



"""simple docstring"""







import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_   						=   logging.get_logger(__name__)

lowerCAmelCase_   						=   '▁'

lowerCAmelCase_   						=   {
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

lowerCAmelCase_   						=   {
    'vocab_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
        ),
    },
    'spm_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
        )
    },
}

lowerCAmelCase_   						=   {
    'facebook/s2t-small-librispeech-asr': 1_024,
}

lowerCAmelCase_   						=   ['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

lowerCAmelCase_   						=   {'mustc': MUSTC_LANGS}
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					Union[str, Any]   	=				VOCAB_FILES_NAMES
		lowerCAmelCase			:					Union[str, Any]   	=				PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase			:					int   	=				MAX_MODEL_INPUT_SIZES
		lowerCAmelCase			:					Optional[int]   	=				["input_ids", "attention_mask"]

		lowerCAmelCase			:					List[int]   	=				[]







		def __init__(	self						:  Optional[Any]			,_snake_case						:  int			,_snake_case						:  Optional[int]			,_snake_case						:  Optional[int]="<s>"			,_snake_case						:  Union[str, Any]="</s>"			,_snake_case						:  Any="<pad>"			,_snake_case						:  Dict="<unk>"			,_snake_case						:  Optional[Any]=False			,_snake_case						:  int=False			,_snake_case						:  Optional[int]=None			,_snake_case						:  Dict=None			,_snake_case						:  Optional[Dict[str, Any]] = None			,**_snake_case						:  int			,)					->		None:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=_snake_case			,eos_token=_snake_case			,unk_token=_snake_case			,pad_token=_snake_case			,do_upper_case=_snake_case			,do_lower_case=_snake_case			,tgt_lang=_snake_case			,lang_codes=_snake_case			,sp_model_kwargs=self.sp_model_kwargs			,**_snake_case			,)
				lowercase__   :    str				=			do_upper_case
				lowercase__   :    List[str]				=			do_lower_case

				lowercase__   :    Any				=			load_json(_snake_case						)
				lowercase__   :    Dict				=			{v: k for k, v in self.encoder.items()}
				lowercase__   :    Dict				=			spm_file
				lowercase__   :    List[str]				=			load_spm(_snake_case			,self.sp_model_kwargs						)

				if lang_codes is not None:
						lowercase__   :    Optional[Any]				=			lang_codes
						lowercase__   :    Any				=			LANGUAGES[lang_codes]
						lowercase__   :    Optional[int]				=			[f"""<lang:{lang}>""" for lang in self.langs]
						lowercase__   :    Union[str, Any]				=			{lang: self.sp_model.PieceToId(f"""<lang:{lang}>"""						) for lang in self.langs}

						lowercase__   :    str				=			self.lang_tokens
						lowercase__   :    Optional[int]				=			tgt_lang if tgt_lang is not None else self.langs[0]

						self.set_tgt_lang_special_tokens(self._tgt_lang						)
				else:
						lowercase__   :    Dict				=			{}







		@property
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		int:






				"""simple docstring"""



				return len(self.encoder						)







		@property
		def 					UpperCAmelCase    (	self						:  str						)					->		str:






				"""simple docstring"""



				return self._tgt_lang







		@tgt_lang.setter
		def 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  List[Any]						)					->		None:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			new_tgt_lang
				self.set_tgt_lang_special_tokens(_snake_case						)







		def 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  str						)					->		None:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			self.lang_code_to_id[tgt_lang]
				lowercase__   :    str				=			[lang_code_id]







		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  str						)					->		List[str]:






				"""simple docstring"""



				return self.sp_model.encode(_snake_case			,out_type=_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  Union[str, Any]						)					->		Tuple:






				"""simple docstring"""



				return self.encoder.get(_snake_case			,self.encoder[self.unk_token]						)







		def 					UpperCAmelCase    (	self						:  List[str]			,_snake_case						:  int						)					->		str:






				"""simple docstring"""



				return self.decoder.get(_snake_case			,self.unk_token						)







		def 					UpperCAmelCase    (	self						:  List[Any]			,_snake_case						:  List[str]						)					->		str:






				"""simple docstring"""



				lowercase__   :    str				=			[]
				lowercase__   :    List[str]				=			''''''
				for token in tokens:
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
								lowercase__   :    int				=			self.sp_model.decode(_snake_case						)
								out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
								lowercase__   :    str				=			[]
						else:
								current_sub_tokens.append(_snake_case						)
				lowercase__   :    Tuple				=			self.sp_model.decode(_snake_case						)
				out_string += decoded.upper() if self.do_upper_case else decoded
				return out_string.strip()







		def 					UpperCAmelCase    (	self						:  str			,_snake_case						:  Tuple			,_snake_case						:  Any=None						)					->		List[int]:






				"""simple docstring"""



				if token_ids_a is None:
						return self.prefix_tokens + token_ids_a + [self.eos_token_id]
				# 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.eos_token_id]







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  List[int]			,_snake_case						:  Optional[List[int]] = None			,_snake_case						:  bool = False						)					->		List[int]:






				"""simple docstring"""



				if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=_snake_case			,token_ids_a=_snake_case			,already_has_special_tokens=_snake_case						)

				lowercase__   :    List[Any]				=			[1] * len(self.prefix_tokens						)
				lowercase__   :    Tuple				=			[1]
				if token_ids_a is None:
						return prefix_ones + ([0] * len(_snake_case						)) + suffix_ones
				return prefix_ones + ([0] * len(_snake_case						)) + ([0] * len(_snake_case						)) + suffix_ones







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    Any				=			self.encoder.copy()
				vocab.update(self.added_tokens_encoder						)
				return vocab







		def __getstate__(	self						:  int						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    int				=			self.__dict__.copy()
				lowercase__   :    Dict				=			None
				return state







		def __setstate__(	self						:  Union[str, Any]			,_snake_case						:  Dict						)					->		None:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			d

				# for backward compatibility
				if not hasattr(self			,'''sp_model_kwargs'''						):
						lowercase__   :    int				=			{}

				lowercase__   :    List[Any]				=			load_spm(self.spm_file			,self.sp_model_kwargs						)







		def 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  str			,_snake_case						:  Optional[str] = None						)					->		Tuple[str]:






				"""simple docstring"""



				lowercase__   :    Tuple				=			Path(_snake_case						)
				assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
				lowercase__   :    List[Any]				=			save_dir / (
				    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
				)
				lowercase__   :    Optional[Any]				=			save_dir / (
				    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
				)

				save_json(self.encoder			,_snake_case						)

				if os.path.abspath(self.spm_file						) != os.path.abspath(_snake_case						) and os.path.isfile(self.spm_file						):
						copyfile(self.spm_file			,_snake_case						)
				elif not os.path.isfile(self.spm_file						):
						with open(_snake_case			,'''wb'''						) as fi:
								lowercase__   :    Dict				=			self.sp_model.serialized_model_proto()
								fi.write(_snake_case						)

				return (str(_snake_case						), str(_snake_case						))





def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      sentencepiece.SentencePieceProcessor:
		lowercase__   :    Any				=			sentencepiece.SentencePieceProcessor(**__lowerCamelCase	)
		spm.Load(str(__lowerCamelCase	)	)
		return spm



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Union[Dict, List]:
		with open(__lowerCamelCase						,     '''r'''	) as f:
				return json.load(__lowerCamelCase	)





def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      None:
		with open(__lowerCamelCase						,     '''w'''	) as f:
				json.dump(__lowerCamelCase						,     __lowerCamelCase						,     indent=2	)





"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase_   						=   {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ['SpeechT5Tokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SpeechT5ForSpeechToText',
							    'SpeechT5ForSpeechToSpeech',
							    'SpeechT5ForTextToSpeech',
							    'SpeechT5Model',
							    'SpeechT5PreTrainedModel',
							    'SpeechT5HifiGan',
							]

if TYPE_CHECKING:
							from .configuration_speechta import (
							    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
							    SpeechTaConfig,
							    SpeechTaHifiGanConfig,
							)
							from .feature_extraction_speechta import SpeechTaFeatureExtractor
							from .processing_speechta import SpeechTaProcessor

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_speechta import SpeechTaTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_speechta import (
														    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SpeechTaForSpeechToSpeech,
														    SpeechTaForSpeechToText,
														    SpeechTaForTextToSpeech,
														    SpeechTaHifiGan,
														    SpeechTaModel,
														    SpeechTaPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase_   						=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
