

from math import factorial
def        UpperCAmelCase_     (		_A = 20       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
     # 2, 3,...
     SCREAMING_SNAKE_CASE__					=					n // 2

     return int(factorial(_A       ) / (factorial(_A       ) * factorial(n - k       ))       )


if __name__ == "__main__":
       import sys

       if len(sys.argv) == 1:
              print(solution(20))
       else:
              try:
                     _SCREAMING_SNAKE_CASE  :						str         =    int(sys.argv[1])
                     print(solution(n))
              except ValueError:
                     print('''Invalid entry - please enter a number.''')






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  UpperCAmelCase__	( A__		,  A__		,  unittest.TestCase ):






  """simple docstring"""


  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    lowercase_	(							self		:	Union[str, Any]      )	->							List[str]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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			,     )
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler()
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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      )
       SCREAMING_SNAKE_CASE__					=					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			,     )
       SCREAMING_SNAKE_CASE__					=					CLIPTextModel(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

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






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	List[str]=0      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''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    lowercase_	(							self		:	Optional[int]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Any      )	->							Optional[Any]:
       super().test_inference_batch_consistent(batch_sizes=[1, 2]      )






  def    lowercase_	(							self		:	List[Any]      )	->							List[Any]:
       super().test_inference_batch_single_identical(batch_size=2			,     expected_max_diff=3.25e-3      )






  def    lowercase_	(							self		:	Union[str, Any]      )	->							str:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

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

       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Tuple      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

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

       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Optional[Any]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					EulerAncestralDiscreteScheduler(
           beta_start=0.00085			,     beta_end=0.012			,     beta_schedule='''scaled_linear'''      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

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

       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Optional[int]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					PNDMScheduler(
           beta_start=0.00085			,     beta_end=0.012			,     beta_schedule='''scaled_linear'''			,     skip_prk_steps=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

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

       SCREAMING_SNAKE_CASE__					=					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  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  def    lowercase_	(							self		:	Tuple      )	->							str:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Dict=0      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''prompt''': '''a photo of the dolomites''',
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs






  def    lowercase_	(							self		:	Union[str, Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					'''stabilityai/stable-diffusion-2-base'''
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler.from_pretrained(__lowerCamelCase			,     subfolder='''scheduler'''      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline.from_pretrained(__lowerCamelCase			,     scheduler=__lowerCamelCase			,     safety_checker=__lowerCamelCase      )
       pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing()

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

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

       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Optional[Any]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					StableDiffusionPanoramaPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-base'''			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
       pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing()

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

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

       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	List[str]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					0

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

                 SCREAMING_SNAKE_CASE__					=					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:
                 SCREAMING_SNAKE_CASE__					=					latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 256)
                 SCREAMING_SNAKE_CASE__					=					latents[0, -3:, -3:, -1]

                 SCREAMING_SNAKE_CASE__					=					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

       SCREAMING_SNAKE_CASE__					=					False

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

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







  def    lowercase_	(							self		:	Tuple      )	->							Tuple:
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

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

       SCREAMING_SNAKE_CASE__					=					self.get_inputs()
       SCREAMING_SNAKE_CASE__					=					pipe(**__lowerCamelCase      )

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


