


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  snake_case_     (lowerCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					Union[str, Any]					   =    DDIMPipeline
					UpperCAmelCase__      :					Dict					   =    UNCONDITIONAL_IMAGE_GENERATION_PARAMS
					UpperCAmelCase__      :					Any					   =    PipelineTesterMixin.required_optional_params - {
					    '''num_images_per_prompt''',
					    '''latents''',
					    '''callback''',
					    '''callback_steps''',
					}
					UpperCAmelCase__      :					List[str]					   =    UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
					UpperCAmelCase__      :					Any					   =    False
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					Any:
												torch.manual_seed(0   )
												a__    =  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')    ,)
												a__    =  DDIMScheduler()
												a__    =  {'unet': unet, 'scheduler': scheduler}
												return components
					def 				lowerCamelCase__(   self     :str    ,__snake_case     :int    ,__snake_case     :List[Any]=0   )      ->					List[str]:
												if str(__snake_case   ).startswith('mps'   ):
																			a__    =  torch.manual_seed(__snake_case   )
												else:
																			a__    =  torch.Generator(device=__snake_case   ).manual_seed(__snake_case   )
												a__    =  {
												    'batch_size': 1,
												    'generator': generator,
												    'num_inference_steps': 2,
												    'output_type': 'numpy',
												}
												return inputs
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					Any:
												a__    =  'cpu'

												a__    =  self.get_dummy_components()
												a__    =  self.pipeline_class(**__snake_case   )
												pipe.to(__snake_case   )
												pipe.set_progress_bar_config(disable=__snake_case   )

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

												self.assertEqual(image.shape    ,(1, 32, 32, 3)   )
												a__    =  np.array(
												    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]   )
												a__    =  np.abs(image_slice.flatten() - expected_slice   ).max()
												self.assertLessEqual(__snake_case    ,1E-3   )
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					Optional[int]:
												super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3   )
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					Tuple:
												super().test_save_load_local(expected_max_difference=3E-3   )
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					List[str]:
												super().test_save_load_optional_components(expected_max_difference=3E-3   )



					def 				lowerCamelCase__(   self     :str   )      ->					Optional[int]:
												super().test_inference_batch_single_identical(expected_max_diff=3E-3   )







@slow
@require_torch_gpu
class  snake_case_     (unittest.TestCase   ):
					def 				lowerCamelCase__(   self     :Optional[int]   )      ->					Any:
												a__    =  'google/ddpm-cifar10-32'

												a__    =  UNetaDModel.from_pretrained(__snake_case   )
												a__    =  DDIMScheduler()

												a__    =  DDIMPipeline(unet=__snake_case    ,scheduler=__snake_case   )
												ddim.to(__snake_case   )
												ddim.set_progress_bar_config(disable=__snake_case   )

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

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

												assert image.shape == (1, 32, 32, 3)
												a__    =  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 				lowerCamelCase__(   self     :Tuple   )      ->					int:
												a__    =  'google/ddpm-ema-bedroom-256'

												a__    =  UNetaDModel.from_pretrained(__snake_case   )
												a__    =  DDIMScheduler.from_pretrained(__snake_case   )

												a__    =  DDIMPipeline(unet=__snake_case    ,scheduler=__snake_case   )
												ddpm.to(__snake_case   )
												ddpm.set_progress_bar_config(disable=__snake_case   )

												a__    =  torch.manual_seed(0   )
												a__    =  ddpm(generator=__snake_case    ,output_type='numpy'   ).images

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

												assert image.shape == (1, 2_56, 2_56, 3)
												a__    =  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








def 							__lowercase     (				__lowerCAmelCase							:       int     ):
							a__    =  generate_pascal_triangle(__lowerCAmelCase     )
							for row_idx in range(__lowerCAmelCase     ):
														# Print left spaces
														for _ in range(num_rows - row_idx - 1     ):
																					print(end=' '     )
														# Print row values
														for col_idx in range(row_idx + 1     ):
																					if col_idx != row_idx:
																												print(triangle[row_idx][col_idx]      ,					end=' '     )
																					else:
																												print(triangle[row_idx][col_idx]      ,					end=''     )
														print()






def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							if not isinstance(__lowerCAmelCase      ,					__lowerCAmelCase     ):
														raise TypeError('The input value of \'num_rows\' should be \'int\''     )

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'     )

							a__    =  []
							for current_row_idx in range(__lowerCAmelCase     ):
														a__    =  populate_current_row(__lowerCAmelCase      ,					__lowerCAmelCase     )
														triangle.append(__lowerCAmelCase     )
							return triangle






def 							__lowercase     (				__lowerCAmelCase							:       list[list[int]]      ,					__lowerCAmelCase							:       int     ):
							a__    =  [-1] * (current_row_idx + 1)
							# first and last elements of current row are equal to 1
							a__   ,      a__    =  1, 1
							for current_col_idx in range(1      ,					__lowerCAmelCase     ):
														calculate_current_element(
														    __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase     )
							return current_row






def 							__lowercase     (				__lowerCAmelCase							:       list[list[int]]      ,					__lowerCAmelCase							:       list[int]      ,					__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int      ,					):
							a__    =  triangle[current_row_idx - 1][current_col_idx - 1]
							a__    =  triangle[current_row_idx - 1][current_col_idx]
							a__    =  above_to_left_elt + above_to_right_elt






def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							if not isinstance(__lowerCAmelCase      ,					__lowerCAmelCase     ):
														raise TypeError('The input value of \'num_rows\' should be \'int\''     )

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'     )

							a__    =  [[1]]

							for row_index in range(1      ,					__lowerCAmelCase     ):
														a__    =  [0] + result[-1] + [0]
														a__    =  row_index + 1
														# Calculate the number of distinct elements in a row
														a__    =  sum(divmod(__lowerCAmelCase      ,					2     )     )
														a__    =  [
														    temp_row[i - 1] + temp_row[i] for i in range(1      ,					distinct_elements + 1     )
														]
														a__    =  row_first_half[: (row_index + 1) // 2]
														row_second_half.reverse()
														a__    =  row_first_half + row_second_half
														result.append(__lowerCAmelCase     )

							return result






def 							__lowercase     (				):
							from collections.abc import Callable
							from timeit import timeit

							def benchmark_a_function(__lowerCAmelCase							:       Callable      ,					__lowerCAmelCase							:       int     ) -> None:
														a__    =  F'{func.__name__}({value})'
														a__    =  timeit(F'__main__.{call}'      ,					setup='import __main__'     )
														# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
														print(F'{call:38} -- {timing:.4f} seconds'     )

							for value in range(1_5     ):  # (1, 7, 14):
														for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
																					benchmark_a_function(__lowerCAmelCase      ,					__lowerCAmelCase     )
														print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()




