

'''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 					lowercase						(  a_ ):
							_lowerCamelCase		:      int=   ["vqvae"]


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			)							->				List[Any]:
								super().__init__()
								self.register_modules(unet=_snake_case    ,			scheduler=_snake_case    ,			mel=_snake_case    ,			vqvae=_snake_case)


							def 						_snake_case							(			self)							->				int:
								return 50 if isinstance(self.scheduler    ,			_snake_case) else 1000


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

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

									if self.vqvae is not None:
										UpperCAmelCase_		:      List[Any]       	=					self.vqvae.encode(torch.unsqueeze(_snake_case    ,			0)).latent_dist.sample(
										    generator=_snake_case)[0]
										UpperCAmelCase_		:      str       	=					self.vqvae.config.scaling_factor * input_images

									if start_step > 0:
										UpperCAmelCase_		:      Dict       	=					self.scheduler.add_noise(_snake_case    ,			_snake_case    ,			self.scheduler.timesteps[start_step - 1])

									UpperCAmelCase_		:      List[str]       	=					(
									    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
									)
									UpperCAmelCase_		:      Any       	=					int(mask_start_secs * pixels_per_second)
									UpperCAmelCase_		:      Union[str, Any]       	=					int(mask_end_secs * pixels_per_second)
									UpperCAmelCase_		:      Tuple       	=					self.scheduler.add_noise(_snake_case    ,			_snake_case    ,			torch.tensor(self.scheduler.timesteps[start_step:]))

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

									if isinstance(self.scheduler    ,			_snake_case):
										UpperCAmelCase_		:      Tuple       	=					self.scheduler.step(
										    model_output=_snake_case    ,			timestep=_snake_case    ,			sample=_snake_case    ,			eta=_snake_case    ,			generator=_snake_case    ,			)['prev_sample']
									else:
										UpperCAmelCase_		:      Optional[int]       	=					self.scheduler.step(
										    model_output=_snake_case    ,			timestep=_snake_case    ,			sample=_snake_case    ,			generator=_snake_case    ,			)['prev_sample']

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

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

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

								UpperCAmelCase_		:      List[str]       	=					[self.mel.image_to_audio(_snake_case) for _ in images]
								if not return_dict:
									return images, (self.mel.get_sample_rate(), audios)

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


							@torch.no_grad()
							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = 50)							->				np.ndarray:
								assert isinstance(self.scheduler    ,			_snake_case)
								self.scheduler.set_timesteps(_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					np.array(
								    [np.frombuffer(image.tobytes()    ,			dtype='uint8').reshape((1, image.height, image.width)) for image in images])
								UpperCAmelCase_		:      Optional[Any]       	=					(sample / 255) * 2 - 1
								UpperCAmelCase_		:      Optional[Any]       	=					torch.Tensor(_snake_case).to(self.device)

								for t in self.progress_bar(torch.flip(self.scheduler.timesteps    ,			(0,))):
									UpperCAmelCase_		:      List[Any]       	=					t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
									UpperCAmelCase_		:      Dict       	=					self.scheduler.alphas_cumprod[t]
									UpperCAmelCase_		:      Optional[int]       	=					(
									    self.scheduler.alphas_cumprod[prev_timestep]
									    if prev_timestep >= 0
									    else self.scheduler.final_alpha_cumprod
									)
									UpperCAmelCase_		:      List[str]       	=					1 - alpha_prod_t
									UpperCAmelCase_		:      Tuple       	=					self.unet(_snake_case    ,			_snake_case)['sample']
									UpperCAmelCase_		:      Union[str, Any]       	=					(1 - alpha_prod_t_prev) ** 0.5 * model_output
									UpperCAmelCase_		:      Union[str, Any]       	=					(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
									UpperCAmelCase_		:      Any       	=					sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

								return sample





							@staticmethod
							def 						_snake_case							(			_snake_case    ,			_snake_case    ,			_snake_case)							->				torch.Tensor:
								UpperCAmelCase_		:      List[str]       	=					acos(torch.dot(torch.flatten(_snake_case)    ,			torch.flatten(_snake_case)) / torch.norm(_snake_case) / torch.norm(_snake_case))
								return sin((1 - alpha) * theta) * xa / sin(_snake_case) + sin(alpha * theta) * xa / sin(_snake_case)









'''simple docstring'''


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      List[str]=   "Wav2Vec2FeatureExtractor"
							_lowerCamelCase		:      Any=   "AutoTokenizer"


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				List[str]:
								super().__init__(_snake_case    ,			_snake_case)
								UpperCAmelCase_		:      int       	=					self.feature_extractor
								UpperCAmelCase_		:      str       	=					False


							@classmethod
							def 						_snake_case							(			cls    ,			_snake_case    ,			**_snake_case)							->				List[Any]:
								try:
									return super().from_pretrained(_snake_case    ,			**_snake_case)
								except OSError:
									warnings.warn(
									    F"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
									    ' include a `tokenizer_class` attribute is deprecated and will be '
									    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
									    ' attribute to either your `config.json` or `tokenizer_config.json` '
									    'file to suppress this warning: '    ,			_snake_case    ,			)

									UpperCAmelCase_		:      Any       	=					WavaVecaFeatureExtractor.from_pretrained(_snake_case    ,			**_snake_case)
									UpperCAmelCase_		:      List[str]       	=					WavaVecaCTCTokenizer.from_pretrained(_snake_case    ,			**_snake_case)

									return cls(feature_extractor=_snake_case    ,			tokenizer=_snake_case)


							def __call__(			self    ,			*_snake_case    ,			**_snake_case)							->				Optional[Any]:
								# For backward compatibility
								if self._in_target_context_manager:
									return self.current_processor(*_snake_case    ,			**_snake_case)

								if "raw_speech" in kwargs:
									warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.')
									UpperCAmelCase_		:      List[str]       	=					kwargs.pop('raw_speech')
								else:
									UpperCAmelCase_		:      Dict       	=					kwargs.pop('audio'    ,			_snake_case)
								UpperCAmelCase_		:      str       	=					kwargs.pop('sampling_rate'    ,			_snake_case)
								UpperCAmelCase_		:      List[str]       	=					kwargs.pop('text'    ,			_snake_case)
								if len(_snake_case) > 0:
									UpperCAmelCase_		:      List[str]       	=					args[0]
									UpperCAmelCase_		:      Tuple       	=					args[1:]

								if audio is None and text is None:
									raise ValueError('You need to specify either an `audio` or `text` input to process.')

								if audio is not None:
									UpperCAmelCase_		:      Union[str, Any]       	=					self.feature_extractor(_snake_case    ,			*_snake_case    ,			sampling_rate=_snake_case    ,			**_snake_case)
								if text is not None:
									UpperCAmelCase_		:      Optional[int]       	=					self.tokenizer(_snake_case    ,			**_snake_case)

								if text is None:
									return inputs
								elif audio is None:
									return encodings
								else:
									UpperCAmelCase_		:      List[str]       	=					encodings['input_ids']
									return inputs


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				str:
								# For backward compatibility
								if self._in_target_context_manager:
									return self.current_processor.pad(*_snake_case    ,			**_snake_case)

								UpperCAmelCase_		:      Union[str, Any]       	=					kwargs.pop('input_features'    ,			_snake_case)
								UpperCAmelCase_		:      Dict       	=					kwargs.pop('labels'    ,			_snake_case)
								if len(_snake_case) > 0:
									UpperCAmelCase_		:      Optional[Any]       	=					args[0]
									UpperCAmelCase_		:      int       	=					args[1:]

								if input_features is not None:
									UpperCAmelCase_		:      Dict       	=					self.feature_extractor.pad(_snake_case    ,			*_snake_case    ,			**_snake_case)
								if labels is not None:
									UpperCAmelCase_		:      Union[str, Any]       	=					self.tokenizer.pad(_snake_case    ,			**_snake_case)

								if labels is None:
									return input_features
								elif input_features is None:
									return labels
								else:
									UpperCAmelCase_		:      Union[str, Any]       	=					labels['input_ids']
									return input_features


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				Dict:
								return self.tokenizer.batch_decode(*_snake_case    ,			**_snake_case)


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				Tuple:
								return self.tokenizer.decode(*_snake_case    ,			**_snake_case)





							@contextmanager
							def 						_snake_case							(			self)							->				Tuple:
								warnings.warn(
								    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
								    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
								    'your audio inputs, or in a separate call.')
								UpperCAmelCase_		:      Optional[Any]       	=					True
								UpperCAmelCase_		:      Tuple       	=					self.tokenizer
								yield
								UpperCAmelCase_		:      Tuple       	=					self.feature_extractor
								UpperCAmelCase_		:      Tuple       	=					False






