


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      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


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


     @property
     def  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       str				    = 1
            _A      :       Tuple				    = 3
            _A      :       List[Any]				    = (3_2, 3_2)

            _A      :       Optional[int]				    = floats_tensor((batch_size, num_channels) + sizes ,						rng=random.Random(0)).to(__lowerCamelCase)
            return image


     @property
     def  _lowerCamelCase (						self)			->				Tuple:
            torch.manual_seed(0)
            _A      :       List[str]				    = UNetaDConditionModel(
                block_out_channels=(3_2, 3_2, 6_4) ,						layers_per_block=2 ,						sample_size=3_2 ,						in_channels=7 ,						out_channels=4 ,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D") ,						up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D") ,						cross_attention_dim=3_2 ,						attention_head_dim=8 ,						use_linear_projection=__lowerCamelCase ,						only_cross_attention=(True, True, False) ,						num_class_embeds=1_0_0 ,						)
            return model


     @property
     def  _lowerCamelCase (						self)			->				Dict:
            torch.manual_seed(0)
            _A      :       Optional[int]				    = AutoencoderKL(
                block_out_channels=[3_2, 3_2, 6_4] ,						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  _lowerCamelCase (						self)			->				List[Any]:
            torch.manual_seed(0)
            _A      :       str				    = CLIPTextConfig(
                bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=3_2 ,						intermediate_size=3_7 ,						layer_norm_eps=1e-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1_0_0_0 ,						hidden_act="gelu" ,						projection_dim=5_1_2 ,						)
            return CLIPTextModel(__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Any:
            _A      :       Any				    = "cpu"  # ensure determinism for the device-dependent torch.Generator
            _A      :       Tuple				    = self.dummy_cond_unet_upscale
            _A      :       Optional[Any]				    = DDPMScheduler()
            _A      :       List[str]				    = DDIMScheduler(prediction_type="v_prediction")
            _A      :       str				    = self.dummy_vae
            _A      :       Tuple				    = self.dummy_text_encoder
            _A      :       Optional[Any]				    = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

            _A      :       Tuple				    = self.dummy_image.cpu().permute(0 ,						2 ,						3 ,						1)[0]
            _A      :       Optional[int]				    = Image.fromarray(np.uinta(__lowerCamelCase)).convert("RGB").resize((6_4, 6_4))

            # make sure here that pndm scheduler skips prk
            _A      :       Dict				    = StableDiffusionUpscalePipeline(
                unet=__lowerCamelCase ,						low_res_scheduler=__lowerCamelCase ,						scheduler=__lowerCamelCase ,						vae=__lowerCamelCase ,						text_encoder=__lowerCamelCase ,						tokenizer=__lowerCamelCase ,						max_noise_level=3_5_0 ,						)
            _A      :       Optional[int]				    = sd_pipe.to(__lowerCamelCase)
            sd_pipe.set_progress_bar_config(disable=__lowerCamelCase)

            _A      :       List[str]				    = "A painting of a squirrel eating a burger"
            _A      :       Any				    = torch.Generator(device=__lowerCamelCase).manual_seed(0)
            _A      :       Optional[int]				    = sd_pipe(
                [prompt] ,						image=__lowerCamelCase ,						generator=__lowerCamelCase ,						guidance_scale=6.0 ,						noise_level=2_0 ,						num_inference_steps=2 ,						output_type="np" ,						)

            _A      :       List[str]				    = output.images

            _A      :       str				    = torch.Generator(device=__lowerCamelCase).manual_seed(0)
            _A      :       Dict				    = sd_pipe(
                [prompt] ,						image=__lowerCamelCase ,						generator=__lowerCamelCase ,						guidance_scale=6.0 ,						noise_level=2_0 ,						num_inference_steps=2 ,						output_type="np" ,						return_dict=__lowerCamelCase ,						)[0]

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

            _A      :       List[Any]				    = low_res_image.size[0] * 4
            assert image.shape == (1, expected_height_width, expected_height_width, 3)
            _A      :       Union[str, Any]				    = 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  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       Optional[Any]				    = "cpu"  # ensure determinism for the device-dependent torch.Generator
            _A      :       Any				    = self.dummy_cond_unet_upscale
            _A      :       Dict				    = DDPMScheduler()
            _A      :       str				    = DDIMScheduler(prediction_type="v_prediction")
            _A      :       List[Any]				    = self.dummy_vae
            _A      :       Union[str, Any]				    = self.dummy_text_encoder
            _A      :       Any				    = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

            _A      :       Optional[int]				    = self.dummy_image.cpu().permute(0 ,						2 ,						3 ,						1)[0]
            _A      :       List[str]				    = Image.fromarray(np.uinta(__lowerCamelCase)).convert("RGB").resize((6_4, 6_4))

            # make sure here that pndm scheduler skips prk
            _A      :       Any				    = StableDiffusionUpscalePipeline(
                unet=__lowerCamelCase ,						low_res_scheduler=__lowerCamelCase ,						scheduler=__lowerCamelCase ,						vae=__lowerCamelCase ,						text_encoder=__lowerCamelCase ,						tokenizer=__lowerCamelCase ,						max_noise_level=3_5_0 ,						)
            _A      :       int				    = sd_pipe.to(__lowerCamelCase)
            sd_pipe.set_progress_bar_config(disable=__lowerCamelCase)

            _A      :       Dict				    = "A painting of a squirrel eating a burger"
            _A      :       str				    = sd_pipe(
                2 * [prompt] ,						image=2 * [low_res_image] ,						guidance_scale=6.0 ,						noise_level=2_0 ,						num_inference_steps=2 ,						output_type="np" ,						)
            _A      :       Tuple				    = output.images
            assert image.shape[0] == 2

            _A      :       Optional[Any]				    = torch.Generator(device=__lowerCamelCase).manual_seed(0)
            _A      :       str				    = sd_pipe(
                [prompt] ,						image=__lowerCamelCase ,						generator=__lowerCamelCase ,						num_images_per_prompt=2 ,						guidance_scale=6.0 ,						noise_level=2_0 ,						num_inference_steps=2 ,						output_type="np" ,						)
            _A      :       List[Any]				    = output.images
            assert image.shape[0] == 2


     @unittest.skipIf(torch_device != "cuda" ,						"This test requires a GPU")
     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       int				    = self.dummy_cond_unet_upscale
            _A      :       Tuple				    = DDPMScheduler()
            _A      :       int				    = DDIMScheduler(prediction_type="v_prediction")
            _A      :       List[Any]				    = self.dummy_vae
            _A      :       Any				    = self.dummy_text_encoder
            _A      :       Optional[int]				    = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

            _A      :       int				    = self.dummy_image.cpu().permute(0 ,						2 ,						3 ,						1)[0]
            _A      :       List[Any]				    = Image.fromarray(np.uinta(__lowerCamelCase)).convert("RGB").resize((6_4, 6_4))

            # put models in fp16, except vae as it overflows in fp16
            _A      :       List[str]				    = unet.half()
            _A      :       List[Any]				    = text_encoder.half()

            # make sure here that pndm scheduler skips prk
            _A      :       List[str]				    = StableDiffusionUpscalePipeline(
                unet=__lowerCamelCase ,						low_res_scheduler=__lowerCamelCase ,						scheduler=__lowerCamelCase ,						vae=__lowerCamelCase ,						text_encoder=__lowerCamelCase ,						tokenizer=__lowerCamelCase ,						max_noise_level=3_5_0 ,						)
            _A      :       Optional[int]				    = sd_pipe.to(__lowerCamelCase)
            sd_pipe.set_progress_bar_config(disable=__lowerCamelCase)

            _A      :       Union[str, Any]				    = "A painting of a squirrel eating a burger"
            _A      :       int				    = torch.manual_seed(0)
            _A      :       List[Any]				    = sd_pipe(
                [prompt] ,						image=__lowerCamelCase ,						generator=__lowerCamelCase ,						num_inference_steps=2 ,						output_type="np" ,						).images

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







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




     '''simple docstring'''


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


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Any				    = load_image(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                "/sd2-upscale/low_res_cat.png")
            _A      :       Dict				    = load_numpy(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
                "/upsampled_cat.npy")

            _A      :       Any				    = "stabilityai/stable-diffusion-x4-upscaler"
            _A      :       Optional[int]				    = StableDiffusionUpscalePipeline.from_pretrained(__lowerCamelCase)
            pipe.to(__lowerCamelCase)
            pipe.set_progress_bar_config(disable=__lowerCamelCase)
            pipe.enable_attention_slicing()

            _A      :       Any				    = "a cat sitting on a park bench"

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

            assert image.shape == (5_1_2, 5_1_2, 3)
            assert np.abs(expected_image - image).max() < 1e-3


     def  _lowerCamelCase (						self)			->				int:
            _A      :       Union[str, Any]				    = load_image(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                "/sd2-upscale/low_res_cat.png")
            _A      :       Union[str, Any]				    = load_numpy(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
                "/upsampled_cat_fp16.npy")

            _A      :       Optional[Any]				    = "stabilityai/stable-diffusion-x4-upscaler"
            _A      :       Optional[int]				    = StableDiffusionUpscalePipeline.from_pretrained(
                __lowerCamelCase ,						torch_dtype=torch.floataa ,						)
            pipe.to(__lowerCamelCase)
            pipe.set_progress_bar_config(disable=__lowerCamelCase)
            pipe.enable_attention_slicing()

            _A      :       str				    = "a cat sitting on a park bench"

            _A      :       Dict				    = torch.manual_seed(0)
            _A      :       Optional[int]				    = pipe(
                prompt=__lowerCamelCase ,						image=__lowerCamelCase ,						generator=__lowerCamelCase ,						output_type="np" ,						)
            _A      :       Optional[int]				    = output.images[0]

            assert image.shape == (5_1_2, 5_1_2, 3)
            assert np.abs(expected_image - image).max() < 5e-1


     def  _lowerCamelCase (						self)			->				Tuple:
            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()
            torch.cuda.reset_peak_memory_stats()

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

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

            _A      :       str				    = "a cat sitting on a park bench"

            _A      :       Union[str, Any]				    = torch.manual_seed(0)
            _A      :       int				    = pipe(
                prompt=__lowerCamelCase ,						image=__lowerCamelCase ,						generator=__lowerCamelCase ,						num_inference_steps=5 ,						output_type="np" ,						)

            _A      :       List[str]				    = torch.cuda.max_memory_allocated()
            # make sure that less than 2.9 GB is allocated
            assert mem_bytes < 2.9 * 1_0**9




import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCAmelCase__      		= logging.get_logger(__name__)







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				None:
            warnings.warn(
                "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                " use BeitImageProcessor instead." ,						__lowerCamelCase ,						)
            super().__init__(*__lowerCamelCase ,						**__lowerCamelCase)
