





from typing import List, Optional, Tuple, Union

import torch

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


a__: Union[str, Any]							   =						logging.get_logger(__name__)  # pylint: disable=invalid-name




class        SCREAMING_SNAKE_CASE__					(		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:
               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					)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__: List[Any]							   =						{
    'configuration_x_clip': [
        'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XCLIPConfig',
        'XCLIPTextConfig',
        'XCLIPVisionConfig',
    ],
    'processing_x_clip': ['XCLIPProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: Any							   =						[
           'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
           'XCLIPModel',
           'XCLIPPreTrainedModel',
           'XCLIPTextModel',
           'XCLIPVisionModel',
       ]

if TYPE_CHECKING:
       from .configuration_x_clip import (
           XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           XCLIPConfig,
           XCLIPTextConfig,
           XCLIPVisionConfig,
       )
       from .processing_x_clip import XCLIPProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_x_clip import (
                  XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XCLIPModel,
                  XCLIPPreTrainedModel,
                  XCLIPTextModel,
                  XCLIPVisionModel,
              )

else:
       import sys

       a__: Optional[Any]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





