



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

class    _A	(     __UpperCAmelCase				,__UpperCAmelCase				,__UpperCAmelCase				,unittest.TestCase   ):
       UpperCamelCase__  :   Union[str, Any]						 =						StableDiffusionLatentUpscalePipeline
       UpperCamelCase__  :   Optional[Any]						 =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           '''height''',
           '''width''',
           '''cross_attention_kwargs''',
           '''negative_prompt_embeds''',
           '''prompt_embeds''',
       }
       UpperCamelCase__  :   Dict						 =						PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
       UpperCamelCase__  :   Union[str, Any]						 =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       UpperCamelCase__  :   Any						 =						frozenset(
           []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       UpperCamelCase__  :   Union[str, Any]						 =						frozenset([]   )

       UpperCamelCase__  :   int						 =						True



       @property
       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    1
           __a							=    4
           __a							=    (16, 16)

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



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           torch.manual_seed(0)
           __a							=    UNetaDConditionModel(
               act_fn='''gelu'''					,		attention_head_dim=8					,		norm_num_groups=__SCREAMING_SNAKE_CASE					,		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=__SCREAMING_SNAKE_CASE					,		only_cross_attention=__SCREAMING_SNAKE_CASE					,		out_channels=5					,		resnet_time_scale_shift='''scale_shift'''					,		time_embedding_type='''fourier'''					,		timestep_post_act='''gelu'''					,		up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')					,		)
           __a							=    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					,		)
           __a							=    EulerDiscreteScheduler(prediction_type='''sample''')
           __a							=    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					,		)
           __a							=    CLIPTextModel(__SCREAMING_SNAKE_CASE)
           __a							=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

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

           return components



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					int=0):


           '''simple docstring'''

           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           else:
               __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    {
               '''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 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    '''cpu'''

           __a							=    self.get_dummy_components()
           __a							=    self.pipeline_class(**__SCREAMING_SNAKE_CASE)
           pipe.to(__SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

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

           self.assertEqual(image.shape					,		(1, 256, 256, 3))
           __a							=    np.array(
               [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55])
           __a							=    np.abs(image_slice.flatten() - expected_slice).max()
           self.assertLessEqual(__SCREAMING_SNAKE_CASE					,		1E-3)



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

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





       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

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

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

           pipe.to(__SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
           __a							=    self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)
           __a							=    2

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

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

           assert check_same_shape(__SCREAMING_SNAKE_CASE)


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



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    torch.manual_seed(33)

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

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

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

           __a							=    pipe(__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE					,		output_type='''latent''').images

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

           __a							=    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 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    torch.manual_seed(33)

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

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

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

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

           __a							=    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 					__snake_case     (      _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						:List[Any]						=					int(input('''Enter the base: ''').strip())
 __snake_case						:Dict						=					int(input('''Enter the exponent: ''').strip())
 __snake_case						:int						=					power(base, abs(exponent))
 if exponent < 0:  # power() does not properly deal w/ negative exponents
  __snake_case						:Optional[Any]						=					1 / result
 print(f'{base} to the power of {exponent} is {result}')





