
"""simple docstring"""

from typing import List, Optional, Tuple, Union

import torch

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


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






class 	_lowercase       (    _UpperCAmelCase		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							int:
			super().__init__()
			self.register_modules(unet=lowercase_		,			scheduler=lowercase_ )




	@torch.no_grad()
	def __call__(			self		,			UpperCAmelCase_ = 1		,			UpperCAmelCase_ = 100		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = True		,			)		->							Optional[Any]:

			if audio_length_in_s is None:
					lowerCamelCase					:      List[Any]          =   self.unet.config.sample_size / self.unet.config.sample_rate

			lowerCamelCase					:      Dict          =   audio_length_in_s * self.unet.config.sample_rate

			lowerCamelCase					:      Any          =   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}.""" )

			lowerCamelCase					:      List[Any]          =   int(lowercase_ )
			if sample_size % down_scale_factor != 0:
					lowerCamelCase					:      int          =   (
					    (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.' )
			lowerCamelCase					:      Any          =   int(lowercase_ )

			lowerCamelCase					:      List[str]          =   next(iter(self.unet.parameters() ) ).dtype
			lowerCamelCase					:      List[str]          =   (batch_size, self.unet.config.in_channels, sample_size)
			if isinstance(lowercase_		,			lowercase_ ) and len(lowercase_ ) != batch_size:
					raise ValueError(
					    F"""You have passed a list of generators of length {len(lowercase_ )}, but requested an effective batch"""
					    F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

			lowerCamelCase					:      Any          =   randn_tensor(lowercase_		,			generator=lowercase_		,			device=self.device		,			dtype=lowercase_ )

			# set step values
			self.scheduler.set_timesteps(lowercase_		,			device=audio.device )
			lowerCamelCase					:      Optional[Any]          =   self.scheduler.timesteps.to(lowercase_ )

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

					# 2. compute previous image: x_t -> t_t-1
					lowerCamelCase					:      List[str]          =   self.scheduler.step(lowercase_		,			lowercase_		,			lowercase_ ).prev_sample

			lowerCamelCase					:      str          =   audio.clamp(-1		,			1 ).float().cpu().numpy()

			lowerCamelCase					:      Union[str, Any]          =   audio[:, :, :original_sample_size]

			if not return_dict:
					return (audio,)

			return AudioPipelineOutput(audios=lowercase_ )








"""simple docstring"""

# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


_A						=			re.compile(r'^(?P<major>\d+)' r'\.(?P<minor>\d+)' r'\.(?P<patch>\d+)$')






@total_ordering
@dataclass
class 	_lowercase       :
	lowercase_				=     42
	lowercase_				=     None
	lowercase_				=     None
	lowercase_				=     None
	lowercase_				=     None




	def       _UpperCamelCase   (			self )		->							List[str]:
			lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   _str_to_version_tuple(self.version_str )




	def __repr__(			self )		->							int:
			return F"""{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"""




	@property
	def       _UpperCamelCase   (			self )		->							Dict:
			return self.major, self.minor, self.patch




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Optional[int]:
			if isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ):
					return Version(UpperCAmelCase_ )
			elif isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ):
					return other
			raise TypeError(F"""{other} (type {type(UpperCAmelCase_ )}) cannot be compared to version.""" )




	def __eq__(			self		,			UpperCAmelCase_ )		->							Optional[Any]:
			try:
					lowerCamelCase					:      List[str]          =   self._validate_operand(UpperCAmelCase_ )
			except (TypeError, ValueError):
					return False
			else:
					return self.tuple == other.tuple




	def __lt__(			self		,			UpperCAmelCase_ )		->							Optional[int]:
			lowerCamelCase					:      Optional[int]          =   self._validate_operand(UpperCAmelCase_ )
			return self.tuple < other.tuple




	def __hash__(			self )		->							Optional[Any]:
			return hash(_version_tuple_to_str(self.tuple ) )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_ )		->							Union[str, Any]:
			lowerCamelCase					:      str          =   {f.name for f in dataclasses.fields(cls )}
			return cls(**{k: v for k, v in dic.items() if k in field_names} )




	def       _UpperCamelCase   (			self )		->							str:
			return self.version_str


def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Tuple          =   _VERSION_REG.match(a_      )
		if not res:
				raise ValueError(F"""Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits."""      )
		return tuple(int(a_      ) for v in [res.group('major'      ), res.group('minor'      ), res.group('patch'      )]      )





def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return ".".join(str(a_      ) for v in version_tuple      )






