




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


lowercase__      :			Tuple           =					logging.get_logger(__name__)

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

lowercase__      :			Tuple           =					{
    "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"
        ),
    },
}

lowercase__      :			Any           =					{
    "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,
}

lowercase__      :			Tuple           =					{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}




class 			a__       (					SCREAMING_SNAKE_CASE_      ):
     a		:			Union[str, Any]							=	VOCAB_FILES_NAMES
     a		:			Tuple							=	PRETRAINED_VOCAB_FILES_MAP
     a		:			Union[str, Any]							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a		:			Optional[Any]							=	["input_ids", "attention_mask", "token_type_ids"]
     a		:			int							=	DebertaTokenizer







     def __init__(	self  ,		A=None  ,		A=None  ,		A=None  ,		A="replace"  ,		A="[CLS]"  ,		A="[SEP]"  ,		A="[SEP]"  ,		A="[CLS]"  ,		A="[UNK]"  ,		A="[PAD]"  ,		A="[MASK]"  ,		A=False  ,		**A  ,		)  ->    List[str]:



          '''simple docstring'''



          super().__init__(
              lowercase_  ,		lowercase_  ,		tokenizer_file=lowercase_  ,		errors=lowercase_  ,		bos_token=lowercase_  ,		eos_token=lowercase_  ,		unk_token=lowercase_  ,		sep_token=lowercase_  ,		cls_token=lowercase_  ,		pad_token=lowercase_  ,		mask_token=lowercase_  ,		add_prefix_space=lowercase_  ,		**lowercase_  ,		)
          a      =      kwargs.pop("add_bos_token"  ,		lowercase_ )

          a      =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
          if pre_tok_state.get("add_prefix_space"  ,		lowercase_ ) != add_prefix_space:
               a      =      getattr(lowercase_  ,		pre_tok_state.pop("type" ) )
               a      =      add_prefix_space
               a      =      pre_tok_class(**lowercase_ )

          a      =      add_prefix_space







     @property
     def 			lowerCAmelCase_				(	self )  ->    str:



          '''simple docstring'''



          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 			lowerCAmelCase_				(	self  ,		A )  ->    Optional[Any]:



          '''simple docstring'''



          a      =      AddedToken(lowercase_  ,		lstrip=lowercase_  ,		rstrip=lowercase_ ) if isinstance(lowercase_  ,		lowercase_ ) else value
          a      =      value







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = None )  ->    List[int]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = None )  ->    List[int]:



          '''simple docstring'''



          a      =      [self.sep_token_id]
          a      =      [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 			lowerCAmelCase_				(	self  ,		*A  ,		**A )  ->    BatchEncoding:



          '''simple docstring'''



          a      =      kwargs.get("is_split_into_words"  ,		lowercase_ )
          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(*lowercase_  ,		**lowercase_ )







     def 			lowerCAmelCase_				(	self  ,		*A  ,		**A )  ->    BatchEncoding:



          '''simple docstring'''



          a      =      kwargs.get("is_split_into_words"  ,		lowercase_ )

          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(*lowercase_  ,		**lowercase_ )







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = None )  ->    Tuple[str]:



          '''simple docstring'''



          a      =      self._tokenizer.model.save(lowercase_  ,		name=lowercase_ )
          return tuple(lowercase_ )







     def 			lowerCAmelCase_				(	self  ,		A )  ->    List[int]:



          '''simple docstring'''



          a      =      []
          for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(lowercase_  ,		add_special_tokens=lowercase_ ) + [self.eos_token_id] )

          if len(lowercase_ ) > self.model_max_length:
               a      =      input_ids[-self.model_max_length :]
          return input_ids



"""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    _UpperCAmelCase							(						unittest.TestCase							):




      def 			a_					(							self							)  ->    Any:
          UpperCAmelCase  	=    '<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  	=    dict(zip(lowercase_      ,				range(len(lowercase_							)							)							)							)

          UpperCAmelCase  	=    {
              'pad_token': '<pad>',
              'unk_token': '<unk>',
              'bos_token': '<s>',
              'eos_token': '</s>',
          }
          UpperCAmelCase  	=    {
              'feature_size': 1,
              'padding_value': 0.0,
              'sampling_rate': 1_6_0_0_0,
              'return_attention_mask': False,
              'do_normalize': True,
          }

          UpperCAmelCase  	=    tempfile.mkdtemp()
          UpperCAmelCase  	=    os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES['vocab_file']							)
          UpperCAmelCase  	=    os.path.join(self.tmpdirname      ,				lowercase_							)
          with open(self.vocab_file      ,				'w'      ,				encoding='utf-8'							) as fp:
              fp.write(json.dumps(lowercase_							) + '\n'							)

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




      def 			a_					(							self      ,				**lowercase_							)  ->    Any:
          UpperCAmelCase  	=    self.add_kwargs_tokens_map.copy()
          kwargs.update(lowercase_							)
          return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,				**lowercase_							)




      def 			a_					(							self      ,				**lowercase_							)  ->    Dict:
          return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,				**lowercase_							)




      def 			a_					(							self							)  ->    Any:
          shutil.rmtree(self.tmpdirname							)




      def 			a_					(							self							)  ->    Tuple:
          UpperCAmelCase  	=    self.get_tokenizer()
          UpperCAmelCase  	=    self.get_feature_extractor()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

          processor.save_pretrained(self.tmpdirname							)
          UpperCAmelCase  	=    WavaVecaProcessor.from_pretrained(self.tmpdirname							)

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

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




      def 			a_					(							self							)  ->    Dict:
          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=self.get_tokenizer()      ,				feature_extractor=self.get_feature_extractor()							)
          processor.save_pretrained(self.tmpdirname							)

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

          UpperCAmelCase  	=    WavaVecaProcessor.from_pretrained(
              self.tmpdirname      ,				bos_token='(BOS)'      ,				eos_token='(EOS)'      ,				do_normalize=lowercase_      ,				padding_value=1.0							)

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

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




      def 			a_					(							self							)  ->    Tuple:
          UpperCAmelCase  	=    self.get_feature_extractor()
          UpperCAmelCase  	=    self.get_tokenizer()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

          UpperCAmelCase  	=    floats_list((3, 1_0_0_0)							)

          UpperCAmelCase  	=    feature_extractor(lowercase_      ,				return_tensors='np'							)
          UpperCAmelCase  	=    processor(lowercase_      ,				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 			a_					(							self							)  ->    List[str]:
          UpperCAmelCase  	=    self.get_feature_extractor()
          UpperCAmelCase  	=    self.get_tokenizer()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

          UpperCAmelCase  	=    'This is a test string'

          UpperCAmelCase  	=    processor(text=lowercase_							)

          UpperCAmelCase  	=    tokenizer(lowercase_							)

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




      def 			a_					(							self							)  ->    List[str]:
          UpperCAmelCase  	=    self.get_feature_extractor()
          UpperCAmelCase  	=    self.get_tokenizer()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

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

          UpperCAmelCase  	=    processor.batch_decode(lowercase_							)
          UpperCAmelCase  	=    tokenizer.batch_decode(lowercase_							)

          self.assertListEqual(lowercase_      ,				lowercase_							)




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

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

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



