


"""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

		a_											=			True
		a_											=			""
except Exception as e:
		a_											=			False
		a_											=			(
		    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 snake_case					(     _UpperCamelCase   , _UpperCamelCase):
		__UpperCamelCase							=	'mel_config.json'







		@register_to_config
		def __init__(    self					:				int			,      a__					:				int = 2_56			,      a__					:				int = 2_56			,      a__					:				int = 2_20_50			,      a__					:				int = 20_48			,      a__					:				int = 5_12			,      a__					:				int = 80			,      a__					:				int = 32			,      )				->  Tuple:





						'''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        a_       (    self					:				List[str]			,      a__					:				int			,      a__					:				int						)				->  List[str]:





						'''simple docstring'''







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







		def        a_       (    self					:				str			,      a__					:				str = None			,      a__					:				np.ndarray = None						)				->  List[Any]:





						'''simple docstring'''







						if audio_file is not None:
										_A     ,						_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        a_       (    self					:				List[str]						)				->  int:





						'''simple docstring'''







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







		def        a_       (    self					:				Tuple			,      a__					:				int = 0						)				->  np.ndarray:





						'''simple docstring'''







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







		def        a_       (    self					:				Tuple						)				->  int:





						'''simple docstring'''







						return self.sr







		def        a_       (    self					:				Tuple			,      a__					:				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) * 2_55 / self.top_db).clip(0			,      2_55						) + 0.5).astype(np.uinta						)
						_A         =	Image.fromarray(a__						)
						return image







		def        a_       (    self					:				Optional[Any]			,      a__					:				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 / 2_55 - 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"""


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		a_											=			None


a_											=			logging.get_logger(__name__)


a_											=			"▁"

a_											=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=			{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

a_											=			{
    "google/reformer-crime-and-punishment": 52_42_88,
}
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	VOCAB_FILES_NAMES
		__UpperCamelCase							=	PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase							=	['input_ids', 'attention_mask']
		__UpperCamelCase							=	ReformerTokenizer







		def __init__(    self					:				Dict			,      a__					:				List[Any]=None			,      a__					:				int=None			,      a__					:				int="</s>"			,      a__					:				List[Any]="<unk>"			,      a__					:				Optional[Any]=[]			,      **a__					:				Tuple			,      )				->  Tuple:





						'''simple docstring'''







						super().__init__(
						    a__			,      tokenizer_file=a__			,      eos_token=a__			,      unk_token=a__			,      additional_special_tokens=a__			,      **a__			,      )

						_A         =	vocab_file
						_A         =	False if not self.vocab_file else True







		def        a_       (    self					:				Any			,      a__					:				str			,      a__					:				Optional[str] = None						)				->  Tuple[str]:





						'''simple docstring'''







						if not self.can_save_slow_tokenizer:
										raise ValueError(
										    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
										    "tokenizer."						)

						if not os.path.isdir(a__						):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
										return
						_A         =	os.path.join(
						    a__			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(a__						):
										copyfile(self.vocab_file			,      a__						)

						return (out_vocab_file,)