






'''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,
)


UpperCAmelCase__						:Optional[int]          =			{
    """configuration_clip""": [
        """CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPConfig""",
        """CLIPOnnxConfig""",
        """CLIPTextConfig""",
        """CLIPVisionConfig""",
    ],
    """processing_clip""": ["""CLIPProcessor"""],
    """tokenization_clip""": ["""CLIPTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:Optional[int]          =			["""CLIPTokenizerFast"""]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:Any          =			["""CLIPFeatureExtractor"""]
   UpperCAmelCase__						:Any          =			["""CLIPImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:Any          =			[
       """CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """CLIPModel""",
       """CLIPPreTrainedModel""",
       """CLIPTextModel""",
       """CLIPTextModelWithProjection""",
       """CLIPVisionModel""",
       """CLIPVisionModelWithProjection""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:Optional[int]          =			[
       """TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFCLIPModel""",
       """TFCLIPPreTrainedModel""",
       """TFCLIPTextModel""",
       """TFCLIPVisionModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:int          =			[
       """FlaxCLIPModel""",
       """FlaxCLIPPreTrainedModel""",
       """FlaxCLIPTextModel""",
       """FlaxCLIPTextPreTrainedModel""",
       """FlaxCLIPVisionModel""",
       """FlaxCLIPVisionPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_clip import (
       CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
       CLIPConfig,
       CLIPOnnxConfig,
       CLIPTextConfig,
       CLIPVisionConfig,
   )
   from .processing_clip import CLIPProcessor
   from .tokenization_clip import CLIPTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_clip_fast import CLIPTokenizerFast

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_clip import CLIPFeatureExtractor
      from .image_processing_clip import CLIPImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_clip import (
          CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          CLIPModel,
          CLIPPreTrainedModel,
          CLIPTextModel,
          CLIPTextModelWithProjection,
          CLIPVisionModel,
          CLIPVisionModelWithProjection,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_clip import (
          TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCLIPModel,
          TFCLIPPreTrainedModel,
          TFCLIPTextModel,
          TFCLIPVisionModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_clip import (
          FlaxCLIPModel,
          FlaxCLIPPreTrainedModel,
          FlaxCLIPTextModel,
          FlaxCLIPTextPreTrainedModel,
          FlaxCLIPVisionModel,
          FlaxCLIPVisionPreTrainedModel,
      )


else:
   import sys

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











'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

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


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







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




       def __init__(					self      : int	,    A__      : List[Any]	,    A__      : Tuple					):


            """simple docstring"""







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




       @torch.no_grad()
       def __call__(					self      : List[str]	,    A__      : int = 1	,    A__      : int = 100	,    A__      : Optional[Union[torch.Generator, List[torch.Generator]]] = None	,    A__      : Optional[float] = None	,    A__      : bool = True	,    ):


            """simple docstring"""







            if audio_length_in_s is None:
                 __lowerCamelCase						:       Union[str, Any]				   =				self.unet.config.sample_size / self.unet.config.sample_rate

            __lowerCamelCase						:       int				   =				audio_length_in_s * self.unet.config.sample_rate

            __lowerCamelCase						:       List[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						:       Any				   =				int(A__					)
            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						:       Dict				   =				int(A__					)

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

            __lowerCamelCase						:       int				   =				randn_tensor(A__	,    generator=A__	,    device=self.device	,    dtype=A__					)

            # set step values
            self.scheduler.set_timesteps(A__	,    device=audio.device					)
            __lowerCamelCase						:       List[Any]				   =				self.scheduler.timesteps.to(A__					)

            for t in self.progress_bar(self.scheduler.timesteps					):
                 # 1. predict noise model_output
                 __lowerCamelCase						:       List[str]				   =				self.unet(A__	,    A__					).sample

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

            __lowerCamelCase						:       Optional[int]				   =				audio.clamp(-1	,    1					).float().cpu().numpy()

            __lowerCamelCase						:       int				   =				audio[:, :, :original_sample_size]

            if not return_dict:
                 return (audio,)

            return AudioPipelineOutput(audios=A__					)



