



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 		UpperCAmelCase						(						UpperCamelCase__				):





			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:
															_lowerCAmelCase 			=  self.unet.config.sample_size / self.unet.config.sample_rate

									_lowerCAmelCase 			=  audio_length_in_s * self.unet.config.sample_rate

									_lowerCAmelCase 			=  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 			=  int(_lowerCAmelCase			)
									if sample_size % down_scale_factor != 0:
															_lowerCAmelCase 			=  (
															    (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 			=  int(_lowerCAmelCase			)

									_lowerCAmelCase 			=  next(iter(self.unet.parameters()			)			).dtype
									_lowerCAmelCase 			=  (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.'''			)

									_lowerCAmelCase 			=  randn_tensor(_lowerCAmelCase       ,     generator=_lowerCAmelCase       ,     device=self.device       ,     dtype=_lowerCAmelCase			)

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

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

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

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

									_lowerCAmelCase 			=  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_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_									=							{"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							["DeiTFeatureExtractor"]
    UpperCAmelCase_									=							["DeiTImageProcessor"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DeiTForImageClassification",
        "DeiTForImageClassificationWithTeacher",
        "DeiTForMaskedImageModeling",
        "DeiTModel",
        "DeiTPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDeiTForImageClassification",
        "TFDeiTForImageClassificationWithTeacher",
        "TFDeiTForMaskedImageModeling",
        "TFDeiTModel",
        "TFDeiTPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_deit import DeiTFeatureExtractor
        from .image_processing_deit import DeiTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deit import (
            DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
            DeiTModel,
            DeiTPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deit import (
            TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
            TFDeiTModel,
            TFDeiTPreTrainedModel,
        )


else:
    import sys

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