


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):




      def  _snake_case					(	self						:   int						)      ->Any:






         """simple docstring"""






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




      @property
      def  _snake_case					(	self						:   Tuple						)      ->List[Any]:






         """simple docstring"""






         a__ :Any						  =    1
         a__ :int						  =    3
         a__ :List[Any]						  =    (32, 32)

         a__ :Optional[Any]						  =    floats_tensor((batch_size, num_channels) + sizes   ,     rng=random.Random(0						)						).to(_a						)
         return image




      @property
      def  _snake_case					(	self						:   str						)      ->str:






         """simple docstring"""






         torch.manual_seed(0						)
         a__ :int						  =    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=100   ,     )
         return model




      @property
      def  _snake_case					(	self						:   Dict						)      ->Dict:






         """simple docstring"""






         torch.manual_seed(0						)
         a__ :str						  =    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  _snake_case					(	self						:   Optional[Any]						)      ->Optional[int]:






         """simple docstring"""






         torch.manual_seed(0						)
         a__ :Union[str, Any]						  =    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=1000   ,     hidden_act="gelu"   ,     projection_dim=512   ,     )
         return CLIPTextModel(_a						)




      def  _snake_case					(	self						:   Tuple						)      ->Union[str, Any]:






         """simple docstring"""






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

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

         # make sure here that pndm scheduler skips prk
         a__ :Optional[int]						  =    StableDiffusionUpscalePipeline(
             unet=_a   ,     low_res_scheduler=_a   ,     scheduler=_a   ,     vae=_a   ,     text_encoder=_a   ,     tokenizer=_a   ,     max_noise_level=350   ,     )
         a__ :Tuple						  =    sd_pipe.to(_a						)
         sd_pipe.set_progress_bar_config(disable=_a						)

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

         a__ :List[str]						  =    output.images

         a__ :str						  =    torch.Generator(device=_a						).manual_seed(0						)
         a__ :Union[str, Any]						  =    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__ :Union[str, Any]						  =    image[0, -3:, -3:, -1]
         a__ :Dict						  =    image_from_tuple[0, -3:, -3:, -1]

         a__ :Union[str, Any]						  =    low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)
         a__ :List[str]						  =    np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]						)

         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  _snake_case					(	self						:   Tuple						)      ->str:






         """simple docstring"""






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

         a__ :List[str]						  =    self.dummy_image.cpu().permute(0   ,     2   ,     3   ,     1						)[0]
         a__ :Optional[Any]						  =    Image.fromarray(np.uinta(_a						)						).convert("RGB"						).resize((64, 64)						)

         # make sure here that pndm scheduler skips prk
         a__ :List[str]						  =    StableDiffusionUpscalePipeline(
             unet=_a   ,     low_res_scheduler=_a   ,     scheduler=_a   ,     vae=_a   ,     text_encoder=_a   ,     tokenizer=_a   ,     max_noise_level=350   ,     )
         a__ :Dict						  =    sd_pipe.to(_a						)
         sd_pipe.set_progress_bar_config(disable=_a						)

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

         a__ :Tuple						  =    torch.Generator(device=_a						).manual_seed(0						)
         a__ :List[str]						  =    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__ :Any						  =    output.images
         assert image.shape[0] == 2






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






         """simple docstring"""






         a__ :Any						  =    self.dummy_cond_unet_upscale
         a__ :Tuple						  =    DDPMScheduler()
         a__ :Tuple						  =    DDIMScheduler(prediction_type="v_prediction"						)
         a__ :Optional[Any]						  =    self.dummy_vae
         a__ :List[str]						  =    self.dummy_text_encoder
         a__ :Tuple						  =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

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

         # put models in fp16, except vae as it overflows in fp16
         a__ :Optional[Any]						  =    unet.half()
         a__ :Tuple						  =    text_encoder.half()

         # make sure here that pndm scheduler skips prk
         a__ :List[Any]						  =    StableDiffusionUpscalePipeline(
             unet=_a   ,     low_res_scheduler=_a   ,     scheduler=_a   ,     vae=_a   ,     text_encoder=_a   ,     tokenizer=_a   ,     max_noise_level=350   ,     )
         a__ :str						  =    sd_pipe.to(_a						)
         sd_pipe.set_progress_bar_config(disable=_a						)

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

         a__ :str						  =    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):




      def  _snake_case					(	self						:   str						)      ->List[Any]:






         """simple docstring"""






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




      def  _snake_case					(	self						:   Dict						)      ->Optional[int]:






         """simple docstring"""






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

         a__ :List[str]						  =    """stabilityai/stable-diffusion-x4-upscaler"""
         a__ :List[str]						  =    StableDiffusionUpscalePipeline.from_pretrained(_a						)
         pipe.to(_a						)
         pipe.set_progress_bar_config(disable=_a						)
         pipe.enable_attention_slicing()

         a__ :Tuple						  =    """a cat sitting on a park bench"""

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

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image						).max() < 1E-3




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






         """simple docstring"""






         a__ :Optional[int]						  =    load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/sd2-upscale/low_res_cat.png"						)
         a__ :List[Any]						  =    load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
             "/upsampled_cat_fp16.npy"						)

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

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

         a__ :Union[str, Any]						  =    torch.manual_seed(0						)
         a__ :List[str]						  =    pipe(
             prompt=_a   ,     image=_a   ,     generator=_a   ,     output_type="np"   ,     )
         a__ :Optional[int]						  =    output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image						).max() < 5E-1






      def  _snake_case					(	self						:   Optional[Any]						)      ->Union[str, Any]:






         """simple docstring"""






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

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

         a__ :Any						  =    """stabilityai/stable-diffusion-x4-upscaler"""
         a__ :Tuple						  =    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__ :Tuple						  =    """a cat sitting on a park bench"""

         a__ :Any						  =    torch.manual_seed(0						)
         a__ :Optional[Any]						  =    pipe(
             prompt=_a   ,     image=_a   ,     generator=_a   ,     num_inference_steps=5   ,     output_type="np"   ,     )

         a__ :List[Any]						  =    torch.cuda.max_memory_allocated()
         # make sure that less than 2.9 GB is allocated
         assert mem_bytes < 2.9 * 10**9






def 				lowerCamelCase__	(      a	:   int	,  a	:   int				)    ->	Any:





			"""simple docstring"""
			# "extended trapezoidal rule"
			# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
			a__ :Optional[int]						  =    (boundary[1] - boundary[0]) / steps
			a__ :str						  =    boundary[0]
			a__ :Optional[int]						  =    boundary[1]
			a__ :str						  =    make_points(a	,  a	,  a				)
			a__ :Any						  =    0.0
			y += (h / 2.0) * f(a				)
			for i in x_i:
						# print(i)
						y += h * f(a				)
			y += (h / 2.0) * f(a				)
			return y


def 				lowerCamelCase__	(      a	:   Any	,  a	:   str	,  a	:   Dict				)    ->	int:





			"""simple docstring"""
			a__ :Union[str, Any]						  =    a + h
			while x < (b - h):
						yield x
						a__ :str						  =    x + h


def 				lowerCamelCase__	(      a	:   Optional[int]				)    ->	List[Any]:  # enter your function here





			"""simple docstring"""
			a__ :Tuple						  =    (x - 0) * (x - 0)
			return y


def 				lowerCamelCase__	(      )    ->	List[str]:





			"""simple docstring"""
			a__ :Optional[Any]						  =    0.0  # Lower bound of integration
			a__ :Optional[Any]						  =    1.0  # Upper bound of integration
			a__ :List[str]						  =    1_0.0  # define number of steps or resolution
			a__ :int						  =    [a, b]  # define boundary of integration
			a__ :str						  =    method_a(a	,  a				)
			print(F'''y = {y}'''				)


if __name__ == "__main__":
		main()


