
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 lowercase__		( self):
            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''')					,					)
            return model







      def lowercase__		( self):
            lowerCAmelCase_						=      '''cpu'''
            lowerCAmelCase_						=      self.dummy_uncond_unet
            lowerCAmelCase_						=      DDPMScheduler()

            lowerCAmelCase_						=      DDPMPipeline(unet=_UpperCAmelCase					,					scheduler=_UpperCAmelCase)
            ddpm.to(_UpperCAmelCase)
            ddpm.set_progress_bar_config(disable=_UpperCAmelCase)

            lowerCAmelCase_						=      torch.Generator(device=_UpperCAmelCase).manual_seed(0)
            lowerCAmelCase_						=      ddpm(generator=_UpperCAmelCase					,					num_inference_steps=2					,					output_type='''numpy''').images

            lowerCAmelCase_						=      torch.Generator(device=_UpperCAmelCase).manual_seed(0)
            lowerCAmelCase_						=      ddpm(generator=_UpperCAmelCase					,					num_inference_steps=2					,					output_type='''numpy'''					,					return_dict=_UpperCAmelCase)[0]

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

            assert image.shape == (1, 32, 32, 3)
            lowerCAmelCase_						=      np.array(
                [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 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 lowercase__		( self):
            lowerCAmelCase_						=      self.dummy_uncond_unet
            lowerCAmelCase_						=      DDPMScheduler(prediction_type='''sample''')

            lowerCAmelCase_						=      DDPMPipeline(unet=_UpperCAmelCase					,					scheduler=_UpperCAmelCase)
            ddpm.to(_UpperCAmelCase)
            ddpm.set_progress_bar_config(disable=_UpperCAmelCase)

            lowerCAmelCase_						=      torch.manual_seed(0)
            lowerCAmelCase_						=      ddpm(generator=_UpperCAmelCase					,					num_inference_steps=2					,					output_type='''numpy''').images

            lowerCAmelCase_						=      torch.manual_seed(0)
            lowerCAmelCase_						=      ddpm(generator=_UpperCAmelCase					,					num_inference_steps=2					,					output_type='''numpy''')[0]

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

            assert image.shape == (1, 32, 32, 3)
            lowerCAmelCase_						=      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 lowercase__		( self):
            lowerCAmelCase_						=      '''google/ddpm-cifar10-32'''

            lowerCAmelCase_						=      UNetaDModel.from_pretrained(_UpperCAmelCase)
            lowerCAmelCase_						=      DDPMScheduler.from_pretrained(_UpperCAmelCase)

            lowerCAmelCase_						=      DDPMPipeline(unet=_UpperCAmelCase					,					scheduler=_UpperCAmelCase)
            ddpm.to(_UpperCAmelCase)
            ddpm.set_progress_bar_config(disable=_UpperCAmelCase)

            lowerCAmelCase_						=      torch.manual_seed(0)
            lowerCAmelCase_						=      ddpm(generator=_UpperCAmelCase					,					output_type='''numpy''').images

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

            assert image.shape == (1, 32, 32, 3)
            lowerCAmelCase_						=      np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385])
            assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2





_snake_case		  =   {
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}
def lowerCamelCase_       (			A			:      List[Any]						):




      """simple docstring"""
      return c.isdigit()
def lowerCamelCase_       (			A			:      List[Any]						):




      """simple docstring"""
      lowerCAmelCase_						=      []

      # iterate over the string in reverse order
      for c in expression.split()[::-1]:
            # push operand to stack
            if is_operand(A						):
                  stack.append(int(A						)						)

            else:
                  # pop values from stack can calculate the result
                  # push the result onto the stack again
                  lowerCAmelCase_						=      stack.pop()
                  lowerCAmelCase_						=      stack.pop()
                  stack.append(calc[c](A		,					A						)						)

      return stack.pop()


# Driver code
if __name__ == "__main__":
 _snake_case		  =   "+ 9 * 2 6"
 print(evaluate(test_expression))

 _snake_case		  =   "/ * 10 2 + 4 1 "
 print(evaluate(test_expression))



