






'''simple docstring'''



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
@skip_mps
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      _lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=StableDiffusionPanoramaPipeline
      __a  					=TEXT_TO_IMAGE_PARAMS
      __a  					=TEXT_TO_IMAGE_BATCH_PARAMS
      __a  					=TEXT_TO_IMAGE_IMAGE_PARAMS
      __a  					=TEXT_TO_IMAGE_IMAGE_PARAMS




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         torch.manual_seed(0      )
         __a				     =    UNetaDConditionModel(
             block_out_channels=(32, 64) ,   layers_per_block=1 ,   sample_size=32 ,   in_channels=4 ,   out_channels=4 ,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,   cross_attention_dim=32 ,   )
         __a				     =    DDIMScheduler()
         torch.manual_seed(0      )
         __a				     =    AutoencoderKL(
             block_out_channels=[32, 64] ,   in_channels=3 ,   out_channels=3 ,   down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,   up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,   latent_channels=4 ,   )
         torch.manual_seed(0      )
         __a				     =    CLIPTextConfig(
             bos_token_id=0 ,   eos_token_id=2 ,   hidden_size=32 ,   intermediate_size=37 ,   layer_norm_eps=1e-05 ,   num_attention_heads=4 ,   num_hidden_layers=5 ,   pad_token_id=1 ,   vocab_size=1000 ,   )
         __a				     =    CLIPTextModel(lowerCamelCase      )
         __a				     =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

         __a				     =    {
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=0      )							->Any:



         '''simple docstring'''
         __a				     =    torch.manual_seed(lowerCamelCase      )
         __a				     =    {
             'prompt': 'a photo of the dolomites',
             'generator': generator,
             # Setting height and width to None to prevent OOMs on CPU.
             'height': None,
             'width': None,
             'num_inference_steps': 1,
             'guidance_scale': 6.0,
             'output_type': 'numpy',
         }
         return inputs




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    'cpu'  # ensure determinism for the device-dependent torch.Generator
         __a				     =    self.get_dummy_components()
         __a				     =    StableDiffusionPanoramaPipeline(**lowerCamelCase      )
         __a				     =    sd_pipe.to(lowerCamelCase      )
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase      )

         __a				     =    self.get_dummy_inputs(lowerCamelCase      )
         __a				     =    sd_pipe(**lowerCamelCase      ).images
         __a				     =    image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)

         __a				     =    np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         super().test_inference_batch_consistent(batch_sizes=[1, 2]      )




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         super().test_inference_batch_single_identical(batch_size=2 ,   expected_max_diff=3.25e-3      )




      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         __a				     =    'cpu'  # ensure determinism for the device-dependent torch.Generator
         __a				     =    self.get_dummy_components()
         __a				     =    StableDiffusionPanoramaPipeline(**lowerCamelCase      )
         __a				     =    sd_pipe.to(lowerCamelCase      )
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase      )

         __a				     =    self.get_dummy_inputs(lowerCamelCase      )
         __a				     =    'french fries'
         __a				     =    sd_pipe(**lowerCamelCase ,   negative_prompt=lowerCamelCase      )
         __a				     =    output.images
         __a				     =    image[0, -3:, -3:, -1]

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

         __a				     =    np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    'cpu'  # ensure determinism for the device-dependent torch.Generator
         __a				     =    self.get_dummy_components()
         __a				     =    StableDiffusionPanoramaPipeline(**lowerCamelCase      )
         __a				     =    sd_pipe.to(lowerCamelCase      )
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase      )

         __a				     =    self.get_dummy_inputs(lowerCamelCase      )
         __a				     =    sd_pipe(**lowerCamelCase ,   view_batch_size=2      )
         __a				     =    output.images
         __a				     =    image[0, -3:, -3:, -1]

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

         __a				     =    np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    'cpu'  # ensure determinism for the device-dependent torch.Generator
         __a				     =    self.get_dummy_components()
         __a				     =    EulerAncestralDiscreteScheduler(
             beta_start=0.00085 ,   beta_end=0.012 ,   beta_schedule='scaled_linear'      )
         __a				     =    StableDiffusionPanoramaPipeline(**lowerCamelCase      )
         __a				     =    sd_pipe.to(lowerCamelCase      )
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase      )

         __a				     =    self.get_dummy_inputs(lowerCamelCase      )
         __a				     =    sd_pipe(**lowerCamelCase      ).images
         __a				     =    image[0, -3:, -3:, -1]

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

         __a				     =    np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2






      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         __a				     =    'cpu'  # ensure determinism for the device-dependent torch.Generator
         __a				     =    self.get_dummy_components()
         __a				     =    PNDMScheduler(
             beta_start=0.00085 ,   beta_end=0.012 ,   beta_schedule='scaled_linear' ,   skip_prk_steps=lowerCamelCase      )
         __a				     =    StableDiffusionPanoramaPipeline(**lowerCamelCase      )
         __a				     =    sd_pipe.to(lowerCamelCase      )
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase      )

         __a				     =    self.get_dummy_inputs(lowerCamelCase      )
         __a				     =    sd_pipe(**lowerCamelCase      ).images
         __a				     =    image[0, -3:, -3:, -1]

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

         __a				     =    np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2







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




      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




      def  __UpperCamelCase							(  self ,   lowerCamelCase=0      )							->List[Any]:



         '''simple docstring'''
         __a				     =    torch.manual_seed(lowerCamelCase      )
         __a				     =    {
             'prompt': 'a photo of the dolomites',
             'generator': generator,
             'num_inference_steps': 3,
             'guidance_scale': 7.5,
             'output_type': 'numpy',
         }
         return inputs




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    'stabilityai/stable-diffusion-2-base'
         __a				     =    DDIMScheduler.from_pretrained(lowerCamelCase ,   subfolder='scheduler'      )
         __a				     =    StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase ,   scheduler=lowerCamelCase ,   safety_checker=lowerCamelCase      )
         pipe.to(lowerCamelCase      )
         pipe.set_progress_bar_config(disable=lowerCamelCase      )
         pipe.enable_attention_slicing()

         __a				     =    self.get_inputs()
         __a				     =    pipe(**lowerCamelCase      ).images
         __a				     =    image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 2048, 3)

         __a				     =    np.array(
             [
                 0.36968392,
                 0.27025372,
                 0.32446766,
                 0.28379387,
                 0.36363274,
                 0.30733347,
                 0.27100027,
                 0.27054125,
                 0.25536096,
             ]      )

         assert np.abs(expected_slice - image_slice      ).max() < 1e-2




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    StableDiffusionPanoramaPipeline.from_pretrained(
             'stabilityai/stable-diffusion-2-base' ,   safety_checker=lowerCamelCase      )
         __a				     =    LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
         pipe.to(lowerCamelCase      )
         pipe.set_progress_bar_config(disable=lowerCamelCase      )
         pipe.enable_attention_slicing()

         __a				     =    self.get_inputs()
         __a				     =    pipe(**lowerCamelCase      ).images
         __a				     =    image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 2048, 3)

         __a				     =    np.array(
             [
                 [
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                 ]
             ]      )

         assert np.abs(expected_slice - image_slice      ).max() < 1e-3




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    0

         def callback_fn(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      ) -> None:
            __a				     =    True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 1:
               __a				     =    latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 256)
               __a				     =    latents[0, -3:, -3:, -1]

               __a				     =    np.array(
                   [
                       0.18681869,
                       0.33907816,
                       0.5361276,
                       0.14432865,
                       -0.02856611,
                       -0.73941123,
                       0.23397987,
                       0.47322682,
                       -0.37823164,
                   ]      )
               assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2
            elif step == 2:
               __a				     =    latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 256)
               __a				     =    latents[0, -3:, -3:, -1]

               __a				     =    np.array(
                   [
                       0.18539645,
                       0.33987248,
                       0.5378559,
                       0.14437142,
                       -0.02455261,
                       -0.7338317,
                       0.23990755,
                       0.47356272,
                       -0.3786505,
                   ]      )

               assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2

         __a				     =    False

         __a				     =    'stabilityai/stable-diffusion-2-base'
         __a				     =    DDIMScheduler.from_pretrained(lowerCamelCase ,   subfolder='scheduler'      )
         __a				     =    StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase ,   scheduler=lowerCamelCase ,   safety_checker=lowerCamelCase      )
         __a				     =    pipe.to(lowerCamelCase      )
         pipe.set_progress_bar_config(disable=lowerCamelCase      )
         pipe.enable_attention_slicing()

         __a				     =    self.get_inputs()
         pipe(**lowerCamelCase ,   callback=lowerCamelCase ,   callback_steps=1      )
         assert callback_fn.has_been_called
         assert number_of_steps == 3






      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __a				     =    'stabilityai/stable-diffusion-2-base'
         __a				     =    DDIMScheduler.from_pretrained(lowerCamelCase ,   subfolder='scheduler'      )
         __a				     =    StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase ,   scheduler=lowerCamelCase ,   safety_checker=lowerCamelCase      )
         __a				     =    pipe.to(lowerCamelCase      )
         pipe.set_progress_bar_config(disable=lowerCamelCase      )
         pipe.enable_attention_slicing(1      )
         pipe.enable_sequential_cpu_offload()

         __a				     =    self.get_inputs()
         __a				     =    pipe(**lowerCamelCase      )

         __a				     =    torch.cuda.max_memory_allocated()
         # make sure that less than 5.2 GB is allocated
         assert mem_bytes < 5.5 * 10**9







'''simple docstring'''



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	bool = False    )  ->			bool:

   """simple docstring"""
   if n == 2:
      return True
   if not n % 2 or n < 2:
      return False
   if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
      return False
   if n > 3317044064679887385961981 and not allow_probable:
      raise ValueError(
          'Warning: upper bound of deterministic test is exceeded. '
          'Pass allow_probable=True to allow probabilistic test. '
          'A return value of True indicates a probable prime.'    )
   # array bounds provided by analysis
   __a				     =    [
       2047,
       1373653,
       25326001,
       3215031751,
       2152302898747,
       3474749660383,
       341550071728321,
       1,
       3825123056546413051,
       1,
       1,
       318665857834031151167461,
       3317044064679887385961981,
   ]

   __a				     =    [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
   for idx, _p in enumerate(SCREAMING_SNAKE_CASE__,					1    ):
      if n < _p:
         # then we have our last prime to check
         __a				     =    primes[:idx]
         break
   __a     ,			__a				     =    n - 1, 0
   # break up n -1 into a power of 2 (s) and
   # remaining odd component
   # essentially, solve for d * 2 ** s == n - 1
   while d % 2 == 0:
      d //= 2
      s += 1
   for prime in plist:
      __a				     =    False
      for r in range(SCREAMING_SNAKE_CASE__    ):
         __a				     =    pow(SCREAMING_SNAKE_CASE__,					d * 2**r,					SCREAMING_SNAKE_CASE__    )
         # see article for analysis explanation for m
         if (r == 0 and m == 1) or ((m + 1) % n == 0):
            __a				     =    True
            # this loop will not determine compositeness
            break
      if pr:
         continue
      # if pr is False, then the above loop never evaluated to true,
      # and the n MUST be composite
      return False
   return True



def    __UpperCAmelCase	(				)  ->			None:

   """simple docstring"""
   assert not miller_rabin(561    )
   assert miller_rabin(563    )
   # 2047

   assert not miller_rabin(838201    )
   assert miller_rabin(838207    )
   # 1_373_653

   assert not miller_rabin(17316001    )
   assert miller_rabin(17316017    )
   # 25_326_001

   assert not miller_rabin(3078386641    )
   assert miller_rabin(3078386653    )
   # 3_215_031_751

   assert not miller_rabin(1713045574801    )
   assert miller_rabin(1713045574819    )
   # 2_152_302_898_747

   assert not miller_rabin(2779799728307    )
   assert miller_rabin(2779799728327    )
   # 3_474_749_660_383

   assert not miller_rabin(113850023909441    )
   assert miller_rabin(113850023909527    )
   # 341_550_071_728_321

   assert not miller_rabin(1275041018848804351    )
   assert miller_rabin(1275041018848804391    )
   # 3_825_123_056_546_413_051

   assert not miller_rabin(79666464458507787791867    )
   assert miller_rabin(79666464458507787791951    )
   # 318_665_857_834_031_151_167_461

   assert not miller_rabin(552840677446647897660333    )
   assert miller_rabin(552840677446647897660359    )
   # 3_317_044_064_679_887_385_961_981
   # upper limit for probabilistic test


if __name__ == "__main__":
 test_miller_rabin()