



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 			__lowerCAmelCase  (						lowerCAmelCase__	,						lowerCAmelCase__	,						unittest.TestCase ):
 lowerCAmelCase__							       =	StableDiffusionSAGPipeline
 lowerCAmelCase__							       =	TEXT_TO_IMAGE_PARAMS
 lowerCAmelCase__							       =	TEXT_TO_IMAGE_BATCH_PARAMS
 lowerCAmelCase__							       =	TEXT_TO_IMAGE_IMAGE_PARAMS
 lowerCAmelCase__							       =	TEXT_TO_IMAGE_IMAGE_PARAMS
 lowerCAmelCase__							       =	False




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    torch.manual_seed(0					)
    __lowerCamelCase    				=     UNetaDConditionModel(
        block_out_channels=(32, 64)						,					layers_per_block=2						,					sample_size=32						,					in_channels=4						,					out_channels=4						,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,					cross_attention_dim=32						,					)
    __lowerCamelCase    				=     DDIMScheduler(
        beta_start=0.00_085						,					beta_end=0.012						,					beta_schedule='''scaled_linear'''						,					clip_sample=__UpperCAmelCase						,					set_alpha_to_one=__UpperCAmelCase						,					)
    torch.manual_seed(0					)
    __lowerCamelCase    				=     AutoencoderKL(
        block_out_channels=[32, 64]						,					in_channels=3						,					out_channels=3						,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,					latent_channels=4						,					)
    torch.manual_seed(0					)
    __lowerCamelCase    				=     CLIPTextConfig(
        bos_token_id=0						,					eos_token_id=2						,					hidden_size=32						,					intermediate_size=37						,					layer_norm_eps=1E-0_5						,					num_attention_heads=4						,					num_hidden_layers=5						,					pad_token_id=1						,					vocab_size=1000						,					)
    __lowerCamelCase    				=     CLIPTextModel(__UpperCAmelCase					)
    __lowerCamelCase    				=     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

    __lowerCamelCase    				=     {
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''safety_checker''': None,
        '''feature_extractor''': None,
    }
    return components




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase=0					):



    '''simple docstring'''
    if str(__UpperCAmelCase					).startswith('''mps'''					):
       __lowerCamelCase    				=     torch.manual_seed(__UpperCAmelCase					)
    else:
       __lowerCamelCase    				=     torch.Generator(device=__UpperCAmelCase					).manual_seed(__UpperCAmelCase					)
    __lowerCamelCase    				=     {
        '''prompt''': '''.''',
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 1.0,
        '''sag_scale''': 1.0,
        '''output_type''': '''numpy''',
    }
    return inputs




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    super().test_inference_batch_single_identical(expected_max_diff=3E-3					)



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




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''					)
    __lowerCamelCase    				=     sag_pipe.to(__UpperCAmelCase					)
    sag_pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

    __lowerCamelCase    				=     '''.'''
    __lowerCamelCase    				=     torch.manual_seed(0					)
    __lowerCamelCase    				=     sag_pipe(
        [prompt]						,					generator=__UpperCAmelCase						,					guidance_scale=7.5						,					sag_scale=1.0						,					num_inference_steps=20						,					output_type='''np'''					)

    __lowerCamelCase    				=     output.images

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

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase    				=     np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949]					)

    assert np.abs(image_slice.flatten() - expected_slice					).max() < 5E-2




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
    __lowerCamelCase    				=     sag_pipe.to(__UpperCAmelCase					)
    sag_pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

    __lowerCamelCase    				=     '''.'''
    __lowerCamelCase    				=     torch.manual_seed(0					)
    __lowerCamelCase    				=     sag_pipe(
        [prompt]						,					generator=__UpperCAmelCase						,					guidance_scale=7.5						,					sag_scale=1.0						,					num_inference_steps=20						,					output_type='''np'''					)

    __lowerCamelCase    				=     output.images

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

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase    				=     np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371]					)

    assert np.abs(image_slice.flatten() - expected_slice					).max() < 5E-2




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
    __lowerCamelCase    				=     sag_pipe.to(__UpperCAmelCase					)
    sag_pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

    __lowerCamelCase    				=     '''.'''
    __lowerCamelCase    				=     torch.manual_seed(0					)
    __lowerCamelCase    				=     sag_pipe(
        [prompt]						,					width=768						,					height=512						,					generator=__UpperCAmelCase						,					guidance_scale=7.5						,					sag_scale=1.0						,					num_inference_steps=20						,					output_type='''np'''						,					)

    __lowerCamelCase    				=     output.images

    assert image.shape == (1, 512, 768, 3)











def 		a__      (		_UpperCamelCase      :			int			,_UpperCamelCase      :			int				):
   while second != 0:
      __lowerCamelCase    				=     first & second
      first ^= second
      __lowerCamelCase    				=     c << 1
   return first


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a_								=       int(input("""Enter the first number: """).strip())
  a_								=       int(input("""Enter the second number: """).strip())
  print(f"{add(first, second) = }")






