




'''simple docstring'''







import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class 			_a  (	__a ):
       __a				:     Optional[Any]   				=					(DEISMultistepScheduler,)
       __a				:     Any   				=					(("""num_inference_steps""", 25),)
       def     A      (	self						:	Any   ,				**lowercase						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		{
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''solver_order''': 2,
           }

           config.update(**lowercase				)
           return config
       def     A      (	self						:	Union[str, Any]   ,				lowercase						:	Optional[Any]=0   ,				**lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		dict(self.forward_default_kwargs				)
           UpperCAmelCase			       =		kwargs.pop('''num_inference_steps'''   ,				lowercase				)
           UpperCAmelCase			       =		self.dummy_sample
           UpperCAmelCase			       =		0.1 * sample
           UpperCAmelCase			       =		[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
               UpperCAmelCase			       =		self.get_scheduler_config(**lowercase				)
               UpperCAmelCase			       =		scheduler_class(**lowercase				)
               scheduler.set_timesteps(lowercase				)
               # copy over dummy past residuals
               UpperCAmelCase			       =		dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowercase				)
                   UpperCAmelCase			       =		scheduler_class.from_pretrained(lowercase				)
                   new_scheduler.set_timesteps(lowercase				)
                   # copy over dummy past residuals
                   UpperCAmelCase			       =		dummy_past_residuals[: new_scheduler.config.solver_order]

               UpperCAmelCase       ,     UpperCAmelCase			       =		sample, sample
               for t in range(lowercase   ,				time_step + scheduler.config.solver_order + 1				):
                   UpperCAmelCase			       =		scheduler.step(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample
                   UpperCAmelCase			       =		new_scheduler.step(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample

                   assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"
       def     A      (	self						:	int				):



           '''simple docstring'''

           pass
       def     A      (	self						:	str   ,				lowercase						:	Any=0   ,				**lowercase						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		dict(self.forward_default_kwargs				)
           UpperCAmelCase			       =		kwargs.pop('''num_inference_steps'''   ,				lowercase				)
           UpperCAmelCase			       =		self.dummy_sample
           UpperCAmelCase			       =		0.1 * sample
           UpperCAmelCase			       =		[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
               UpperCAmelCase			       =		self.get_scheduler_config()
               UpperCAmelCase			       =		scheduler_class(**lowercase				)
               scheduler.set_timesteps(lowercase				)

               # copy over dummy past residuals (must be after setting timesteps)
               UpperCAmelCase			       =		dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowercase				)
                   UpperCAmelCase			       =		scheduler_class.from_pretrained(lowercase				)
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(lowercase				)

                   # copy over dummy past residual (must be after setting timesteps)
                   UpperCAmelCase			       =		dummy_past_residuals[: new_scheduler.config.solver_order]

               UpperCAmelCase			       =		scheduler.step(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample
               UpperCAmelCase			       =		new_scheduler.step(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample

               assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"
       def     A      (	self						:	Any   ,				lowercase						:	List[str]=None   ,				**lowercase						:	List[Any]				):



           '''simple docstring'''

           if scheduler is None:
               UpperCAmelCase			       =		self.scheduler_classes[0]
               UpperCAmelCase			       =		self.get_scheduler_config(**lowercase				)
               UpperCAmelCase			       =		scheduler_class(**lowercase				)

           UpperCAmelCase			       =		self.scheduler_classes[0]
           UpperCAmelCase			       =		self.get_scheduler_config(**lowercase				)
           UpperCAmelCase			       =		scheduler_class(**lowercase				)

           UpperCAmelCase			       =		10
           UpperCAmelCase			       =		self.dummy_model()
           UpperCAmelCase			       =		self.dummy_sample_deter
           scheduler.set_timesteps(lowercase				)

           for i, t in enumerate(scheduler.timesteps				):
               UpperCAmelCase			       =		model(lowercase   ,				lowercase				)
               UpperCAmelCase			       =		scheduler.step(lowercase   ,				lowercase   ,				lowercase				).prev_sample

           return sample
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		dict(self.forward_default_kwargs				)

           UpperCAmelCase			       =		kwargs.pop('''num_inference_steps'''   ,				lowercase				)

           for scheduler_class in self.scheduler_classes:
               UpperCAmelCase			       =		self.get_scheduler_config()
               UpperCAmelCase			       =		scheduler_class(**lowercase				)

               UpperCAmelCase			       =		self.dummy_sample
               UpperCAmelCase			       =		0.1 * sample

               if num_inference_steps is not None and hasattr(lowercase   ,				'''set_timesteps'''				):
                   scheduler.set_timesteps(lowercase				)
               elif num_inference_steps is not None and not hasattr(lowercase   ,				'''set_timesteps'''				):
                   UpperCAmelCase			       =		num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               UpperCAmelCase			       =		[residual + 0.2, residual + 0.15, residual + 0.10]
               UpperCAmelCase			       =		dummy_past_residuals[: scheduler.config.solver_order]

               UpperCAmelCase			       =		scheduler.timesteps[5]
               UpperCAmelCase			       =		scheduler.timesteps[6]

               UpperCAmelCase			       =		scheduler.step(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample
               UpperCAmelCase			       =		scheduler.step(lowercase   ,				lowercase   ,				lowercase   ,				**lowercase				).prev_sample

               self.assertEqual(output_a.shape   ,				sample.shape				)
               self.assertEqual(output_a.shape   ,				output_a.shape				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		DEISMultistepScheduler(**self.get_scheduler_config()				)
           UpperCAmelCase			       =		self.full_loop(scheduler=lowercase				)
           UpperCAmelCase			       =		torch.mean(torch.abs(lowercase				)				)

           assert abs(result_mean.item() - 0.2_3916				) < 1E-3

           UpperCAmelCase			       =		DPMSolverSinglestepScheduler.from_config(scheduler.config				)
           UpperCAmelCase			       =		DPMSolverMultistepScheduler.from_config(scheduler.config				)
           UpperCAmelCase			       =		UniPCMultistepScheduler.from_config(scheduler.config				)
           UpperCAmelCase			       =		DEISMultistepScheduler.from_config(scheduler.config				)

           UpperCAmelCase			       =		self.full_loop(scheduler=lowercase				)
           UpperCAmelCase			       =		torch.mean(torch.abs(lowercase				)				)

           assert abs(result_mean.item() - 0.2_3916				) < 1E-3
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           for timesteps in [25, 50, 100, 999, 1_000]:
               self.check_over_configs(num_train_timesteps=lowercase				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           self.check_over_configs(thresholding=lowercase				)
           for order in [1, 2, 3]:
               for solver_type in ["logrho"]:
                   for threshold in [0.5, 1.0, 2.0]:
                       for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               thresholding=lowercase   ,				prediction_type=lowercase   ,				sample_max_value=lowercase   ,				algorithm_type='''deis'''   ,				solver_order=lowercase   ,				solver_type=lowercase   ,				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowercase				)
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           for algorithm_type in ["deis"]:
               for solver_type in ["logrho"]:
                   for order in [1, 2, 3]:
                       for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               solver_order=lowercase   ,				solver_type=lowercase   ,				prediction_type=lowercase   ,				algorithm_type=lowercase   ,				)
                           UpperCAmelCase			       =		self.full_loop(
                               solver_order=lowercase   ,				solver_type=lowercase   ,				prediction_type=lowercase   ,				algorithm_type=lowercase   ,				)
                           assert not torch.isnan(lowercase				).any(), "Samples have nan numbers"
       def     A      (	self						:	int				):



           '''simple docstring'''

           self.check_over_configs(lower_order_final=lowercase				)
           self.check_over_configs(lower_order_final=lowercase				)
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
               self.check_over_forward(num_inference_steps=lowercase   ,				time_step=0				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.full_loop()
           UpperCAmelCase			       =		torch.mean(torch.abs(lowercase				)				)

           assert abs(result_mean.item() - 0.2_3916				) < 1E-3
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.full_loop(prediction_type='''v_prediction'''				)
           UpperCAmelCase			       =		torch.mean(torch.abs(lowercase				)				)

           assert abs(result_mean.item() - 0.091				) < 1E-3







       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.scheduler_classes[0]
           UpperCAmelCase			       =		self.get_scheduler_config(thresholding=lowercase   ,				dynamic_thresholding_ratio=0				)
           UpperCAmelCase			       =		scheduler_class(**lowercase				)

           UpperCAmelCase			       =		10
           UpperCAmelCase			       =		self.dummy_model()
           UpperCAmelCase			       =		self.dummy_sample_deter.half()
           scheduler.set_timesteps(lowercase				)

           for i, t in enumerate(scheduler.timesteps				):
               UpperCAmelCase			       =		model(lowercase   ,				lowercase				)
               UpperCAmelCase			       =		scheduler.step(lowercase   ,				lowercase   ,				lowercase				).prev_sample

           assert sample.dtype == torch.floataa










'''simple docstring'''







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 			_a  (	__a ):
       __a				:     str   				=					["""vqvae"""]
       def __init__(	self						:	str   ,				lowercase						:	AutoencoderKL   ,				lowercase						:	UNetaDConditionModel   ,				lowercase						:	Mel   ,				lowercase						:	Union[DDIMScheduler, DDPMScheduler]   ,				):



           '''simple docstring'''

           super().__init__()
           self.register_modules(unet=lowercase   ,				scheduler=lowercase   ,				mel=lowercase   ,				vqvae=lowercase				)
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           return 50 if isinstance(self.scheduler   ,				lowercase				) else 1_000
       @torch.no_grad()
       def __call__(	self						:	Optional[Any]   ,				lowercase						:	int = 1   ,				lowercase						:	str = None   ,				lowercase						:	np.ndarray = None   ,				lowercase						:	int = 0   ,				lowercase						:	int = 0   ,				lowercase						:	int = None   ,				lowercase						:	torch.Generator = None   ,				lowercase						:	float = 0   ,				lowercase						:	float = 0   ,				lowercase						:	torch.Generator = None   ,				lowercase						:	float = 0   ,				lowercase						:	torch.Tensor = None   ,				lowercase						:	torch.Tensor = None   ,				lowercase						:	Tuple=True   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		steps or self.get_default_steps()
           self.scheduler.set_timesteps(lowercase				)
           UpperCAmelCase			       =		step_generator or generator
           # For backwards compatibility
           if type(self.unet.config.sample_size				) == int:
               UpperCAmelCase			       =		(self.unet.config.sample_size, self.unet.config.sample_size)
           if noise is None:
               UpperCAmelCase			       =		randn_tensor(
                   (
                       batch_size,
                       self.unet.config.in_channels,
                       self.unet.config.sample_size[0],
                       self.unet.config.sample_size[1],
                   )   ,				generator=lowercase   ,				device=self.device   ,				)
           UpperCAmelCase			       =		noise
           UpperCAmelCase			       =		None

           if audio_file is not None or raw_audio is not None:
               self.mel.load_audio(lowercase   ,				lowercase				)
               UpperCAmelCase			       =		self.mel.audio_slice_to_image(lowercase				)
               UpperCAmelCase			       =		np.frombuffer(input_image.tobytes()   ,				dtype='''uint8'''				).reshape(
                   (input_image.height, input_image.width)				)
               UpperCAmelCase			       =		(input_image / 255) * 2 - 1
               UpperCAmelCase			       =		torch.tensor(input_image[np.newaxis, :, :]   ,				dtype=torch.float				).to(self.device				)

               if self.vqvae is not None:
                   UpperCAmelCase			       =		self.vqvae.encode(torch.unsqueeze(lowercase   ,				0				)				).latent_dist.sample(
                       generator=lowercase				)[0]
                   UpperCAmelCase			       =		self.vqvae.config.scaling_factor * input_images

               if start_step > 0:
                   UpperCAmelCase			       =		self.scheduler.add_noise(lowercase   ,				lowercase   ,				self.scheduler.timesteps[start_step - 1]				)

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

           for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]				)				):
               if isinstance(self.unet   ,				lowercase				):
                   UpperCAmelCase			       =		self.unet(lowercase   ,				lowercase   ,				lowercase				)['''sample''']
               else:
                   UpperCAmelCase			       =		self.unet(lowercase   ,				lowercase				)['''sample''']

               if isinstance(self.scheduler   ,				lowercase				):
                   UpperCAmelCase			       =		self.scheduler.step(
                       model_output=lowercase   ,				timestep=lowercase   ,				sample=lowercase   ,				eta=lowercase   ,				generator=lowercase   ,				)['''prev_sample''']
               else:
                   UpperCAmelCase			       =		self.scheduler.step(
                       model_output=lowercase   ,				timestep=lowercase   ,				sample=lowercase   ,				generator=lowercase   ,				)['''prev_sample''']

               if mask is not None:
                   if mask_start > 0:
                       UpperCAmelCase			       =		mask[:, step, :, :mask_start]
                   if mask_end > 0:
                       UpperCAmelCase			       =		mask[:, step, :, -mask_end:]

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

           UpperCAmelCase			       =		(images / 2 + 0.5).clamp(0   ,				1				)
           UpperCAmelCase			       =		images.cpu().permute(0   ,				2   ,				3   ,				1				).numpy()
           UpperCAmelCase			       =		(images * 255).round().astype('''uint8'''				)
           UpperCAmelCase			       =		list(
               (Image.fromarray(_[:, :, 0]				) for _ in images)
               if images.shape[3] == 1
               else (Image.fromarray(lowercase   ,				mode='''RGB'''				).convert('''L'''				) for _ in images)				)

           UpperCAmelCase			       =		[self.mel.image_to_audio(lowercase				) for _ in images]
           if not return_dict:
               return images, (self.mel.get_sample_rate(), audios)

           return BaseOutput(**AudioPipelineOutput(np.array(lowercase				)[:, np.newaxis, :]				)   ,				**ImagePipelineOutput(lowercase				)				)
       @torch.no_grad()
       def     A      (	self						:	Dict   ,				lowercase						:	List[Image.Image]   ,				lowercase						:	int = 50				):



           '''simple docstring'''

           assert isinstance(self.scheduler   ,				lowercase				)
           self.scheduler.set_timesteps(lowercase				)
           UpperCAmelCase			       =		np.array(
               [np.frombuffer(image.tobytes()   ,				dtype='''uint8'''				).reshape((1, image.height, image.width)				) for image in images]				)
           UpperCAmelCase			       =		(sample / 255) * 2 - 1
           UpperCAmelCase			       =		torch.Tensor(lowercase				).to(self.device				)

           for t in self.progress_bar(torch.flip(self.scheduler.timesteps   ,				(0,)				)				):
               UpperCAmelCase			       =		t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
               UpperCAmelCase			       =		self.scheduler.alphas_cumprod[t]
               UpperCAmelCase			       =		(
                   self.scheduler.alphas_cumprod[prev_timestep]
                   if prev_timestep >= 0
                   else self.scheduler.final_alpha_cumprod
               )
               UpperCAmelCase			       =		1 - alpha_prod_t
               UpperCAmelCase			       =		self.unet(lowercase   ,				lowercase				)['''sample''']
               UpperCAmelCase			       =		(1 - alpha_prod_t_prev) ** 0.5 * model_output
               UpperCAmelCase			       =		(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
               UpperCAmelCase			       =		sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

           return sample







       @staticmethod
       def     A      (	lowercase						:	torch.Tensor   ,				lowercase						:	torch.Tensor   ,				lowercase						:	float				):



           '''simple docstring'''

           UpperCAmelCase			       =		acos(torch.dot(torch.flatten(lowercase				)   ,				torch.flatten(lowercase				)				) / torch.norm(lowercase				) / torch.norm(lowercase				)				)
           return sin((1 - alpha) * theta				) * xa / sin(lowercase				) + sin(alpha * theta				) * xa / sin(lowercase				)




