

import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
def 	a (     A__      :  str	)       -> List[str]:





      """simple docstring"""



      _lowercase      			=[tensor.shape for tensor in tensor_list]
      return all(shape == shapes[0] for shape in shapes[1:]	)





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     unittest.TestCase				):
  _a		     =    StableDiffusionLatentUpscalePipeline
  _a		     =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      """height""",
      """width""",
      """cross_attention_kwargs""",
      """negative_prompt_embeds""",
      """prompt_embeds""",
  }
  _a		     =    PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
  _a		     =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  _a		     =    frozenset(
      []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  _a		     =    frozenset([]				)

  _a		     =    True




  @property
  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        _lowercase      			=1
        _lowercase      			=4
        _lowercase      			=(16, 16)

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




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        torch.manual_seed(0   )
        _lowercase      			=UNetaDConditionModel(
            act_fn='gelu'				,  attention_head_dim=8				,  norm_num_groups=lowerCAmelCase				,  block_out_channels=[32, 32, 64, 64]				,  time_cond_proj_dim=160				,  conv_in_kernel=1				,  conv_out_kernel=1				,  cross_attention_dim=32				,  down_block_types=(
                'KDownBlock2D',
                'KCrossAttnDownBlock2D',
                'KCrossAttnDownBlock2D',
                'KCrossAttnDownBlock2D',
            )				,  in_channels=8				,  mid_block_type=lowerCAmelCase				,  only_cross_attention=lowerCAmelCase				,  out_channels=5				,  resnet_time_scale_shift='scale_shift'				,  time_embedding_type='fourier'				,  timestep_post_act='gelu'				,  up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')				,  )
        _lowercase      			=AutoencoderKL(
            block_out_channels=[32, 32, 64, 64]				,  in_channels=3				,  out_channels=3				,  down_block_types=[
                'DownEncoderBlock2D',
                'DownEncoderBlock2D',
                'DownEncoderBlock2D',
                'DownEncoderBlock2D',
            ]				,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']				,  latent_channels=4				,  )
        _lowercase      			=EulerDiscreteScheduler(prediction_type='sample'   )
        _lowercase      			=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=1_000				,  hidden_act='quick_gelu'				,  projection_dim=512				,  )
        _lowercase      			=CLIPTextModel(lowerCAmelCase   )
        _lowercase      			=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

        _lowercase      			={
            'unet': model.eval(),
            'vae': vae.eval(),
            'scheduler': scheduler,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
        }

        return components




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=0   )    ->					List[Any]:
        '''simple docstring'''






        if str(lowerCAmelCase   ).startswith('mps'   ):
              _lowercase      			=torch.manual_seed(lowerCAmelCase   )
        else:
              _lowercase      			=torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
        _lowercase      			={
            'prompt': 'A painting of a squirrel eating a burger',
            'image': self.dummy_image.cpu(),
            'generator': generator,
            'num_inference_steps': 2,
            'output_type': 'numpy',
        }
        return inputs




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			='cpu'

        _lowercase      			=self.get_dummy_components()
        _lowercase      			=self.pipeline_class(**lowerCAmelCase   )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )

        _lowercase      			=self.get_dummy_inputs(lowerCAmelCase   )
        _lowercase      			=pipe(**lowerCAmelCase   ).images
        _lowercase      			=image[0, -3:, -3:, -1]

        self.assertEqual(image.shape				,  (1, 256, 256, 3)   )
        _lowercase      			=np.array(
            [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]   )
        _lowercase      			=np.abs(image_slice.flatten() - expected_slice   ).max()
        self.assertLessEqual(lowerCAmelCase				,  1e-3   )




  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        super().test_attention_slicing_forward_pass(expected_max_diff=7e-3   )




  def     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        super().test_cpu_offload_forward_pass(expected_max_diff=3e-3   )




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3   )




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        super().test_inference_batch_single_identical(expected_max_diff=7e-3   )




  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3   )




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        super().test_save_load_local(expected_max_difference=3e-3   )




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        super().test_save_load_optional_components(expected_max_difference=3e-3   )




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=[
            'DDIMScheduler',
            'DDPMScheduler',
            'PNDMScheduler',
            'HeunDiscreteScheduler',
            'EulerAncestralDiscreteScheduler',
            'KDPM2DiscreteScheduler',
            'KDPM2AncestralDiscreteScheduler',
            'DPMSolverSDEScheduler',
        ]
        _lowercase      			=self.get_dummy_components()
        _lowercase      			=self.pipeline_class(**lowerCAmelCase   )

        # make sure that PNDM does not need warm-up
        pipe.scheduler.register_to_config(skip_prk_steps=lowerCAmelCase   )

        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )
        _lowercase      			=self.get_dummy_inputs(lowerCAmelCase   )
        _lowercase      			=2

        _lowercase      			=[]
        for scheduler_enum in KarrasDiffusionSchedulers:
              if scheduler_enum.name in skip_schedulers:
                    # no sigma schedulers are not supported
                    # no schedulers
                    continue

              _lowercase      			=getattr(lowerCAmelCase				,  scheduler_enum.name   )
              _lowercase      			=scheduler_cls.from_config(pipe.scheduler.config   )
              _lowercase      			=pipe(**lowerCAmelCase   )[0]
              outputs.append(lowerCAmelCase   )

        assert check_same_shape(lowerCAmelCase   )





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




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






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




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=torch.manual_seed(33   )

        _lowercase      			=StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'				,  torch_dtype=torch.floataa   )
        pipe.to('cuda'   )

        _lowercase      			=StableDiffusionLatentUpscalePipeline.from_pretrained(
            'stabilityai/sd-x2-latent-upscaler'				,  torch_dtype=torch.floataa   )
        upscaler.to('cuda'   )

        _lowercase      			='a photo of an astronaut high resolution, unreal engine, ultra realistic'

        _lowercase      			=pipe(lowerCAmelCase				,  generator=lowerCAmelCase				,  output_type='latent'   ).images

        _lowercase      			=upscaler(
            prompt=lowerCAmelCase				,  image=lowerCAmelCase				,  num_inference_steps=20				,  guidance_scale=0				,  generator=lowerCAmelCase				,  output_type='np'				,  ).images[0]

        _lowercase      			=load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'   )
        assert np.abs((expected_image - image).mean()   ) < 5e-2




  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase      			=torch.manual_seed(33   )

        _lowercase      			=StableDiffusionLatentUpscalePipeline.from_pretrained(
            'stabilityai/sd-x2-latent-upscaler'				,  torch_dtype=torch.floataa   )
        upscaler.to('cuda'   )

        _lowercase      			='the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

        _lowercase      			=load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'   )

        _lowercase      			=upscaler(
            prompt=lowerCAmelCase				,  image=lowerCAmelCase				,  num_inference_steps=20				,  guidance_scale=0				,  generator=lowerCAmelCase				,  output_type='np'				,  ).images[0]

        _lowercase      			=load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'   )
        assert np.abs((expected_image - image).max()   ) < 5e-2





def 	a (     A__      :  int	)       -> bool:





      """simple docstring"""



      return sum(i for i in range(1 ,  number // 2 + 1	) if number % i == 0	) == number


if __name__ == "__main__":
  print('Program to check whether a number is a Perfect number or not...')
  lowercase_   							=  int(input('Enter number: ').strip())
  print(f"{number} is {'' if perfect(number) else 'not '}a Perfect Number.")


