






'''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_nllb import NllbTokenizer
else:
   UpperCAmelCase__						:Any          =			None


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


UpperCAmelCase__						:List[Any]          =			{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase__						:List[str]          =			{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
        ),
    },
}

UpperCAmelCase__						:int          =			{
    'facebook/nllb-large-en-ro': 1_024,
    'facebook/nllb-200-distilled-600M': 1_024,
}

# fmt: off
UpperCAmelCase__						:Optional[int]          =			['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']







class 	SCREAMING_SNAKE_CASE	(      a__  ):
       snake_case__    :     Dict   		=			VOCAB_FILES_NAMES
       snake_case__    :     List[str]   		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case__    :     List[str]   		=			PRETRAINED_VOCAB_FILES_MAP
       snake_case__    :     Optional[Any]   		=			["""input_ids""", """attention_mask"""]
       snake_case__    :     List[Any]   		=			NllbTokenizer

       snake_case__    :     List[int]   		=			[]
       snake_case__    :     List[int]   		=			[]




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


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				AddedToken(lowercase__	,    lstrip=lowercase__	,    rstrip=lowercase__					) if isinstance(lowercase__	,    lowercase__					) else mask_token
            __lowerCamelCase						:       Union[str, Any]				   =				legacy_behaviour
            super().__init__(
                vocab_file=lowercase__	,    tokenizer_file=lowercase__	,    bos_token=lowercase__	,    eos_token=lowercase__	,    sep_token=lowercase__	,    cls_token=lowercase__	,    unk_token=lowercase__	,    pad_token=lowercase__	,    mask_token=lowercase__	,    src_lang=lowercase__	,    tgt_lang=lowercase__	,    additional_special_tokens=lowercase__	,    legacy_behaviour=lowercase__	,    **lowercase__	,    )

            __lowerCamelCase						:       Tuple				   =				vocab_file
            __lowerCamelCase						:       int				   =				False if not self.vocab_file else True

            __lowerCamelCase						:       Tuple				   =				FAIRSEQ_LANGUAGE_CODES.copy()

            if additional_special_tokens is not None:
                 # Only add those special tokens if they are not already there.
                 _additional_special_tokens.extend(
                     [t for t in additional_special_tokens if t not in _additional_special_tokens]					)

            self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}					)
            __lowerCamelCase						:       Union[str, Any]				   =				{
                lang_code: self.convert_tokens_to_ids(lowercase__					) for lang_code in FAIRSEQ_LANGUAGE_CODES
            }

            __lowerCamelCase						:       List[str]				   =				src_lang if src_lang is not None else '''eng_Latn'''
            __lowerCamelCase						:       List[str]				   =				self.convert_tokens_to_ids(self._src_lang					)
            __lowerCamelCase						:       Union[str, Any]				   =				tgt_lang
            self.set_src_lang_special_tokens(self._src_lang					)




       @property
       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            return self._src_lang




       @src_lang.setter
       def 					a_					(					self      : Optional[Any]	,    A__      : str					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				new_src_lang
            self.set_src_lang_special_tokens(self._src_lang					)




       def 					a_					(					self      : Tuple	,    A__      : Optional[int]	,    A__      : Dict = None					):


            """simple docstring"""







            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 					a_					(					self      : List[str]	,    A__      : int	,    A__      : Optional[int] = None					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				[self.sep_token_id]
            __lowerCamelCase						:       Optional[Any]				   =				[self.cls_token_id]

            if token_ids_a is None:
                 return len(cls + token_ids_a + sep					) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]




       def 					a_					(					self      : List[Any]	,    A__      : Optional[int]	,    A__      : Optional[int]	,    A__      : Optional[Any]	,    A__      : Dict	,    **A__      : str					):


            """simple docstring"""







            if src_lang is None or tgt_lang is None:
                 raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""					)
            __lowerCamelCase						:       List[str]				   =				src_lang
            __lowerCamelCase						:       Optional[Any]				   =				self(lowercase__	,    add_special_tokens=lowercase__	,    return_tensors=lowercase__	,    **lowercase__					)
            __lowerCamelCase						:       str				   =				self.convert_tokens_to_ids(lowercase__					)
            __lowerCamelCase						:       str				   =				tgt_lang_id
            return inputs




       def 					a_					(					self      : List[str]	,    A__      : Dict	,    A__      : Dict = "eng_Latn"	,    A__      : Any = None	,    A__      : Optional[int] = "fra_Latn"	,    **A__      : Optional[Any]	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				src_lang
            __lowerCamelCase						:       Tuple				   =				tgt_lang
            return super().prepare_seqaseq_batch(lowercase__	,    lowercase__	,    **lowercase__					)




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            return self.set_src_lang_special_tokens(self.src_lang					)




       def 					a_					(					self      : Any					):


            """simple docstring"""







            return self.set_tgt_lang_special_tokens(self.tgt_lang					)




       def 					a_					(					self      : int	,    A__      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.convert_tokens_to_ids(lowercase__					)

            if self.legacy_behaviour:
                 __lowerCamelCase						:       Any				   =				[]
                 __lowerCamelCase						:       Optional[int]				   =				[self.eos_token_id, self.cur_lang_code]
            else:
                 __lowerCamelCase						:       int				   =				[self.cur_lang_code]
                 __lowerCamelCase						:       List[Any]				   =				[self.eos_token_id]

            __lowerCamelCase						:       List[Any]				   =				self.convert_ids_to_tokens(self.prefix_tokens					)
            __lowerCamelCase						:       List[Any]				   =				self.convert_ids_to_tokens(self.suffix_tokens					)

            __lowerCamelCase						:       Dict				   =				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 					a_					(					self      : str	,    A__      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				self.convert_tokens_to_ids(lowercase__					)
            if self.legacy_behaviour:
                 __lowerCamelCase						:       List[str]				   =				[]
                 __lowerCamelCase						:       Optional[int]				   =				[self.eos_token_id, self.cur_lang_code]
            else:
                 __lowerCamelCase						:       int				   =				[self.cur_lang_code]
                 __lowerCamelCase						:       str				   =				[self.eos_token_id]

            __lowerCamelCase						:       Any				   =				self.convert_ids_to_tokens(self.prefix_tokens					)
            __lowerCamelCase						:       int				   =				self.convert_ids_to_tokens(self.suffix_tokens					)

            __lowerCamelCase						:       Dict				   =				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 					a_					(					self      : Optional[int]	,    A__      : str	,    A__      : Tuple = None					):


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

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

            return (out_vocab_file,)











'''simple docstring'''

from ...processing_utils import ProcessorMixin







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     List[Any]   		=			'SpeechT5FeatureExtractor'
       snake_case__    :     Union[str, Any]   		=			'SpeechT5Tokenizer'




       def __init__(					self      : Union[str, Any]	,    A__      : str	,    A__      : Tuple					):


            """simple docstring"""







            super().__init__(A__	,    A__					)




       def __call__(					self      : List[Any]	,    *A__      : Any	,    **A__      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				kwargs.pop("""audio"""	,    A__					)
            __lowerCamelCase						:       Any				   =				kwargs.pop("""text"""	,    A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				kwargs.pop("""text_target"""	,    A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				kwargs.pop("""audio_target"""	,    A__					)
            __lowerCamelCase						:       Any				   =				kwargs.pop("""sampling_rate"""	,    A__					)

            if audio is not None and text is not None:
                 raise ValueError(
                     """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""					)
            if audio_target is not None and text_target is not None:
                 raise ValueError(
                     """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""					)
            if audio is None and audio_target is None and text is None and text_target is None:
                 raise ValueError(
                     """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""					)

            if audio is not None:
                 __lowerCamelCase						:       Optional[int]				   =				self.feature_extractor(A__	,    *A__	,    sampling_rate=A__	,    **A__					)
            elif text is not None:
                 __lowerCamelCase						:       Optional[int]				   =				self.tokenizer(A__	,    **A__					)
            else:
                 __lowerCamelCase						:       Tuple				   =				None

            if audio_target is not None:
                 __lowerCamelCase						:       Optional[int]				   =				self.feature_extractor(audio_target=A__	,    *A__	,    sampling_rate=A__	,    **A__					)
                 __lowerCamelCase						:       Union[str, Any]				   =				targets["""input_values"""]
            elif text_target is not None:
                 __lowerCamelCase						:       Tuple				   =				self.tokenizer(A__	,    **A__					)
                 __lowerCamelCase						:       List[str]				   =				targets["""input_ids"""]
            else:
                 __lowerCamelCase						:       Dict				   =				None

            if inputs is None:
                 return targets

            if targets is not None:
                 __lowerCamelCase						:       str				   =				labels

                 __lowerCamelCase						:       int				   =				targets.get("""attention_mask"""					)
                 if decoder_attention_mask is not None:
                      __lowerCamelCase						:       Optional[int]				   =				decoder_attention_mask

            return inputs




       def 					a_					(					self      : Any	,    *A__      : Union[str, Any]	,    **A__      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				kwargs.pop("""input_values"""	,    A__					)
            __lowerCamelCase						:       List[Any]				   =				kwargs.pop("""input_ids"""	,    A__					)
            __lowerCamelCase						:       List[Any]				   =				kwargs.pop("""labels"""	,    A__					)

            if input_values is not None and input_ids is not None:
                 raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""					)
            if input_values is None and input_ids is None and labels is None:
                 raise ValueError(
                     """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""					)

            if input_values is not None:
                 __lowerCamelCase						:       List[Any]				   =				self.feature_extractor.pad(A__	,    *A__	,    **A__					)
            elif input_ids is not None:
                 __lowerCamelCase						:       Any				   =				self.tokenizer.pad(A__	,    **A__					)
            else:
                 __lowerCamelCase						:       Any				   =				None

            if labels is not None:
                 if "input_ids" in labels or (isinstance(A__	,    A__					) and "input_ids" in labels[0]):
                      __lowerCamelCase						:       List[Any]				   =				self.tokenizer.pad(A__	,    **A__					)
                      __lowerCamelCase						:       Tuple				   =				targets["""input_ids"""]
                 else:
                      __lowerCamelCase						:       Optional[int]				   =				self.feature_extractor.feature_size
                      __lowerCamelCase						:       Any				   =				self.feature_extractor.num_mel_bins
                      __lowerCamelCase						:       List[str]				   =				self.feature_extractor.pad(A__	,    *A__	,    **A__					)
                      __lowerCamelCase						:       Dict				   =				feature_size_hack
                      __lowerCamelCase						:       List[Any]				   =				targets["""input_values"""]
            else:
                 __lowerCamelCase						:       Optional[int]				   =				None

            if inputs is None:
                 return targets

            if targets is not None:
                 __lowerCamelCase						:       Dict				   =				labels

                 __lowerCamelCase						:       List[str]				   =				targets.get("""attention_mask"""					)
                 if decoder_attention_mask is not None:
                      __lowerCamelCase						:       int				   =				decoder_attention_mask

            return inputs




       def 					a_					(					self      : Optional[Any]	,    *A__      : List[Any]	,    **A__      : Optional[int]					):


            """simple docstring"""







            return self.tokenizer.batch_decode(*A__	,    **A__					)




       def 					a_					(					self      : Any	,    *A__      : int	,    **A__      : Dict					):


            """simple docstring"""







            return self.tokenizer.decode(*A__	,    **A__					)



