






import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class     lowerCamelCase_					(   lowerCamelCase      ,					lowerCamelCase      ,					unittest.TestCase			):
				a__       							=	StableDiffusionDiffEditPipeline
				a__       							=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
				a__       							=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
				a__       							=	frozenset(
				    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				a__       							=	frozenset([]			)







				def       A						(    self  ):
								"""simple docstring"""
								torch.manual_seed(0  )
								__magic_name__		:int   =   UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)							,       layers_per_block=2							,       sample_size=3_2							,       in_channels=4							,       out_channels=4							,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')							,       cross_attention_dim=3_2							,       attention_head_dim=(2, 4)							,       use_linear_projection=__lowerCAmelCase							,       )
								__magic_name__		:Dict   =   DDIMScheduler(
								    beta_start=0.00085							,       beta_end=0.012							,       beta_schedule='''scaled_linear'''							,       clip_sample=__lowerCAmelCase							,       set_alpha_to_one=__lowerCAmelCase							,       )
								__magic_name__		:Union[str, Any]   =   DDIMInverseScheduler(
								    beta_start=0.00085							,       beta_end=0.012							,       beta_schedule='''scaled_linear'''							,       clip_sample=__lowerCAmelCase							,       set_alpha_to_zero=__lowerCAmelCase							,       )
								torch.manual_seed(0  )
								__magic_name__		:Optional[int]   =   AutoencoderKL(
								    block_out_channels=[3_2, 6_4]							,       in_channels=3							,       out_channels=3							,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']							,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']							,       latent_channels=4							,       sample_size=1_2_8							,       )
								torch.manual_seed(0  )
								__magic_name__		:Tuple   =   CLIPTextConfig(
								    bos_token_id=0							,       eos_token_id=2							,       hidden_size=3_2							,       intermediate_size=3_7							,       layer_norm_eps=1E-05							,       num_attention_heads=4							,       num_hidden_layers=5							,       pad_token_id=1							,       vocab_size=1_0_0_0							,       hidden_act='''gelu'''							,       projection_dim=5_1_2							,       )
								__magic_name__		:Dict   =   CLIPTextModel(__lowerCAmelCase  )
								__magic_name__		:Any   =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

								__magic_name__		:Tuple   =   {
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''inverse_scheduler''': inverse_scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}

								return components







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=0  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   floats_tensor((1, 1_6, 1_6)							,       rng=random.Random(__lowerCAmelCase  )  ).to(__lowerCAmelCase  )
								__magic_name__		:List[str]   =   floats_tensor((1, 2, 4, 1_6, 1_6)							,       rng=random.Random(__lowerCAmelCase  )  ).to(__lowerCAmelCase  )
								if str(__lowerCAmelCase  ).startswith('''mps'''  ):
												__magic_name__		:Dict   =   torch.manual_seed(__lowerCAmelCase  )
								else:
												__magic_name__		:Dict   =   torch.Generator(device=__lowerCAmelCase  ).manual_seed(__lowerCAmelCase  )
								__magic_name__		:Any   =   {
								    '''prompt''': '''a dog and a newt''',
								    '''mask_image''': mask,
								    '''image_latents''': latents,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''inpaint_strength''': 1.0,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''numpy''',
								}

								return inputs







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=0  ):
								"""simple docstring"""
								__magic_name__		:int   =   floats_tensor((1, 3, 3_2, 3_2)							,       rng=random.Random(__lowerCAmelCase  )  ).to(__lowerCAmelCase  )
								__magic_name__		:Optional[int]   =   image.cpu().permute(0							,       2							,       3							,       1  )[0]
								__magic_name__		:List[Any]   =   Image.fromarray(np.uinta(__lowerCAmelCase  )  ).convert('''RGB'''  )
								if str(__lowerCAmelCase  ).startswith('''mps'''  ):
												__magic_name__		:str   =   torch.manual_seed(__lowerCAmelCase  )
								else:
												__magic_name__		:str   =   torch.Generator(device=__lowerCAmelCase  ).manual_seed(__lowerCAmelCase  )
								__magic_name__		:str   =   {
								    '''image''': image,
								    '''source_prompt''': '''a cat and a frog''',
								    '''target_prompt''': '''a dog and a newt''',
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''num_maps_per_mask''': 2,
								    '''mask_encode_strength''': 1.0,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''numpy''',
								}

								return inputs







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=0  ):
								"""simple docstring"""
								__magic_name__		:str   =   floats_tensor((1, 3, 3_2, 3_2)							,       rng=random.Random(__lowerCAmelCase  )  ).to(__lowerCAmelCase  )
								__magic_name__		:str   =   image.cpu().permute(0							,       2							,       3							,       1  )[0]
								__magic_name__		:List[Any]   =   Image.fromarray(np.uinta(__lowerCAmelCase  )  ).convert('''RGB'''  )
								if str(__lowerCAmelCase  ).startswith('''mps'''  ):
												__magic_name__		:Optional[Any]   =   torch.manual_seed(__lowerCAmelCase  )
								else:
												__magic_name__		:Dict   =   torch.Generator(device=__lowerCAmelCase  ).manual_seed(__lowerCAmelCase  )
								__magic_name__		:Dict   =   {
								    '''image''': image,
								    '''prompt''': '''a cat and a frog''',
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''inpaint_strength''': 1.0,
								    '''guidance_scale''': 6.0,
								    '''decode_latents''': True,
								    '''output_type''': '''numpy''',
								}
								return inputs







				def       A						(    self  ):
								"""simple docstring"""
								if not hasattr(self.pipeline_class							,       '''_optional_components'''  ):
												return

								__magic_name__		:Optional[int]   =   self.get_dummy_components()
								__magic_name__		:str   =   self.pipeline_class(**__lowerCAmelCase  )
								pipe.to(__lowerCAmelCase  )
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

								# set all optional components to None and update pipeline config accordingly
								for optional_component in pipe._optional_components:
												setattr(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  )
								pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}  )

								__magic_name__		:Tuple   =   self.get_dummy_inputs(__lowerCAmelCase  )
								__magic_name__		:List[Any]   =   pipe(**__lowerCAmelCase  )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
												pipe.save_pretrained(__lowerCAmelCase  )
												__magic_name__		:Any   =   self.pipeline_class.from_pretrained(__lowerCAmelCase  )
												pipe_loaded.to(__lowerCAmelCase  )
												pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase  )

								for optional_component in pipe._optional_components:
												self.assertTrue(
												    getattr(__lowerCAmelCase							,       __lowerCAmelCase  ) is None							,       F'''`{optional_component}` did not stay set to None after loading.'''							,       )

								__magic_name__		:int   =   self.get_dummy_inputs(__lowerCAmelCase  )
								__magic_name__		:str   =   pipe_loaded(**__lowerCAmelCase  )[0]

								__magic_name__		:int   =   np.abs(output - output_loaded  ).max()
								self.assertLess(__lowerCAmelCase							,       1E-4  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   '''cpu'''

								__magic_name__		:List[Any]   =   self.get_dummy_components()
								__magic_name__		:Tuple   =   self.pipeline_class(**__lowerCAmelCase  )
								pipe.to(__lowerCAmelCase  )
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

								__magic_name__		:str   =   self.get_dummy_mask_inputs(__lowerCAmelCase  )
								__magic_name__		:Union[str, Any]   =   pipe.generate_mask(**__lowerCAmelCase  )
								__magic_name__		:str   =   mask[0, -3:, -3:]

								self.assertEqual(mask.shape							,       (1, 1_6, 1_6)  )
								__magic_name__		:List[str]   =   np.array([0] * 9  )
								__magic_name__		:Optional[int]   =   np.abs(mask_slice.flatten() - expected_slice  ).max()
								self.assertLessEqual(__lowerCAmelCase							,       1E-3  )
								self.assertEqual(mask[0, -3, -4]							,       0  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   '''cpu'''

								__magic_name__		:Tuple   =   self.get_dummy_components()
								__magic_name__		:int   =   self.pipeline_class(**__lowerCAmelCase  )
								pipe.to(__lowerCAmelCase  )
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

								__magic_name__		:Optional[Any]   =   self.get_dummy_inversion_inputs(__lowerCAmelCase  )
								__magic_name__		:str   =   pipe.invert(**__lowerCAmelCase  ).images
								__magic_name__		:List[Any]   =   image[0, -1, -3:, -3:]

								self.assertEqual(image.shape							,       (2, 3_2, 3_2, 3)  )
								__magic_name__		:Any   =   np.array(
								    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]							,       )
								__magic_name__		:List[Any]   =   np.abs(image_slice.flatten() - expected_slice  ).max()
								self.assertLessEqual(__lowerCAmelCase							,       1E-3  )







				def       A						(    self  ):
								"""simple docstring"""
								super().test_inference_batch_single_identical(expected_max_diff=5E-3  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   '''cpu'''

								__magic_name__		:Optional[int]   =   self.get_dummy_components()

								__magic_name__		:int   =   {'''beta_start''': 0.00085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
								__magic_name__		:Dict   =   DPMSolverMultistepScheduler(**__lowerCAmelCase  )
								__magic_name__		:List[str]   =   DPMSolverMultistepInverseScheduler(**__lowerCAmelCase  )

								__magic_name__		:Union[str, Any]   =   self.pipeline_class(**__lowerCAmelCase  )
								pipe.to(__lowerCAmelCase  )
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

								__magic_name__		:List[str]   =   self.get_dummy_inversion_inputs(__lowerCAmelCase  )
								__magic_name__		:Dict   =   pipe.invert(**__lowerCAmelCase  ).images
								__magic_name__		:List[str]   =   image[0, -1, -3:, -3:]

								self.assertEqual(image.shape							,       (2, 3_2, 3_2, 3)  )
								__magic_name__		:List[str]   =   np.array(
								    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]							,       )
								__magic_name__		:List[str]   =   np.abs(image_slice.flatten() - expected_slice  ).max()
								self.assertLessEqual(__lowerCAmelCase							,       1E-3  )







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







				def       A						(    self  ):
								"""simple docstring"""
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







				@classmethod
				def       A						(    cls  ):
								"""simple docstring"""
								__magic_name__		:Any   =   load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''  )

								__magic_name__		:Optional[Any]   =   raw_image.convert('''RGB'''  ).resize((7_6_8, 7_6_8)  )

								__magic_name__		:Optional[Any]   =   raw_image







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   torch.manual_seed(0  )

								__magic_name__		:Dict   =   StableDiffusionDiffEditPipeline.from_pretrained(
								    '''stabilityai/stable-diffusion-2-1'''							,       safety_checker=__lowerCAmelCase							,       torch_dtype=torch.floataa  )
								__magic_name__		:Tuple   =   DDIMScheduler.from_config(pipe.scheduler.config  )
								__magic_name__		:str   =   DDIMInverseScheduler.from_config(pipe.scheduler.config  )
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

								__magic_name__		:Optional[Any]   =   '''a bowl of fruit'''
								__magic_name__		:Dict   =   '''a bowl of pears'''

								__magic_name__		:Dict   =   pipe.generate_mask(
								    image=self.raw_image							,       source_prompt=__lowerCAmelCase							,       target_prompt=__lowerCAmelCase							,       generator=__lowerCAmelCase							,       )

								__magic_name__		:Optional[int]   =   pipe.invert(
								    prompt=__lowerCAmelCase							,       image=self.raw_image							,       inpaint_strength=0.7							,       generator=__lowerCAmelCase  ).latents

								__magic_name__		:List[Any]   =   pipe(
								    prompt=__lowerCAmelCase							,       mask_image=__lowerCAmelCase							,       image_latents=__lowerCAmelCase							,       generator=__lowerCAmelCase							,       negative_prompt=__lowerCAmelCase							,       inpaint_strength=0.7							,       output_type='''numpy'''							,       ).images[0]

								__magic_name__		:str   =   (
								    np.array(
								        load_image(
								            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								            '''/diffedit/pears.png'''  ).resize((7_6_8, 7_6_8)  )  )
								    / 2_5_5
								)
								assert np.abs((expected_image - image).max()  ) < 5E-1







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   torch.manual_seed(0  )

								__magic_name__		:Dict   =   StableDiffusionDiffEditPipeline.from_pretrained(
								    '''stabilityai/stable-diffusion-2-1'''							,       safety_checker=__lowerCAmelCase							,       torch_dtype=torch.floataa  )
								__magic_name__		:Optional[int]   =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
								__magic_name__		:Any   =   DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config  )
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

								__magic_name__		:str   =   '''a bowl of fruit'''
								__magic_name__		:Any   =   '''a bowl of pears'''

								__magic_name__		:List[Any]   =   pipe.generate_mask(
								    image=self.raw_image							,       source_prompt=__lowerCAmelCase							,       target_prompt=__lowerCAmelCase							,       generator=__lowerCAmelCase							,       )

								__magic_name__		:int   =   pipe.invert(
								    prompt=__lowerCAmelCase							,       image=self.raw_image							,       inpaint_strength=0.7							,       generator=__lowerCAmelCase							,       num_inference_steps=2_5							,       ).latents

								__magic_name__		:List[Any]   =   pipe(
								    prompt=__lowerCAmelCase							,       mask_image=__lowerCAmelCase							,       image_latents=__lowerCAmelCase							,       generator=__lowerCAmelCase							,       negative_prompt=__lowerCAmelCase							,       inpaint_strength=0.7							,       num_inference_steps=2_5							,       output_type='''numpy'''							,       ).images[0]

								__magic_name__		:List[str]   =   (
								    np.array(
								        load_image(
								            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								            '''/diffedit/pears.png'''  ).resize((7_6_8, 7_6_8)  )  )
								    / 2_5_5
								)
								assert np.abs((expected_image - image).max()  ) < 5E-1











'''simple docstring'''

from __future__ import annotations

from math import gcd



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_ = 2		,     lowerCAmelCase_ = 1		,     lowerCAmelCase_ = 3		,     )     -> int | None:
		# A value less than 2 can cause an infinite loop in the algorithm.
		if num < 2:
				raise ValueError('The input value cannot be less than 2'    )

		# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
		# algorithm struggles to find factors that are divisible by two.
		# As a workaround, we specifically check for two and even inputs.
		#   See: https://math.stackexchange.com/a/2856214/165820
		if num > 2 and num % 2 == 0:
				return 2

		# Pollard's Rho algorithm requires a function that returns pseudorandom
		# values between 0 <= X < ``num``.  It doesn't need to be random in the
		# sense that the output value is cryptographically secure or difficult
		# to calculate, it only needs to be random in the sense that all output
		# values should be equally likely to appear.
		# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
		# However, the success of Pollard's algorithm isn't guaranteed and is
		# determined in part by the initial seed and the chosen random function.
		# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
		# where ``C`` is a value that we can modify between each attempt.
		def rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    ) -> int:
				return (pow(lowerCAmelCase_		,     2    ) + step) % modulus

		for _ in range(lowerCAmelCase_    ):
				# These track the position within the cycle detection logic.
				_a	: Optional[int]								=				seed
				_a	: str								=				seed

				while True:
						# At each iteration, the tortoise moves one step and the hare moves two.
						_a	: str								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )
						_a	: Dict								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )
						_a	: Tuple								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )

						# At some point both the tortoise and the hare will enter a cycle whose
						# length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
						# the tortoise and hare will end up on the same value modulo ``p``.
						# We can detect when this happens because the position difference between
						# the tortoise and the hare will share a common divisor with ``num``.
						_a	: Optional[Any]								=				gcd(hare - tortoise		,     lowerCAmelCase_    )

						if divisor == 1:
								# No common divisor yet, just keep searching.
								continue
						else:
								# We found a common divisor!
								if divisor == num:
										# Unfortunately, the divisor is ``num`` itself and is useless.
										break
								else:
										# The divisor is a nontrivial factor of ``num``!
										return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
				_a	: Union[str, Any]								=				hare

				# The new step value for the random function can just be incremented.
				# At first the results will be similar to what the old function would
				# have produced, but the value will quickly diverge after a bit.
				step += 1

		# We haven't found a divisor within the requested number of attempts.
		# We were unlucky or ``num`` itself is actually prime.
		return None


if __name__ == "__main__":
							import argparse

							__lowerCAmelCase		     =  argparse.ArgumentParser()
							parser.add_argument(
							    '''num''',
							    type=int,
							    help='''The value to find a divisor of''',
							)
							parser.add_argument(
							    '''--attempts''',
							    type=int,
							    default=3,
							    help='''The number of attempts before giving up''',
							)
							__lowerCAmelCase		     =  parser.parse_args()

							__lowerCAmelCase		     =  pollard_rho(args.num, attempts=args.attempts)
							if divisor is None:
														print(f"""{args.num} is probably prime""")
							else:
														__lowerCAmelCase		     =  args.num // divisor
														print(f"""{args.num} = {divisor} * {quotient}""")




