



"""simple docstring"""







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  UpperCamelCase__  (      unittest.TestCase    ):




      """simple docstring"""




      @property
      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        torch.manual_seed(0			)
        lowerCAmelCase_  :							List[str]								=					UNetaDModel(
            block_out_channels=(3_2, 6_4)       ,     layers_per_block=2       ,     sample_size=3_2       ,     in_channels=3       ,     out_channels=3       ,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')       ,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')       ,     )
        return model



      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        lowerCAmelCase_  :							Optional[int]								=					'cpu'
        lowerCAmelCase_  :							List[str]								=					self.dummy_uncond_unet
        lowerCAmelCase_  :							Any								=					DDPMScheduler()

        lowerCAmelCase_  :							Union[str, Any]								=					DDPMPipeline(unet=SCREAMING_SNAKE_CASE_       ,     scheduler=SCREAMING_SNAKE_CASE_			)
        ddpm.to(SCREAMING_SNAKE_CASE_			)
        ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Optional[Any]								=					torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(0			)
        lowerCAmelCase_  :							Optional[Any]								=					ddpm(generator=SCREAMING_SNAKE_CASE_       ,     num_inference_steps=2       ,     output_type='numpy'			).images

        lowerCAmelCase_  :							Any								=					torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(0			)
        lowerCAmelCase_  :							Union[str, Any]								=					ddpm(generator=SCREAMING_SNAKE_CASE_       ,     num_inference_steps=2       ,     output_type='numpy'       ,     return_dict=SCREAMING_SNAKE_CASE_			)[0]

        lowerCAmelCase_  :							Any								=					image[0, -3:, -3:, -1]
        lowerCAmelCase_  :							str								=					image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 3_2, 3_2, 3)
        lowerCAmelCase_  :							Union[str, Any]								=					np.array(
            [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.0_00, 1.1_99E-03, 2.6_48E-04, 5.1_01E-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 	SCREAMING_SNAKE_CASE__					( self							: Tuple			):
        lowerCAmelCase_  :							Dict								=					self.dummy_uncond_unet
        lowerCAmelCase_  :							int								=					DDPMScheduler(prediction_type='sample'			)

        lowerCAmelCase_  :							str								=					DDPMPipeline(unet=SCREAMING_SNAKE_CASE_       ,     scheduler=SCREAMING_SNAKE_CASE_			)
        ddpm.to(SCREAMING_SNAKE_CASE_			)
        ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							str								=					torch.manual_seed(0			)
        lowerCAmelCase_  :							Optional[Any]								=					ddpm(generator=SCREAMING_SNAKE_CASE_       ,     num_inference_steps=2       ,     output_type='numpy'			).images

        lowerCAmelCase_  :							str								=					torch.manual_seed(0			)
        lowerCAmelCase_  :							Any								=					ddpm(generator=SCREAMING_SNAKE_CASE_       ,     num_inference_steps=2       ,     output_type='numpy'			)[0]

        lowerCAmelCase_  :							Optional[Any]								=					image[0, -3:, -3:, -1]
        lowerCAmelCase_  :							List[str]								=					image_eps[0, -3:, -3:, -1]

        assert image.shape == (1, 3_2, 3_2, 3)
        lowerCAmelCase_  :							Tuple								=					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  UpperCamelCase__  (      unittest.TestCase    ):




      """simple docstring"""




      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[Any]			):
        lowerCAmelCase_  :							int								=					'google/ddpm-cifar10-32'

        lowerCAmelCase_  :							str								=					UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							int								=					DDPMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Any								=					DDPMPipeline(unet=SCREAMING_SNAKE_CASE_       ,     scheduler=SCREAMING_SNAKE_CASE_			)
        ddpm.to(SCREAMING_SNAKE_CASE_			)
        ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							int								=					torch.manual_seed(0			)
        lowerCAmelCase_  :							int								=					ddpm(generator=SCREAMING_SNAKE_CASE_       ,     output_type='numpy'			).images

        lowerCAmelCase_  :							Optional[Any]								=					image[0, -3:, -3:, -1]

        assert image.shape == (1, 3_2, 3_2, 3)
        lowerCAmelCase_  :							Dict								=					np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85]			)
        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2










"""simple docstring"""







from .stack import Stack

def 							UpperCamelCase_       (       lowerCAmelCase__ :							str		)		->  bool:

  """simple docstring"""




  lowerCAmelCase_  :							Stack[str]								=					Stack()
  lowerCAmelCase_  :							List[str]								=					{'(': ')', '[': ']', '{': '}'}
  for bracket in parentheses:
    if bracket in bracket_pairs:
      stack.push(lowerCAmelCase__		)
    elif bracket in (")", "]", "}"):
      if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
        return False
  return stack.is_empty()


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     lowercase__						:							List[str]        =      ["""((()))""", """((())""", """(()))"""]
     print("""Balanced parentheses demonstration:\n""")
     for example in examples:
          lowercase__						:							List[Any]        =      """""" if balanced_parentheses(example) else """not """
          print(f'{example} is {not_str}balanced')





