




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()



@skip_mps
class 					__lowerCamelCase	(_a							,     _a							,     _a							,     unittest.TestCase		):
       _lowercase    							=      StableDiffusionModelEditingPipeline
       _lowercase    							=      TEXT_TO_IMAGE_PARAMS
       _lowercase    							=      TEXT_TO_IMAGE_BATCH_PARAMS
       _lowercase    							=      TEXT_TO_IMAGE_IMAGE_PARAMS
       _lowercase    							=      TEXT_TO_IMAGE_IMAGE_PARAMS







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            torch.manual_seed(0			)
            __UpperCamelCase				=	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,)
            __UpperCamelCase				=	DDIMScheduler()
            torch.manual_seed(0			)
            __UpperCamelCase				=	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			)
            __UpperCamelCase				=	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,)
            __UpperCamelCase				=	CLIPTextModel(A_			)
            __UpperCamelCase				=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

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







       def 						snake_case_					(	self:				Any,A_:				List[Any],A_:				Union[str, Any]=0			):






            '''simple docstring'''




            __UpperCamelCase				=	torch.manual_seed(A_			)
            __UpperCamelCase				=	{
                'prompt': 'A field of roses',
                'generator': generator,
                # Setting height and width to None to prevent OOMs on CPU.
                'height': None,
                'width': None,
                'num_inference_steps': 2,
                'guidance_scale': 6.0,
                'output_type': 'numpy',
            }
            return inputs







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            __UpperCamelCase				=	'cpu'  # ensure determinism for the device-dependent torch.Generator
            __UpperCamelCase				=	self.get_dummy_components()
            __UpperCamelCase				=	StableDiffusionModelEditingPipeline(**A_			)
            __UpperCamelCase				=	sd_pipe.to(A_			)
            sd_pipe.set_progress_bar_config(disable=A_			)

            __UpperCamelCase				=	self.get_dummy_inputs(A_			)
            __UpperCamelCase				=	sd_pipe(**A_			).images
            __UpperCamelCase				=	image[0, -3:, -3:, -1]
            assert image.shape == (1, 64, 64, 3)

            __UpperCamelCase				=	np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]			)

            assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            __UpperCamelCase				=	'cpu'  # ensure determinism for the device-dependent torch.Generator
            __UpperCamelCase				=	self.get_dummy_components()
            __UpperCamelCase				=	StableDiffusionModelEditingPipeline(**A_			)
            __UpperCamelCase				=	sd_pipe.to(A_			)
            sd_pipe.set_progress_bar_config(disable=A_			)

            __UpperCamelCase				=	self.get_dummy_inputs(A_			)
            __UpperCamelCase				=	'french fries'
            __UpperCamelCase				=	sd_pipe(**A_,negative_prompt=A_			)
            __UpperCamelCase				=	output.images
            __UpperCamelCase				=	image[0, -3:, -3:, -1]

            assert image.shape == (1, 64, 64, 3)

            __UpperCamelCase				=	np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]			)

            assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2







       def 						snake_case_					(	self:				Union[str, Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	'cpu'  # ensure determinism for the device-dependent torch.Generator
            __UpperCamelCase				=	self.get_dummy_components()
            __UpperCamelCase				=	EulerAncestralDiscreteScheduler(
                beta_start=0.0_0_0_8_5,beta_end=0.0_1_2,beta_schedule='scaled_linear'			)
            __UpperCamelCase				=	StableDiffusionModelEditingPipeline(**A_			)
            __UpperCamelCase				=	sd_pipe.to(A_			)
            sd_pipe.set_progress_bar_config(disable=A_			)

            __UpperCamelCase				=	self.get_dummy_inputs(A_			)
            __UpperCamelCase				=	sd_pipe(**A_			).images
            __UpperCamelCase				=	image[0, -3:, -3:, -1]

            assert image.shape == (1, 64, 64, 3)

            __UpperCamelCase				=	np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]			)

            assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2







       def 						snake_case_					(	self:				Union[str, Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	'cpu'  # ensure determinism for the device-dependent torch.Generator
            __UpperCamelCase				=	self.get_dummy_components()
            __UpperCamelCase				=	PNDMScheduler()
            __UpperCamelCase				=	StableDiffusionModelEditingPipeline(**A_			)
            __UpperCamelCase				=	sd_pipe.to(A_			)
            sd_pipe.set_progress_bar_config(disable=A_			)

            __UpperCamelCase				=	self.get_dummy_inputs(A_			)
            # the pipeline does not expect pndm so test if it raises error.
            with self.assertRaises(A_			):
                 __UpperCamelCase				=	sd_pipe(**A_			).images







       def 						snake_case_					(	self:				List[str]			):






            '''simple docstring'''




            super().test_inference_batch_single_identical(expected_max_diff=5E-3			)







       def 						snake_case_					(	self:				List[Any]			):






            '''simple docstring'''




            super().test_attention_slicing_forward_pass(expected_max_diff=5E-3			)



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







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				Optional[Any],A_:				Optional[Any]=0			):






            '''simple docstring'''




            __UpperCamelCase				=	torch.manual_seed(A_			)
            __UpperCamelCase				=	{
                'prompt': 'A field of roses',
                'generator': generator,
                'num_inference_steps': 3,
                'guidance_scale': 7.5,
                'output_type': 'numpy',
            }
            return inputs







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            __UpperCamelCase				=	'CompVis/stable-diffusion-v1-4'
            __UpperCamelCase				=	StableDiffusionModelEditingPipeline.from_pretrained(A_,safety_checker=A_			)
            pipe.to(A_			)
            pipe.set_progress_bar_config(disable=A_			)
            pipe.enable_attention_slicing()

            __UpperCamelCase				=	self.get_inputs()
            __UpperCamelCase				=	pipe(**A_			).images
            __UpperCamelCase				=	image[0, -3:, -3:, -1].flatten()

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

            __UpperCamelCase				=	np.array(
                [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]			)

            assert np.abs(expected_slice - image_slice			).max() < 1E-2

            # make sure image changes after editing
            pipe.edit_model('A pack of roses','A pack of blue roses'			)

            __UpperCamelCase				=	pipe(**A_			).images
            __UpperCamelCase				=	image[0, -3:, -3:, -1].flatten()

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

            assert np.abs(expected_slice - image_slice			).max() > 1E-1







       def 						snake_case_					(	self:				Union[str, Any]			):






            '''simple docstring'''




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

            __UpperCamelCase				=	'CompVis/stable-diffusion-v1-4'
            __UpperCamelCase				=	DDIMScheduler.from_pretrained(A_,subfolder='scheduler'			)
            __UpperCamelCase				=	StableDiffusionModelEditingPipeline.from_pretrained(
                A_,scheduler=A_,safety_checker=A_			)
            __UpperCamelCase				=	pipe.to(A_			)
            pipe.set_progress_bar_config(disable=A_			)
            pipe.enable_attention_slicing(1			)
            pipe.enable_sequential_cpu_offload()

            __UpperCamelCase				=	self.get_inputs()
            __UpperCamelCase				=	pipe(**A_			)

            __UpperCamelCase				=	torch.cuda.max_memory_allocated()
            # make sure that less than 4.4 GB is allocated
            assert mem_bytes < 4.4 * 10**9






import math






def 						_A						(						_lowercase				)			->				list:


     """simple docstring"""


     if n <= 0:
          raise ValueError('Only positive integers have prime factors'				)
     __UpperCamelCase				=	[]
     while n % 2 == 0:
          pf.append(2				)
          __UpperCamelCase				=	int(n / 2				)
     for i in range(3			,       int(math.sqrt(_lowercase				)				) + 1			,       2				):
          while n % i == 0:
               pf.append(_lowercase				)
               __UpperCamelCase				=	int(n / i				)
     if n > 2:
          pf.append(_lowercase				)
     return pf






def 						_A						(						_lowercase				)			->				int:


     """simple docstring"""


     if n <= 0:
          raise ValueError('Only positive numbers are accepted'				)
     __UpperCamelCase				=	1
     __UpperCamelCase				=	1
     while n % 2 == 0:
          temp += 1
          __UpperCamelCase				=	int(n / 2				)
     div *= temp
     for i in range(3			,       int(math.sqrt(_lowercase				)				) + 1			,       2				):
          __UpperCamelCase				=	1
          while n % i == 0:
               temp += 1
               __UpperCamelCase				=	int(n / i				)
          div *= temp
     if n > 1:
          div *= 2
     return div






def 						_A						(						_lowercase				)			->				int:


     """simple docstring"""


     if n <= 0:
          raise ValueError('Only positive numbers are accepted'				)
     __UpperCamelCase				=	1
     __UpperCamelCase				=	1
     while n % 2 == 0:
          temp += 1
          __UpperCamelCase				=	int(n / 2				)
     if temp > 1:
          s *= (2**temp - 1) / (2 - 1)
     for i in range(3			,       int(math.sqrt(_lowercase				)				) + 1			,       2				):
          __UpperCamelCase				=	1
          while n % i == 0:
               temp += 1
               __UpperCamelCase				=	int(n / i				)
          if temp > 1:
               s *= (i**temp - 1) / (i - 1)
     return int(_lowercase				)






def 						_A						(						_lowercase				)			->				int:


     """simple docstring"""


     __UpperCamelCase				=	n
     for x in set(prime_factors(_lowercase				)				):
          s *= (x - 1) / x
     return int(_lowercase				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


