



from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)  # pylint: disable=invalid-name



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							super().__init__()
							self.register_modules(unet=lowerCAmelCase    ,	scheduler=lowerCAmelCase     )



					@torch.no_grad()
					def __call__(			self    ,	lowerCAmelCase = 1    ,	lowerCAmelCase = 100    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = True    ,	):

							if audio_length_in_s is None:
									UpperCAmelCase_						=				self.unet.config.sample_size / self.unet.config.sample_rate

							UpperCAmelCase_						=				audio_length_in_s * self.unet.config.sample_rate

							UpperCAmelCase_						=				2 ** len(self.unet.up_blocks     )
							if sample_size < 3 * down_scale_factor:
									raise ValueError(
									    f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
									    f''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''     )

							UpperCAmelCase_						=				int(lowerCAmelCase     )
							if sample_size % down_scale_factor != 0:
									UpperCAmelCase_						=				(
									    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
									) * down_scale_factor
									logger.info(
									    f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
									    f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
									    " process."     )
							UpperCAmelCase_						=				int(lowerCAmelCase     )

							UpperCAmelCase_						=				next(iter(self.unet.parameters()     )     ).dtype
							UpperCAmelCase_						=				(batch_size, self.unet.config.in_channels, sample_size)
							if isinstance(lowerCAmelCase    ,	lowerCAmelCase     ) and len(lowerCAmelCase     ) != batch_size:
									raise ValueError(
									    f'''You have passed a list of generators of length {len(lowerCAmelCase     )}, but requested an effective batch'''
									    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''     )

							UpperCAmelCase_						=				randn_tensor(lowerCAmelCase    ,	generator=lowerCAmelCase    ,	device=self.device    ,	dtype=lowerCAmelCase     )

							# set step values
							self.scheduler.set_timesteps(lowerCAmelCase    ,	device=audio.device     )
							UpperCAmelCase_						=				self.scheduler.timesteps.to(lowerCAmelCase     )

							for t in self.progress_bar(self.scheduler.timesteps     ):
									# 1. predict noise model_output
									UpperCAmelCase_						=				self.unet(lowerCAmelCase    ,	lowerCAmelCase     ).sample

									# 2. compute previous image: x_t -> t_t-1
									UpperCAmelCase_						=				self.scheduler.step(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ).prev_sample

							UpperCAmelCase_						=				audio.clamp(-1    ,	1     ).float().cpu().numpy()

							UpperCAmelCase_						=				audio[:, :, :original_sample_size]

							if not return_dict:
									return (audio,)

							return AudioPipelineOutput(audios=lowerCAmelCase     )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE     				=						{"tokenization_herbert": ["HerbertTokenizer"]}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["HerbertTokenizerFast"]


if TYPE_CHECKING:
			from .tokenization_herbert import HerbertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_herbert_fast import HerbertTokenizerFast

else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

