
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        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Optional[int]       =['''vqvae''']




							def __init__(       self     ,A__     ,A__     ,A__     ,A__     ,):
								super().__init__()
								self.register_modules(unet=A__     ,scheduler=A__     ,mel=A__     ,vqvae=A__)




							def 				A__   (       self):
								return 5_0 if isinstance(self.scheduler     ,A__) else 1_0_0_0




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

								if audio_file is not None or raw_audio is not None:
									self.mel.load_audio(A__     ,A__)
									lowercase					=		self.mel.audio_slice_to_image(A__)
									lowercase					=		np.frombuffer(input_image.tobytes()     ,dtype='''uint8''').reshape(
									    (input_image.height, input_image.width))
									lowercase					=		(input_image / 2_5_5) * 2 - 1
									lowercase					=		torch.tensor(input_image[np.newaxis, :, :]     ,dtype=torch.float).to(self.device)

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

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

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

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

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

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

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

								lowercase					=		(images / 2 + 0.5).clamp(0     ,1)
								lowercase					=		images.cpu().permute(0     ,2     ,3     ,1).numpy()
								lowercase					=		(images * 2_5_5).round().astype('''uint8''')
								lowercase					=		list(
								    (Image.fromarray(_[:, :, 0]) for _ in images)
								    if images.shape[3] == 1
								    else (Image.fromarray(A__     ,mode='''RGB''').convert('''L''') for _ in images))

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

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




							@torch.no_grad()
							def 				A__   (       self     ,A__     ,A__ = 5_0):
								assert isinstance(self.scheduler     ,A__)
								self.scheduler.set_timesteps(A__)
								lowercase					=		np.array(
								    [np.frombuffer(image.tobytes()     ,dtype='''uint8''').reshape((1, image.height, image.width)) for image in images])
								lowercase					=		(sample / 2_5_5) * 2 - 1
								lowercase					=		torch.Tensor(A__).to(self.device)

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

								return sample





							@staticmethod
							def 				A__   (       A__     ,A__     ,A__):
								lowercase					=		acos(torch.dot(torch.flatten(A__)     ,torch.flatten(A__)) / torch.norm(A__) / torch.norm(A__))
								return sin((1 - alpha) * theta) * xa / sin(A__) + sin(alpha * theta) * xa / sin(A__)






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : List[str]       =(UniPCMultistepScheduler,)
							lowercase_ : Tuple       =(('''num_inference_steps''', 25),)




							def 				A__   (       self     ,**A__):
												lowercase					=		{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''solver_order''': 2,
												    '''solver_type''': '''bh2''',
												}

												config.update(**A__)
												return config




							def 				A__   (       self     ,A__=0     ,**A__):
												lowercase					=		dict(self.forward_default_kwargs)
												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
												lowercase					=		self.dummy_sample
												lowercase					=		0.1 * sample
												lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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




							def 				A__   (       self     ,A__=0     ,**A__):
												lowercase					=		dict(self.forward_default_kwargs)
												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
												lowercase					=		self.dummy_sample
												lowercase					=		0.1 * sample
												lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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

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

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




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

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

												lowercase					=		1_0
												lowercase					=		self.dummy_model()
												lowercase					=		self.dummy_sample_deter
												scheduler.set_timesteps(A__)

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

												return sample




							def 				A__   (       self):
												lowercase					=		dict(self.forward_default_kwargs)

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

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

																	lowercase					=		self.dummy_sample
																	lowercase					=		0.1 * sample

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

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

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

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

																	self.assertEqual(output_a.shape     ,sample.shape)
																	self.assertEqual(output_a.shape     ,output_a.shape)




							def 				A__   (       self):
												# make sure that iterating over schedulers with same config names gives same results
												# for defaults
												lowercase					=		UniPCMultistepScheduler(**self.get_scheduler_config())
												lowercase					=		self.full_loop(scheduler=A__)
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3

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

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

												assert abs(result_mean.item() - 0.2464) < 1E-3




							def 				A__   (       self):
												for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=A__)




							def 				A__   (       self):
												self.check_over_configs(thresholding=A__)
												for order in [1, 2, 3]:
																	for solver_type in ["bh1", "bh2"]:
																						for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    thresholding=A__     ,prediction_type=A__     ,sample_max_value=A__     ,solver_order=A__     ,solver_type=A__     ,)




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




							def 				A__   (       self):
												for solver_type in ["bh1", "bh2"]:
																	for order in [1, 2, 3]:
																						for prediction_type in ["epsilon", "sample"]:
																											self.check_over_configs(
																											    solver_order=A__     ,solver_type=A__     ,prediction_type=A__     ,)
																											lowercase					=		self.full_loop(
																											    solver_order=A__     ,solver_type=A__     ,prediction_type=A__     ,)
																											assert not torch.isnan(A__).any(), "Samples have nan numbers"




							def 				A__   (       self):
												self.check_over_configs(lower_order_final=A__)
												self.check_over_configs(lower_order_final=A__)




							def 				A__   (       self):
												for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_forward(num_inference_steps=A__     ,time_step=0)




							def 				A__   (       self):
												lowercase					=		self.full_loop()
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3




							def 				A__   (       self):
												lowercase					=		self.full_loop(prediction_type='''v_prediction''')
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.1014) < 1E-3




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

												lowercase					=		1_0
												lowercase					=		self.dummy_model()
												lowercase					=		self.dummy_sample_deter.half()
												scheduler.set_timesteps(A__)

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

												assert sample.dtype == torch.floataa





							def 				A__   (       self     ,**A__):
												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)

																	scheduler.set_timesteps(scheduler.config.num_train_timesteps)
																	assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps




