





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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   A							(						__lowercase		,   unittest.TestCase    ):
      _snake_case				  =KandinskyVaaControlnetPipeline
      _snake_case				  =['''image_embeds''', '''negative_image_embeds''', '''hint''']
      _snake_case				  =['''image_embeds''', '''negative_image_embeds''', '''hint''']
      _snake_case				  =[
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      _snake_case				  =False



      @property
      def 						lowerCAmelCase__     (						self:    int							)    ->     Dict:
         '''simple docstring'''

         return 32



      @property
      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     str:
         '''simple docstring'''

         return 32



      @property
      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Dict:
         '''simple docstring'''

         return self.time_input_dim



      @property
      def 						lowerCAmelCase__     (						self:    int							)    ->     int:
         '''simple docstring'''

         return self.time_input_dim * 4



      @property
      def 						lowerCAmelCase__     (						self:    Tuple							)    ->     Dict:
         '''simple docstring'''

         return 100



      @property
      def 						lowerCAmelCase__     (						self:    Dict							)    ->     Union[str, Any]:
         '''simple docstring'''

         torch.manual_seed(0							)

         UpperCAmelCase_           ={
             "in_channels": 8,
             # Out channels is double in channels because predicts mean and variance
             "out_channels": 8,
             "addition_embed_type": "image_hint",
             "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,
         }

         UpperCAmelCase_           =UNetaDConditionModel(**_lowerCAmelCase							)
         return model



      @property
      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     Optional[int]:
         '''simple docstring'''

         return {
             "block_out_channels": [32, 32, 64, 64],
             "down_block_types": [
                 "DownEncoderBlock2D",
                 "DownEncoderBlock2D",
                 "DownEncoderBlock2D",
                 "AttnDownEncoderBlock2D",
             ],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
             "vq_embed_dim": 4,
         }



      @property
      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Dict:
         '''simple docstring'''

         torch.manual_seed(0							)
         UpperCAmelCase_           =VQModel(**self.dummy_movq_kwargs							)
         return model



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =self.dummy_unet
         UpperCAmelCase_           =self.dummy_movq

         UpperCAmelCase_           =DDIMScheduler(
             num_train_timesteps=1000		,	beta_schedule="linear"		,	beta_start=0.0_00_85		,	beta_end=0.0_12		,	clip_sample=_lowerCAmelCase		,	set_alpha_to_one=_lowerCAmelCase		,	steps_offset=1		,	prediction_type="epsilon"		,	thresholding=_lowerCAmelCase		,	)

         UpperCAmelCase_           ={
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components



      def 						lowerCAmelCase__     (						self:    Dict		,	_lowerCAmelCase:    Optional[int]		,	_lowerCAmelCase:    List[Any]=0							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =floats_tensor((1, self.text_embedder_hidden_size)		,	rng=random.Random(_lowerCAmelCase							)							).to(_lowerCAmelCase							)
         UpperCAmelCase_           =floats_tensor((1, self.text_embedder_hidden_size)		,	rng=random.Random(seed + 1							)							).to(
             _lowerCAmelCase							)

         # create hint
         UpperCAmelCase_           =floats_tensor((1, 3, 64, 64)		,	rng=random.Random(_lowerCAmelCase							)							).to(_lowerCAmelCase							)

         if str(_lowerCAmelCase							).startswith("mps"							):
            UpperCAmelCase_           =torch.manual_seed(_lowerCAmelCase							)
         else:
            UpperCAmelCase_           =torch.Generator(device=_lowerCAmelCase							).manual_seed(_lowerCAmelCase							)
         UpperCAmelCase_           ={
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "hint": hint,
             "generator": generator,
             "height": 64,
             "width": 64,
             "guidance_scale": 4.0,
             "num_inference_steps": 2,
             "output_type": "np",
         }
         return inputs






      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           ="cpu"

         UpperCAmelCase_           =self.get_dummy_components()

         UpperCAmelCase_           =self.pipeline_class(**_lowerCAmelCase							)
         UpperCAmelCase_           =pipe.to(_lowerCAmelCase							)

         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =pipe(**self.get_dummy_inputs(_lowerCAmelCase							)							)
         UpperCAmelCase_           =output.images

         UpperCAmelCase_           =pipe(
             **self.get_dummy_inputs(_lowerCAmelCase							)		,	return_dict=_lowerCAmelCase		,	)[0]

         UpperCAmelCase_           =image[0, -3:, -3:, -1]
         UpperCAmelCase_           =image_from_tuple[0, -3:, -3:, -1]

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

         UpperCAmelCase_           =np.array(
             [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95]							)

         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()}'

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



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     List[Any]:
         '''simple docstring'''

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






      def 						lowerCAmelCase__     (						self:    int							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           =load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"							)

         UpperCAmelCase_           =load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/hint_image_cat.png"							)
         UpperCAmelCase_           =torch.from_numpy(np.array(_lowerCAmelCase							)							).float() / 2_55.0
         UpperCAmelCase_           =hint.permute(2		,	0		,	1							).unsqueeze(0							)

         UpperCAmelCase_           =KandinskyVaaPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-prior"		,	torch_dtype=torch.floataa							)
         pipe_prior.to(_lowerCAmelCase							)

         UpperCAmelCase_           =KandinskyVaaControlnetPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-controlnet-depth"		,	torch_dtype=torch.floataa							)
         UpperCAmelCase_           =pipeline.to(_lowerCAmelCase							)
         pipeline.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           ="A robot, 4k photo"

         UpperCAmelCase_           =torch.Generator(device="cuda"							).manual_seed(0							)
         UpperCAmelCase_    ,							UpperCAmelCase_           =pipe_prior(
             _lowerCAmelCase		,	generator=_lowerCAmelCase		,	num_inference_steps=5		,	negative_prompt=""		,	).to_tuple()

         UpperCAmelCase_           =torch.Generator(device="cuda"							).manual_seed(0							)
         UpperCAmelCase_           =pipeline(
             image_embeds=_lowerCAmelCase		,	negative_image_embeds=_lowerCAmelCase		,	hint=_lowerCAmelCase		,	generator=_lowerCAmelCase		,	num_inference_steps=100		,	output_type="np"		,	)

         UpperCAmelCase_           =output.images[0]

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

         assert_mean_pixel_difference(_lowerCAmelCase		,	_lowerCAmelCase							)








from __future__ import annotations

from math import gcd



def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:int = 2		,    lowerCAmelCase_				:int = 1		,    lowerCAmelCase_				:int = 3		,    )->int | None:
   '''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(lowerCAmelCase_				:int		,    lowerCAmelCase_				:int		,    lowerCAmelCase_				:int				) -> int:
      return (pow(lowerCAmelCase_		,    2				) + step) % modulus

   for _ in range(lowerCAmelCase_				):
      # These track the position within the cycle detection logic.
      snake_case_            =       seed
      snake_case_            =       seed

      while True:
         # At each iteration, the tortoise moves one step and the hare moves two.
         snake_case_            =       rand_fn(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)
         snake_case_            =       rand_fn(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)
         snake_case_            =       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``.
         snake_case_            =       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.
      snake_case_            =       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

   SCREAMING_SNAKE_CASE						:Union[str, Any]			=  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''',
   )
   SCREAMING_SNAKE_CASE						:List[str]			=  parser.parse_args()

   SCREAMING_SNAKE_CASE						:List[Any]			=  pollard_rho(args.num, attempts=args.attempts)
   if divisor is None:
      print(F'''{args.num} is probably prime''')
   else:
      SCREAMING_SNAKE_CASE						:List[str]			=  args.num // divisor
      print(F'''{args.num} = {divisor} * {quotient}''')




