




'''simple docstring'''







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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_mbartaa import MBartaaTokenizer
else:
      lowercase__     :  str    					=			None


lowercase__     :  int    					=			logging.get_logger(__name__)

lowercase__     :  Optional[int]    					=			{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowercase__     :  Optional[Any]    					=			{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

lowercase__     :  Union[str, Any]    					=			{
    'facebook/mbart-large-50-one-to-many-mmt': 10_24,
}

# fmt: off
lowercase__     :  Dict    					=			['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']

class       lowerCamelCase_							(	__lowerCamelCase  ):






   '''simple docstring'''

   A :     List[str]				=	VOCAB_FILES_NAMES
   A :     Optional[Any]				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A :     Tuple				=	PRETRAINED_VOCAB_FILES_MAP
   A :     List[Any]				=	['input_ids', 'attention_mask']
   A :     List[str]				=	MBartaaTokenizer

   A :     List[int]				=	[]
   A :     List[int]				=	[]


   def __init__(  self     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase="</s>"     ,						__UpperCamelCase="</s>"     ,						__UpperCamelCase="<s>"     ,						__UpperCamelCase="<unk>"     ,						__UpperCamelCase="<pad>"     ,						__UpperCamelCase="<mask>"     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			AddedToken(__UpperCamelCase     ,						lstrip=__UpperCamelCase     ,						rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase     ,						__UpperCamelCase ) else mask_token

       UpperCamelCase__      =			kwargs.get("additional_special_tokens"     ,						[] )
       kwargs["additional_special_tokens"] += [
           code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
       ]

       super().__init__(
           __UpperCamelCase     ,						src_lang=__UpperCamelCase     ,						tgt_lang=__UpperCamelCase     ,						tokenizer_file=__UpperCamelCase     ,						eos_token=__UpperCamelCase     ,						sep_token=__UpperCamelCase     ,						cls_token=__UpperCamelCase     ,						unk_token=__UpperCamelCase     ,						pad_token=__UpperCamelCase     ,						mask_token=__UpperCamelCase     ,						**__UpperCamelCase     ,						)

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

       UpperCamelCase__      =			{
           lang_code: self.convert_tokens_to_ids(__UpperCamelCase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       UpperCamelCase__      =			src_lang if src_lang is not None else "en_XX"
       UpperCamelCase__      =			tgt_lang
       UpperCamelCase__      =			self.lang_code_to_id[self._src_lang]
       self.set_src_lang_special_tokens(self._src_lang )


   @property
   def 	_UpperCamelCase       (  self ):
       return self._src_lang


   @src_lang.setter
   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			new_src_lang
       self.set_src_lang_special_tokens(self._src_lang )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase = None ):






       if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase = "en_XX"     ,						__UpperCamelCase = None     ,						__UpperCamelCase = "ro_RO"     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			src_lang
       UpperCamelCase__      =			tgt_lang
       return super().prepare_seqaseq_batch(__UpperCamelCase     ,						__UpperCamelCase     ,						**__UpperCamelCase )


   def 	_UpperCamelCase       (  self ):
       return self.set_src_lang_special_tokens(self.src_lang )


   def 	_UpperCamelCase       (  self ):
       return self.set_tgt_lang_special_tokens(self.tgt_lang )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.convert_tokens_to_ids(__UpperCamelCase )
       UpperCamelCase__      =			[self.cur_lang_code_id]
       UpperCamelCase__      =			[self.eos_token_id]

       UpperCamelCase__      =			self.convert_ids_to_tokens(self.prefix_tokens )
       UpperCamelCase__      =			self.convert_ids_to_tokens(self.suffix_tokens )

       UpperCamelCase__      =			processors.TemplateProcessing(
           single=prefix_tokens_str + ["$A"] + suffix_tokens_str     ,						pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str     ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,						self.prefix_tokens + self.suffix_tokens ) )     ,						)


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.convert_tokens_to_ids(__UpperCamelCase )
       UpperCamelCase__      =			[self.cur_lang_code_id]
       UpperCamelCase__      =			[self.eos_token_id]

       UpperCamelCase__      =			self.convert_ids_to_tokens(self.prefix_tokens )
       UpperCamelCase__      =			self.convert_ids_to_tokens(self.suffix_tokens )

       UpperCamelCase__      =			processors.TemplateProcessing(
           single=prefix_tokens_str + ["$A"] + suffix_tokens_str     ,						pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str     ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,						self.prefix_tokens + self.suffix_tokens ) )     ,						)


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						**__UpperCamelCase ):






       if src_lang is None or tgt_lang is None:
           raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
       UpperCamelCase__      =			src_lang
       UpperCamelCase__      =			self(__UpperCamelCase     ,						add_special_tokens=__UpperCamelCase     ,						return_tensors=__UpperCamelCase     ,						**__UpperCamelCase )
       UpperCamelCase__      =			self.convert_tokens_to_ids(__UpperCamelCase )
       UpperCamelCase__      =			tgt_lang_id
       return inputs







   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase = None ):






       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(__UpperCamelCase ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
           return
       UpperCamelCase__      =			os.path.join(
           __UpperCamelCase     ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

       return (out_vocab_file,)









'''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

      lowercase__     :  Optional[Any]    					=			True
      lowercase__     :  int    					=			''
except Exception as e:
      lowercase__     :  Union[str, Any]    					=			False
      lowercase__     :  List[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       lowerCamelCase_							(	lowerCAmelCase_					,    lowerCAmelCase_  ):






   '''simple docstring'''

   A :     int				=	"mel_config.json"


   @register_to_config
   def __init__(  self     ,						__UpperCamelCase = 2_56     ,						__UpperCamelCase = 2_56     ,						__UpperCamelCase = 2_20_50     ,						__UpperCamelCase = 20_48     ,						__UpperCamelCase = 5_12     ,						__UpperCamelCase = 80     ,						__UpperCamelCase = 32     ,						):
       UpperCamelCase__      =			hop_length
       UpperCamelCase__      =			sample_rate
       UpperCamelCase__      =			n_fft
       UpperCamelCase__      =			top_db
       UpperCamelCase__      =			n_iter
       self.set_resolution(__UpperCamelCase     ,						__UpperCamelCase )
       UpperCamelCase__      =			None

       if not _librosa_can_be_imported:
           raise ValueError(_import_error )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       UpperCamelCase__      =			x_res
       UpperCamelCase__      =			y_res
       UpperCamelCase__      =			self.y_res
       UpperCamelCase__      =			self.x_res * self.hop_length - 1


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None ):
       if audio_file is not None:
           UpperCamelCase__    ,  UpperCamelCase__      =			librosa.load(__UpperCamelCase     ,						mono=__UpperCamelCase     ,						sr=self.sr )
       else:
           UpperCamelCase__      =			raw_audio

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


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


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


   def 	_UpperCamelCase       (  self ):
       return self.sr


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			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 )
       UpperCamelCase__      =			librosa.power_to_db(__UpperCamelCase     ,						ref=np.max     ,						top_db=self.top_db )
       UpperCamelCase__      =			(((log_S + self.top_db) * 2_55 / self.top_db).clip(0     ,						2_55 ) + 0.5).astype(np.uinta )
       UpperCamelCase__      =			Image.fromarray(__UpperCamelCase )
       return image







   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			np.frombuffer(image.tobytes()     ,						dtype="uint8" ).reshape((image.height, image.width) )
       UpperCamelCase__      =			bytedata.astype("float" ) * self.top_db / 2_55 - self.top_db
       UpperCamelCase__      =			librosa.db_to_power(__UpperCamelCase )
       UpperCamelCase__      =			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




