


"""simple docstring"""


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      :       Dict		)       ->						List[str]:





						'''simple docstring'''







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







		@property
		def  a_ (					self      :       Union[str, Any]		)       ->						Any:





						'''simple docstring'''







						_A					  =			1
						_A					  =			3
						_A					  =			(32, 32)

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







		@property
		def  a_ (					self      :       Optional[int]		)       ->						Dict:





						'''simple docstring'''







						torch.manual_seed(0		)
						_A					  =			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=a__    ,   only_cross_attention=(True, True, False)    ,   num_class_embeds=1_00    ,   )
						return model







		@property
		def  a_ (					self      :       str		)       ->						Dict:





						'''simple docstring'''







						torch.manual_seed(0		)
						_A					  =			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      :       Any		)       ->						List[str]:





						'''simple docstring'''







						torch.manual_seed(0		)
						_A					  =			CLIPTextConfig(
						    bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=32    ,   intermediate_size=37    ,   layer_norm_eps=1E-0_5    ,   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(a__		)







		def  a_ (					self      :       int		)       ->						Union[str, Any]:





						'''simple docstring'''







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

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

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

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

						_A					  =			output.images

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

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

						_A					  =			low_res_image.size[0] * 4
						assert image.shape == (1, expected_height_width, expected_height_width, 3)
						_A					  =			np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]		)

						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      :       str		)       ->						str:





						'''simple docstring'''







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

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

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

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

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







		@unittest.skipIf(torch_device != "cuda"    ,   "This test requires a GPU"		)
		def  a_ (					self      :       Dict		)       ->						Union[str, Any]:





						'''simple docstring'''







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

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

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

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

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

						_A					  =			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      :       Dict		)       ->						Union[str, Any]:





						'''simple docstring'''







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







		def  a_ (					self      :       Tuple		)       ->						List[Any]:





						'''simple docstring'''







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

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

						_A					  =			"a cat sitting on a park bench"

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

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







		def  a_ (					self      :       Optional[Any]		)       ->						Optional[int]:





						'''simple docstring'''







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

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

						_A					  =			"a cat sitting on a park bench"

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

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







		def  a_ (					self      :       Optional[Any]		)       ->						Dict:





						'''simple docstring'''







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

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

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

						_A					  =			"a cat sitting on a park bench"

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

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



"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a_       							=    logging.get_logger(__name__)
class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       str    ,   *a__      :       Dict    ,   **a__      :       Optional[int]		)       ->						None:





						'''simple docstring'''







						warnings.warn(
						    "The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
						    " use GLPNImageProcessor instead."    ,   a__    ,   )
						super().__init__(*a__    ,   **a__		)