



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


a_       :Dict				  =				logging.get_logger(__name__)

a_       :Union[str, Any]				  =				{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_       :Any				  =				{
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

a_       :int				  =				{
    "microsoft/deberta-base": 512,
    "microsoft/deberta-large": 512,
    "microsoft/deberta-xlarge": 512,
    "microsoft/deberta-base-mnli": 512,
    "microsoft/deberta-large-mnli": 512,
    "microsoft/deberta-xlarge-mnli": 512,
}

a_       :Dict				  =				{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}




class  snake_case__						(	lowerCAmelCase_					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE            =     PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE            =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE            =     ["""input_ids""", """attention_mask""", """token_type_ids"""]
     _SCREAMING_SNAKE_CASE            =     DebertaTokenizer






     def __init__(	self  :  List[str],			_snake_case  :  Optional[int]=None,			_snake_case  :  Any=None,			_snake_case  :  int=None,			_snake_case  :  str="replace",			_snake_case  :  Any="[CLS]",			_snake_case  :  Any="[SEP]",			_snake_case  :  Tuple="[SEP]",			_snake_case  :  int="[CLS]",			_snake_case  :  Union[str, Any]="[UNK]",			_snake_case  :  Optional[Any]="[PAD]",			_snake_case  :  int="[MASK]",			_snake_case  :  str=False,			**_snake_case  :  Union[str, Any],			)       ->Dict:
            super().__init__(
                _snake_case,			_snake_case,			tokenizer_file=_snake_case,			errors=_snake_case,			bos_token=_snake_case,			eos_token=_snake_case,			unk_token=_snake_case,			sep_token=_snake_case,			cls_token=_snake_case,			pad_token=_snake_case,			mask_token=_snake_case,			add_prefix_space=_snake_case,			**_snake_case,			)
            snake_case__							:     int            =     kwargs.pop('add_bos_token',			_snake_case )

            snake_case__							:     Optional[int]            =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
            if pre_tok_state.get('add_prefix_space',			_snake_case ) != add_prefix_space:
                   snake_case__							:     Dict            =     getattr(_snake_case,			pre_tok_state.pop('type' ) )
                   snake_case__							:     Optional[Any]            =     add_prefix_space
                   snake_case__							:     List[Any]            =     pre_tok_class(**_snake_case )

            snake_case__							:     Any            =     add_prefix_space






     @property
     def        lowercase_       (	self  :  Dict )       ->str:
            if self._mask_token is None:
                   if self.verbose:
                          logger.error('Using mask_token, but it is not set yet.' )
                   return None
            return str(self._mask_token )






     @mask_token.setter
     def        lowercase_       (	self  :  Dict,			_snake_case  :  Union[str, Any] )       ->Union[str, Any]:
            snake_case__							:     Any            =     AddedToken(_snake_case,			lstrip=_snake_case,			rstrip=_snake_case ) if isinstance(_snake_case,			_snake_case ) else value
            snake_case__							:     Dict            =     value






     def        lowercase_       (	self  :  Tuple,			_snake_case  :  List[int],			_snake_case  :  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__							:     Dict            =     [self.cls_token_id]
            snake_case__							:     str            =     [self.sep_token_id]
            return cls + token_ids_a + sep + token_ids_a + sep






     def        lowercase_       (	self  :  List[Any],			_snake_case  :  List[int],			_snake_case  :  Optional[List[int]] = None )       ->List[int]:
            snake_case__							:     List[Any]            =     [self.sep_token_id]
            snake_case__							:     int            =     [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 ) * [0] + len(token_ids_a + sep ) * [1]






     def        lowercase_       (	self  :  Any,			*_snake_case  :  Union[str, Any],			**_snake_case  :  int )       ->BatchEncoding:
            snake_case__							:     List[Any]            =     kwargs.get('is_split_into_words',			_snake_case )
            assert self.add_prefix_space or not is_split_into_words, (
                F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                "to use it with pretokenized inputs."
            )

            return super()._batch_encode_plus(*_snake_case,			**_snake_case )






     def        lowercase_       (	self  :  Union[str, Any],			*_snake_case  :  str,			**_snake_case  :  Any )       ->BatchEncoding:
            snake_case__							:     Any            =     kwargs.get('is_split_into_words',			_snake_case )

            assert self.add_prefix_space or not is_split_into_words, (
                F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                "to use it with pretokenized inputs."
            )

            return super()._encode_plus(*_snake_case,			**_snake_case )






     def        lowercase_       (	self  :  int,			_snake_case  :  str,			_snake_case  :  Optional[str] = None )       ->Tuple[str]:
            snake_case__							:     List[Any]            =     self._tokenizer.model.save(_snake_case,			name=_snake_case )
            return tuple(_snake_case )






     def        lowercase_       (	self  :  List[Any],			_snake_case  :  "Conversation" )       ->List[int]:
            snake_case__							:     Dict            =     []
            for is_user, text in conversation.iter_texts():
                   input_ids.extend(self.encode(_snake_case,			add_special_tokens=_snake_case ) + [self.eos_token_id] )

            if len(_snake_case ) > self.model_max_length:
                   snake_case__							:     Union[str, Any]            =     input_ids[-self.model_max_length :]
            return input_ids











import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class  snake_case__						(	unittest.TestCase					):






     """simple docstring"""


     def        lowercase_       (	self  :  Optional[int] )       ->Optional[Any]:
            snake_case__							:     Tuple            =     '<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' ' )
            snake_case__							:     Any            =     dict(zip(_snake_case,			range(len(_snake_case ) ) ) )

            snake_case__							:     List[str]            =     {
                'pad_token': '<pad>',
                'unk_token': '<unk>',
                'bos_token': '<s>',
                'eos_token': '</s>',
            }
            snake_case__							:     Optional[int]            =     {
                'feature_size': 1,
                'padding_value': 0.0,
                'sampling_rate': 1_6_0_0_0,
                'return_attention_mask': False,
                'do_normalize': True,
            }

            snake_case__							:     Union[str, Any]            =     tempfile.mkdtemp()
            snake_case__							:     Optional[int]            =     os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['vocab_file'] )
            snake_case__							:     int            =     os.path.join(self.tmpdirname,			_snake_case )
            with open(self.vocab_file,			'w',			encoding='utf-8' ) as fp:
                   fp.write(json.dumps(_snake_case ) + '\n' )

            with open(self.feature_extraction_file,			'w',			encoding='utf-8' ) as fp:
                   fp.write(json.dumps(_snake_case ) + '\n' )






     def        lowercase_       (	self  :  Tuple,			**_snake_case  :  Dict )       ->Union[str, Any]:
            snake_case__							:     Tuple            =     self.add_kwargs_tokens_map.copy()
            kwargs.update(_snake_case )
            return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,			**_snake_case )






     def        lowercase_       (	self  :  Optional[Any],			**_snake_case  :  Dict )       ->Union[str, Any]:
            return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,			**_snake_case )






     def        lowercase_       (	self  :  List[Any] )       ->str:
            shutil.rmtree(self.tmpdirname )






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

            snake_case__							:     Tuple            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            processor.save_pretrained(self.tmpdirname )
            snake_case__							:     List[Any]            =     WavaVecaProcessor.from_pretrained(self.tmpdirname )

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

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






     def        lowercase_       (	self  :  Optional[Any] )       ->Any:
            snake_case__							:     str            =     WavaVecaProcessor(tokenizer=self.get_tokenizer(),			feature_extractor=self.get_feature_extractor() )
            processor.save_pretrained(self.tmpdirname )

            snake_case__							:     Optional[int]            =     self.get_tokenizer(bos_token='(BOS)',			eos_token='(EOS)' )
            snake_case__							:     List[str]            =     self.get_feature_extractor(do_normalize=_snake_case,			padding_value=1.0 )

            snake_case__							:     List[str]            =     WavaVecaProcessor.from_pretrained(
                self.tmpdirname,			bos_token='(BOS)',			eos_token='(EOS)',			do_normalize=_snake_case,			padding_value=1.0 )

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

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






     def        lowercase_       (	self  :  str )       ->Optional[Any]:
            snake_case__							:     Optional[Any]            =     self.get_feature_extractor()
            snake_case__							:     Tuple            =     self.get_tokenizer()

            snake_case__							:     List[Any]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            snake_case__							:     Any            =     floats_list((3, 1_0_0_0) )

            snake_case__							:     int            =     feature_extractor(_snake_case,			return_tensors='np' )
            snake_case__							:     Union[str, Any]            =     processor(_snake_case,			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        lowercase_       (	self  :  Optional[int] )       ->str:
            snake_case__							:     int            =     self.get_feature_extractor()
            snake_case__							:     Any            =     self.get_tokenizer()

            snake_case__							:     List[str]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            snake_case__							:     Optional[Any]            =     'This is a test string'

            snake_case__							:     Optional[Any]            =     processor(text=_snake_case )

            snake_case__							:     Any            =     tokenizer(_snake_case )

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






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

            snake_case__							:     Union[str, Any]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            snake_case__							:     Optional[Any]            =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            snake_case__							:     Dict            =     processor.batch_decode(_snake_case )
            snake_case__							:     Optional[int]            =     tokenizer.batch_decode(_snake_case )

            self.assertListEqual(_snake_case,			_snake_case )






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

            snake_case__							:     Union[str, Any]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

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






