





"""simple docstring"""

from typing import List, Optional, Tuple, Union

import torch

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


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


class       lowerCAmelCase__				(   __a  ):







 def __init__(   self		,	UpperCamelCase__		,	UpperCamelCase__					):

    '''simple docstring'''


    super().__init__()
    self.register_modules(unet=_lowerCamelCase		,	scheduler=_lowerCamelCase					)







 @torch.no_grad()
 def __call__(   self		,	UpperCamelCase__ = 1		,	UpperCamelCase__ = 1_00		,	UpperCamelCase__ = None		,	UpperCamelCase__ = None		,	UpperCamelCase__ = True		,	):

    '''simple docstring'''


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

    A__											=					audio_length_in_s * self.unet.config.sample_rate

    A__											=					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}."""					)

    A__											=					int(_lowerCamelCase					)
    if sample_size % down_scale_factor != 0:
       A__											=					(
           (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."					)
    A__											=					int(_lowerCamelCase					)

    A__											=					next(iter(self.unet.parameters()					)					).dtype
    A__											=					(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."""					)

    A__											=					randn_tensor(_lowerCamelCase		,	generator=_lowerCamelCase		,	device=self.device		,	dtype=_lowerCamelCase					)

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

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

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

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

    A__											=					audio[:, :, :original_sample_size]

    if not return_dict:
       return (audio,)

    return AudioPipelineOutput(audios=_lowerCamelCase					)

"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase							=				{
    '''configuration_mask2former''': [
        '''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Mask2FormerConfig''',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				['''Mask2FormerImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''Mask2FormerForUniversalSegmentation''',
					    '''Mask2FormerModel''',
					    '''Mask2FormerPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					_lowercase							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure)





