




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 			lowerCAmelCase__						(				self: Tuple   ):
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()
      @property
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						1
             __lowerCamelCase   =						3
             __lowerCamelCase   =						(32, 32)

             __lowerCamelCase   =						floats_tensor((batch_size, num_channels) + sizes    ,       rng=random.Random(0   )   ).to(UpperCamelCase_   )
             return image
      @property
      def 			lowerCAmelCase__						(				self: int   ):
             torch.manual_seed(0   )
             __lowerCamelCase   =						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=UpperCamelCase_    ,       only_cross_attention=(True, True, False)    ,       num_class_embeds=1_00    ,       )
             return model
      @property
      def 			lowerCAmelCase__						(				self: Tuple   ):
             torch.manual_seed(0   )
             __lowerCamelCase   =						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 			lowerCAmelCase__						(				self: Tuple   ):
             torch.manual_seed(0   )
             __lowerCamelCase   =						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(UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						"cpu"  # ensure determinism for the device-dependent torch.Generator
             __lowerCamelCase   =						self.dummy_cond_unet_upscale
             __lowerCamelCase   =						DDPMScheduler()
             __lowerCamelCase   =						DDIMScheduler(prediction_type="""v_prediction"""   )
             __lowerCamelCase   =						self.dummy_vae
             __lowerCamelCase   =						self.dummy_text_encoder
             __lowerCamelCase   =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

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

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

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

             __lowerCamelCase   =						output.images

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

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

             __lowerCamelCase   =						low_res_image.size[0] * 4
             assert image.shape == (1, expected_height_width, expected_height_width, 3)
             __lowerCamelCase   =						np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]   )

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

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

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

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

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




      @unittest.skipIf(torch_device != """cuda"""    ,       """This test requires a GPU"""   )
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						self.dummy_cond_unet_upscale
             __lowerCamelCase   =						DDPMScheduler()
             __lowerCamelCase   =						DDIMScheduler(prediction_type="""v_prediction"""   )
             __lowerCamelCase   =						self.dummy_vae
             __lowerCamelCase   =						self.dummy_text_encoder
             __lowerCamelCase   =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

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

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

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

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

             __lowerCamelCase   =						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 			lowerCAmelCase__						(				self: List[str]   ):
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                 """/sd2-upscale/low_res_cat.png"""   )
             __lowerCamelCase   =						load_numpy(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
                 """/upsampled_cat.npy"""   )

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

             __lowerCamelCase   =						"a cat sitting on a park bench"

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

             assert image.shape == (5_12, 5_12, 3)
             assert np.abs(expected_image - image   ).max() < 1E-3
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                 """/sd2-upscale/low_res_cat.png"""   )
             __lowerCamelCase   =						load_numpy(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
                 """/upsampled_cat_fp16.npy"""   )

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

             __lowerCamelCase   =						"a cat sitting on a park bench"

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

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




      def 			lowerCAmelCase__						(				self: List[str]   ):
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

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

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

             __lowerCamelCase   =						"a cat sitting on a park bench"

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

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



def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			int)					->    float:






							'''simple docstring'''





							return base * power(_lowerCamelCase						,  (exponent - 1)) if exponent else 1


if __name__ == "__main__":
		print('Raise base to the power of exponent using recursion...')
		lowercase  :					Optional[int]													=       int(input('Enter the base: ').strip())
		lowercase  :					Tuple													=       int(input('Enter the exponent: ').strip())
		lowercase  :					str													=       power(base, abs(exponent))
		if exponent < 0:  # power() does not properly deal w/ negative exponents
				lowercase  :					Tuple													=       1 / result
		print(f"{base} to the power of {exponent} is {result}")