





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 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Tuple      ):





         """simple docstring"""



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


      @property
      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   1
         __snake_case							       =   3
         __snake_case							       =   (32, 32)

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


      @property
      def    a (self       :      List[str]      ):





         """simple docstring"""



         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=a__				,			only_cross_attention=(True, True, False)				,			num_class_embeds=100				,			)
         return model


      @property
      def    a (self       :      List[str]      ):





         """simple docstring"""



         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    a (self       :      int      ):





         """simple docstring"""



         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=1000				,			hidden_act='''gelu'''				,			projection_dim=512				,			)
         return CLIPTextModel(a__      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __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(a__      )      ).convert('''RGB'''      ).resize((64, 64)      )

         # make sure here that pndm scheduler skips prk
         __snake_case							       =   StableDiffusionUpscalePipeline(
             unet=a__				,			low_res_scheduler=a__				,			scheduler=a__				,			vae=a__				,			text_encoder=a__				,			tokenizer=a__				,			max_noise_level=350				,			)
         __snake_case							       =   sd_pipe.to(a__      )
         sd_pipe.set_progress_bar_config(disable=a__      )

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

         __snake_case							       =   output.images

         __snake_case							       =   torch.Generator(device=a__      ).manual_seed(0      )
         __snake_case							       =   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]

         __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_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      ):





         """simple docstring"""



         __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(a__      )      ).convert('''RGB'''      ).resize((64, 64)      )

         # make sure here that pndm scheduler skips prk
         __snake_case							       =   StableDiffusionUpscalePipeline(
             unet=a__				,			low_res_scheduler=a__				,			scheduler=a__				,			vae=a__				,			text_encoder=a__				,			tokenizer=a__				,			max_noise_level=350				,			)
         __snake_case							       =   sd_pipe.to(a__      )
         sd_pipe.set_progress_bar_config(disable=a__      )

         __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=a__      ).manual_seed(0      )
         __snake_case							       =   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'''				,			)
         __snake_case							       =   output.images
         assert image.shape[0] == 2


      @unittest.skipIf(torch_device != '''cuda'''				,			'''This test requires a GPU'''      )
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __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(a__      )      ).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=a__				,			low_res_scheduler=a__				,			scheduler=a__				,			vae=a__				,			text_encoder=a__				,			tokenizer=a__				,			max_noise_level=350				,			)
         __snake_case							       =   sd_pipe.to(a__      )
         sd_pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   '''A painting of a squirrel eating a burger'''
         __snake_case							       =   torch.manual_seed(0      )
         __snake_case							       =   sd_pipe(
             [prompt]				,			image=a__				,			generator=a__				,			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 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



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


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __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(a__      )
         pipe.to(a__      )
         pipe.set_progress_bar_config(disable=a__      )
         pipe.enable_attention_slicing()

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

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

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


      def    a (self       :      int      ):





         """simple docstring"""



         __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(
             a__				,			torch_dtype=torch.floataa				,			)
         pipe.to(a__      )
         pipe.set_progress_bar_config(disable=a__      )
         pipe.enable_attention_slicing()

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

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

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


      def    a (self       :      str      ):





         """simple docstring"""



         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(
             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()

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

         __snake_case							       =   torch.manual_seed(0      )
         __snake_case							       =   pipe(
             prompt=a__				,			image=a__				,			generator=a__				,			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








from __future__ import annotations






def lowerCamelCase__    (							snake_case_	:  list[int]       )      ->				list[int]:  # This function is recursive
   __snake_case							       =   len(snake_case_       )
   # If the array contains only one element, we return it (it's the stop condition of
   # recursion)
   if array_length <= 1:
      return array
      # Else
   __snake_case							       =   array[0]
   __snake_case							       =   False
   __snake_case							       =   1
   __snake_case							       =   []
   while not is_found and i < array_length:
      if array[i] < pivot:
         __snake_case							       =   True
         __snake_case							       =   [element for element in array[i:] if element >= array[i]]
         __snake_case							       =   longest_subsequence(snake_case_       )
         if len(snake_case_       ) > len(snake_case_       ):
            __snake_case							       =   temp_array
      else:
         i += 1

   __snake_case							       =   [element for element in array[1:] if element >= pivot]
   __snake_case							       =   [pivot, *longest_subsequence(snake_case_       )]
   if len(snake_case_       ) > len(snake_case_       ):
      return temp_array
   else:
      return longest_subseq


if __name__ == "__main__":
    import doctest

    doctest.testmod()

