
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:
       _UpperCamelCase:					Any     =None


_UpperCamelCase:					Union[str, Any]     =logging.get_logger(__name__)

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

_UpperCamelCase:					List[str]     ={
    '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'
        ),
    },
}

_UpperCamelCase:					Optional[Any]     ={
    'facebook/mbart-large-50-one-to-many-mmt': 1_024,
}

# fmt: off
_UpperCamelCase:					Optional[Any]     =['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 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							VOCAB_FILES_NAMES
       UpperCamelCase_				 =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_				 =							PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_				 =							['''input_ids''', '''attention_mask''']
       UpperCamelCase_				 =							MBartaaTokenizer

       UpperCamelCase_				 =							[]
       UpperCamelCase_				 =							[]



       def __init__(     self					:			List[str]  ,      _lowerCAmelCase					:			List[Any]=None  ,      _lowerCAmelCase					:			Any=None  ,      _lowerCAmelCase					:			List[Any]=None  ,      _lowerCAmelCase					:			Optional[Any]=None  ,      _lowerCAmelCase					:			str="</s>"  ,      _lowerCAmelCase					:			Optional[int]="</s>"  ,      _lowerCAmelCase					:			Optional[Any]="<s>"  ,      _lowerCAmelCase					:			Union[str, Any]="<unk>"  ,      _lowerCAmelCase					:			Dict="<pad>"  ,      _lowerCAmelCase					:			Dict="<mask>"  ,      **_lowerCAmelCase					:			int  ,      )					->   Any:
         # Mask token behave like a normal word, i.e. include the space before it
         _lowerCAmelCase						=		AddedToken(_lowerCAmelCase  ,      lstrip=_lowerCAmelCase  ,      rstrip=_lowerCAmelCase       ) if isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ) else mask_token

         _lowerCAmelCase						=		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__(
             _lowerCAmelCase  ,      src_lang=_lowerCAmelCase  ,      tgt_lang=_lowerCAmelCase  ,      tokenizer_file=_lowerCAmelCase  ,      eos_token=_lowerCAmelCase  ,      sep_token=_lowerCAmelCase  ,      cls_token=_lowerCAmelCase  ,      unk_token=_lowerCAmelCase  ,      pad_token=_lowerCAmelCase  ,      mask_token=_lowerCAmelCase  ,      **_lowerCAmelCase  ,      )

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

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

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



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]       )					->   str:
         return self._src_lang



       @src_lang.setter
       def SCREAMING_SNAKE_CASE_				(     self					:			str  ,      _lowerCAmelCase					:			str       )					->   None:
         _lowerCAmelCase						=		new_src_lang
         self.set_src_lang_special_tokens(self._src_lang       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			List[int]  ,      _lowerCAmelCase					:			Optional[List[int]] = None       )					->   List[int]:
         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 SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]  ,      _lowerCAmelCase					:			List[str]  ,      _lowerCAmelCase					:			str = "en_XX"  ,      _lowerCAmelCase					:			Optional[List[str]] = None  ,      _lowerCAmelCase					:			str = "ro_RO"  ,      **_lowerCAmelCase					:			Union[str, Any]  ,      )					->   BatchEncoding:
         _lowerCAmelCase						=		src_lang
         _lowerCAmelCase						=		tgt_lang
         return super().prepare_seqaseq_batch(_lowerCAmelCase  ,      _lowerCAmelCase  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   str:
         return self.set_src_lang_special_tokens(self.src_lang       )



       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   List[str]:
         return self.set_tgt_lang_special_tokens(self.tgt_lang       )



       def SCREAMING_SNAKE_CASE_				(     self					:			str  ,      _lowerCAmelCase					:			str       )					->   None:
         _lowerCAmelCase						=		self.convert_tokens_to_ids(_lowerCAmelCase       )
         _lowerCAmelCase						=		[self.cur_lang_code_id]
         _lowerCAmelCase						=		[self.eos_token_id]

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

         _lowerCAmelCase						=		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 SCREAMING_SNAKE_CASE_				(     self					:			str  ,      _lowerCAmelCase					:			str       )					->   None:
         _lowerCAmelCase						=		self.convert_tokens_to_ids(_lowerCAmelCase       )
         _lowerCAmelCase						=		[self.cur_lang_code_id]
         _lowerCAmelCase						=		[self.eos_token_id]

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

         _lowerCAmelCase						=		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 SCREAMING_SNAKE_CASE_				(     self					:			Dict  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			str  ,      _lowerCAmelCase					:			Optional[str]  ,      _lowerCAmelCase					:			Optional[str]  ,      **_lowerCAmelCase					:			Union[str, Any]       )					->   List[str]:
         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						=		src_lang
         _lowerCAmelCase						=		self(_lowerCAmelCase  ,      add_special_tokens=_lowerCAmelCase  ,      return_tensors=_lowerCAmelCase  ,      **_lowerCAmelCase       )
         _lowerCAmelCase						=		self.convert_tokens_to_ids(_lowerCAmelCase       )
         _lowerCAmelCase						=		tgt_lang_id
         return inputs






       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]  ,      _lowerCAmelCase					:			str  ,      _lowerCAmelCase					:			Optional[str] = None       )					->   Tuple[str]:
         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(_lowerCAmelCase       ):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
           return
         _lowerCAmelCase						=		os.path.join(
             _lowerCAmelCase  ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

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

         return (out_vocab_file,)


import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
       from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


_UpperCamelCase:					str     =get_tests_dir('fixtures/test_sentencepiece.model')





@require_torch
@require_torchaudio
@require_sentencepiece
class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Any:
         _lowerCAmelCase						=		tempfile.mkdtemp()

         _lowerCAmelCase						=		['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
         _lowerCAmelCase						=		dict(zip(_lowerCAmelCase  ,      range(len(_lowerCAmelCase       )       )       )       )
         _lowerCAmelCase						=		Path(self.tmpdirname       )
         save_json(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['vocab_file']       )
         if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
           copyfile(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['spm_file']       )

         _lowerCAmelCase						=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )
         tokenizer.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		{
             'feature_size': 24,
             'num_mel_bins': 24,
             'padding_value': 0.0,
             'sampling_rate': 1_6000,
             'return_attention_mask': False,
             'do_normalize': True,
         }
         save_json(_lowerCAmelCase  ,      save_dir / FEATURE_EXTRACTOR_NAME       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      **_lowerCAmelCase					:			Optional[Any]       )					->   List[str]:
         return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      **_lowerCAmelCase					:			Dict       )					->   Tuple:
         return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   int:
         shutil.rmtree(self.tmpdirname       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Any:
         _lowerCAmelCase						=		self.get_tokenizer()
         _lowerCAmelCase						=		self.get_feature_extractor()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         processor.save_pretrained(self.tmpdirname       )
         _lowerCAmelCase						=		SpeechaTextProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer.get_vocab()       )
         self.assertIsInstance(processor.tokenizer  ,      _lowerCAmelCase       )

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   int:
         _lowerCAmelCase						=		SpeechaTextProcessor(
             tokenizer=self.get_tokenizer()  ,      feature_extractor=self.get_feature_extractor()       )
         processor.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		self.get_tokenizer(bos_token='(BOS)'  ,      eos_token='(EOS)'       )
         _lowerCAmelCase						=		self.get_feature_extractor(do_normalize=_lowerCAmelCase  ,      padding_value=1.0       )

         _lowerCAmelCase						=		SpeechaTextProcessor.from_pretrained(
             self.tmpdirname  ,      bos_token='(BOS)'  ,      eos_token='(EOS)'  ,      do_normalize=_lowerCAmelCase  ,      padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer  ,      _lowerCAmelCase       )

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		floats_list((3, 1000)       )

         _lowerCAmelCase						=		feature_extractor(_lowerCAmelCase  ,      return_tensors='np'       )
         _lowerCAmelCase						=		processor(_lowerCAmelCase  ,      return_tensors='np'       )

         for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()  ,      input_processor[key].sum()  ,      delta=1e-2       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		'This is a test string'

         _lowerCAmelCase						=		processor(text=_lowerCAmelCase       )

         _lowerCAmelCase						=		tokenizer(_lowerCAmelCase       )

         for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]  ,      encoded_processor[key]       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Tuple:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         _lowerCAmelCase						=		processor.batch_decode(_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer.batch_decode(_lowerCAmelCase       )

         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         self.assertListEqual(
             processor.model_input_names  ,      feature_extractor.model_input_names  ,      msg='`processor` and `feature_extractor` model input names do not match'  ,      )
