

'''simple docstring'''
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 							A__    (       unittest.TestCase  ):





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





       '''simple docstring'''






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





  @property
  def 						A  (		self      :	Any )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =1
       _SCREAMING_SNAKE_CASE							    =3
       _SCREAMING_SNAKE_CASE							    =(32, 32)

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





  @property
  def 						A  (		self      :	Union[str, Any] )	->    int:





       '''simple docstring'''






       torch.manual_seed(0 )
       _SCREAMING_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      :	str )	->    List[Any]:





       '''simple docstring'''






       torch.manual_seed(0 )
       _SCREAMING_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      :	Dict )	->    Union[str, Any]:





       '''simple docstring'''






       torch.manual_seed(0 )
       _SCREAMING_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 )	->    Optional[Any]:





       '''simple docstring'''






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

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

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

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

       _SCREAMING_SNAKE_CASE							    =output.images

       _SCREAMING_SNAKE_CASE							    =torch.Generator(device=_a ).manual_seed(0 )
       _SCREAMING_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]

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

       _SCREAMING_SNAKE_CASE							    =low_res_image.size[0] * 4
       assert image.shape == (1, expected_height_width, expected_height_width, 3)
       _SCREAMING_SNAKE_CASE							    =np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61] )

       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 )	->    str:





       '''simple docstring'''






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

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

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

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

       _SCREAMING_SNAKE_CASE							    =torch.Generator(device=_a ).manual_seed(0 )
       _SCREAMING_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'							,					)
       _SCREAMING_SNAKE_CASE							    =output.images
       assert image.shape[0] == 2





  @unittest.skipIf(torch_device != 'cuda'							,					'This test requires a GPU' )
  def 						A  (		self      :	Dict )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.dummy_cond_unet_upscale
       _SCREAMING_SNAKE_CASE							    =DDPMScheduler()
       _SCREAMING_SNAKE_CASE							    =DDIMScheduler(prediction_type='v_prediction' )
       _SCREAMING_SNAKE_CASE							    =self.dummy_vae
       _SCREAMING_SNAKE_CASE							    =self.dummy_text_encoder
       _SCREAMING_SNAKE_CASE							    =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

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

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

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

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

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







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





  def 						A  (		self      :	int )	->    Tuple:





       '''simple docstring'''






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





  def 						A  (		self      :	List[Any] )	->    List[Any]:





       '''simple docstring'''






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

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

       _SCREAMING_SNAKE_CASE							    ='a cat sitting on a park bench'

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

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





  def 						A  (		self      :	List[Any] )	->    Optional[Any]:





       '''simple docstring'''






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

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

       _SCREAMING_SNAKE_CASE							    ='a cat sitting on a park bench'

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

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





  def 						A  (		self      :	Union[str, Any] )	->    Dict:





       '''simple docstring'''






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

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

       _SCREAMING_SNAKE_CASE							    ='stabilityai/stable-diffusion-x4-upscaler'
       _SCREAMING_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()

       _SCREAMING_SNAKE_CASE							    ='a cat sitting on a park bench'

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

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

'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


_A			:      Optional[int]					      =				logging.get_logger(__name__)







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 def __init__(      self   :	Dict      ,       *SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       **SCREAMING_SNAKE_CASE__   :	List[str]							)  ->    None:
  warnings.warn(
      """The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
      """ Please use SegformerImageProcessor instead."""      ,       SCREAMING_SNAKE_CASE__      ,       )
  super().__init__(*SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)






