





import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        UpperCAmelCase     (	SCREAMING_SNAKE_CASE_      ,	unittest.TestCase    ):
 __lowercase       		=    KandinskyVaaPriorPipeline
 __lowercase       		=    ["""prompt"""]
 __lowercase       		=    ["""prompt""", """negative_prompt"""]
 __lowercase       		=    [
     """num_images_per_prompt""",
     """generator""",
     """num_inference_steps""",
     """latents""",
     """negative_prompt""",
     """guidance_scale""",
     """output_type""",
     """return_dict""",
 ]
 __lowercase       		=    False
 @property
 def 					UpperCAmelCase_							(    self				:Dict							)->							List[str]:
     return 32
 @property
 def 					UpperCAmelCase_							(    self				:Any							)->							str:
     return 32
 @property
 def 					UpperCAmelCase_							(    self				:str							)->							Optional[int]:
     return self.time_input_dim
 @property
 def 					UpperCAmelCase_							(    self				:str							)->							int:
     return self.time_input_dim * 4
 @property
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Union[str, Any]:
     return 1_00
 @property
 def 					UpperCAmelCase_							(    self				:Tuple							)->							List[str]:
     A__    =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)
     return tokenizer
 @property
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[int]:
     torch.manual_seed(0							)
     A__    =      CLIPTextConfig(
         bos_token_id=0						,	eos_token_id=2						,	hidden_size=self.text_embedder_hidden_size						,	projection_dim=self.text_embedder_hidden_size						,	intermediate_size=37						,	layer_norm_eps=1E-05						,	num_attention_heads=4						,	num_hidden_layers=5						,	pad_token_id=1						,	vocab_size=10_00						,	)
     return CLIPTextModelWithProjection(__a							)
 @property
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Any:
     torch.manual_seed(0							)

     A__    =      {
         'num_attention_heads': 2,
         'attention_head_dim': 12,
         'embedding_dim': self.text_embedder_hidden_size,
         'num_layers': 1,
     }

     A__    =      PriorTransformer(**__a							)
     # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
     A__    =      nn.Parameter(torch.ones(model.clip_std.shape							)							)
     return model
 @property
 def 					UpperCAmelCase_							(    self				:List[str]							)->							List[str]:
     torch.manual_seed(0							)
     A__    =      CLIPVisionConfig(
         hidden_size=self.text_embedder_hidden_size						,	image_size=2_24						,	projection_dim=self.text_embedder_hidden_size						,	intermediate_size=37						,	num_attention_heads=4						,	num_channels=3						,	num_hidden_layers=5						,	patch_size=14						,	)

     A__    =      CLIPVisionModelWithProjection(__a							)
     return model
 @property
 def 					UpperCAmelCase_							(    self				:Dict							)->							List[Any]:
     A__    =      CLIPImageProcessor(
         crop_size=2_24						,	do_center_crop=__a						,	do_normalize=__a						,	do_resize=__a						,	image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]						,	image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]						,	resample=3						,	size=2_24						,	)

     return image_processor
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[int]:
     A__    =      self.dummy_prior
     A__    =      self.dummy_image_encoder
     A__    =      self.dummy_text_encoder
     A__    =      self.dummy_tokenizer
     A__    =      self.dummy_image_processor

     A__    =      UnCLIPScheduler(
         variance_type="fixed_small_log"						,	prediction_type="sample"						,	num_train_timesteps=10_00						,	clip_sample=__a						,	clip_sample_range=10.0						,	)

     A__    =      {
         'prior': prior,
         'image_encoder': image_encoder,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'scheduler': scheduler,
         'image_processor': image_processor,
     }

     return components
 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:Optional[Any]						,	lowercase_				:Tuple=0							)->							Any:
     if str(__a							).startswith("mps"							):
         A__    =      torch.manual_seed(__a							)
     else:
         A__    =      torch.Generator(device=__a							).manual_seed(__a							)
     A__    =      {
         'prompt': 'horse',
         'generator': generator,
         'guidance_scale': 4.0,
         'num_inference_steps': 2,
         'output_type': 'np',
     }
     return inputs
 def 					UpperCAmelCase_							(    self				:str							)->							Dict:
     A__    =      'cpu'

     A__    =      self.get_dummy_components()

     A__    =      self.pipeline_class(**__a							)
     A__    =      pipe.to(__a							)

     pipe.set_progress_bar_config(disable=__a							)

     A__    =      pipe(**self.get_dummy_inputs(__a							)							)
     A__    =      output.image_embeds

     A__    =      pipe(
         **self.get_dummy_inputs(__a							)						,	return_dict=__a						,	)[0]

     A__    =      image[0, -10:]
     A__    =      image_from_tuple[0, -10:]

     assert image.shape == (1, 32)

     A__    =      np.array(
         [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]							)

     assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
 @skip_mps
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Optional[int]:
     A__    =      torch_device == 'cpu'
     A__    =      True
     A__    =      False

     self._test_inference_batch_single_identical(
         test_max_difference=__a						,	relax_max_difference=__a						,	test_mean_pixel_difference=__a						,	)



 @skip_mps
 def 					UpperCAmelCase_							(    self				:str							)->							Union[str, Any]:
     A__    =      torch_device == 'cpu'
     A__    =      False

     self._test_attention_slicing_forward_pass(
         test_max_difference=__a						,	test_mean_pixel_difference=__a						,	)










'''simple docstring'''

from __future__ import annotations

from math import gcd





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : int = 2						,	_lowerCamelCase : int = 1						,	_lowerCamelCase : int = 3						,	):
    # 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 : 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.
        A__    =      seed
        A__    =      seed

        while True:
            # At each iteration, the tortoise moves one step and the hare moves two.
            A__    =      rand_fn(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
            A__    =      rand_fn(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
            A__    =      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__    =      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__    =      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							:     Optional[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",
  )
  __lowerCAmelCase							:     Optional[int]		     =parser.parse_args()

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



