




import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
      import librosa  # noqa: E402

      SCREAMING_SNAKE_CASE       = True
      SCREAMING_SNAKE_CASE       = ''''''
except Exception as e:
      SCREAMING_SNAKE_CASE       = False
      SCREAMING_SNAKE_CASE       = (
          f'Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'
      )


from PIL import Image  # noqa: E402




class 			UpperCAmelCase_						(		lowercase__,     lowercase__							):
    lowercase__		      =   """mel_config.json"""




    @register_to_config
    def __init__(					self		: Union[str, Any]			,				snake_case_		: int = 256			,				snake_case_		: int = 256			,				snake_case_		: int = 22_050			,				snake_case_		: int = 2_048			,				snake_case_		: int = 512			,				snake_case_		: int = 80			,				snake_case_		: int = 32			,				)   ->							Any:




     '''simple docstring'''

     A__ =       hop_length
     A__ =       sample_rate
     A__ =       n_fft
     A__ =       top_db
     A__ =       n_iter
     self.set_resolution(_A			,				_A   )
     A__ =       None

     if not _librosa_can_be_imported:
      raise ValueError(_import_error   )




    def 							__magic_name__							(					self		: Optional[Any]			,				snake_case_		: int			,				snake_case_		: int   )   ->							Tuple:




     '''simple docstring'''

     A__ =       x_res
     A__ =       y_res
     A__ =       self.y_res
     A__ =       self.x_res * self.hop_length - 1




    def 							__magic_name__							(					self		: List[str]			,				snake_case_		: str = None			,				snake_case_		: np.ndarray = None   )   ->							Optional[int]:




     '''simple docstring'''

     if audio_file is not None:
      A__ =       librosa.load(_A			,				mono=_A			,				sr=self.sr   )
     else:
      A__ =       raw_audio

     # Pad with silence if necessary.
     if len(self.audio   ) < self.x_res * self.hop_length:
      A__ =       np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio   ),)   )]   )




    def 							__magic_name__							(					self		: List[Any]   )   ->							int:




     '''simple docstring'''

     return len(self.audio   ) // self.slice_size




    def 							__magic_name__							(					self		: Optional[Any]			,				snake_case_		: int = 0   )   ->							np.ndarray:




     '''simple docstring'''

     return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]




    def 							__magic_name__							(					self		: int   )   ->							int:




     '''simple docstring'''

     return self.sr




    def 							__magic_name__							(					self		: Dict			,				snake_case_		: int   )   ->							Image.Image:




     '''simple docstring'''

     A__ =       librosa.feature.melspectrogram(
         y=self.get_audio_slice(_A   )			,				sr=self.sr			,				n_fft=self.n_fft			,				hop_length=self.hop_length			,				n_mels=self.n_mels   )
     A__ =       librosa.power_to_db(_A			,				ref=np.max			,				top_db=self.top_db   )
     A__ =       (((log_S + self.top_db) * 255 / self.top_db).clip(0			,				255   ) + 0.5).astype(np.uinta   )
     A__ =       Image.fromarray(_A   )
     return image




    def 							__magic_name__							(					self		: List[Any]			,				snake_case_		: Image.Image   )   ->							np.ndarray:




     '''simple docstring'''

     A__ =       np.frombuffer(image.tobytes()			,				dtype="uint8"   ).reshape((image.height, image.width)   )
     A__ =       bytedata.astype("float"   ) * self.top_db / 255 - self.top_db
     A__ =       librosa.db_to_power(_A   )
     A__ =       librosa.feature.inverse.mel_to_audio(
         _A			,				sr=self.sr			,				n_fft=self.n_fft			,				hop_length=self.hop_length			,				n_iter=self.n_iter   )
     return audio











"""simple docstring"""



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE       = {
    "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE       = ["DetaImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE       = [
          "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DetaForObjectDetection",
          "DetaModel",
          "DetaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_deta import DetaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_deta import (
                DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                DetaForObjectDetection,
                DetaModel,
                DetaPreTrainedModel,
            )

else:
      import sys

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





