
"""simple docstring"""

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


SCREAMING_SNAKE_CASE_			    =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_			    =  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE_			    =  {
    '''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'''
        ),
    },
}

SCREAMING_SNAKE_CASE_			    =  {
    '''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,
}

SCREAMING_SNAKE_CASE_			    =  {
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}
class    _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE      :							Union[str, Any]												=		VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE      :							Tuple												=		PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE      :							Union[str, Any]												=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE      :							Optional[Any]												=		["input_ids", "attention_mask", "token_type_ids"]
      __SCREAMING_SNAKE_CASE      :							int												=		DebertaTokenizer




      def __init__(							self      ,				lowercase_=None      ,				lowercase_=None      ,				lowercase_=None      ,				lowercase_="replace"      ,				lowercase_="[CLS]"      ,				lowercase_="[SEP]"      ,				lowercase_="[SEP]"      ,				lowercase_="[CLS]"      ,				lowercase_="[UNK]"      ,				lowercase_="[PAD]"      ,				lowercase_="[MASK]"      ,				lowercase_=False      ,				**lowercase_      ,				)  ->    List[str]:
          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_      ,				)
          UpperCAmelCase  	=    kwargs.pop('add_bos_token'      ,				lowercase_							)

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

          UpperCAmelCase  	=    add_prefix_space




      @property
      def 			a_					(							self							)  ->    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 			a_					(							self      ,				lowercase_							)  ->    Optional[Any]:
          UpperCAmelCase  	=    AddedToken(lowercase_      ,				lstrip=lowercase_      ,				rstrip=lowercase_							) if isinstance(lowercase_      ,				lowercase_							) else value
          UpperCAmelCase  	=    value




      def 			a_					(							self      ,				lowercase_      ,				lowercase_ = None							)  ->    List[int]:
          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          UpperCAmelCase  	=    [self.cls_token_id]
          UpperCAmelCase  	=    [self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep




      def 			a_					(							self      ,				lowercase_      ,				lowercase_ = None							)  ->    List[int]:
          UpperCAmelCase  	=    [self.sep_token_id]
          UpperCAmelCase  	=    [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 			a_					(							self      ,				*lowercase_      ,				**lowercase_							)  ->    BatchEncoding:
          UpperCAmelCase  	=    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 			a_					(							self      ,				*lowercase_      ,				**lowercase_							)  ->    BatchEncoding:
          UpperCAmelCase  	=    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 			a_					(							self      ,				lowercase_      ,				lowercase_ = None							)  ->    Tuple[str]:
          UpperCAmelCase  	=    self._tokenizer.model.save(lowercase_      ,				name=lowercase_							)
          return tuple(lowercase_							)




      def 			a_					(							self      ,				lowercase_							)  ->    List[int]:
          UpperCAmelCase  	=    []
          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:
              UpperCAmelCase  	=    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'      ,				)



