

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 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     ['''vqvae''']





							def __init__(					self       :	List[Any]       ,     a       :	AutoencoderKL       ,     a       :	UNetaDConditionModel       ,     a       :	Mel       ,     a       :	Union[DDIMScheduler, DDPMScheduler]       ,     )			->					Optional[int]:
										super().__init__()
										self.register_modules(unet=a       ,     scheduler=a       ,     mel=a       ,     vqvae=a			)





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					int:
										return 50 if isinstance(self.scheduler       ,     a			) else 1_000





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

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

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

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

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

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

													if mask is not None:
																if mask_start > 0:
																			SCREAMING_SNAKE_CASE           =					mask[:, step, :, :mask_start]
																if mask_end > 0:
																			SCREAMING_SNAKE_CASE           =					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           =					1 / self.vqvae.config.scaling_factor * images
													SCREAMING_SNAKE_CASE           =					self.vqvae.decode(a			)["""sample"""]

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

										SCREAMING_SNAKE_CASE           =					[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    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	List[Image.Image]       ,     a       :	int = 50			)			->					np.ndarray:
										assert isinstance(self.scheduler       ,     a			)
										self.scheduler.set_timesteps(a			)
										SCREAMING_SNAKE_CASE           =					np.array(
										    [np.frombuffer(image.tobytes()       ,     dtype="""uint8"""			).reshape((1, image.height, image.width)			) for image in images]			)
										SCREAMING_SNAKE_CASE           =					(sample / 255) * 2 - 1
										SCREAMING_SNAKE_CASE           =					torch.Tensor(a			).to(self.device			)

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

										return sample







							@staticmethod
							def    _UpperCAmelCase     (					a       :	torch.Tensor       ,     a       :	torch.Tensor       ,     a       :	float			)			->					torch.Tensor:
										SCREAMING_SNAKE_CASE           =					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			)












from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						:							Any							=  logging.get_logger(__name__)

__A						:							int							=  {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''megatron-bert'''





							def __init__(					self       :	str       ,     a       :	Union[str, Any]=29_056       ,     a       :	Optional[int]=1_024       ,     a       :	str=24       ,     a       :	str=16       ,     a       :	str=4_096       ,     a       :	Any="gelu"       ,     a       :	Any=0.1       ,     a       :	Optional[int]=0.1       ,     a       :	Optional[int]=512       ,     a       :	Optional[Any]=2       ,     a       :	List[str]=0.02       ,     a       :	List[Any]=1E-1_2       ,     a       :	Any=0       ,     a       :	List[Any]="absolute"       ,     a       :	List[str]=True       ,     **a       :	Optional[Any]       ,     )			->					List[str]:
										super().__init__(pad_token_id=a       ,     **a			)

										SCREAMING_SNAKE_CASE           =					vocab_size
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					num_hidden_layers
										SCREAMING_SNAKE_CASE           =					num_attention_heads
										SCREAMING_SNAKE_CASE           =					hidden_act
										SCREAMING_SNAKE_CASE           =					intermediate_size
										SCREAMING_SNAKE_CASE           =					hidden_dropout_prob
										SCREAMING_SNAKE_CASE           =					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE           =					max_position_embeddings
										SCREAMING_SNAKE_CASE           =					type_vocab_size
										SCREAMING_SNAKE_CASE           =					initializer_range
										SCREAMING_SNAKE_CASE           =					layer_norm_eps
										SCREAMING_SNAKE_CASE           =					position_embedding_type
										SCREAMING_SNAKE_CASE           =					use_cache






