

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     snake_case_			(				_UpperCAmelCase							):


							"""simple docstring"""





							__UpperCAmelCase       =["""vqvae"""]




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




							def        A__  ( self							):
												return 5_0 if isinstance(self.scheduler					,						lowercase__							) 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					,						):
												__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_5_5) * 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_5_5).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        A__  ( self					,						_A					,						_A = 5_0							):
												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_5_5) * 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        A__  ( _A					,						_A					,						_A							):
												__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__							)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase      				=     {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''UniSpeechForCTC''',
		    '''UniSpeechForPreTraining''',
		    '''UniSpeechForSequenceClassification''',
		    '''UniSpeechModel''',
		    '''UniSpeechPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech import (
				    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechForCTC,
				    UniSpeechForPreTraining,
				    UniSpeechForSequenceClassification,
				    UniSpeechModel,
				    UniSpeechPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase      				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






