'''simple docstring'''

import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
       from .tokenization_deberta_va import DebertaVaTokenizer
else:
       lowerCAmelCase_			:			List[str]    	=   None

lowerCAmelCase_			:			Tuple    	=   logging.get_logger(__name__)

lowerCAmelCase_			:			List[Any]    	=   {"""vocab_file""": """spm.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase_			:			List[str]    	=   {
    """vocab_file""": {
        """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"""
        ),
        """microsoft/deberta-v2-xxlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"""
        ),
    }
}

lowerCAmelCase_			:			Optional[Any]    	=   {
    """microsoft/deberta-v2-xlarge""": 512,
    """microsoft/deberta-v2-xxlarge""": 512,
    """microsoft/deberta-v2-xlarge-mnli""": 512,
    """microsoft/deberta-v2-xxlarge-mnli""": 512,
}

lowerCAmelCase_			:			Optional[int]    	=   {
    """microsoft/deberta-v2-xlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xlarge-mnli""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge-mnli""": {"""do_lower_case""": False},
}





class 				lowercase    (    __snake_case			):
   lowerCamelCase_            =VOCAB_FILES_NAMES
   lowerCamelCase_            =PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_            =PRETRAINED_INIT_CONFIGURATION
   lowerCamelCase_            =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_            =DebertaVaTokenizer


   def __init__( self					:						Optional[int] ,		__lowerCAmelCase					:						Dict=None ,		__lowerCAmelCase					:						List[Any]=None ,		__lowerCAmelCase					:						int=False ,		__lowerCAmelCase					:						Union[str, Any]=False ,		__lowerCAmelCase					:						Any="[CLS]" ,		__lowerCAmelCase					:						Dict="[SEP]" ,		__lowerCAmelCase					:						Union[str, Any]="[UNK]" ,		__lowerCAmelCase					:						Optional[int]="[SEP]" ,		__lowerCAmelCase					:						Dict="[PAD]" ,		__lowerCAmelCase					:						str="[CLS]" ,		__lowerCAmelCase					:						Optional[int]="[MASK]" ,		**__lowerCAmelCase					:						Optional[int] ,		)   ->       None:
         super().__init__(
             lowerCAmelCase__ ,		tokenizer_file=lowerCAmelCase__ ,		do_lower_case=lowerCAmelCase__ ,		bos_token=lowerCAmelCase__ ,		eos_token=lowerCAmelCase__ ,		unk_token=lowerCAmelCase__ ,		sep_token=lowerCAmelCase__ ,		pad_token=lowerCAmelCase__ ,		cls_token=lowerCAmelCase__ ,		mask_token=lowerCAmelCase__ ,		split_by_punct=lowerCAmelCase__ ,		**lowerCAmelCase__ ,		)

         lowercase_     			=   do_lower_case
         lowercase_     			=   split_by_punct
         lowercase_     			=   vocab_file
         lowercase_     			=   False if not self.vocab_file else True


   def __UpperCAmelCase							( self					:						Dict ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						Optional[int]=None)   ->       int:





         if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowercase_     			=   [self.cls_token_id]
         lowercase_     			=   [self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep


   def __UpperCAmelCase							( self					:						Optional[Any] ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						Union[str, Any]=None ,		__lowerCAmelCase					:						Union[str, Any]=False)   ->       Optional[Any]:





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

         if token_ids_a is not None:
               return [1] + ([0] * len(lowerCAmelCase__)) + [1] + ([0] * len(lowerCAmelCase__)) + [1]
         return [1] + ([0] * len(lowerCAmelCase__)) + [1]


   def __UpperCAmelCase							( self					:						int ,		__lowerCAmelCase					:						Dict ,		__lowerCAmelCase					:						Optional[Any]=None)   ->       str:
         lowercase_     			=   [self.sep_token_id]
         lowercase_     			=   [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 __UpperCAmelCase							( self					:						List[str] ,		__lowerCAmelCase					:						List[Any] ,		__lowerCAmelCase					:						List[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
         lowercase_     			=   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,)







'''simple docstring'''

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    lowerCamelCase__  (   unittest.TestCase     ):



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :Any       ="""<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(""" """     )
        _UpperCamelCase  :Optional[Any]       =dict(zip(lowerCAmelCase__					,      range(len(lowerCAmelCase__     )     )     )     )

        _UpperCamelCase  :int       ={
            """pad_token""": """<pad>""",
            """unk_token""": """<unk>""",
            """bos_token""": """<s>""",
            """eos_token""": """</s>""",
        }
        _UpperCamelCase  :Union[str, Any]       ={
            """feature_size""": 1,
            """padding_value""": 0.0,
            """sampling_rate""": 16_000,
            """return_attention_mask""": False,
            """do_normalize""": True,
        }

        _UpperCamelCase  :List[str]       =tempfile.mkdtemp()
        _UpperCamelCase  :Optional[Any]       =os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["""vocab_file"""]     )
        _UpperCamelCase  :Tuple       =os.path.join(self.tmpdirname					,      lowerCAmelCase__     )
        with open(self.vocab_file					,      """w"""					,      encoding="""utf-8"""     ) as fp:
             fp.write(json.dumps(lowerCAmelCase__     ) + """\n"""     )

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



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :int       =self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCAmelCase__     )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname					,      **lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     str:
        """simple docstring"""





        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname					,      **lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        shutil.rmtree(self.tmpdirname     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =self.get_tokenizer()
        _UpperCamelCase  :int       =self.get_feature_extractor()

        _UpperCamelCase  :Union[str, Any]       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        processor.save_pretrained(self.tmpdirname     )
        _UpperCamelCase  :Dict       =WavaVecaProcessor.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  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =WavaVecaProcessor(tokenizer=self.get_tokenizer()					,      feature_extractor=self.get_feature_extractor()     )
        processor.save_pretrained(self.tmpdirname     )

        _UpperCamelCase  :Optional[int]       =self.get_tokenizer(bos_token="""(BOS)"""					,      eos_token="""(EOS)"""     )
        _UpperCamelCase  :Optional[int]       =self.get_feature_extractor(do_normalize=lowerCAmelCase__					,      padding_value=1.0     )

        _UpperCamelCase  :Optional[int]       =WavaVecaProcessor.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  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =self.get_feature_extractor()
        _UpperCamelCase  :List[str]       =self.get_tokenizer()

        _UpperCamelCase  :Any       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        _UpperCamelCase  :Tuple       =floats_list((3, 1_000)     )

        _UpperCamelCase  :Tuple       =feature_extractor(lowerCAmelCase__					,      return_tensors="""np"""     )
        _UpperCamelCase  :Union[str, Any]       =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  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Any       =self.get_feature_extractor()
        _UpperCamelCase  :List[Any]       =self.get_tokenizer()

        _UpperCamelCase  :Tuple       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[Any]       ="""This is a test string"""

        _UpperCamelCase  :Union[str, Any]       =processor(text=lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =tokenizer(lowerCAmelCase__     )

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



   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :int       =self.get_feature_extractor()
        _UpperCamelCase  :Any       =self.get_tokenizer()

        _UpperCamelCase  :str       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[int]       =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCamelCase  :List[Any]       =processor.batch_decode(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =tokenizer.batch_decode(lowerCAmelCase__     )

        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )




   def  _UpperCamelCase		(    self     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =self.get_feature_extractor()
        _UpperCamelCase  :Any       =self.get_tokenizer()

        _UpperCamelCase  :List[str]       =WavaVecaProcessor(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"""					,      )