


'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

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


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



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

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







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

            '''simple docstring'''

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

            _lowerCAmelCase     :				Optional[int]       =							audio_length_in_s * self.unet.config.sample_rate

            _lowerCAmelCase     :				List[str]       =							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     :				Tuple       =							int(snake_case__ )
            if sample_size % down_scale_factor != 0:
                   _lowerCAmelCase     :				Optional[Any]       =							(
                       (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(snake_case__ )

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

            _lowerCAmelCase     :				Union[str, Any]       =							randn_tensor(snake_case__   ,						generator=snake_case__   ,						device=self.device   ,						dtype=snake_case__ )

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

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

                   # 2. compute previous image: x_t -> t_t-1
                   _lowerCAmelCase     :				Union[str, Any]       =							self.scheduler.step(snake_case__   ,						snake_case__   ,						snake_case__ ).prev_sample

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

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

            if not return_dict:
                   return (audio,)

            return AudioPipelineOutput(audios=snake_case__ )





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase     :  int											=    {
    """configuration_owlvit""": [
        """OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """OwlViTConfig""",
        """OwlViTOnnxConfig""",
        """OwlViTTextConfig""",
        """OwlViTVisionConfig""",
    ],
    """processing_owlvit""": ["""OwlViTProcessor"""],
}


try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Dict											=    ["""OwlViTFeatureExtractor"""]
       lowerCAmelCase     :  int											=    ["""OwlViTImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Optional[Any]											=    [
           """OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """OwlViTModel""",
           """OwlViTPreTrainedModel""",
           """OwlViTTextModel""",
           """OwlViTVisionModel""",
           """OwlViTForObjectDetection""",
       ]

if TYPE_CHECKING:
       from .configuration_owlvit import (
           OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           OwlViTConfig,
           OwlViTOnnxConfig,
           OwlViTTextConfig,
           OwlViTVisionConfig,
       )
       from .processing_owlvit import OwlViTProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_owlvit import OwlViTFeatureExtractor
              from .image_processing_owlvit import OwlViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_owlvit import (
                  OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OwlViTForObjectDetection,
                  OwlViTModel,
                  OwlViTPreTrainedModel,
                  OwlViTTextModel,
                  OwlViTVisionModel,
              )

else:
       import sys

       lowerCAmelCase     :  Union[str, Any]											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

