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:    int     						=							True
			_SCREAMING_SNAKE_CASE:    int     						=							''''''
except Exception as e:
			_SCREAMING_SNAKE_CASE:    List[Any]     						=							False
			_SCREAMING_SNAKE_CASE:    Optional[int]     						=							(
			    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 						_SCREAMING_SNAKE_CASE				(  _lowercase   ,  _lowercase				):






		"""simple docstring"""







		A_    			=   '''mel_config.json'''



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






					"""simple docstring"""
					_snake_case														=   hop_length
					_snake_case														=   sample_rate
					_snake_case														=   n_fft
					_snake_case														=   top_db
					_snake_case														=   n_iter
					self.set_resolution(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
					_snake_case														=   None

					if not _librosa_can_be_imported:
								raise ValueError(_import_error )



		def 					_a					(      self     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					int	,       __SCREAMING_SNAKE_CASE     :					int ):






					"""simple docstring"""
					_snake_case														=   x_res
					_snake_case														=   y_res
					_snake_case														=   self.y_res
					_snake_case														=   self.x_res * self.hop_length - 1



		def 					_a					(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					str = None	,       __SCREAMING_SNAKE_CASE     :					np.ndarray = None ):






					"""simple docstring"""
					if audio_file is not None:
								_snake_case							,			_snake_case														=   librosa.load(__SCREAMING_SNAKE_CASE	,       mono=__SCREAMING_SNAKE_CASE	,       sr=self.sr )
					else:
								_snake_case														=   raw_audio

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



		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					return len(self.audio ) // self.slice_size



		def 					_a					(      self     :					List[Any]	,       __SCREAMING_SNAKE_CASE     :					int = 0 ):






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



		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					return self.sr



		def 					_a					(      self     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					int ):






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







		def 					_a					(      self     :					str	,       __SCREAMING_SNAKE_CASE     :					Image.Image ):






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


import sys

from transformers import AutoTokenizer


_SCREAMING_SNAKE_CASE:    Optional[int]     						=							sys.argv[1]
_SCREAMING_SNAKE_CASE:    int     						=							sys.argv[2]
_SCREAMING_SNAKE_CASE:    str     						=							int(sys.argv[3])

_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							0

_SCREAMING_SNAKE_CASE:    Any     						=							AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
			for line in f_p:
						_SCREAMING_SNAKE_CASE:    Any     						=							line.rstrip()

						if not line:
									print(line)
									_SCREAMING_SNAKE_CASE:    str     						=							0
									continue

						_SCREAMING_SNAKE_CASE:    Optional[int]     						=							line.split()[0]

						_SCREAMING_SNAKE_CASE:    Tuple     						=							len(tokenizer.tokenize(token))

						# Token contains strange control characters like \x96 or \x95
						# Just filter out the complete line
						if current_subwords_len == 0:
									continue

						if (subword_len_counter + current_subwords_len) > max_len:
									print('''''')
									print(line)
									_SCREAMING_SNAKE_CASE:    Any     						=							current_subwords_len
									continue

						subword_len_counter += current_subwords_len

						print(line)

