"""simple docstring"""







from typing import List, Union

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


_SCREAMING_SNAKE_CASE	:		Dict   =   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	:		Tuple   =   {
    '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/config.json''',
    # See all LED models at https://huggingface.co/models?filter=led
}






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Optional[int]    =    "led"
   __UpperCAmelCase	:							List[str]    =    {
       "num_attention_heads": "encoder_attention_heads",
       "hidden_size": "d_model",
       "attention_probs_dropout_prob": "attention_dropout",
       "initializer_range": "init_std",
   }







   def __init__(	self ,				_SCREAMING_SNAKE_CASE=50265 ,				_SCREAMING_SNAKE_CASE=16384 ,				_SCREAMING_SNAKE_CASE=1024 ,				_SCREAMING_SNAKE_CASE=12 ,				_SCREAMING_SNAKE_CASE=4096 ,				_SCREAMING_SNAKE_CASE=16 ,				_SCREAMING_SNAKE_CASE=12 ,				_SCREAMING_SNAKE_CASE=4096 ,				_SCREAMING_SNAKE_CASE=16 ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE="gelu" ,				_SCREAMING_SNAKE_CASE=1024 ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=0.02 ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=1 ,				_SCREAMING_SNAKE_CASE=0 ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE = 512 ,				**_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					vocab_size
      __lowerCamelCase    		=					max_encoder_position_embeddings
      __lowerCamelCase    		=					max_decoder_position_embeddings
      __lowerCamelCase    		=					d_model
      __lowerCamelCase    		=					encoder_ffn_dim
      __lowerCamelCase    		=					encoder_layers
      __lowerCamelCase    		=					encoder_attention_heads
      __lowerCamelCase    		=					decoder_ffn_dim
      __lowerCamelCase    		=					decoder_layers
      __lowerCamelCase    		=					decoder_attention_heads
      __lowerCamelCase    		=					dropout
      __lowerCamelCase    		=					attention_dropout
      __lowerCamelCase    		=					activation_dropout
      __lowerCamelCase    		=					activation_function
      __lowerCamelCase    		=					init_std
      __lowerCamelCase    		=					encoder_layerdrop
      __lowerCamelCase    		=					decoder_layerdrop
      __lowerCamelCase    		=					classifier_dropout
      __lowerCamelCase    		=					use_cache
      __lowerCamelCase    		=					encoder_layers
      __lowerCamelCase    		=					attention_window

      super().__init__(
          pad_token_id=_SCREAMING_SNAKE_CASE ,				bos_token_id=_SCREAMING_SNAKE_CASE ,				eos_token_id=_SCREAMING_SNAKE_CASE ,				is_encoder_decoder=_SCREAMING_SNAKE_CASE ,				decoder_start_token_id=_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE ,				)








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     :   Tuple							 =      True
					_SCREAMING_SNAKE_CASE     :   List[Any]							 =      ''
except Exception as e:
					_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      False
					_SCREAMING_SNAKE_CASE     :   Any							 =      (
					    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__		(     SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ ):







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





	@register_to_config
	def __init__(      self					,							UpperCamelCase__ = 256					,							UpperCamelCase__ = 256					,							UpperCamelCase__ = 2_2050					,							UpperCamelCase__ = 2048					,							UpperCamelCase__ = 512					,							UpperCamelCase__ = 80					,							UpperCamelCase__ = 32					,							):
					A__	:						Any								=				hop_length
					A__	:						List[Any]								=				sample_rate
					A__	:						Union[str, Any]								=				n_fft
					A__	:						Optional[int]								=				top_db
					A__	:						Optional[Any]								=				n_iter
					self.set_resolution(UpperCamelCase__					,							UpperCamelCase__		)
					A__	:						str								=				None

					if not _librosa_can_be_imported:
									raise ValueError(_import_error		)





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__		):
					A__	:						int								=				x_res
					A__	:						List[Any]								=				y_res
					A__	:						List[str]								=				self.y_res
					A__	:						int								=				self.x_res * self.hop_length - 1





	def 	__snake_case					(      self					,							UpperCamelCase__ = None					,							UpperCamelCase__ = None		):
					if audio_file is not None:
									A__							,				A__	:						Union[str, Any]								=				librosa.load(UpperCamelCase__					,							mono=UpperCamelCase__					,							sr=self.sr		)
					else:
									A__	:						Tuple								=				raw_audio

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





	def 	__snake_case					(      self		):
					return len(self.audio		) // self.slice_size





	def 	__snake_case					(      self					,							UpperCamelCase__ = 0		):
					return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]





	def 	__snake_case					(      self		):
					return self.sr





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						Optional[Any]								=				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__	:						str								=				librosa.power_to_db(UpperCamelCase__					,							ref=np.max					,							top_db=self.top_db		)
					A__	:						str								=				(((log_S + self.top_db) * 255 / self.top_db).clip(0					,							255		) + 0.5).astype(np.uinta		)
					A__	:						Dict								=				Image.fromarray(UpperCamelCase__		)
					return image





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						List[Any]								=				np.frombuffer(image.tobytes()					,							dtype='''uint8'''		).reshape((image.height, image.width)		)
					A__	:						Dict								=				bytedata.astype('''float'''		) * self.top_db / 255 - self.top_db
					A__	:						Union[str, Any]								=				librosa.db_to_power(UpperCamelCase__		)
					A__	:						Dict								=				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