



import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 raise RuntimeError("CUDA out of memory."     )

class       _SCREAMING_SNAKE_CASE	(nn.Module ):





     def __init__(       self :							Optional[int]				)->Optional[int]:
      super().__init__()
      __SCREAMING_SNAKE_CASE       :    Tuple       =	nn.Linear(3     ,  4				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	nn.BatchNormad(4				)
      __SCREAMING_SNAKE_CASE       :    Any       =	nn.Linear(4     ,  5				)






     def 	__snake_case    (       self :							str     ,  UpperCamelCase :							Any				)->List[Any]:
      return self.lineara(self.batchnorm(self.lineara(UpperCamelCase				)				)				)






class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def 	__snake_case    (       self :							Optional[Any]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	[]

      @find_executable_batch_size(starting_batch_size=1_2_8				)
      def mock_training_loop_function(UpperCamelCase :							Any				):
       nonlocal batch_sizes
       batch_sizes.append(UpperCamelCase				)
       if batch_size != 8:
        raise_fake_out_of_memory()

      mock_training_loop_function()
      self.assertListEqual(UpperCamelCase     ,  [1_2_8, 6_4, 3_2, 1_6, 8]				)





     def 	__snake_case    (       self :							int				)->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    str       =	[]

      @find_executable_batch_size(starting_batch_size=1_2_8				)
      def mock_training_loop_function(UpperCamelCase :							Tuple     ,  UpperCamelCase :							int				):
       nonlocal batch_sizes
       batch_sizes.append(UpperCamelCase				)
       if batch_size != 8:
        raise_fake_out_of_memory()
       return batch_size, arga

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[str]       =	mock_training_loop_function("hello"				)
      self.assertListEqual(UpperCamelCase     ,  [1_2_8, 6_4, 3_2, 1_6, 8]				)
      self.assertListEqual([bs, arga]     ,  [8, "hello"]				)





     def 	__snake_case    (       self :							Union[str, Any]				)->Any:
      @find_executable_batch_size(starting_batch_size=0				)
      def mock_training_loop_function(UpperCamelCase :							Dict				):
       pass

      with self.assertRaises(UpperCamelCase				) as cm:
       mock_training_loop_function()
       self.assertIn("No executable batch size found, reached zero."     ,  cm.exception.args[0]				)





     def 	__snake_case    (       self :							str				)->List[str]:
      @find_executable_batch_size(starting_batch_size=1_6				)
      def mock_training_loop_function(UpperCamelCase :							Tuple				):
       if batch_size > 0:
        raise_fake_out_of_memory()
       pass

      with self.assertRaises(UpperCamelCase				) as cm:
       mock_training_loop_function()
       self.assertIn("No executable batch size found, reached zero."     ,  cm.exception.args[0]				)





     def 	__snake_case    (       self :							Union[str, Any]				)->Dict:
      @find_executable_batch_size(starting_batch_size=1_2_8				)
      def mock_training_loop_function(UpperCamelCase :							Any     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							List[Any]				):
       if batch_size != 8:
        raise raise_fake_out_of_memory()

      with self.assertRaises(UpperCamelCase				) as cm:
       mock_training_loop_function(1_2_8     ,  "hello"     ,  "world"				)
       self.assertIn("Batch size was passed into `f`"     ,  cm.exception.args[0]				)
       self.assertIn("`f(arg1='hello', arg2='world')"     ,  cm.exception.args[0]				)





     def 	__snake_case    (       self :							Tuple				)->Union[str, Any]:
      @find_executable_batch_size(starting_batch_size=1_6				)
      def mock_training_loop_function(UpperCamelCase :							Any				):
       raise ValueError("Oops, we had an error!"				)

      with self.assertRaises(UpperCamelCase				) as cm:
       mock_training_loop_function()
       self.assertIn("Oops, we had an error!"     ,  cm.exception.args[0]				)






     @require_cuda
     def 	__snake_case    (       self :							Dict				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    str       =	torch.cuda.memory_allocated()
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	ModelForTest()
      model.cuda()
      self.assertGreater(torch.cuda.memory_allocated()     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    str       =	release_memory(UpperCamelCase				)
      self.assertEqual(torch.cuda.memory_allocated()     ,  UpperCamelCase				)











from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			["""vqvae"""]





     def __init__(       self :							Tuple     ,  UpperCamelCase :							AutoencoderKL     ,  UpperCamelCase :							UNetaDConditionModel     ,  UpperCamelCase :							Mel     ,  UpperCamelCase :							Union[DDIMScheduler, DDPMScheduler]     ,  )->Tuple:
      super().__init__()
      self.register_modules(unet=UpperCamelCase     ,  scheduler=UpperCamelCase     ,  mel=UpperCamelCase     ,  vqvae=UpperCamelCase				)





     def 	__snake_case    (       self :							List[Any]				)->int:
      return 5_0 if isinstance(self.scheduler     ,  UpperCamelCase				) else 1_0_0_0





     @torch.no_grad()
     def __call__(       self :							Dict     ,  UpperCamelCase :							int = 1     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							np.ndarray = None     ,  UpperCamelCase :							int = 0     ,  UpperCamelCase :							int = 0     ,  UpperCamelCase :							int = None     ,  UpperCamelCase :							torch.Generator = None     ,  UpperCamelCase :							float = 0     ,  UpperCamelCase :							float = 0     ,  UpperCamelCase :							torch.Generator = None     ,  UpperCamelCase :							float = 0     ,  UpperCamelCase :							torch.Tensor = None     ,  UpperCamelCase :							torch.Tensor = None     ,  UpperCamelCase :							Any=True     ,  )->Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	steps or self.get_default_steps()
      self.scheduler.set_timesteps(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	step_generator or generator
      # For backwards compatibility
      if type(self.unet.config.sample_size				) == int:
       __SCREAMING_SNAKE_CASE       :    Dict       =	(self.unet.config.sample_size, self.unet.config.sample_size)
      if noise is None:
       __SCREAMING_SNAKE_CASE       :    Any       =	randn_tensor(
           (
               batch_size,
               self.unet.config.in_channels,
               self.unet.config.sample_size[0],
               self.unet.config.sample_size[1],
           )     ,  generator=UpperCamelCase     ,  device=self.device     ,  )
      __SCREAMING_SNAKE_CASE       :    Any       =	noise
      __SCREAMING_SNAKE_CASE       :    Any       =	None

      if audio_file is not None or raw_audio is not None:
       self.mel.load_audio(UpperCamelCase     ,  UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    str       =	self.mel.audio_slice_to_image(UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	np.frombuffer(input_image.tobytes()     ,  dtype="uint8"				).reshape(
           (input_image.height, input_image.width)				)
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(input_image / 2_5_5) * 2 - 1
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.tensor(input_image[np.newaxis, :, :]     ,  dtype=torch.float				).to(self.device				)

       if self.vqvae is not None:
        __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.vqvae.encode(torch.unsqueeze(UpperCamelCase     ,  0				)				).latent_dist.sample(
            generator=UpperCamelCase				)[0]
        __SCREAMING_SNAKE_CASE       :    int       =	self.vqvae.config.scaling_factor * input_images

       if start_step > 0:
        __SCREAMING_SNAKE_CASE       :    List[str]       =	self.scheduler.add_noise(UpperCamelCase     ,  UpperCamelCase     ,  self.scheduler.timesteps[start_step - 1]				)

       __SCREAMING_SNAKE_CASE       :    int       =	(
           self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
       )
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	int(mask_start_secs * pixels_per_second				)
       __SCREAMING_SNAKE_CASE       :    int       =	int(mask_end_secs * pixels_per_second				)
       __SCREAMING_SNAKE_CASE       :    Any       =	self.scheduler.add_noise(UpperCamelCase     ,  UpperCamelCase     ,  torch.tensor(self.scheduler.timesteps[start_step:]				)				)

      for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]				)				):
       if isinstance(self.unet     ,  UpperCamelCase				):
        __SCREAMING_SNAKE_CASE       :    str       =	self.unet(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)["sample"]
       else:
        __SCREAMING_SNAKE_CASE       :    int       =	self.unet(UpperCamelCase     ,  UpperCamelCase				)["sample"]

       if isinstance(self.scheduler     ,  UpperCamelCase				):
        __SCREAMING_SNAKE_CASE       :    int       =	self.scheduler.step(
            model_output=UpperCamelCase     ,  timestep=UpperCamelCase     ,  sample=UpperCamelCase     ,  eta=UpperCamelCase     ,  generator=UpperCamelCase     ,  )["prev_sample"]
       else:
        __SCREAMING_SNAKE_CASE       :    Tuple       =	self.scheduler.step(
            model_output=UpperCamelCase     ,  timestep=UpperCamelCase     ,  sample=UpperCamelCase     ,  generator=UpperCamelCase     ,  )["prev_sample"]

       if mask is not None:
        if mask_start > 0:
         __SCREAMING_SNAKE_CASE       :    int       =	mask[:, step, :, :mask_start]
        if mask_end > 0:
         __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	mask[:, step, :, -mask_end:]

      if self.vqvae is not None:
       # 0.18215 was scaling factor used in training to ensure unit variance
       __SCREAMING_SNAKE_CASE       :    Any       =	1 / self.vqvae.config.scaling_factor * images
       __SCREAMING_SNAKE_CASE       :    Any       =	self.vqvae.decode(UpperCamelCase				)["sample"]

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(images / 2 + 0.5).clamp(0     ,  1				)
      __SCREAMING_SNAKE_CASE       :    str       =	images.cpu().permute(0     ,  2     ,  3     ,  1				).numpy()
      __SCREAMING_SNAKE_CASE       :    Tuple       =	(images * 2_5_5).round().astype("uint8"				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	list(
          (Image.fromarray(_[:, :, 0]				) for _ in images)
          if images.shape[3] == 1
          else (Image.fromarray(UpperCamelCase     ,  mode="RGB"				).convert("L"				) for _ in images)				)

      __SCREAMING_SNAKE_CASE       :    List[str]       =	[self.mel.image_to_audio(UpperCamelCase				) for _ in images]
      if not return_dict:
       return images, (self.mel.get_sample_rate(), audios)

      return BaseOutput(**AudioPipelineOutput(np.array(UpperCamelCase				)[:, np.newaxis, :]				)     ,  **ImagePipelineOutput(UpperCamelCase				)				)





     @torch.no_grad()
     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[Image.Image]     ,  UpperCamelCase :							int = 5_0				)->np.ndarray:
      assert isinstance(self.scheduler     ,  UpperCamelCase				)
      self.scheduler.set_timesteps(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    int       =	np.array(
          [np.frombuffer(image.tobytes()     ,  dtype="uint8"				).reshape((1, image.height, image.width)				) for image in images]				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	(sample / 2_5_5) * 2 - 1
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.Tensor(UpperCamelCase				).to(self.device				)

      for t in self.progress_bar(torch.flip(self.scheduler.timesteps     ,  (0,)				)				):
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.scheduler.alphas_cumprod[t]
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	(
           self.scheduler.alphas_cumprod[prev_timestep]
           if prev_timestep >= 0
           else self.scheduler.final_alpha_cumprod
       )
       __SCREAMING_SNAKE_CASE       :    Dict       =	1 - alpha_prod_t
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.unet(UpperCamelCase     ,  UpperCamelCase				)["sample"]
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(1 - alpha_prod_t_prev) ** 0.5 * model_output
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

      return sample






     @staticmethod
     def 	__snake_case    (       UpperCamelCase :							torch.Tensor     ,  UpperCamelCase :							torch.Tensor     ,  UpperCamelCase :							float				)->torch.Tensor:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	acos(torch.dot(torch.flatten(UpperCamelCase				)     ,  torch.flatten(UpperCamelCase				)				) / torch.norm(UpperCamelCase				) / torch.norm(UpperCamelCase				)				)
      return sin((1 - alpha) * theta				) * xa / sin(UpperCamelCase				) + sin(alpha * theta				) * xa / sin(UpperCamelCase				)






