


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 		UpperCamelCase__  (			self      ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 @property
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   1
       snake_case_ =   3
       snake_case_ =   (32, 32)

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



 @property
 def 		UpperCamelCase__  (			self      ):
       torch.manual_seed(0      )
       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=_UpperCAmelCase	,   only_cross_attention=(True, True, False)	,   num_class_embeds=1_00	,   )
       return model



 @property
 def 		UpperCamelCase__  (			self      ):
       torch.manual_seed(0      )
       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 		UpperCamelCase__  (			self      ):
       torch.manual_seed(0      )
       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(_UpperCAmelCase      )



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

       snake_case_ =   self.dummy_image.cpu().permute(0	,   2	,   3	,   1      )[0]
       snake_case_ =   Image.fromarray(np.uinta(_UpperCAmelCase      )      ).convert('''RGB'''      ).resize((64, 64)      )

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

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

       snake_case_ =   output.images

       snake_case_ =   torch.Generator(device=_UpperCAmelCase      ).manual_seed(0      )
       snake_case_ =   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]

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

       snake_case_ =   low_res_image.size[0] * 4
       assert image.shape == (1, expected_height_width, expected_height_width, 3)
       snake_case_ =   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 		UpperCamelCase__  (			self      ):
       snake_case_ =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case_ =   self.dummy_cond_unet_upscale
       snake_case_ =   DDPMScheduler()
       snake_case_ =   DDIMScheduler(prediction_type='''v_prediction'''      )
       snake_case_ =   self.dummy_vae
       snake_case_ =   self.dummy_text_encoder
       snake_case_ =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

       snake_case_ =   self.dummy_image.cpu().permute(0	,   2	,   3	,   1      )[0]
       snake_case_ =   Image.fromarray(np.uinta(_UpperCAmelCase      )      ).convert('''RGB'''      ).resize((64, 64)      )

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

       snake_case_ =   '''A painting of a squirrel eating a burger'''
       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'''	,   )
       snake_case_ =   output.images
       assert image.shape[0] == 2

       snake_case_ =   torch.Generator(device=_UpperCAmelCase      ).manual_seed(0      )
       snake_case_ =   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'''	,   )
       snake_case_ =   output.images
       assert image.shape[0] == 2





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

       snake_case_ =   self.dummy_image.cpu().permute(0	,   2	,   3	,   1      )[0]
       snake_case_ =   Image.fromarray(np.uinta(_UpperCAmelCase      )      ).convert('''RGB'''      ).resize((64, 64)      )

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

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

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

       snake_case_ =   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 		UpperCamelCase__  (			self      ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-upscale/low_res_cat.png'''      )
       snake_case_ =   load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
           '''/upsampled_cat.npy'''      )

       snake_case_ =   '''stabilityai/stable-diffusion-x4-upscaler'''
       snake_case_ =   StableDiffusionUpscalePipeline.from_pretrained(_UpperCAmelCase      )
       pipe.to(_UpperCAmelCase      )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )
       pipe.enable_attention_slicing()

       snake_case_ =   '''a cat sitting on a park bench'''

       snake_case_ =   torch.manual_seed(0      )
       snake_case_ =   pipe(
           prompt=_UpperCAmelCase	,   image=_UpperCAmelCase	,   generator=_UpperCAmelCase	,   output_type='''np'''	,   )
       snake_case_ =   output.images[0]

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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-upscale/low_res_cat.png'''      )
       snake_case_ =   load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
           '''/upsampled_cat_fp16.npy'''      )

       snake_case_ =   '''stabilityai/stable-diffusion-x4-upscaler'''
       snake_case_ =   StableDiffusionUpscalePipeline.from_pretrained(
           _UpperCAmelCase	,   torch_dtype=torch.floataa	,   )
       pipe.to(_UpperCAmelCase      )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )
       pipe.enable_attention_slicing()

       snake_case_ =   '''a cat sitting on a park bench'''

       snake_case_ =   torch.manual_seed(0      )
       snake_case_ =   pipe(
           prompt=_UpperCAmelCase	,   image=_UpperCAmelCase	,   generator=_UpperCAmelCase	,   output_type='''np'''	,   )
       snake_case_ =   output.images[0]

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





 def 		UpperCamelCase__  (			self      ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

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

       snake_case_ =   '''stabilityai/stable-diffusion-x4-upscaler'''
       snake_case_ =   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()

       snake_case_ =   '''a cat sitting on a park bench'''

       snake_case_ =   torch.manual_seed(0      )
       snake_case_ =   pipe(
           prompt=_UpperCAmelCase	,   image=_UpperCAmelCase	,   generator=_UpperCAmelCase	,   num_inference_steps=5	,   output_type='''np'''	,   )

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



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = 3      ,      SCREAMING_SNAKE_CASE = 7      ,      SCREAMING_SNAKE_CASE = 100_0000      )->						int:




      """simple docstring"""

      snake_case_ =   0
      snake_case_ =   1

      for current_denominator in range(1      ,      limit + 1      ):
            snake_case_ =   current_denominator * numerator // denominator
            if current_denominator % denominator == 0:
                  current_numerator -= 1
            if current_numerator * max_denominator > current_denominator * max_numerator:
                  snake_case_ =   current_numerator
                  snake_case_ =   current_denominator
      return max_numerator


if __name__ == "__main__":
   print(solution(numerator=3, denominator=7, limit=100_0000))