'''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 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE   , unittest.TestCase	):







				'''simple docstring'''
				UpperCAmelCase__							=							DDIMPipeline
				UpperCAmelCase__							=							UNCONDITIONAL_IMAGE_GENERATION_PARAMS
				UpperCAmelCase__							=							PipelineTesterMixin.required_optional_params - {
				    '''num_images_per_prompt''',
				    '''latents''',
				    '''callback''',
				    '''callback_steps''',
				}
				UpperCAmelCase__							=							UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
				UpperCAmelCase__							=							False





				def  snake_case__				(						self					:						Union[str, Any]					)							->Optional[Any]:


										'''simple docstring'''




										torch.manual_seed(0					)
										_UpperCamelCase			:			Optional[int]   			=						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")       ,		)
										_UpperCamelCase			:			Any   			=						DDIMScheduler()
										_UpperCamelCase			:			List[str]   			=						{"unet": unet, "scheduler": scheduler}
										return components





				def  snake_case__				(						self					:						Union[str, Any]       ,		lowercase__					:						int       ,		lowercase__					:						Optional[Any]=0					)							->Tuple:


										'''simple docstring'''




										if str(lowercase__					).startswith("mps"					):
																_UpperCamelCase			:			Optional[int]   			=						torch.manual_seed(lowercase__					)
										else:
																_UpperCamelCase			:			List[Any]   			=						torch.Generator(device=lowercase__					).manual_seed(lowercase__					)
										_UpperCamelCase			:			List[str]   			=						{
										    "batch_size": 1,
										    "generator": generator,
										    "num_inference_steps": 2,
										    "output_type": "numpy",
										}
										return inputs





				def  snake_case__				(						self					:						str					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[int]   			=						"cpu"

										_UpperCamelCase			:			Optional[Any]   			=						self.get_dummy_components()
										_UpperCamelCase			:			int   			=						self.pipeline_class(**lowercase__					)
										pipe.to(lowercase__					)
										pipe.set_progress_bar_config(disable=lowercase__					)

										_UpperCamelCase			:			Dict   			=						self.get_dummy_inputs(lowercase__					)
										_UpperCamelCase			:			List[Any]   			=						pipe(**lowercase__					).images
										_UpperCamelCase			:			List[Any]   			=						image[0, -3:, -3:, -1]

										self.assertEqual(image.shape       ,		(1, 32, 32, 3)					)
										_UpperCamelCase			:			Union[str, Any]   			=						np.array(
										    [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]					)
										_UpperCamelCase			:			str   			=						np.abs(image_slice.flatten() - expected_slice					).max()
										self.assertLessEqual(lowercase__       ,		1e-3					)





				def  snake_case__				(						self					:						Any					)							->List[str]:


										'''simple docstring'''




										super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)





				def  snake_case__				(						self					:						int					)							->Optional[Any]:


										'''simple docstring'''




										super().test_save_load_local(expected_max_difference=3e-3					)





				def  snake_case__				(						self					:						int					)							->int:


										'''simple docstring'''




										super().test_save_load_optional_components(expected_max_difference=3e-3					)







				def  snake_case__				(						self					:						Union[str, Any]					)							->List[str]:


										'''simple docstring'''




										super().test_inference_batch_single_identical(expected_max_diff=3e-3					)





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







				'''simple docstring'''





				def  snake_case__				(						self					:						Union[str, Any]					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						"google/ddpm-cifar10-32"

										_UpperCamelCase			:			List[Any]   			=						UNetaDModel.from_pretrained(lowercase__					)
										_UpperCamelCase			:			Any   			=						DDIMScheduler()

										_UpperCamelCase			:			Optional[Any]   			=						DDIMPipeline(unet=lowercase__       ,		scheduler=lowercase__					)
										ddim.to(lowercase__					)
										ddim.set_progress_bar_config(disable=lowercase__					)

										_UpperCamelCase			:			Optional[Any]   			=						torch.manual_seed(0					)
										_UpperCamelCase			:			Any   			=						ddim(generator=lowercase__       ,		eta=0.0       ,		output_type="numpy"					).images

										_UpperCamelCase			:			List[str]   			=						image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										_UpperCamelCase			:			Union[str, Any]   			=						np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]					)

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







				def  snake_case__				(						self					:						Tuple					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			Dict   			=						"google/ddpm-ema-bedroom-256"

										_UpperCamelCase			:			List[str]   			=						UNetaDModel.from_pretrained(lowercase__					)
										_UpperCamelCase			:			Union[str, Any]   			=						DDIMScheduler.from_pretrained(lowercase__					)

										_UpperCamelCase			:			Any   			=						DDIMPipeline(unet=lowercase__       ,		scheduler=lowercase__					)
										ddpm.to(lowercase__					)
										ddpm.set_progress_bar_config(disable=lowercase__					)

										_UpperCamelCase			:			Optional[Any]   			=						torch.manual_seed(0					)
										_UpperCamelCase			:			Tuple   			=						ddpm(generator=lowercase__       ,		output_type="numpy"					).images

										_UpperCamelCase			:			Optional[int]   			=						image[0, -3:, -3:, -1]

										assert image.shape == (1, 256, 256, 3)
										_UpperCamelCase			:			Optional[int]   			=						np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]					)

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



'''simple docstring'''





def 						__A							(     UpperCAmelCase					)     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						[]
						_UpperCamelCase			:			Optional[Any]   			=						set({"(", "[", "{"}					)
						_UpperCamelCase			:			int   			=						set({")", "]", "}"}					)
						_UpperCamelCase			:			Dict   			=						{"{": "}", "[": "]", "(": ")"}

						for i in range(len(UpperCAmelCase					)					):
												if s[i] in open_brackets:
																		stack.append(s[i]					)

												elif s[i] in closed_brackets and (
												    len(UpperCAmelCase					) == 0 or (len(UpperCAmelCase					) > 0 and open_to_closed[stack.pop()] != s[i])
												):
																		return False

						return len(UpperCAmelCase					) == 0
def 						__A							(     )     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Any   			=						input("Enter sequence of brackets: "					)
						if is_balanced(UpperCAmelCase					):
												print(UpperCAmelCase     ,"is balanced"					)
						else:
												print(UpperCAmelCase     ,"is not balanced"					)


if __name__ == "__main__":
			main()


