






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

  __a :    int								=				True
  __a :    List[Any]								=				""
except Exception as e:
  __a :    int								=				False
  __a :    Dict								=				(
      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  __lowercase (		lowercase_     , lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"mel_config.json"




     @register_to_config
     def __init__(			self    :     int	,		UpperCamelCase_    :     int = 256	,		UpperCamelCase_    :     int = 256	,		UpperCamelCase_    :     int = 22_050	,		UpperCamelCase_    :     int = 2_048	,		UpperCamelCase_    :     int = 512	,		UpperCamelCase_    :     int = 80	,		UpperCamelCase_    :     int = 32	,		):


            """simple docstring"""



            __A             =		hop_length
            __A             =		sample_rate
            __A             =		n_fft
            __A             =		top_db
            __A             =		n_iter
            self.set_resolution(UpperCamelCase_	,		UpperCamelCase_			)
            __A             =		None

            if not _librosa_can_be_imported:
                   raise ValueError(_import_error			)




     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     int	,		UpperCamelCase_    :     int			):


            """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     str = None	,		UpperCamelCase_    :     np.ndarray = None			):


            """simple docstring"""



            if audio_file is not None:
                   __A      ,		__A             =		librosa.load(UpperCamelCase_	,		mono=UpperCamelCase_	,		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 						lowerCAmelCase_       (			self    :     Tuple			):


            """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     Any	,		UpperCamelCase_    :     int = 0			):


            """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


            """simple docstring"""



            return self.sr




     def 						lowerCAmelCase_       (			self    :     List[str]	,		UpperCamelCase_    :     int			):


            """simple docstring"""



            __A             =		librosa.feature.melspectrogram(
                y=self.get_audio_slice(UpperCamelCase_			)	,		sr=self.sr	,		n_fft=self.n_fft	,		hop_length=self.hop_length	,		n_mels=self.n_mels			)
            __A             =		librosa.power_to_db(UpperCamelCase_	,		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(UpperCamelCase_			)
            return image





     def 						lowerCAmelCase_       (			self    :     Union[str, Any]	,		UpperCamelCase_    :     Image.Image			):


            """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(UpperCamelCase_			)
            __A             =		librosa.feature.inverse.mel_to_audio(
                UpperCamelCase_	,		sr=self.sr	,		n_fft=self.n_fft	,		hop_length=self.hop_length	,		n_iter=self.n_iter			)
            return audio












import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


__a :    Tuple								=				logging.get_logger(__name__)


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"hybrid-clip"
     SCREAMING_SNAKE_CASE				  =			True




     def __init__(			self    :     Optional[Any]	,		UpperCamelCase_    :     Optional[Any]=512	,		**UpperCamelCase_    :     List[Any]			):


         """simple docstring"""



         super().__init__(**UpperCamelCase_			)

         if "text_config" not in kwargs:
             raise ValueError("""`text_config` can not be `None`."""			)

         if "vision_config" not in kwargs:
             raise ValueError("""`vision_config` can not be `None`."""			)

         __A             =		kwargs.pop("""text_config"""			)
         __A             =		kwargs.pop("""vision_config"""			)

         __A             =		text_config.pop("""model_type"""			)
         __A             =		vision_config.pop("""model_type"""			)

         from transformers import AutoConfig

         __A             =		AutoConfig.for_model(UpperCamelCase_	,		**UpperCamelCase_			)

         if vision_model_type == "clip":
             __A             =		AutoConfig.for_model(UpperCamelCase_	,		**UpperCamelCase_			).vision_config
         elif vision_model_type == "clip_vision_model":
             from transformers import CLIPVisionConfig

             __A             =		CLIPVisionConfig(**UpperCamelCase_			)
         else:
             __A             =		AutoConfig.for_model(UpperCamelCase_	,		**UpperCamelCase_			)

         __A             =		projection_dim
         __A             =		1.0




     @classmethod
     def 						lowerCAmelCase_       (			cls    :     int	,		UpperCamelCase_    :     PretrainedConfig	,		UpperCamelCase_    :     PretrainedConfig	,		**UpperCamelCase_    :     Union[str, Any]			):


         """simple docstring"""



         return cls(text_config=text_config.to_dict()	,		vision_config=vision_config.to_dict()	,		**UpperCamelCase_			)





     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



         __A             =		copy.deepcopy(self.__dict__			)
         __A             =		self.text_config.to_dict()
         __A             =		self.vision_config.to_dict()
         __A             =		self.__class__.model_type
         return output




