


'''simple docstring'''




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

				_lowerCAmelCase	:		Optional[int]		  =						True
				_lowerCAmelCase	:		str		  =						''
except Exception as e:
				_lowerCAmelCase	:		Optional[int]		  =						False
				_lowerCAmelCase	:		List[str]		  =						(
				    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 			lowerCAmelCase					(					a   ,					a				):
	_lowerCamelCase :       Optional[Any]         =			"""mel_config.json"""



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

		if not _librosa_can_be_imported:
			raise ValueError(_import_error     )



	def 					lowercase  (       self			,       snake_case__			,       snake_case__     ):
		lowerCAmelCase       :     Any   				=			x_res
		lowerCAmelCase       :     List[str]   				=			y_res
		lowerCAmelCase       :     Optional[Any]   				=			self.y_res
		lowerCAmelCase       :     Optional[int]   				=			self.x_res * self.hop_length - 1



	def 					lowercase  (       self			,       snake_case__ = None			,       snake_case__ = None     ):
		if audio_file is not None:
			lowerCAmelCase				, lowerCAmelCase       :     List[Any]   				=			librosa.load(snake_case__			,       mono=snake_case__			,       sr=self.sr     )
		else:
			lowerCAmelCase       :     Tuple   				=			raw_audio

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



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



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



	def 					lowercase  (       self     ):
		return self.sr



	def 					lowercase  (       self			,       snake_case__     ):
		lowerCAmelCase       :     Optional[int]   				=			librosa.feature.melspectrogram(
		    y=self.get_audio_slice(snake_case__     )			,       sr=self.sr			,       n_fft=self.n_fft			,       hop_length=self.hop_length			,       n_mels=self.n_mels     )
		lowerCAmelCase       :     int   				=			librosa.power_to_db(snake_case__			,       ref=np.max			,       top_db=self.top_db     )
		lowerCAmelCase       :     Optional[int]   				=			(((log_S + self.top_db) * 255 / self.top_db).clip(0			,       255     ) + 0.5).astype(np.uinta     )
		lowerCAmelCase       :     Dict   				=			Image.fromarray(snake_case__     )
		return image



	def 					lowercase  (       self			,       snake_case__     ):
		lowerCAmelCase       :     Dict   				=			np.frombuffer(image.tobytes()			,       dtype='uint8'     ).reshape((image.height, image.width)     )
		lowerCAmelCase       :     Dict   				=			bytedata.astype('float'     ) * self.top_db / 255 - self.top_db
		lowerCAmelCase       :     Tuple   				=			librosa.db_to_power(snake_case__     )
		lowerCAmelCase       :     Union[str, Any]   				=			librosa.feature.inverse.mel_to_audio(
		    snake_case__			,       sr=self.sr			,       n_fft=self.n_fft			,       hop_length=self.hop_length			,       n_iter=self.n_iter     )
		return audio




'''simple docstring'''




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


_lowerCAmelCase	:		List[str]		  =						logging.get_logger(__name__)

_lowerCAmelCase	:		int		  =						{
    'facebook/opt-125m': 'https://huggingface.co/facebook/opt-125m/blob/main/config.json',
    'facebook/opt-350m': 'https://huggingface.co/facebook/opt-350m/blob/main/config.json',
    'facebook/opt-1.3b': 'https://huggingface.co/facebook/opt-1.3b/blob/main/config.json',
    'facebook/opt-2.7b': 'https://huggingface.co/facebook/opt-2.7b/blob/main/config.json',
    'facebook/opt-6.7b': 'https://huggingface.co/facebook/opt-6.7b/blob/main/config.json',
    'facebook/opt-13b': 'https://huggingface.co/facebook/opt-13b/blob/main/config.json',
}



class 			lowerCAmelCase					(					a				):
	_lowerCamelCase :       Dict         =			"""opt"""
	_lowerCamelCase :       Optional[Any]         =			["""past_key_values"""]



	def __init__(       self			,       snake_case__=5_0272			,       snake_case__=768			,       snake_case__=12			,       snake_case__=3072			,       snake_case__=2048			,       snake_case__=True			,       snake_case__=False			,       snake_case__=None			,       snake_case__=0.1			,       snake_case__=0.0			,       snake_case__=12			,       snake_case__="relu"			,       snake_case__=0.0			,       snake_case__=0.0_2			,       snake_case__=True			,       snake_case__=1			,       snake_case__=2			,       snake_case__=2			,       snake_case__=True			,       snake_case__=True			,       **snake_case__			,       ):
		super().__init__(
		    pad_token_id=snake_case__			,       bos_token_id=snake_case__			,       eos_token_id=snake_case__			,       **snake_case__			,       )
		lowerCAmelCase       :     Union[str, Any]   				=			vocab_size
		lowerCAmelCase       :     List[Any]   				=			max_position_embeddings
		lowerCAmelCase       :     List[str]   				=			num_attention_heads
		lowerCAmelCase       :     Dict   				=			word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
		lowerCAmelCase       :     Optional[Any]   				=			ffn_dim
		lowerCAmelCase       :     Optional[int]   				=			hidden_size
		lowerCAmelCase       :     List[str]   				=			num_hidden_layers
		lowerCAmelCase       :     Union[str, Any]   				=			dropout
		lowerCAmelCase       :     List[str]   				=			attention_dropout
		lowerCAmelCase       :     Union[str, Any]   				=			activation_function
		lowerCAmelCase       :     Optional[Any]   				=			init_std
		lowerCAmelCase       :     List[str]   				=			layerdrop
		lowerCAmelCase       :     Dict   				=			use_cache
		lowerCAmelCase       :     List[Any]   				=			do_layer_norm_before
		# We keep these variables at `True` for backward compatibility.
		lowerCAmelCase       :     List[str]   				=			enable_bias
		lowerCAmelCase       :     Dict   				=			layer_norm_elementwise_affine

		# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
		# with checkpoints that have been fine-tuned before transformers v4.20.1
		# see https://github.com/facebookresearch/metaseq/pull/164
		lowerCAmelCase       :     Tuple   				=			_remove_final_layer_norm
