert("""RGB"""				).resize((64, 64)				)

     # put models in fp16, except vae as it overflows in fp16
     SCREAMING_SNAKE_CASE__      							=				unet.half()
     SCREAMING_SNAKE_CASE__      							=				text_encoder.half()

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE__      							=				StableDiffusionUpscalePipeline(
         unet=_lowercase	,     low_res_scheduler=_lowercase	,     scheduler=_lowercase	,     vae=_lowercase	,     text_encoder=_lowercase	,     tokenizer=_lowercase	,     max_noise_level=3_50	,     )
     SCREAMING_SNAKE_CASE__      							=				sd_pipe.to(_lowercase				)
     sd_pipe.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				"""A painting of a squirrel eating a burger"""
     SCREAMING_SNAKE_CASE__      							=				torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				sd_pipe(
         [prompt]	,     image=_lowercase	,     generator=_lowercase	,     num_inference_steps=2	,     output_type="""np"""	,     ).images

     SCREAMING_SNAKE_CASE__      							=				low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)







@slow
@require_torch_gpu
class __snake_case    (						unittest.TestCase		):




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/sd2-upscale/low_res_cat.png"""				)
     SCREAMING_SNAKE_CASE__      							=				load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
         """/upsampled_cat.npy"""				)

     SCREAMING_SNAKE_CASE__      							=				"""stabilityai/stable-diffusion-x4-upscaler"""
     SCREAMING_SNAKE_CASE__      							=				StableDiffusionUpscalePipeline.from_pretrained(_lowercase				)
     pipe.to(_lowercase				)
     pipe.set_progress_bar_config(disable=_lowercase				)
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE__      							=				"""a cat sitting on a park bench"""

     SCREAMING_SNAKE_CASE__      							=				torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				pipe(
         prompt=_lowercase	,     image=_lowercase	,     generator=_lowercase	,     output_type="""np"""	,     )
     SCREAMING_SNAKE_CASE__      							=				output.images[0]

     assert image.shape == (5_12, 5_12, 3)
     assert np.abs(expected_image - image				).max() < 1E-3




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/sd2-upscale/low_res_cat.png"""				)
     SCREAMING_SNAKE_CASE__      							=				load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
         """/upsampled_cat_fp16.npy"""				)

     SCREAMING_SNAKE_CASE__      							=				"""stabilityai/stable-diffusion-x4-upscaler"""
     SCREAMING_SNAKE_CASE__      							=				StableDiffusionUpscalePipeline.from_pretrained(
         _lowercase	,     torch_dtype=torch.floataa	,     )
     pipe.to(_lowercase				)
     pipe.set_progress_bar_config(disable=_lowercase				)
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE__      							=				"""a cat sitting on a park bench"""

     SCREAMING_SNAKE_CASE__      							=				torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				pipe(
         prompt=_lowercase	,     image=_lowercase	,     generator=_lowercase	,     output_type="""np"""	,     )
     SCREAMING_SNAKE_CASE__      							=				output.images[0]

     assert image.shape == (5_12, 5_12, 3)
     assert np.abs(expected_image - image				).max() < 5E-1




   def      __a						(   self      :    Any				):



     """simple docstring"""






     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     SCREAMING_SNAKE_CASE__      							=				load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/sd2-upscale/low_res_cat.png"""				)

     SCREAMING_SNAKE_CASE__      							=				"""stabilityai/stable-diffusion-x4-upscaler"""
     SCREAMING_SNAKE_CASE__      							=				StableDiffusionUpscalePipeline.from_pretrained(
         _lowercase	,     torch_dtype=torch.floataa	,     )
     pipe.to(_lowercase				)
     pipe.set_progress_bar_config(disable=_lowercase				)
     pipe.enable_attention_slicing(1				)
     pipe.enable_sequential_cpu_offload()

     SCREAMING_SNAKE_CASE__      							=				"""a cat sitting on a park bench"""

     SCREAMING_SNAKE_CASE__      							=				torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				pipe(
         prompt=_lowercase	,     image=_lowercase	,     generator=_lowercase	,     num_inference_steps=5	,     output_type="""np"""	,     )

     SCREAMING_SNAKE_CASE__      							=				torch.cuda.max_memory_allocated()
     # make sure that less than 2.9 GB is allocated
     assert mem_bytes < 2.9 * 10**9
