import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()

class 						_SCREAMING_SNAKE_CASE				(  unittest.TestCase				):






		"""simple docstring"""







		@property
		def 					_a					(      self     :					Any ):






					"""simple docstring"""
					torch.manual_seed(0 )
					_snake_case														=   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")	,       )
					return model



		def 					_a					(      self     :					Optional[Any] ):






					"""simple docstring"""
					_snake_case														=   """cpu"""
					_snake_case														=   self.dummy_uncond_unet
					_snake_case														=   DDPMScheduler()

					_snake_case														=   DDPMPipeline(unet=__SCREAMING_SNAKE_CASE	,       scheduler=__SCREAMING_SNAKE_CASE )
					ddpm.to(__SCREAMING_SNAKE_CASE )
					ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

					_snake_case														=   torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(0 )
					_snake_case														=   ddpm(generator=__SCREAMING_SNAKE_CASE	,       num_inference_steps=2	,       output_type="numpy" ).images

					_snake_case														=   torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(0 )
					_snake_case														=   ddpm(generator=__SCREAMING_SNAKE_CASE	,       num_inference_steps=2	,       output_type="numpy"	,       return_dict=__SCREAMING_SNAKE_CASE )[0]

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

					assert image.shape == (1, 32, 32, 3)
					_snake_case														=   np.array(
					    [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.0_0_0, 1.199e-03, 2.648e-04, 5.101e-04] )

					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 					_a					(      self     :					int ):






					"""simple docstring"""
					_snake_case														=   self.dummy_uncond_unet
					_snake_case														=   DDPMScheduler(prediction_type="sample" )

					_snake_case														=   DDPMPipeline(unet=__SCREAMING_SNAKE_CASE	,       scheduler=__SCREAMING_SNAKE_CASE )
					ddpm.to(__SCREAMING_SNAKE_CASE )
					ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

					_snake_case														=   torch.manual_seed(0 )
					_snake_case														=   ddpm(generator=__SCREAMING_SNAKE_CASE	,       num_inference_steps=2	,       output_type="numpy" ).images

					_snake_case														=   torch.manual_seed(0 )
					_snake_case														=   ddpm(generator=__SCREAMING_SNAKE_CASE	,       num_inference_steps=2	,       output_type="numpy" )[0]

					_snake_case														=   image[0, -3:, -3:, -1]
					_snake_case														=   image_eps[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					_snake_case														=   1e-2 if torch_device != """mps""" else 3e-2
					assert np.abs(image_slice.flatten() - image_eps_slice.flatten() ).max() < tolerance

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






		"""simple docstring"""







		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					_snake_case														=   """google/ddpm-cifar10-32"""

					_snake_case														=   UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE )
					_snake_case														=   DDPMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE )

					_snake_case														=   DDPMPipeline(unet=__SCREAMING_SNAKE_CASE	,       scheduler=__SCREAMING_SNAKE_CASE )
					ddpm.to(__SCREAMING_SNAKE_CASE )
					ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

					_snake_case														=   torch.manual_seed(0 )
					_snake_case														=   ddpm(generator=__SCREAMING_SNAKE_CASE	,       output_type="numpy" ).images

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

					assert image.shape == (1, 32, 32, 3)
					_snake_case														=   np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5] )
					assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2



'''simple docstring'''
from __future__ import annotations







def 							A_  (    __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  )					->							list[float | int]:





  """simple docstring"""

  if not all((nth_term, start_term_a, common_ratio_r)			):
    return []
  __A		:  list[float | int]       				=		[]
  __A		:  Union[str, Any]       				=		1
  __A		:  List[str]       				=		common_ratio_r
  for _ in range(int(__SCREAMING_SNAKE_CASE			)			):
    if not series:
      series.append(__SCREAMING_SNAKE_CASE			)
    else:
      power += 1
      series.append(float(start_term_a * multiple			)			)
      __A		:  List[str]       				=		pow(float(__SCREAMING_SNAKE_CASE			)      ,  __SCREAMING_SNAKE_CASE			)
  return series


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A__ :		Optional[int]					=float(input('Enter the last number (n term) of the Geometric Series'))
     A__ :		List[Any]					=float(input('Enter the starting term (a) of the Geometric Series'))
     A__ :		List[Any]					=float(
         input('Enter the common ratio between two terms (r) of the Geometric Series')
     )
     print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
     print(geometric_series(nth_term, start_term_a, common_ratio_r))



