

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   UpperCamelCase			(  SCREAMING_SNAKE_CASE						,      SCREAMING_SNAKE_CASE						,      unittest.TestCase ):
    __UpperCamelCase    =StableDiffusionDiffEditPipeline
    __UpperCamelCase    =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
    __UpperCamelCase    =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
    __UpperCamelCase    =frozenset(
        [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    __UpperCamelCase    =frozenset([] )

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      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=snake_case__				,     )
     SCREAMING_SNAKE_CASE		    =      DDIMScheduler(
         beta_start=0.00_085				,     beta_end=0.012				,     beta_schedule='scaled_linear'				,     clip_sample=snake_case__				,     set_alpha_to_one=snake_case__				,     )
     SCREAMING_SNAKE_CASE		    =      DDIMInverseScheduler(
         beta_start=0.00_085				,     beta_end=0.012				,     beta_schedule='scaled_linear'				,     clip_sample=snake_case__				,     set_alpha_to_zero=snake_case__				,     )
     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      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     )
     SCREAMING_SNAKE_CASE		    =      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				,     )
     SCREAMING_SNAKE_CASE		    =      CLIPTextModel(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

     SCREAMING_SNAKE_CASE		    =      {
         '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 UpperCamelCase      (      self			:   Any				,     snake_case__			:   Optional[Any]				,     snake_case__			:   Any=0     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      floats_tensor((1, 1_6, 1_6)				,     rng=random.Random(snake_case__     )     ).to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      floats_tensor((1, 2, 4, 1_6, 1_6)				,     rng=random.Random(snake_case__     )     ).to(snake_case__     )
     if str(snake_case__     ).startswith('mps'     ):
      SCREAMING_SNAKE_CASE		    =      torch.manual_seed(snake_case__     )
     else:
      SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      {
         '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 UpperCamelCase      (      self			:   Dict				,     snake_case__			:   Optional[int]				,     snake_case__			:   List[str]=0     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      floats_tensor((1, 3, 3_2, 3_2)				,     rng=random.Random(snake_case__     )     ).to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      image.cpu().permute(0				,     2				,     3				,     1     )[0]
     SCREAMING_SNAKE_CASE		    =      Image.fromarray(np.uinta(snake_case__     )     ).convert('RGB'     )
     if str(snake_case__     ).startswith('mps'     ):
      SCREAMING_SNAKE_CASE		    =      torch.manual_seed(snake_case__     )
     else:
      SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      {
         '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 UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   Optional[int]				,     snake_case__			:   int=0     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      floats_tensor((1, 3, 3_2, 3_2)				,     rng=random.Random(snake_case__     )     ).to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      image.cpu().permute(0				,     2				,     3				,     1     )[0]
     SCREAMING_SNAKE_CASE		    =      Image.fromarray(np.uinta(snake_case__     )     ).convert('RGB'     )
     if str(snake_case__     ).startswith('mps'     ):
      SCREAMING_SNAKE_CASE		    =      torch.manual_seed(snake_case__     )
     else:
      SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      {
         '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 UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     if not hasattr(self.pipeline_class				,     '_optional_components'     ):
      return

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_components()
     SCREAMING_SNAKE_CASE		    =      self.pipeline_class(**snake_case__     )
     pipe.to(snake_case__     )
     pipe.set_progress_bar_config(disable=snake_case__     )

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

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      pipe(**snake_case__     )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
      pipe.save_pretrained(snake_case__     )
      SCREAMING_SNAKE_CASE		    =      self.pipeline_class.from_pretrained(snake_case__     )
      pipe_loaded.to(snake_case__     )
      pipe_loaded.set_progress_bar_config(disable=snake_case__     )

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

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      pipe_loaded(**snake_case__     )[0]

     SCREAMING_SNAKE_CASE		    =      np.abs(output - output_loaded     ).max()
     self.assertLess(snake_case__				,     1E-4     )

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'cpu'

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_components()
     SCREAMING_SNAKE_CASE		    =      self.pipeline_class(**snake_case__     )
     pipe.to(snake_case__     )
     pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_mask_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      pipe.generate_mask(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      mask[0, -3:, -3:]

     self.assertEqual(mask.shape				,     (1, 1_6, 1_6)     )
     SCREAMING_SNAKE_CASE		    =      np.array([0] * 9     )
     SCREAMING_SNAKE_CASE		    =      np.abs(mask_slice.flatten() - expected_slice     ).max()
     self.assertLessEqual(snake_case__				,     1E-3     )
     self.assertEqual(mask[0, -3, -4]				,     0     )

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'cpu'

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_components()
     SCREAMING_SNAKE_CASE		    =      self.pipeline_class(**snake_case__     )
     pipe.to(snake_case__     )
     pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_inversion_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      pipe.invert(**snake_case__     ).images
     SCREAMING_SNAKE_CASE		    =      image[0, -1, -3:, -3:]

     self.assertEqual(image.shape				,     (2, 3_2, 3_2, 3)     )
     SCREAMING_SNAKE_CASE		    =      np.array(
         [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]				,     )
     SCREAMING_SNAKE_CASE		    =      np.abs(image_slice.flatten() - expected_slice     ).max()
     self.assertLessEqual(snake_case__				,     1E-3     )

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






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




    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'cpu'

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_components()

     SCREAMING_SNAKE_CASE		    =      {'beta_start': 0.00_085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
     SCREAMING_SNAKE_CASE		    =      DPMSolverMultistepScheduler(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      DPMSolverMultistepInverseScheduler(**snake_case__     )

     SCREAMING_SNAKE_CASE		    =      self.pipeline_class(**snake_case__     )
     pipe.to(snake_case__     )
     pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_inversion_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      pipe.invert(**snake_case__     ).images
     SCREAMING_SNAKE_CASE		    =      image[0, -1, -3:, -3:]

     self.assertEqual(image.shape				,     (2, 3_2, 3_2, 3)     )
     SCREAMING_SNAKE_CASE		    =      np.array(
         [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]				,     )
     SCREAMING_SNAKE_CASE		    =      np.abs(image_slice.flatten() - expected_slice     ).max()
     self.assertLessEqual(snake_case__				,     1E-3     )




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

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






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

    @classmethod
    def UpperCamelCase      (      cls			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'     )

     SCREAMING_SNAKE_CASE		    =      raw_image.convert('RGB'     ).resize((7_6_8, 7_6_8)     )

     SCREAMING_SNAKE_CASE		    =      raw_image

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )

     SCREAMING_SNAKE_CASE		    =      StableDiffusionDiffEditPipeline.from_pretrained(
         'stabilityai/stable-diffusion-2-1'				,     safety_checker=snake_case__				,     torch_dtype=torch.floataa     )
     SCREAMING_SNAKE_CASE		    =      DDIMScheduler.from_config(pipe.scheduler.config     )
     SCREAMING_SNAKE_CASE		    =      DDIMInverseScheduler.from_config(pipe.scheduler.config     )
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      'a bowl of fruit'
     SCREAMING_SNAKE_CASE		    =      'a bowl of pears'

     SCREAMING_SNAKE_CASE		    =      pipe.generate_mask(
         image=self.raw_image				,     source_prompt=snake_case__				,     target_prompt=snake_case__				,     generator=snake_case__				,     )

     SCREAMING_SNAKE_CASE		    =      pipe.invert(
         prompt=snake_case__				,     image=self.raw_image				,     inpaint_strength=0.7				,     generator=snake_case__     ).latents

     SCREAMING_SNAKE_CASE		    =      pipe(
         prompt=snake_case__				,     mask_image=snake_case__				,     image_latents=snake_case__				,     generator=snake_case__				,     negative_prompt=snake_case__				,     inpaint_strength=0.7				,     output_type='numpy'				,     ).images[0]

     SCREAMING_SNAKE_CASE		    =      (
         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 UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      torch.manual_seed(0     )

     SCREAMING_SNAKE_CASE		    =      StableDiffusionDiffEditPipeline.from_pretrained(
         'stabilityai/stable-diffusion-2-1'				,     safety_checker=snake_case__				,     torch_dtype=torch.floataa     )
     SCREAMING_SNAKE_CASE		    =      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
     SCREAMING_SNAKE_CASE		    =      DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config     )
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      'a bowl of fruit'
     SCREAMING_SNAKE_CASE		    =      'a bowl of pears'

     SCREAMING_SNAKE_CASE		    =      pipe.generate_mask(
         image=self.raw_image				,     source_prompt=snake_case__				,     target_prompt=snake_case__				,     generator=snake_case__				,     )

     SCREAMING_SNAKE_CASE		    =      pipe.invert(
         prompt=snake_case__				,     image=self.raw_image				,     inpaint_strength=0.7				,     generator=snake_case__				,     num_inference_steps=2_5				,     ).latents

     SCREAMING_SNAKE_CASE		    =      pipe(
         prompt=snake_case__				,     mask_image=snake_case__				,     image_latents=snake_case__				,     generator=snake_case__				,     negative_prompt=snake_case__				,     inpaint_strength=0.7				,     num_inference_steps=2_5				,     output_type='numpy'				,     ).images[0]

     SCREAMING_SNAKE_CASE		    =      (
         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




from __future__ import annotations

from math import gcd


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	int = 2		,				_UpperCamelCase		:	int = 1		,				_UpperCamelCase		:	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(_UpperCamelCase		:	int		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	int						) -> int:
  return (pow(_UpperCamelCase		,				2						) + step) % modulus

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

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

   # 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``.
   SCREAMING_SNAKE_CASE		    =      gcd(hare - tortoise		,				_UpperCamelCase						)

   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.
  SCREAMING_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

      a_     :    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",
      )
      a_     :    Tuple     		=   parser.parse_args()

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

