
'''simple docstring'''


from __future__ import annotations

from math import gcd




def lowercase_	(   _lowercase			,       _lowercase = 2			,       _lowercase = 1			,       _lowercase = 3			,       )     ->							int:
					'''simple docstring'''

					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(_lowercase			,       _lowercase			,       _lowercase				) -> int:
										return (pow(_lowercase			,       2				) + step) % modulus

					for _ in range(_lowercase				):
										# These track the position within the cycle detection logic.
										lowerCamelCase_					:      Any     =		seed
										lowerCamelCase_					:      Dict     =		seed

										while True:
															# At each iteration, the tortoise moves one step and the hare moves two.
															lowerCamelCase_					:      Optional[Any]     =		rand_fn(_lowercase			,       _lowercase			,       _lowercase				)
															lowerCamelCase_					:      Any     =		rand_fn(_lowercase			,       _lowercase			,       _lowercase				)
															lowerCamelCase_					:      Optional[Any]     =		rand_fn(_lowercase			,       _lowercase			,       _lowercase				)

															# 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``.
															lowerCamelCase_					:      Optional[Any]     =		gcd(hare - tortoise			,       _lowercase				)

															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.
										lowerCamelCase_					:      Dict     =		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

					__lowercase							:   int					=     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''',
					)
					__lowercase							:   int					=     parser.parse_args()

					__lowercase							:   int					=     pollard_rho(args.num, attempts=args.attempts)
					if divisor is None:
										print(F'{args.num} is probably prime')
					else:
										__lowercase							:   str					=     args.num // divisor
										print(F'{args.num} = {divisor} * {quotient}')




'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 	__lowercase				(  _lowercase      ,     unittest.TestCase    ):
			lowerCamelCase			:		Optional[Any]	      =			KandinskyVaaInpaintPipeline
			lowerCamelCase			:		Tuple	      =			["image_embeds", "negative_image_embeds", "image", "mask_image"]
			lowerCamelCase			:		List[str]	      =			[
			    "image_embeds",
			    "negative_image_embeds",
			    "image",
			    "mask_image",
			]
			lowerCamelCase			:		Optional[int]	      =			[
			    "generator",
			    "height",
			    "width",
			    "latents",
			    "guidance_scale",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			lowerCamelCase			:		List[Any]	      =			False

			@property
			def        UpperCAmelCase__ (self      ):
								return 3_2

			@property
			def        UpperCAmelCase__ (self      ):
								return 3_2

			@property
			def        UpperCAmelCase__ (self      ):
								return self.time_input_dim

			@property
			def        UpperCAmelCase__ (self      ):
								return self.time_input_dim * 4

			@property
			def        UpperCAmelCase__ (self      ):
								return 1_0_0

			@property
			def        UpperCAmelCase__ (self      ):
								torch.manual_seed(0      )

								lowerCamelCase_					:      Optional[Any]     =		{
								    '''in_channels''': 9,
								    # Out channels is double in channels because predicts mean and variance
								    '''out_channels''': 8,
								    '''addition_embed_type''': '''image''',
								    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
								    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
								    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
								    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
								    '''layers_per_block''': 1,
								    '''encoder_hid_dim''': self.text_embedder_hidden_size,
								    '''encoder_hid_dim_type''': '''image_proj''',
								    '''cross_attention_dim''': self.cross_attention_dim,
								    '''attention_head_dim''': 4,
								    '''resnet_time_scale_shift''': '''scale_shift''',
								    '''class_embed_type''': None,
								}

								lowerCamelCase_					:      str     =		UNetaDConditionModel(**A      )
								return model

			@property
			def        UpperCAmelCase__ (self      ):
								return {
								    "block_out_channels": [3_2, 6_4],
								    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
								    "in_channels": 3,
								    "latent_channels": 4,
								    "layers_per_block": 1,
								    "norm_num_groups": 8,
								    "norm_type": "spatial",
								    "num_vq_embeddings": 1_2,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}

			@property
			def        UpperCAmelCase__ (self      ):
								torch.manual_seed(0      )
								lowerCamelCase_					:      Optional[Any]     =		VQModel(**self.dummy_movq_kwargs      )
								return model

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[str]     =		self.dummy_unet
								lowerCamelCase_					:      Optional[int]     =		self.dummy_movq

								lowerCamelCase_					:      Union[str, Any]     =		DDIMScheduler(
								    num_train_timesteps=1_0_0_0			,   beta_schedule='''linear'''			,   beta_start=0.0_00_85			,   beta_end=0.0_12			,   clip_sample=A			,   set_alpha_to_one=A			,   steps_offset=1			,   prediction_type='''epsilon'''			,   thresholding=A			,   )

								lowerCamelCase_					:      Dict     =		{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}

								return components

			def        UpperCAmelCase__ (self			,   A			,   A=0      ):
								lowerCamelCase_					:      List[Any]     =		floats_tensor((1, self.text_embedder_hidden_size)			,   rng=random.Random(A      )      ).to(A      )
								lowerCamelCase_					:      Tuple     =		floats_tensor((1, self.text_embedder_hidden_size)			,   rng=random.Random(seed + 1      )      ).to(
								    A      )
								# create init_image
								lowerCamelCase_					:      Dict     =		floats_tensor((1, 3, 6_4, 6_4)			,   rng=random.Random(A      )      ).to(A      )
								lowerCamelCase_					:      List[str]     =		image.cpu().permute(0			,   2			,   3			,   1      )[0]
								lowerCamelCase_					:      Any     =		Image.fromarray(np.uinta(A      )      ).convert('''RGB'''      ).resize((2_5_6, 2_5_6)      )
								# create mask
								lowerCamelCase_					:      List[str]     =		np.ones((6_4, 6_4)			,   dtype=np.floataa      )
								lowerCamelCase_					:      Union[str, Any]     =		0

								if str(A      ).startswith('''mps'''      ):
													lowerCamelCase_					:      int     =		torch.manual_seed(A      )
								else:
													lowerCamelCase_					:      Union[str, Any]     =		torch.Generator(device=A      ).manual_seed(A      )
								lowerCamelCase_					:      Optional[Any]     =		{
								    '''image''': init_image,
								    '''mask_image''': mask,
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''generator''': generator,
								    '''height''': 6_4,
								    '''width''': 6_4,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 4.0,
								    '''output_type''': '''np''',
								}
								return inputs

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		'''cpu'''

								lowerCamelCase_					:      str     =		self.get_dummy_components()

								lowerCamelCase_					:      Tuple     =		self.pipeline_class(**A      )
								lowerCamelCase_					:      Union[str, Any]     =		pipe.to(A      )

								pipe.set_progress_bar_config(disable=A      )

								lowerCamelCase_					:      str     =		pipe(**self.get_dummy_inputs(A      )      )
								lowerCamelCase_					:      Any     =		output.images

								lowerCamelCase_					:      Union[str, Any]     =		pipe(
								    **self.get_dummy_inputs(A      )			,   return_dict=A			,   )[0]

								lowerCamelCase_					:      List[Any]     =		image[0, -3:, -3:, -1]
								lowerCamelCase_					:      Tuple     =		image_from_tuple[0, -3:, -3:, -1]

								print(F"""image.shape {image.shape}"""      )

								assert image.shape == (1, 6_4, 6_4, 3)

								lowerCamelCase_					:      Any     =		np.array(
								    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]      )

								assert (
								    np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
								), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2
								), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""






			def        UpperCAmelCase__ (self      ):
								super().test_inference_batch_single_identical(expected_max_diff=3E-3      )

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

			def        UpperCAmelCase__ (self      ):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''      )

								lowerCamelCase_					:      Optional[Any]     =		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
								lowerCamelCase_					:      List[str]     =		np.ones((7_6_8, 7_6_8)			,   dtype=np.floataa      )
								lowerCamelCase_					:      int     =		0

								lowerCamelCase_					:      Union[str, Any]     =		'''a hat'''

								lowerCamelCase_					:      Optional[int]     =		KandinskyVaaPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-prior'''			,   torch_dtype=torch.floataa      )
								pipe_prior.to(A      )

								lowerCamelCase_					:      Any     =		KandinskyVaaInpaintPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''			,   torch_dtype=torch.floataa      )
								lowerCamelCase_					:      Any     =		pipeline.to(A      )
								pipeline.set_progress_bar_config(disable=A      )

								lowerCamelCase_					:      Any     =		torch.Generator(device='''cpu'''      ).manual_seed(0      )
								lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		pipe_prior(
								    A			,   generator=A			,   num_inference_steps=5			,   negative_prompt=''''''			,   ).to_tuple()

								lowerCamelCase_					:      Union[str, Any]     =		pipeline(
								    image=A			,   mask_image=A			,   image_embeds=A			,   negative_image_embeds=A			,   generator=A			,   num_inference_steps=1_0_0			,   height=7_6_8			,   width=7_6_8			,   output_type='''np'''			,   )

								lowerCamelCase_					:      List[str]     =		output.images[0]

								assert image.shape == (7_6_8, 7_6_8, 3)

								assert_mean_pixel_difference(A			,   A      )


