"""simple docstring"""







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      lowercase__(			__SCREAMING_SNAKE_CASE				:	Optional[int] ):
     lowercase_      :							int						=      [tensor.shape for tensor in tensor_list]
     return all(shape == shapes[0] for shape in shapes[1:] )







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

   lowercase				=    True
   @property
   def      _UpperCAmelCase  (     self     )      -> str:


        '''simple docstring'''


        lowercase_      :							Dict						=      1
        lowercase_      :							str						=      4
        lowercase_      :							Optional[int]						=      (16, 16)

        lowercase_      :							Optional[int]						=      floats_tensor((batch_size, num_channels) + sizes	,rng=random.Random(0     )     ).to(lowerCamelCase__     )
        return image
   def      _UpperCAmelCase  (     self     )      -> Union[str, Any]:


        '''simple docstring'''


        torch.manual_seed(0     )
        lowercase_      :							List[str]						=      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_      :							Dict						=      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_      :							List[Any]						=      EulerDiscreteScheduler(prediction_type='sample'     )
        lowercase_      :							List[Any]						=      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='quick_gelu'	,projection_dim=512	,)
        lowercase_      :							int						=      CLIPTextModel(lowerCamelCase__     )
        lowercase_      :							str						=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

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

        return components
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase=0     )      -> List[Any]:


        '''simple docstring'''


        if str(lowerCamelCase__     ).startswith('mps'     ):
             lowercase_      :							int						=      torch.manual_seed(lowerCamelCase__     )
        else:
             lowercase_      :							Union[str, Any]						=      torch.Generator(device=lowerCamelCase__     ).manual_seed(lowerCamelCase__     )
        lowercase_      :							List[str]						=      {
            '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      _UpperCAmelCase  (     self     )      -> Any:


        '''simple docstring'''


        lowercase_      :							Any						=      'cpu'

        lowercase_      :							Any						=      self.get_dummy_components()
        lowercase_      :							Any						=      self.pipeline_class(**lowerCamelCase__     )
        pipe.to(lowerCamelCase__     )
        pipe.set_progress_bar_config(disable=lowerCamelCase__     )

        lowercase_      :							Optional[int]						=      self.get_dummy_inputs(lowerCamelCase__     )
        lowercase_      :							int						=      pipe(**lowerCamelCase__     ).images
        lowercase_      :							str						=      image[0, -3:, -3:, -1]

        self.assertEqual(image.shape	,(1, 256, 256, 3)     )
        lowercase_      :							List[str]						=      np.array(
            [0.4722_2412, 0.4192_1633, 0.4471_7434, 0.4687_4192, 0.4258_8258, 0.4615_0726, 0.467_7534, 0.4558_3832, 0.4857_9055]     )
        lowercase_      :							Union[str, Any]						=      np.abs(image_slice.flatten() - expected_slice     ).max()
        self.assertLessEqual(lowerCamelCase__	,1e-3     )
   def      _UpperCAmelCase  (     self     )      -> Optional[int]:


        '''simple docstring'''


        super().test_attention_slicing_forward_pass(expected_max_diff=7e-3     )
   def      _UpperCAmelCase  (     self     )      -> Optional[Any]:


        '''simple docstring'''


        super().test_cpu_offload_forward_pass(expected_max_diff=3e-3     )
   def      _UpperCAmelCase  (     self     )      -> Optional[int]:


        '''simple docstring'''


        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3     )
   def      _UpperCAmelCase  (     self     )      -> Optional[int]:


        '''simple docstring'''


        super().test_inference_batch_single_identical(expected_max_diff=7e-3     )
   def      _UpperCAmelCase  (     self     )      -> str:


        '''simple docstring'''


        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3     )
   def      _UpperCAmelCase  (     self     )      -> Optional[int]:


        '''simple docstring'''


        super().test_save_load_local(expected_max_difference=3e-3     )
   def      _UpperCAmelCase  (     self     )      -> Tuple:


        '''simple docstring'''


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







   def      _UpperCAmelCase  (     self     )      -> Any:


        '''simple docstring'''


        lowercase_      :							int						=      [
            'DDIMScheduler',
            'DDPMScheduler',
            'PNDMScheduler',
            'HeunDiscreteScheduler',
            'EulerAncestralDiscreteScheduler',
            'KDPM2DiscreteScheduler',
            'KDPM2AncestralDiscreteScheduler',
            'DPMSolverSDEScheduler',
        ]
        lowercase_      :							str						=      self.get_dummy_components()
        lowercase_      :							int						=      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_      :							Any						=      self.get_dummy_inputs(lowerCamelCase__     )
        lowercase_      :							Tuple						=      2

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

             lowercase_      :							Dict						=      getattr(lowerCamelCase__	,scheduler_enum.name     )
             lowercase_      :							List[Any]						=      scheduler_cls.from_config(pipe.scheduler.config     )
             lowercase_      :							Optional[Any]						=      pipe(**lowerCamelCase__     )[0]
             outputs.append(lowerCamelCase__     )

        assert check_same_shape(lowerCamelCase__     )







@require_torch_gpu
@slow
class 			UpperCamelCase						(     unittest.TestCase    ):
   def      _UpperCAmelCase  (     self     )      -> str:


        '''simple docstring'''


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   def      _UpperCAmelCase  (     self     )      -> Union[str, Any]:


        '''simple docstring'''


        lowercase_      :							str						=      torch.manual_seed(33     )

        lowercase_      :							Optional[int]						=      StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'	,torch_dtype=torch.floataa     )
        pipe.to('cuda'     )

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

        lowercase_      :							str						=      'a photo of an astronaut high resolution, unreal engine, ultra realistic'

        lowercase_      :							str						=      pipe(lowerCamelCase__	,generator=lowerCamelCase__	,output_type='latent'     ).images

        lowercase_      :							Any						=      upscaler(
            prompt=lowerCamelCase__	,image=lowerCamelCase__	,num_inference_steps=20	,guidance_scale=0	,generator=lowerCamelCase__	,output_type='np'	,).images[0]

        lowercase_      :							Optional[int]						=      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      _UpperCAmelCase  (     self     )      -> Optional[Any]:


        '''simple docstring'''


        lowercase_      :							Union[str, Any]						=      torch.manual_seed(33     )

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

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

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

        lowercase_      :							List[str]						=      upscaler(
            prompt=lowerCamelCase__	,image=lowerCamelCase__	,num_inference_steps=20	,guidance_scale=0	,generator=lowerCamelCase__	,output_type='np'	,).images[0]

        lowercase_      :							Dict						=      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






'''simple docstring'''
def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						return base * power(UpperCAmelCase_    ,    (exponent - 1)				) if exponent else 1


if __name__ == "__main__":
					print('Raise base to the power of exponent using recursion...')
					snake_case_					:      int    			=    int(input('Enter the base: ').strip())
					snake_case_					:      Optional[int]    			=    int(input('Enter the exponent: ').strip())
					snake_case_					:      Optional[int]    			=    power(base, abs(exponent))
					if exponent < 0:  # power() does not properly deal w/ negative exponents
										snake_case_					:      List[Any]    			=    1 / result
					print(F"""{base} to the power of {exponent} is {result}""")


