

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


lowerCAmelCase_	     =     get_tests_dir('''fixtures/test_sentencepiece.model''')





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







			'''simple docstring'''

			def     snake_case__(					self			:    Dict							)				->List[Any]:
									snake_case_       =     tempfile.mkdtemp()

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

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

									snake_case_       =     {
									    '''feature_size''': 2_4,
									    '''num_mel_bins''': 2_4,
									    '''padding_value''': 0.0,
									    '''sampling_rate''': 1_6_0_0_0,
									    '''return_attention_mask''': False,
									    '''do_normalize''': True,
									}
									save_json(_UpperCamelCase							,       save_dir / FEATURE_EXTRACTOR_NAME							)







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







			def     snake_case__(					self			:    Union[str, Any]							,       **_UpperCamelCase			:    Any							)				->List[Any]:
									return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname							,       **_UpperCamelCase							)







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







			def     snake_case__(					self			:    List[Any]							)				->Tuple:
									snake_case_       =     self.get_tokenizer()
									snake_case_       =     self.get_feature_extractor()

									snake_case_       =     SpeechaTextProcessor(tokenizer=_UpperCamelCase							,       feature_extractor=_UpperCamelCase							)

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

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

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







			def     snake_case__(					self			:    Any							)				->Union[str, Any]:
									snake_case_       =     SpeechaTextProcessor(
									    tokenizer=self.get_tokenizer()							,       feature_extractor=self.get_feature_extractor()							)
									processor.save_pretrained(self.tmpdirname							)

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

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

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

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







			def     snake_case__(					self			:    Any							)				->List[Any]:
									snake_case_       =     self.get_feature_extractor()
									snake_case_       =     self.get_tokenizer()

									snake_case_       =     SpeechaTextProcessor(tokenizer=_UpperCamelCase							,       feature_extractor=_UpperCamelCase							)

									snake_case_       =     floats_list((3, 1_0_0_0)							)

									snake_case_       =     feature_extractor(_UpperCamelCase							,       return_tensors='''np'''							)
									snake_case_       =     processor(_UpperCamelCase							,       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     snake_case__(					self			:    Union[str, Any]							)				->Dict:
									snake_case_       =     self.get_feature_extractor()
									snake_case_       =     self.get_tokenizer()

									snake_case_       =     SpeechaTextProcessor(tokenizer=_UpperCamelCase							,       feature_extractor=_UpperCamelCase							)

									snake_case_       =     '''This is a test string'''

									snake_case_       =     processor(text=_UpperCamelCase							)

									snake_case_       =     tokenizer(_UpperCamelCase							)

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







			def     snake_case__(					self			:    List[str]							)				->List[str]:
									snake_case_       =     self.get_feature_extractor()
									snake_case_       =     self.get_tokenizer()

									snake_case_       =     SpeechaTextProcessor(tokenizer=_UpperCamelCase							,       feature_extractor=_UpperCamelCase							)

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

									snake_case_       =     processor.batch_decode(_UpperCamelCase							)
									snake_case_       =     tokenizer.batch_decode(_UpperCamelCase							)

									self.assertListEqual(_UpperCamelCase							,       _UpperCamelCase							)







			def     snake_case__(					self			:    int							)				->Union[str, Any]:
									snake_case_       =     self.get_feature_extractor()
									snake_case_       =     self.get_tokenizer()

									snake_case_       =     SpeechaTextProcessor(tokenizer=_UpperCamelCase							,       feature_extractor=_UpperCamelCase							)

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


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


lowerCAmelCase_	     =     logging.get_logger(__name__)

lowerCAmelCase_	     =     {'''vocab_file''': '''vocab.txt'''}

lowerCAmelCase_	     =     {
    '''vocab_file''': {
        '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt''',
        '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt''',
        '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt''',
        '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt''',
    }
}

lowerCAmelCase_	     =     {
    '''tau/splinter-base''': 5_12,
    '''tau/splinter-base-qass''': 5_12,
    '''tau/splinter-large''': 5_12,
    '''tau/splinter-large-qass''': 5_12,
}

lowerCAmelCase_	     =     {
    '''tau/splinter-base''': {'''do_lower_case''': False},
    '''tau/splinter-base-qass''': {'''do_lower_case''': False},
    '''tau/splinter-large''': {'''do_lower_case''': False},
    '''tau/splinter-large-qass''': {'''do_lower_case''': False},
}





class      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       List[str]						      =    VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE					:       List[str]						      =    PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE					:       Optional[int]						      =    PRETRAINED_INIT_CONFIGURATION
			SCREAMING_SNAKE_CASE					:       List[str]						      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE					:       Any						      =    SplinterTokenizer







			def __init__(					self			:    List[str]							,       _UpperCamelCase			:    Dict=None							,       _UpperCamelCase			:    Union[str, Any]=None							,       _UpperCamelCase			:    Union[str, Any]=True							,       _UpperCamelCase			:    List[Any]="[UNK]"							,       _UpperCamelCase			:    List[str]="[SEP]"							,       _UpperCamelCase			:    Optional[int]="[PAD]"							,       _UpperCamelCase			:    Tuple="[CLS]"							,       _UpperCamelCase			:    Optional[int]="[MASK]"							,       _UpperCamelCase			:    str="[QUESTION]"							,       _UpperCamelCase			:    Optional[Any]=True							,       _UpperCamelCase			:    Dict=None							,       **_UpperCamelCase			:    Optional[int]							,       )				->Union[str, Any]:
									super().__init__(
									    _UpperCamelCase							,       tokenizer_file=_UpperCamelCase							,       do_lower_case=_UpperCamelCase							,       unk_token=_UpperCamelCase							,       sep_token=_UpperCamelCase							,       pad_token=_UpperCamelCase							,       cls_token=_UpperCamelCase							,       mask_token=_UpperCamelCase							,       tokenize_chinese_chars=_UpperCamelCase							,       strip_accents=_UpperCamelCase							,       additional_special_tokens=(question_token,)							,       **_UpperCamelCase							,       )

									snake_case_       =     json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
									if (
									    pre_tok_state.get('''lowercase'''							,       _UpperCamelCase							) != do_lower_case
									    or pre_tok_state.get('''strip_accents'''							,       _UpperCamelCase							) != strip_accents
									):
															snake_case_       =     getattr(_UpperCamelCase							,       pre_tok_state.pop('''type'''							)							)
															snake_case_       =     do_lower_case
															snake_case_       =     strip_accents
															snake_case_       =     pre_tok_class(**_UpperCamelCase							)

									snake_case_       =     do_lower_case







			@property
			def     snake_case__(					self			:    Optional[int]							)				->int:
									return self.convert_tokens_to_ids(self.question_token							)







			def     snake_case__(					self			:    Dict							,       _UpperCamelCase			:    List[int]							,       _UpperCamelCase			:    Optional[List[int]] = None							)				->List[int]:
									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

									snake_case_       =     [self.cls_token_id]
									snake_case_       =     [self.sep_token_id]
									snake_case_       =     [self.question_token_id] + [self.convert_tokens_to_ids('''.'''							)]
									if self.padding_side == "right":
															# Input is question-then-context
															return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
									else:
															# Input is context-then-question
															return cls + token_ids_a + sep + token_ids_a + question_suffix + sep







			def     snake_case__(					self			:    str							,       _UpperCamelCase			:    List[int]							,       _UpperCamelCase			:    Optional[List[int]] = None							)				->List[int]:
									snake_case_       =     [self.sep_token_id]
									snake_case_       =     [self.cls_token_id]
									snake_case_       =     [self.question_token_id] + [self.convert_tokens_to_ids('''.'''							)]
									if token_ids_a is None:
															return len(cls + token_ids_a + sep							) * [0]

									if self.padding_side == "right":
															# Input is question-then-context
															return len(cls + token_ids_a + question_suffix + sep							) * [0] + len(token_ids_a + sep							) * [1]
									else:
															# Input is context-then-question
															return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + question_suffix + sep							) * [1]







			def     snake_case__(					self			:    Union[str, Any]							,       _UpperCamelCase			:    str							,       _UpperCamelCase			:    Optional[str] = None							)				->Tuple[str]:
									snake_case_       =     self._tokenizer.model.save(_UpperCamelCase							,       name=_UpperCamelCase							)
									return tuple(_UpperCamelCase							)