

"""simple docstring"""




import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class        __lowercase     (      unittest.TestCase   ):

  def 				__lowercase    (					self						:     Any      ):






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

  @property
  def 				__lowercase    (					self						:     List[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   1
   UpperCAmelCase__   :					Union[str, Any]   				=   3
   UpperCAmelCase__   :					Optional[Any]   				=   (32, 32)

   UpperCAmelCase__   :					List[str]   				=   floats_tensor((batch_size, num_channels) + sizes   ,rng=random.Random(0      )      ).to(A      )
   return image

  @property
  def 				__lowercase    (					self						:     int      ):






   '''simple docstring'''
   torch.manual_seed(0      )
   UpperCAmelCase__   :					Tuple   				=   UNetaDConditionModel(
       block_out_channels=(32, 64)   ,layers_per_block=2   ,sample_size=32   ,in_channels=4   ,out_channels=4   ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")   ,cross_attention_dim=32   ,)
   return model

  @property
  def 				__lowercase    (					self						:     int      ):






   '''simple docstring'''
   torch.manual_seed(0      )
   UpperCAmelCase__   :					int   				=   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   ,)
   return model

  @property
  def 				__lowercase    (					self						:     int      ):






   '''simple docstring'''
   torch.manual_seed(0      )
   UpperCAmelCase__   :					Tuple   				=   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=1_000   ,)
   return CLIPTextModel(A      )

  @property
  def 				__lowercase    (					self						:     Tuple      ):






   '''simple docstring'''



   def extract(*A						:     Optional[int]   ,**A						:     int      ):







    class        __lowercase     :

      def __init__(					self						:     Optional[Any]      ):






       '''simple docstring'''
       UpperCAmelCase__   :					List[str]   				=   torch.ones([0]      )




      def 				__lowercase    (					self						:     Union[str, Any]   ,A						:     List[str]      ):






       '''simple docstring'''
       self.pixel_values.to(A      )
       return self







    return Out()

   return extract

  def 				__lowercase    (					self						:     List[str]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Union[str, Any]   				=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
   UpperCAmelCase__   :					str   				=   self.dummy_cond_unet
   UpperCAmelCase__   :					int   				=   DDIMScheduler(
       beta_start=0.0_0_0_8_5   ,beta_end=0.0_1_2   ,beta_schedule="""scaled_linear"""   ,clip_sample=A   ,set_alpha_to_one=A   ,)

   UpperCAmelCase__   :					Dict   				=   self.dummy_vae
   UpperCAmelCase__   :					int   				=   self.dummy_text_encoder
   UpperCAmelCase__   :					Tuple   				=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

   # make sure here that pndm scheduler skips prk
   UpperCAmelCase__   :					List[str]   				=   StableDiffusionPipeline(
       unet=A   ,scheduler=A   ,vae=A   ,text_encoder=A   ,tokenizer=A   ,safety_checker=A   ,feature_extractor=self.dummy_extractor   ,)
   UpperCAmelCase__   :					List[str]   				=   sd_pipe.to(A      )
   sd_pipe.set_progress_bar_config(disable=A      )

   UpperCAmelCase__   :					Optional[Any]   				=   """A painting of a squirrel eating a burger"""

   UpperCAmelCase__   :					Tuple   				=   torch.Generator(device=A      ).manual_seed(0      )
   UpperCAmelCase__   :					int   				=   sd_pipe([prompt]   ,generator=A   ,guidance_scale=6.0   ,num_inference_steps=2   ,output_type="""np"""      )
   UpperCAmelCase__   :					Optional[int]   				=   output.images

   UpperCAmelCase__   :					str   				=   torch.Generator(device=A      ).manual_seed(0      )
   UpperCAmelCase__   :					int   				=   sd_pipe(
       [prompt]   ,generator=A   ,guidance_scale=6.0   ,num_inference_steps=2   ,output_type="""np"""   ,return_dict=A   ,)[0]

   UpperCAmelCase__   :					List[Any]   				=   image[0, -3:, -3:, -1]
   UpperCAmelCase__   :					List[Any]   				=   image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)
   UpperCAmelCase__   :					int   				=   np.array([0.5_7_5_3, 0.6_1_1_4, 0.5_0_0_1, 0.5_0_3_4, 0.5_4_7_0, 0.4_7_2_9, 0.4_9_7_1, 0.4_8_6_7, 0.4_8_6_7]      )

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

  def 				__lowercase    (					self						:     Any      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
   UpperCAmelCase__   :					Any   				=   self.dummy_cond_unet
   UpperCAmelCase__   :					List[Any]   				=   PNDMScheduler(skip_prk_steps=A      )
   UpperCAmelCase__   :					Any   				=   self.dummy_vae
   UpperCAmelCase__   :					List[Any]   				=   self.dummy_text_encoder
   UpperCAmelCase__   :					Optional[Any]   				=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

   # make sure here that pndm scheduler skips prk
   UpperCAmelCase__   :					int   				=   StableDiffusionPipeline(
       unet=A   ,scheduler=A   ,vae=A   ,text_encoder=A   ,tokenizer=A   ,safety_checker=A   ,feature_extractor=self.dummy_extractor   ,)
   UpperCAmelCase__   :					Any   				=   sd_pipe.to(A      )
   sd_pipe.set_progress_bar_config(disable=A      )

   UpperCAmelCase__   :					int   				=   """A painting of a squirrel eating a burger"""
   UpperCAmelCase__   :					List[Any]   				=   torch.Generator(device=A      ).manual_seed(0      )
   UpperCAmelCase__   :					str   				=   sd_pipe([prompt]   ,generator=A   ,guidance_scale=6.0   ,num_inference_steps=2   ,output_type="""np"""      )

   UpperCAmelCase__   :					Optional[Any]   				=   output.images

   UpperCAmelCase__   :					Optional[int]   				=   torch.Generator(device=A      ).manual_seed(0      )
   UpperCAmelCase__   :					int   				=   sd_pipe(
       [prompt]   ,generator=A   ,guidance_scale=6.0   ,num_inference_steps=2   ,output_type="""np"""   ,return_dict=A   ,)[0]

   UpperCAmelCase__   :					str   				=   image[0, -3:, -3:, -1]
   UpperCAmelCase__   :					str   				=   image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)
   UpperCAmelCase__   :					Union[str, Any]   				=   np.array([0.5_1_2_2, 0.5_7_1_2, 0.4_8_2_5, 0.5_0_5_3, 0.5_6_4_6, 0.4_7_6_9, 0.5_1_7_9, 0.4_8_9_4, 0.4_9_9_4]      )

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

  def 				__lowercase    (					self						:     List[str]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Tuple   				=   StableDiffusionPipeline.from_pretrained(
       """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""   ,safety_checker=A      )
   assert isinstance(A   ,A      )
   assert isinstance(pipe.scheduler   ,A      )
   assert pipe.safety_checker is None

   UpperCAmelCase__   :					List[str]   				=   pipe("""example prompt"""   ,num_inference_steps=2      ).images[0]
   assert image is not None

   # check that there's no error when saving a pipeline with one of the models being None
   with tempfile.TemporaryDirectory() as tmpdirname:
    pipe.save_pretrained(A      )
    UpperCAmelCase__   :					str   				=   StableDiffusionPipeline.from_pretrained(A      )

   # sanity check that the pipeline still works
   assert pipe.safety_checker is None
   UpperCAmelCase__   :					Union[str, Any]   				=   pipe("""example prompt"""   ,num_inference_steps=2      ).images[0]
   assert image is not None




  @unittest.skipIf(torch_device != """cuda"""   ,"""This test requires a GPU"""      )
  def 				__lowercase    (					self						:     Union[str, Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[Any]   				=   self.dummy_cond_unet
   UpperCAmelCase__   :					Union[str, Any]   				=   PNDMScheduler(skip_prk_steps=A      )
   UpperCAmelCase__   :					Any   				=   self.dummy_vae
   UpperCAmelCase__   :					List[str]   				=   self.dummy_text_encoder
   UpperCAmelCase__   :					List[str]   				=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

   # put models in fp16
   UpperCAmelCase__   :					Tuple   				=   unet.half()
   UpperCAmelCase__   :					List[Any]   				=   vae.half()
   UpperCAmelCase__   :					Dict   				=   bert.half()

   # make sure here that pndm scheduler skips prk
   UpperCAmelCase__   :					Optional[int]   				=   StableDiffusionPipeline(
       unet=A   ,scheduler=A   ,vae=A   ,text_encoder=A   ,tokenizer=A   ,safety_checker=A   ,feature_extractor=self.dummy_extractor   ,)
   UpperCAmelCase__   :					Any   				=   sd_pipe.to(A      )
   sd_pipe.set_progress_bar_config(disable=A      )

   UpperCAmelCase__   :					Union[str, Any]   				=   """A painting of a squirrel eating a burger"""
   UpperCAmelCase__   :					Dict   				=   sd_pipe([prompt]   ,num_inference_steps=2   ,output_type="""np"""      ).images

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







@nightly
@require_torch_gpu
class        __lowercase     (      unittest.TestCase   ):

  def 				__lowercase    (					self						:     List[Any]      ):






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

  def 				__lowercase    (					self						:     Tuple      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Union[str, Any]   				=   """cuda"""
   UpperCAmelCase__   :					Tuple   				=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""      )
   UpperCAmelCase__   :					str   				=   pipe.to(A      )
   pipe.set_progress_bar_config(disable=A      )

   UpperCAmelCase__   :					List[Any]   				=   """a photo of a cat"""
   UpperCAmelCase__   :					Any   				=   {
       """editing_prompt""": ["""sunglasses"""],
       """reverse_editing_direction""": [False],
       """edit_warmup_steps""": 10,
       """edit_guidance_scale""": 6,
       """edit_threshold""": 0.9_5,
       """edit_momentum_scale""": 0.5,
       """edit_mom_beta""": 0.6,
   }

   UpperCAmelCase__   :					Dict   				=   3
   UpperCAmelCase__   :					List[str]   				=   7

   # no sega enabled
   UpperCAmelCase__   :					List[str]   				=   torch.Generator(A      )
   generator.manual_seed(A      )
   UpperCAmelCase__   :					List[Any]   				=   pipe(
       [prompt]   ,generator=A   ,guidance_scale=A   ,num_inference_steps=50   ,output_type="""np"""   ,width=512   ,height=512   ,)

   UpperCAmelCase__   :					int   				=   output.images
   UpperCAmelCase__   :					Dict   				=   image[0, -3:, -3:, -1]
   UpperCAmelCase__   :					Union[str, Any]   				=   [
       0.3_4_6_7_3_1_1_3,
       0.3_8_4_9_2_7_3_3,
       0.3_7_5_9_7_3_5_2,
       0.3_4_0_8_6_3_3_5,
       0.3_5_6_5_0_7_4_8,
       0.3_5_5_7_9_2_0_5,
       0.3_3_8_4_7_6_3,
       0.3_4_3_4_0_2_3_6,
       0.3_5_7_3_2_7_1,
   ]

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

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

   # with sega enabled
   # generator = torch.manual_seed(seed)
   generator.manual_seed(A      )
   UpperCAmelCase__   :					List[str]   				=   pipe(
       [prompt]   ,generator=A   ,guidance_scale=A   ,num_inference_steps=50   ,output_type="""np"""   ,width=512   ,height=512   ,**A   ,)

   UpperCAmelCase__   :					int   				=   output.images
   UpperCAmelCase__   :					List[Any]   				=   image[0, -3:, -3:, -1]
   UpperCAmelCase__   :					List[Any]   				=   [
       0.4_1_8_8_7_8_2_6,
       0.3_7_7_2_8_7_6_6,
       0.3_0_1_3_8_2_7_2,
       0.4_1_4_1_6_3_3_5,
       0.4_1_6_6_4_9_8_5,
       0.3_6_2_8_3_3_9_2,
       0.3_6_1_9_1_2_4_6,
       0.4_3_3_6_4_4_6_5,
       0.4_3_0_0_1_7_3_2,
   ]

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

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

  def 				__lowercase    (					self						:     Optional[int]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   """cuda"""
   UpperCAmelCase__   :					str   				=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""      )
   UpperCAmelCase__   :					str   				=   pipe.to(A      )
   pipe.set_progress_bar_config(disable=A      )

   UpperCAmelCase__   :					Dict   				=   """an image of a crowded boulevard, realistic, 4k"""
   UpperCAmelCase__   :					Optional[Any]   				=   {
       """editing_prompt""": """crowd, crowded, people""",
       """reverse_editing_direction""": True,
       """edit_warmup_steps""": 10,
       """edit_guidance_scale""": 8.3,
       """edit_threshold""": 0.9,
       """edit_momentum_scale""": 0.5,
       """edit_mom_beta""": 0.6,
   }

   UpperCAmelCase__   :					Optional[Any]   				=   9
   UpperCAmelCase__   :					Dict   				=   7

   # no sega enabled
   UpperCAmelCase__   :					Any   				=   torch.Generator(A      )
   generator.manual_seed(A      )
   UpperCAmelCase__   :					Optional[int]   				=   pipe(
       [prompt]   ,generator=A   ,guidance_scale=A   ,num_inference_steps=50   ,output_type="""np"""   ,width=512   ,height=512   ,)

   UpperCAmelCase__   :					Any   				=   output.images
   UpperCAmelCase__   :					Any   				=   image[0, -3:, -3:, -1]
   UpperCAmelCase__   :					Optional[int]   				=   [
       0.4_3_4_9_7_9_9_8,
       0.9_1_8_1_4_0_6_5,
       0.7_5_4_0_7_3_9,
       0.5_5_5_8_0_2_0_5,
       0.8_4_6_7_2_6_5,
       0.5_3_8_9_6_9_1,
       0.6_2_5_7_4_5_0_6,
       0.5_8_8_9_7_7_6_3,
       0.5_0_9_2_6_7_5_7,
   ]

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

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

   # with sega enabled
   # generator = torch.manual_seed(seed)
   generator.manual_seed(A      )
   UpperCAmelCase__   :					Dict   				=   pipe(
       [prompt]   ,generator=A   ,guidance_scale=A   ,num_inference_steps=50   ,output_type="""np"""   ,width=512   ,height=512   ,**A   ,)

   UpperCAmelCase__   :					List[Any]   				=   output.images
   UpperCAmelCase__   :					Any   				=   image[0, -3:, -3:, -1]
   UpperCAmelCase__   :					str   				=   [
       0.3_0_8_9_7_1_9,
       0.3_0_5_0_0_1_4_4,
       0.2_9_0_1_6_0_4_2,
       0.3_0_6_3_0_9_6_4,
       0.3_2_5_6_8_7,
       0.2_9_4_1_9_2_2_5,
       0.2_9_0_8_0_9_1,
       0.2_8_7_2_3_5_9_8,
       0.2_7_6_9_6_2_9_4,
   ]

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

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

  def 				__lowercase    (					self						:     Any      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   """cuda"""
   UpperCAmelCase__   :					List[str]   				=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""      )
   UpperCAmelCase__   :					Union[str, Any]   				=   pipe.to(A      )
   pipe.set_progress_bar_config(disable=A      )

   UpperCAmelCase__   :					Union[str, Any]   				=   """a castle next to a river"""
   UpperCAmelCase__   :					Optional[Any]   				=   {
       """editing_prompt""": ["""boat on a river, boat""", """monet, impression, sunrise"""],
       """reverse_editing_direction""": False,
       """edit_warmup_steps""": [15, 18],
       """edit_guidance_scale""": 6,
       """edit_threshold""": [0.9, 0.8],
       """edit_momentum_scale""": 0.5,
       """edit_mom_beta""": 0.6,
   }

   UpperCAmelCase__   :					List[str]   				=   48
   UpperCAmelCase__   :					Tuple   				=   7

   # no sega enabled
   UpperCAmelCase__   :					Any   				=   torch.Generator(A      )
   generator.manual_seed(A      )
   UpperCAmelCase__   :					Union[str, Any]   				=   pipe(
       [prompt]   ,generator=A   ,guidance_scale=A   ,num_inference_steps=50   ,output_type="""np"""   ,width=512   ,height=512   ,)

   UpperCAmelCase__   :					Optional[Any]   				=   output.images
   UpperCAmelCase__   :					str   				=   image[0, -3:, -3:, -1]
   UpperCAmelCase__   :					List[Any]   				=   [
       0.7_5_1_6_3_5_5_5,
       0.7_6_0_3_7_1_4_5,
       0.6_1_7_8_5,
       0.9_1_8_9_6_7_3,
       0.8_6_2_7_7_0_1,
       0.8_5_1_8_9_6_9_4,
       0.8_5_1_2_8_1_3,
       0.8_7_0_1_2_0_7_6,
       0.8_3_1_2_8_5_7,
   ]

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

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

   # with sega enabled
   # generator = torch.manual_seed(seed)
   generator.manual_seed(A      )
   UpperCAmelCase__   :					str   				=   pipe(
       [prompt]   ,generator=A   ,guidance_scale=A   ,num_inference_steps=50   ,output_type="""np"""   ,width=512   ,height=512   ,**A   ,)

   UpperCAmelCase__   :					Optional[Any]   				=   output.images
   UpperCAmelCase__   :					int   				=   image[0, -3:, -3:, -1]
   UpperCAmelCase__   :					Union[str, Any]   				=   [
       0.7_3_5_5_3_3_6_5,
       0.7_5_3_7_2_7_1,
       0.7_4_3_4_1_9_0_5,
       0.6_6_4_8_0_3_5_6,
       0.6_4_7_2_9_2_5,
       0.6_3_0_3_9_4_1_6,
       0.6_4_8_1_2_9_0_5,
       0.6_7_4_9_7_1_7,
       0.6_5_1_7_1_0_2,
   ]

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

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




  def 				__lowercase    (					self						:     Optional[int]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   """cuda"""
   UpperCAmelCase__   :					int   				=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""   ,torch_dtype=torch.floataa      )
   UpperCAmelCase__   :					List[Any]   				=   pipe.to(A      )
   pipe.set_progress_bar_config(disable=A      )

   UpperCAmelCase__   :					Any   				=   """a photo of a cat"""
   UpperCAmelCase__   :					List[Any]   				=   {
       """editing_prompt""": ["""sunglasses"""],
       """reverse_editing_direction""": [False],
       """edit_warmup_steps""": 10,
       """edit_guidance_scale""": 6,
       """edit_threshold""": 0.9_5,
       """edit_momentum_scale""": 0.5,
       """edit_mom_beta""": 0.6,
   }

   UpperCAmelCase__   :					List[Any]   				=   3
   UpperCAmelCase__   :					Optional[Any]   				=   7

   # no sega enabled
   UpperCAmelCase__   :					Union[str, Any]   				=   torch.Generator(A      )
   generator.manual_seed(A      )
   UpperCAmelCase__   :					Union[str, Any]   				=   pipe(
       [prompt]   ,generator=A   ,guidance_scale=A   ,num_inference_steps=50   ,output_type="""np"""   ,width=512   ,height=512   ,)

   UpperCAmelCase__   :					Any   				=   output.images
   UpperCAmelCase__   :					Optional[int]   				=   image[0, -3:, -3:, -1]
   UpperCAmelCase__   :					Dict   				=   [
       0.3_4_8_8_7_6_9_5,
       0.3_8_7_6_9_5_3,
       0.3_7_5,
       0.3_4_4_2_3_8_2_8,
       0.3_5_8_1_5_4_3,
       0.3_5_7_1_7_7_7_3,
       0.3_3_8_3_7_8_9,
       0.3_4_5_7_0_3_1_2,
       0.3_5_9_3_7_5,
   ]

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

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

   # with sega enabled
   # generator = torch.manual_seed(seed)
   generator.manual_seed(A      )
   UpperCAmelCase__   :					Optional[Any]   				=   pipe(
       [prompt]   ,generator=A   ,guidance_scale=A   ,num_inference_steps=50   ,output_type="""np"""   ,width=512   ,height=512   ,**A   ,)

   UpperCAmelCase__   :					int   				=   output.images
   UpperCAmelCase__   :					int   				=   image[0, -3:, -3:, -1]
   UpperCAmelCase__   :					Dict   				=   [
       0.4_2_2_8_5_1_5_6,
       0.3_6_9_1_4_0_6_2,
       0.2_9_0_7_7_1_4_8,
       0.4_2_0_4_1_0_1_6,
       0.4_1_9_1_8_9_4_5,
       0.3_5_4_9_8_0_4_7,
       0.3_6_1_8_1_6_4,
       0.4_4_2_3_8_2_8,
       0.4_3_1_1_5_2_3_4,
   ]

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

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








"""simple docstring"""




from math import factorial
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 return factorial(__UpperCamelCase ) / (factorial(__UpperCamelCase ) * factorial(n - r ))
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					Any   				=   0

 for i in range(1							,      101 ):
  for j in range(1							,      i + 1 ):
   if combinations(__UpperCamelCase							,      __UpperCamelCase ) > 1e6:
    total += 1
 return total


if __name__ == "__main__":
      print(solution())





