



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class     _A       (	unittest.TestCase    ):



       @property
       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           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''')     ,		)
           return model





       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.dummy_uncond_unet
           __a						       =				PNDMScheduler()

           __a						       =				PNDMPipeline(unet=__SCREAMING_SNAKE_CASE     ,		scheduler=__SCREAMING_SNAKE_CASE)
           pndm.to(__SCREAMING_SNAKE_CASE)
           pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a						       =				torch.manual_seed(0)
           __a						       =				pndm(generator=__SCREAMING_SNAKE_CASE     ,		num_inference_steps=20     ,		output_type='''numpy''').images

           __a						       =				torch.manual_seed(0)
           __a						       =				pndm(generator=__SCREAMING_SNAKE_CASE     ,		num_inference_steps=20     ,		output_type='''numpy'''     ,		return_dict=__SCREAMING_SNAKE_CASE)[0]

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

           assert image.shape == (1, 32, 32, 3)
           __a						       =				np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])

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


@slow
@require_torch
class     _A       (	unittest.TestCase    ):



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           __a						       =				'''google/ddpm-cifar10-32'''

           __a						       =				UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE)
           __a						       =				PNDMScheduler()

           __a						       =				PNDMPipeline(unet=__SCREAMING_SNAKE_CASE     ,		scheduler=__SCREAMING_SNAKE_CASE)
           pndm.to(__SCREAMING_SNAKE_CASE)
           pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
           __a						       =				torch.manual_seed(0)
           __a						       =				pndm(generator=__SCREAMING_SNAKE_CASE     ,		output_type='''numpy''').images

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

           assert image.shape == (1, 32, 32, 3)
           __a						       =				np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25])

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










from __future__ import annotations

from typing import Any





def      __snake_case	(			_UpperCAmelCase				):
    if not postfix_notation:
        return 0

    __a						       =				{'''+''', '''-''', '''*''', '''/'''}
    __a						       =				[]

    for token in postfix_notation:
        if token in operations:
            __a       ,   __a						       =				stack.pop(), stack.pop()
            if token == "+":
                stack.append(a + b				)
            elif token == "-":
                stack.append(a - b				)
            elif token == "*":
                stack.append(a * b				)
            else:
                if a * b < 0 and a % b != 0:
                    stack.append(a // b + 1				)
                else:
                    stack.append(a // b				)
        else:
            stack.append(int(_UpperCAmelCase				)				)

    return stack.pop()


if __name__ == "__main__":
 import doctest

 doctest.testmod()





