



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()



class        _SCREAMING_SNAKE_CASE		(       unittest.TestCase ):





   def 	A__      (self):
       '''simple docstring'''







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





   @property
   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =1
       __UpperCAmelCase			 =3
       __UpperCAmelCase			 =(3_2, 3_2)

       __UpperCAmelCase			 =floats_tensor((batch_size, num_channels) + sizes    ,  rng=random.Random(0)).to(UpperCAmelCase)
       return image





   @property
   def 	A__      (self):
       '''simple docstring'''







       torch.manual_seed(0)
       __UpperCAmelCase			 =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    ,  )
       return model





   @property
   def 	A__      (self):
       '''simple docstring'''







       torch.manual_seed(0)
       __UpperCAmelCase			 =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    ,  )
       return model





   @property
   def 	A__      (self):
       '''simple docstring'''







       torch.manual_seed(0)
       __UpperCAmelCase			 =RobertaSeriesConfig(
           hidden_size=3_2    ,  project_dim=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=5_0_0_6    ,  )
       return RobertaSeriesModelWithTransformation(UpperCAmelCase)





   @property
   def 	A__      (self):
       '''simple docstring'''







       def extract(*UpperCAmelCase    ,  **UpperCAmelCase):



           class        _SCREAMING_SNAKE_CASE		:





              def __init__(self):
                  '''simple docstring'''







                  __UpperCAmelCase			 =torch.ones([0])






              def 	A__      (self    ,  UpperCAmelCase):
                  '''simple docstring'''







                  self.pixel_values.to(UpperCAmelCase)
                  return self







           return Out()

       return extract





   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
       __UpperCAmelCase			 =self.dummy_cond_unet
       __UpperCAmelCase			 =PNDMScheduler(skip_prk_steps=UpperCAmelCase)
       __UpperCAmelCase			 =self.dummy_vae
       __UpperCAmelCase			 =self.dummy_text_encoder
       __UpperCAmelCase			 =XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''')
       __UpperCAmelCase			 =7_7

       __UpperCAmelCase			 =self.dummy_image.to(UpperCAmelCase)
       __UpperCAmelCase			 =init_image / 2 + 0.5

       # make sure here that pndm scheduler skips prk
       __UpperCAmelCase			 =AltDiffusionImgaImgPipeline(
           unet=UpperCAmelCase    ,  scheduler=UpperCAmelCase    ,  vae=UpperCAmelCase    ,  text_encoder=UpperCAmelCase    ,  tokenizer=UpperCAmelCase    ,  safety_checker=UpperCAmelCase    ,  feature_extractor=self.dummy_extractor    ,  )
       __UpperCAmelCase			 =VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor    ,  do_normalize=UpperCAmelCase)
       __UpperCAmelCase			 =alt_pipe.to(UpperCAmelCase)
       alt_pipe.set_progress_bar_config(disable=UpperCAmelCase)

       __UpperCAmelCase			 ='''A painting of a squirrel eating a burger'''
       __UpperCAmelCase			 =torch.Generator(device=UpperCAmelCase).manual_seed(0)
       __UpperCAmelCase			 =alt_pipe(
           [prompt]    ,  generator=UpperCAmelCase    ,  guidance_scale=6.0    ,  num_inference_steps=2    ,  output_type='''np'''    ,  image=UpperCAmelCase    ,  )

       __UpperCAmelCase			 =output.images

       __UpperCAmelCase			 =torch.Generator(device=UpperCAmelCase).manual_seed(0)
       __UpperCAmelCase			 =alt_pipe(
           [prompt]    ,  generator=UpperCAmelCase    ,  guidance_scale=6.0    ,  num_inference_steps=2    ,  output_type='''np'''    ,  image=UpperCAmelCase    ,  return_dict=UpperCAmelCase    ,  )[0]

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

       assert image.shape == (1, 3_2, 3_2, 3)
       __UpperCAmelCase			 =np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499])

       assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5e-3





   @unittest.skipIf(torch_device != '''cuda'''    ,  '''This test requires a GPU''')
   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =self.dummy_cond_unet
       __UpperCAmelCase			 =PNDMScheduler(skip_prk_steps=UpperCAmelCase)
       __UpperCAmelCase			 =self.dummy_vae
       __UpperCAmelCase			 =self.dummy_text_encoder
       __UpperCAmelCase			 =XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''')
       __UpperCAmelCase			 =7_7

       __UpperCAmelCase			 =self.dummy_image.to(UpperCAmelCase)

       # put models in fp16
       __UpperCAmelCase			 =unet.half()
       __UpperCAmelCase			 =vae.half()
       __UpperCAmelCase			 =bert.half()

       # make sure here that pndm scheduler skips prk
       __UpperCAmelCase			 =AltDiffusionImgaImgPipeline(
           unet=UpperCAmelCase    ,  scheduler=UpperCAmelCase    ,  vae=UpperCAmelCase    ,  text_encoder=UpperCAmelCase    ,  tokenizer=UpperCAmelCase    ,  safety_checker=UpperCAmelCase    ,  feature_extractor=self.dummy_extractor    ,  )
       __UpperCAmelCase			 =VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor    ,  do_normalize=UpperCAmelCase)
       __UpperCAmelCase			 =alt_pipe.to(UpperCAmelCase)
       alt_pipe.set_progress_bar_config(disable=UpperCAmelCase)

       __UpperCAmelCase			 ='''A painting of a squirrel eating a burger'''
       __UpperCAmelCase			 =torch.manual_seed(0)
       __UpperCAmelCase			 =alt_pipe(
           [prompt]    ,  generator=UpperCAmelCase    ,  num_inference_steps=2    ,  output_type='''np'''    ,  image=UpperCAmelCase    ,  ).images

       assert image.shape == (1, 3_2, 3_2, 3)






   @unittest.skipIf(torch_device != '''cuda'''    ,  '''This test requires a GPU''')
   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/img2img/sketch-mountains-input.jpg''')
       # resize to resolution that is divisible by 8 but not 16 or 32
       __UpperCAmelCase			 =init_image.resize((7_6_0, 5_0_4))

       __UpperCAmelCase			 ='''BAAI/AltDiffusion'''
       __UpperCAmelCase			 =AltDiffusionImgaImgPipeline.from_pretrained(
           UpperCAmelCase    ,  safety_checker=UpperCAmelCase    ,  )
       pipe.to(UpperCAmelCase)
       pipe.set_progress_bar_config(disable=UpperCAmelCase)
       pipe.enable_attention_slicing()

       __UpperCAmelCase			 ='''A fantasy landscape, trending on artstation'''

       __UpperCAmelCase			 =torch.manual_seed(0)
       __UpperCAmelCase			 =pipe(
           prompt=UpperCAmelCase    ,  image=UpperCAmelCase    ,  strength=0.75    ,  guidance_scale=7.5    ,  generator=UpperCAmelCase    ,  output_type='''np'''    ,  )
       __UpperCAmelCase			 =output.images[0]

       __UpperCAmelCase			 =image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

       assert image.shape == (5_0_4, 7_6_0, 3)
       __UpperCAmelCase			 =np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000])

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







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





   def 	A__      (self):
       '''simple docstring'''







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






   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/img2img/sketch-mountains-input.jpg''')
       __UpperCAmelCase			 =init_image.resize((7_6_8, 5_1_2))
       __UpperCAmelCase			 =load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy''')

       __UpperCAmelCase			 ='''BAAI/AltDiffusion'''
       __UpperCAmelCase			 =AltDiffusionImgaImgPipeline.from_pretrained(
           UpperCAmelCase    ,  safety_checker=UpperCAmelCase    ,  )
       pipe.to(UpperCAmelCase)
       pipe.set_progress_bar_config(disable=UpperCAmelCase)
       pipe.enable_attention_slicing()

       __UpperCAmelCase			 ='''A fantasy landscape, trending on artstation'''

       __UpperCAmelCase			 =torch.manual_seed(0)
       __UpperCAmelCase			 =pipe(
           prompt=UpperCAmelCase    ,  image=UpperCAmelCase    ,  strength=0.75    ,  guidance_scale=7.5    ,  generator=UpperCAmelCase    ,  output_type='''np'''    ,  )
       __UpperCAmelCase			 =output.images[0]

       assert image.shape == (5_1_2, 7_6_8, 3)
       # img2img is flaky across GPUs even in fp32, so using MAE here
       assert np.abs(expected_image - image).max() < 1e-2











'''simple docstring'''



from math import isqrt



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int     )	->      bool:

   """simple docstring"""
   return all(number % divisor != 0 for divisor in range(2,       isqrt(SCREAMING_SNAKE_CASE__     ) + 1     )     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int = 10**6     )	->      int:

   """simple docstring"""
   __a					  =     0
   __a					  =     1
   __a					  =     7
   while prime_candidate < max_prime:
      primes_count += is_prime(SCREAMING_SNAKE_CASE__     )

      cube_index += 1
      prime_candidate += 6 * cube_index

   return primes_count


if __name__ == "__main__":
 print(f"""{solution() = }""")