




import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Any						)     ->			List[Any]:

       """simple docstring"""






       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

 @property
 def        a__				(       self      :						str						)     ->			Any:

       """simple docstring"""






       torch.manual_seed(0						)
       lowerCamelCase_	  =					UNetaDModel(
           sample_size=(32, 64)		,							in_channels=1		,							out_channels=1		,							layers_per_block=2		,							block_out_channels=(128, 128)		,							down_block_types=('AttnDownBlock2D', 'DownBlock2D')		,							up_block_types=('UpBlock2D', 'AttnUpBlock2D')		,							)
       return model

 @property
 def        a__				(       self      :						Optional[Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       torch.manual_seed(0						)
       lowerCamelCase_	  =					UNetaDConditionModel(
           sample_size=(64, 32)		,							in_channels=1		,							out_channels=1		,							layers_per_block=2		,							block_out_channels=(128, 128)		,							down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')		,							up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')		,							cross_attention_dim=10		,							)
       return model

 @property
 def        a__				(       self      :						Tuple						)     ->			int:

       """simple docstring"""






       torch.manual_seed(0						)
       lowerCamelCase_	  =					AutoencoderKL(
           sample_size=(128, 64)		,							in_channels=1		,							out_channels=1		,							latent_channels=1		,							layers_per_block=2		,							block_out_channels=(128, 128)		,							down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')		,							up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')		,							)
       lowerCamelCase_	  =					UNetaDModel(
           sample_size=(64, 32)		,							in_channels=1		,							out_channels=1		,							layers_per_block=2		,							block_out_channels=(128, 128)		,							down_block_types=('AttnDownBlock2D', 'DownBlock2D')		,							up_block_types=('UpBlock2D', 'AttnUpBlock2D')		,							)
       return vqvae, unet





 @slow
 def        a__				(       self      :						Dict						)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_	  =					'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowerCamelCase_	  =					Mel(
           x_res=self.dummy_unet.config.sample_size[1]		,							y_res=self.dummy_unet.config.sample_size[0]		,							)

       lowerCamelCase_	  =					DDPMScheduler()
       lowerCamelCase_	  =					AudioDiffusionPipeline(vqvae=A_		,							unet=self.dummy_unet		,							mel=A_		,							scheduler=A_						)
       lowerCamelCase_	  =					pipe.to(A_						)
       pipe.set_progress_bar_config(disable=A_						)

       lowerCamelCase_	  =					torch.Generator(device=A_						).manual_seed(42						)
       lowerCamelCase_	  =					pipe(generator=A_		,							steps=4						)
       lowerCamelCase_	  =					output.audios[0]
       lowerCamelCase_	  =					output.images[0]

       lowerCamelCase_	  =					torch.Generator(device=A_						).manual_seed(42						)
       lowerCamelCase_	  =					pipe(generator=A_		,							steps=4		,							return_dict=A_						)
       lowerCamelCase_	  =					output[0][0]

       assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
       assert (
           image.height == self.dummy_unet.config.sample_size[0]
           and image.width == self.dummy_unet.config.sample_size[1]
       )
       lowerCamelCase_	  =					np.frombuffer(image.tobytes()		,							dtype='uint8'						)[:10]
       lowerCamelCase_	  =					np.frombuffer(image_from_tuple.tobytes()		,							dtype='uint8'						)[:10]
       lowerCamelCase_	  =					np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() == 0
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() == 0

       lowerCamelCase_	  =					Mel(
           x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]		,							y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]		,							)

       lowerCamelCase_	  =					DDIMScheduler()
       lowerCamelCase_	  =					self.dummy_vqvae_and_unet
       lowerCamelCase_	  =					AudioDiffusionPipeline(
           vqvae=self.dummy_vqvae_and_unet[0]		,							unet=dummy_vqvae_and_unet[1]		,							mel=A_		,							scheduler=A_						)
       lowerCamelCase_	  =					pipe.to(A_						)
       pipe.set_progress_bar_config(disable=A_						)

       np.random.seed(0						)
       lowerCamelCase_	  =					np.random.uniform(-1		,							1		,							((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)						)
       lowerCamelCase_	  =					torch.Generator(device=A_						).manual_seed(42						)
       lowerCamelCase_	  =					pipe(raw_audio=A_		,							generator=A_		,							start_step=5		,							steps=10						)
       lowerCamelCase_	  =					output.images[0]

       assert (
           image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
           and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
       )
       lowerCamelCase_	  =					np.frombuffer(image.tobytes()		,							dtype='uint8'						)[:10]
       lowerCamelCase_	  =					np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() == 0

       lowerCamelCase_	  =					self.dummy_unet_condition
       lowerCamelCase_	  =					AudioDiffusionPipeline(
           vqvae=self.dummy_vqvae_and_unet[0]		,							unet=A_		,							mel=A_		,							scheduler=A_						)
       lowerCamelCase_	  =					pipe.to(A_						)
       pipe.set_progress_bar_config(disable=A_						)

       np.random.seed(0						)
       lowerCamelCase_	  =					torch.rand((1, 1, 10)						)
       lowerCamelCase_	  =					pipe(generator=A_		,							encoding=A_						)
       lowerCamelCase_	  =					output.images[0]
       lowerCamelCase_	  =					np.frombuffer(image.tobytes()		,							dtype='uint8'						)[:10]
       lowerCamelCase_	  =					np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() == 0







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



 '''simple docstring'''


 def        a__				(       self      :						str						)     ->			List[str]:

       """simple docstring"""






       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





 def        a__				(       self      :						str						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					torch_device

       lowerCamelCase_	  =					DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'						)
       lowerCamelCase_	  =					pipe.to(A_						)
       pipe.set_progress_bar_config(disable=A_						)

       lowerCamelCase_	  =					torch.Generator(device=A_						).manual_seed(42						)
       lowerCamelCase_	  =					pipe(generator=A_						)
       lowerCamelCase_	  =					output.audios[0]
       lowerCamelCase_	  =					output.images[0]

       assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
       assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
       lowerCamelCase_	  =					np.frombuffer(image.tobytes()		,							dtype='uint8'						)[:10]
       lowerCamelCase_	  =					np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() == 0









lowerCamelCase       :	Dict									=     "Alexander Joslin"

import operator as op

from .stack import Stack

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      lowerCamelCase_	  =					{'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

      lowerCamelCase_	  =					Stack()
      lowerCamelCase_	  =					Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(lowercase  )  )
            elif i in operators:
                  # RULE 2
                  operator_stack.push(lowercase  )
            elif i == ")":
                  # RULE 4
                  lowerCamelCase_	  =					operator_stack.peek()
                  operator_stack.pop()
                  lowerCamelCase_	  =					operand_stack.peek()
                  operand_stack.pop()
                  lowerCamelCase_	  =					operand_stack.peek()
                  operand_stack.pop()

                  lowerCamelCase_	  =					operators[opr](lowercase			,							lowercase  )
                  operand_stack.push(lowercase  )

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
 lowerCamelCase       :	Any									=     "(5 + ((4 * 2) * (2 + 3)))"
 # answer = 45
 print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



