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        lowerCamelCase__				(   _UpperCamelCase				):
			UpperCamelCase__    							=['vqvae']





			def __init__(       self	:	Union[str, Any]  ,  lowercase__	:	AutoencoderKL  ,  lowercase__	:	UNetaDConditionModel  ,  lowercase__	:	Mel  ,  lowercase__	:	Union[DDIMScheduler, DDPMScheduler]  ,  ):
										super().__init__()
										self.register_modules(unet=lowercase__  ,  scheduler=lowercase__  ,  mel=lowercase__  ,  vqvae=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										return 50 if isinstance(self.scheduler  ,  lowercase__						) else 10_00





			@torch.no_grad()
			def __call__(       self	:	str  ,  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__	:	Optional[int]=True  ,  ):
										_lowerCAmelCase							=							steps or self.get_default_steps()
										self.scheduler.set_timesteps(lowercase__						)
										_lowerCAmelCase							=							step_generator or generator
										# For backwards compatibility
										if type(self.unet.config.sample_size						) == int:
																	_lowerCAmelCase							=							(self.unet.config.sample_size, self.unet.config.sample_size)
										if noise is None:
																	_lowerCAmelCase							=							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  ,  )
										_lowerCAmelCase							=							noise
										_lowerCAmelCase							=							None

										if audio_file is not None or raw_audio is not None:
																	self.mel.load_audio(lowercase__  ,  lowercase__						)
																	_lowerCAmelCase							=							self.mel.audio_slice_to_image(lowercase__						)
																	_lowerCAmelCase							=							np.frombuffer(input_image.tobytes()  ,  dtype='uint8'						).reshape(
																	    (input_image.height, input_image.width)						)
																	_lowerCAmelCase							=							(input_image / 2_55) * 2 - 1
																	_lowerCAmelCase							=							torch.tensor(input_image[np.newaxis, :, :]  ,  dtype=torch.float						).to(self.device						)

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

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

																	_lowerCAmelCase							=							(
																	    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
																	)
																	_lowerCAmelCase							=							int(mask_start_secs * pixels_per_second						)
																	_lowerCAmelCase							=							int(mask_end_secs * pixels_per_second						)
																	_lowerCAmelCase							=							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__						):
																								_lowerCAmelCase							=							self.unet(lowercase__  ,  lowercase__  ,  lowercase__						)["""sample"""]
																	else:
																								_lowerCAmelCase							=							self.unet(lowercase__  ,  lowercase__						)["""sample"""]

																	if isinstance(self.scheduler  ,  lowercase__						):
																								_lowerCAmelCase							=							self.scheduler.step(
																								    model_output=lowercase__  ,  timestep=lowercase__  ,  sample=lowercase__  ,  eta=lowercase__  ,  generator=lowercase__  ,  )["""prev_sample"""]
																	else:
																								_lowerCAmelCase							=							self.scheduler.step(
																								    model_output=lowercase__  ,  timestep=lowercase__  ,  sample=lowercase__  ,  generator=lowercase__  ,  )["""prev_sample"""]

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

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

										_lowerCAmelCase							=							(images / 2 + 0.5).clamp(0  ,  1						)
										_lowerCAmelCase							=							images.cpu().permute(0  ,  2  ,  3  ,  1						).numpy()
										_lowerCAmelCase							=							(images * 2_55).round().astype('uint8'						)
										_lowerCAmelCase							=							list(
										    (Image.fromarray(_[:, :, 0]						) for _ in images)
										    if images.shape[3] == 1
										    else (Image.fromarray(lowercase__  ,  mode='RGB'						).convert('L'						) for _ in images)						)

										_lowerCAmelCase							=							[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   SCREAMING_SNAKE_CASE__     (       self	:	Tuple  ,  lowercase__	:	List[Image.Image]  ,  lowercase__	:	int = 50						):
										assert isinstance(self.scheduler  ,  lowercase__						)
										self.scheduler.set_timesteps(lowercase__						)
										_lowerCAmelCase							=							np.array(
										    [np.frombuffer(image.tobytes()  ,  dtype='uint8'						).reshape((1, image.height, image.width)						) for image in images]						)
										_lowerCAmelCase							=							(sample / 2_55) * 2 - 1
										_lowerCAmelCase							=							torch.Tensor(lowercase__						).to(self.device						)

										for t in self.progress_bar(torch.flip(self.scheduler.timesteps  ,  (0,)						)						):
																	_lowerCAmelCase							=							t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
																	_lowerCAmelCase							=							self.scheduler.alphas_cumprod[t]
																	_lowerCAmelCase							=							(
																	    self.scheduler.alphas_cumprod[prev_timestep]
																	    if prev_timestep >= 0
																	    else self.scheduler.final_alpha_cumprod
																	)
																	_lowerCAmelCase							=							1 - alpha_prod_t
																	_lowerCAmelCase							=							self.unet(lowercase__  ,  lowercase__						)["""sample"""]
																	_lowerCAmelCase							=							(1 - alpha_prod_t_prev) ** 0.5 * model_output
																	_lowerCAmelCase							=							(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
																	_lowerCAmelCase							=							sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

										return sample





			@staticmethod
			def   SCREAMING_SNAKE_CASE__     (       lowercase__	:	torch.Tensor  ,  lowercase__	:	torch.Tensor  ,  lowercase__	:	float						):
										_lowerCAmelCase							=							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__						)












import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


a__		     =     logging.get_logger(__name__)


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Dict      	=		'linear'
	__magic_name__   :       Dict      	=		'cosine'
	__magic_name__   :       Optional[int]      	=		'cosine_with_restarts'
	__magic_name__   :       List[str]      	=		'polynomial'
	__magic_name__   :       Any      	=		'constant'
	__magic_name__   :       Union[str, Any]      	=		'constant_with_warmup'
	__magic_name__   :       str      	=		'piecewise_constant'






def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int = -1				)     ->						Optional[Any]:
					return LambdaLR(snake_case		,	lambda snake_case				: 1		,	last_epoch=snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int		,	snake_case   :		int = -1				)     ->						List[Any]:

					def lr_lambda(snake_case   :		int				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1.0		,	snake_case				)				)
										return 1.0

					return LambdaLR(snake_case		,	snake_case		,	last_epoch=snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		str		,	snake_case   :		int = -1				)     ->						Union[str, Any]:
					__UpperCamelCase					:	Optional[Any]             =      {}
					__UpperCamelCase					:	int             =      step_rules.split(""","""				)
					for rule_str in rule_list[:-1]:
										__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      rule_str.split(""":"""				)
										__UpperCamelCase					:	int             =      int(snake_case				)
										__UpperCamelCase					:	Union[str, Any]             =      float(snake_case				)
										__UpperCamelCase					:	Optional[int]             =      value
					__UpperCamelCase					:	Dict             =      float(rule_list[-1]				)

					def create_rules_function(snake_case   :		List[str]		,	snake_case   :		Any				):
										def rule_func(snake_case   :		int				) -> float:
															__UpperCamelCase					:	Any             =      sorted(rules_dict.keys()				)
															for i, sorted_step in enumerate(snake_case				):
																				if steps < sorted_step:
																									return rules_dict[sorted_steps[i]]
															return last_lr_multiple

										return rule_func

					__UpperCamelCase					:	Tuple             =      create_rules_function(snake_case		,	snake_case				)

					return LambdaLR(snake_case		,	snake_case		,	last_epoch=snake_case				)





def 				A__ (snake_case   :		int		,	snake_case   :		Optional[Any]		,	snake_case   :		Union[str, Any]		,	snake_case   :		str=-1				)     ->						str:

					def lr_lambda(snake_case   :		int				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										return max(
										    0.0		,	float(num_training_steps - current_step				) / float(max(1		,	num_training_steps - num_warmup_steps				)				)				)

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int		,	snake_case   :		int		,	snake_case   :		float = 0.5		,	snake_case   :		int = -1				)     ->						List[str]:

					def lr_lambda(snake_case   :		Dict				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										__UpperCamelCase					:	Dict             =      float(current_step - num_warmup_steps				) / float(max(1		,	num_training_steps - num_warmup_steps				)				)
										return max(0.0		,	0.5 * (1.0 + math.cos(math.pi * float(snake_case				) * 2.0 * progress				))				)

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int		,	snake_case   :		int		,	snake_case   :		int = 1		,	snake_case   :		int = -1				)     ->						Tuple:

					def lr_lambda(snake_case   :		Optional[int]				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										__UpperCamelCase					:	List[str]             =      float(current_step - num_warmup_steps				) / float(max(1		,	num_training_steps - num_warmup_steps				)				)
										if progress >= 1.0:
															return 0.0
										return max(0.0		,	0.5 * (1.0 + math.cos(math.pi * ((float(snake_case				) * progress) % 1.0)				))				)

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)





def 				A__ (snake_case   :		List[str]		,	snake_case   :		Optional[Any]		,	snake_case   :		Optional[int]		,	snake_case   :		str=1e-7		,	snake_case   :		List[str]=1.0		,	snake_case   :		Dict=-1				)     ->						Tuple:
					__UpperCamelCase					:	Tuple             =      optimizer.defaults["""lr"""]
					if not (lr_init > lr_end):
										raise ValueError(F'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'''				)

					def lr_lambda(snake_case   :		int				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										elif current_step > num_training_steps:
															return lr_end / lr_init  # as LambdaLR multiplies by lr_init
										else:
															__UpperCamelCase					:	List[str]             =      lr_init - lr_end
															__UpperCamelCase					:	Any             =      num_training_steps - num_warmup_steps
															__UpperCamelCase					:	List[str]             =      1 - (current_step - num_warmup_steps) / decay_steps
															__UpperCamelCase					:	List[Any]             =      lr_range * pct_remaining**power + lr_end
															return decay / lr_init  # as LambdaLR multiplies by lr_init

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)


a__		     =     {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def 				A__ (snake_case   :		Union[str, SchedulerType]		,	snake_case   :		Optimizer		,	snake_case   :		Optional[str] = None		,	snake_case   :		Optional[int] = None		,	snake_case   :		Optional[int] = None		,	snake_case   :		int = 1		,	snake_case   :		float = 1.0		,	snake_case   :		int = -1		,	)     ->						Dict:
					__UpperCamelCase					:	List[str]             =      SchedulerType(snake_case				)
					__UpperCamelCase					:	int             =      TYPE_TO_SCHEDULER_FUNCTION[name]
					if name == SchedulerType.CONSTANT:
										return schedule_func(snake_case		,	last_epoch=snake_case				)

					if name == SchedulerType.PIECEWISE_CONSTANT:
										return schedule_func(snake_case		,	step_rules=snake_case		,	last_epoch=snake_case				)

					# All other schedulers require `num_warmup_steps`
					if num_warmup_steps is None:
										raise ValueError(F'''{name} requires `num_warmup_steps`, please provide that argument.'''				)

					if name == SchedulerType.CONSTANT_WITH_WARMUP:
										return schedule_func(snake_case		,	num_warmup_steps=snake_case		,	last_epoch=snake_case				)

					# All other schedulers require `num_training_steps`
					if num_training_steps is None:
										raise ValueError(F'''{name} requires `num_training_steps`, please provide that argument.'''				)

					if name == SchedulerType.COSINE_WITH_RESTARTS:
										return schedule_func(
										    snake_case		,	num_warmup_steps=snake_case		,	num_training_steps=snake_case		,	num_cycles=snake_case		,	last_epoch=snake_case		,	)

					if name == SchedulerType.POLYNOMIAL:
										return schedule_func(
										    snake_case		,	num_warmup_steps=snake_case		,	num_training_steps=snake_case		,	power=snake_case		,	last_epoch=snake_case		,	)

					return schedule_func(
					    snake_case		,	num_warmup_steps=snake_case		,	num_training_steps=snake_case		,	last_epoch=snake_case				)






