


"""simple docstring"""


import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 	_a   (   SCREAMING_SNAKE_CASE_		,		unittest.TestCase			):
    a_     :					List[str]   							=     DDIMPipeline
    a_     :					Union[str, Any]   							=     UNCONDITIONAL_IMAGE_GENERATION_PARAMS
    a_     :					str   							=     PipelineTesterMixin.required_optional_params - {
        'num_images_per_prompt',
        'latents',
        'callback',
        'callback_steps',
    }
    a_     :					Dict   							=     UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
    a_     :					int   							=     False
    def   _UpperCamelCase							(			self				:       Union[str, Any]					):
          torch.manual_seed(0					)
          lowerCamelCase__							     =		UNetaDModel(
              block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=3				,       out_channels=3				,       down_block_types=('DownBlock2D', 'AttnDownBlock2D')				,       up_block_types=('AttnUpBlock2D', 'UpBlock2D')				,       )
          lowerCamelCase__							     =		DDIMScheduler()
          lowerCamelCase__							     =		{'unet': unet, 'scheduler': scheduler}
          return components
    def   _UpperCamelCase							(			self				:       Any				,       SCREAMING_SNAKE_CASE__				:       str				,       SCREAMING_SNAKE_CASE__				:       List[Any]=0					):
          if str(_lowerCAmelCase					).startswith('mps'					):
                lowerCamelCase__							     =		torch.manual_seed(_lowerCAmelCase					)
          else:
                lowerCamelCase__							     =		torch.Generator(device=_lowerCAmelCase					).manual_seed(_lowerCAmelCase					)
          lowerCamelCase__							     =		{
              'batch_size': 1,
              'generator': generator,
              'num_inference_steps': 2,
              'output_type': 'numpy',
          }
          return inputs
    def   _UpperCamelCase							(			self				:       List[Any]					):
          lowerCamelCase__							     =		'cpu'

          lowerCamelCase__							     =		self.get_dummy_components()
          lowerCamelCase__							     =		self.pipeline_class(**_lowerCAmelCase					)
          pipe.to(_lowerCAmelCase					)
          pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

          lowerCamelCase__							     =		self.get_dummy_inputs(_lowerCAmelCase					)
          lowerCamelCase__							     =		pipe(**_lowerCAmelCase					).images
          lowerCamelCase__							     =		image[0, -3:, -3:, -1]

          self.assertEqual(image.shape				,       (1, 32, 32, 3)					)
          lowerCamelCase__							     =		np.array(
              [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]					)
          lowerCamelCase__							     =		np.abs(image_slice.flatten() - expected_slice					).max()
          self.assertLessEqual(_lowerCAmelCase				,       1e-3					)
    def   _UpperCamelCase							(			self				:       Union[str, Any]					):
          super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)
    def   _UpperCamelCase							(			self				:       Dict					):
          super().test_save_load_local(expected_max_difference=3e-3					)
    def   _UpperCamelCase							(			self				:       Optional[int]					):
          super().test_save_load_optional_components(expected_max_difference=3e-3					)


    def   _UpperCamelCase							(			self				:       Optional[int]					):
          super().test_inference_batch_single_identical(expected_max_diff=3e-3					)





@slow
@require_torch_gpu
class 	_a   (   unittest.TestCase			):
    def   _UpperCamelCase							(			self				:       int					):
          lowerCamelCase__							     =		'google/ddpm-cifar10-32'

          lowerCamelCase__							     =		UNetaDModel.from_pretrained(_lowerCAmelCase					)
          lowerCamelCase__							     =		DDIMScheduler()

          lowerCamelCase__							     =		DDIMPipeline(unet=_lowerCAmelCase				,       scheduler=_lowerCAmelCase					)
          ddim.to(_lowerCAmelCase					)
          ddim.set_progress_bar_config(disable=_lowerCAmelCase					)

          lowerCamelCase__							     =		torch.manual_seed(0					)
          lowerCamelCase__							     =		ddim(generator=_lowerCAmelCase				,       eta=0.0				,       output_type='numpy'					).images

          lowerCamelCase__							     =		image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowerCamelCase__							     =		np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]					)

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


    def   _UpperCamelCase							(			self				:       str					):
          lowerCamelCase__							     =		'google/ddpm-ema-bedroom-256'

          lowerCamelCase__							     =		UNetaDModel.from_pretrained(_lowerCAmelCase					)
          lowerCamelCase__							     =		DDIMScheduler.from_pretrained(_lowerCAmelCase					)

          lowerCamelCase__							     =		DDIMPipeline(unet=_lowerCAmelCase				,       scheduler=_lowerCAmelCase					)
          ddpm.to(_lowerCAmelCase					)
          ddpm.set_progress_bar_config(disable=_lowerCAmelCase					)

          lowerCamelCase__							     =		torch.manual_seed(0					)
          lowerCamelCase__							     =		ddpm(generator=_lowerCAmelCase				,       output_type='numpy'					).images

          lowerCamelCase__							     =		image[0, -3:, -3:, -1]

          assert image.shape == (1, 2_56, 2_56, 3)
          lowerCamelCase__							     =		np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]					)

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







from math import factorial



def       _lowerCAmelCase    (				lowerCAmelCase_				:int = 20				)->int:
   '''simple docstring'''






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

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


if __name__ == "__main__":
   import sys

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




