






'''simple docstring'''







from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase					   =       logging.get_logger(__name__)




class      __SCREAMING_SNAKE_CASE					(A_					):


		"""simple docstring"""


		_a     :   Any			 =    ['''input_ids''', '''attention_mask''']







		def __init__( self      ,  UpperCamelCase__="[PAD]"      ,  UpperCamelCase__="[BOS]"      ,  UpperCamelCase__="[EOS]"      ,  UpperCamelCase__="[MASK]"      ,  UpperCamelCase__="[CLS]"      ,  UpperCamelCase__="[SEP]"      ,  UpperCamelCase__=2_048      ,  **UpperCamelCase__      ,  ):






									"""simple docstring"""


									a_	    = AddedToken(UpperCamelCase__      ,  lstrip=UpperCamelCase__      ,  rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__      ,  UpperCamelCase__  ) else pad_token
									a_	    = AddedToken(UpperCamelCase__      ,  lstrip=UpperCamelCase__      ,  rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__      ,  UpperCamelCase__  ) else bos_token
									a_	    = AddedToken(UpperCamelCase__      ,  lstrip=UpperCamelCase__      ,  rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__      ,  UpperCamelCase__  ) else eos_token
									a_	    = AddedToken(UpperCamelCase__      ,  lstrip=UpperCamelCase__      ,  rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__      ,  UpperCamelCase__  ) else mask_token
									a_	    = AddedToken(UpperCamelCase__      ,  lstrip=UpperCamelCase__      ,  rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__      ,  UpperCamelCase__  ) else cls_token
									a_	    = AddedToken(UpperCamelCase__      ,  lstrip=UpperCamelCase__      ,  rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__      ,  UpperCamelCase__  ) else sep_token

									super().__init__(
									    pad_token=UpperCamelCase__      ,  bos_token=UpperCamelCase__      ,  eos_token=UpperCamelCase__      ,  mask_token=UpperCamelCase__      ,  cls_token=UpperCamelCase__      ,  sep_token=UpperCamelCase__      ,  model_max_length=UpperCamelCase__      ,  **UpperCamelCase__      ,  )

									a_	    = 2**8  # utf is 8 bits

									# define special tokens dict
									a_	    = {
									    self.pad_token: 0,
									    self.bos_token: 1,
									    self.eos_token: 2,
									    self.mask_token: 3,
									    self.cls_token: 4,
									    self.sep_token: 5,
									}
									a_	    = len(self.special_tokens_encoder  )
									a_	    = {v: k for k, v in self.special_tokens_encoder.items()}







		def       _a					( self  ):






									"""simple docstring"""


									a_	    = self.special_tokens_encoder.copy()
									vocab.update(self.added_tokens_encoder  )
									for i in range(self._utf_vocab_size  ):
																a_	    = chr(UpperCamelCase__  )
																a_	    = i + len(self.special_tokens_encoder  )
									return vocab







		@property
		def       _a					( self  ):






									"""simple docstring"""


									return self._utf_vocab_size + self._num_special_tokens







		def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = False  ):






									"""simple docstring"""


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

									# normal case: some special tokens
									if token_ids_a is None:
																return [1] + [0] * len(UpperCamelCase__  ) + [1]
									return [1] + ([0] * len(UpperCamelCase__  )) + [1] + ([0] * len(UpperCamelCase__  )) + [1]







		def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__ = None  ):






									"""simple docstring"""


									if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									else:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]







		def       _a					( self      ,  UpperCamelCase__  ):






									"""simple docstring"""


									a_	    = [chr(UpperCamelCase__  ) for i in text.encode('utf-8'  )]
									return tokens







		def       _a					( self      ,  UpperCamelCase__  ):






									"""simple docstring"""


									if token in self.special_tokens_encoder:
																a_	    = self.special_tokens_encoder[token]
									elif token in self.added_tokens_encoder:
																a_	    = self.added_tokens_encoder[token]
									elif len(UpperCamelCase__  ) != 1:
																a_	    = self.unk_token_id
									else:
																a_	    = ord(UpperCamelCase__  ) + self._num_special_tokens
									return token_id







		def       _a					( self      ,  UpperCamelCase__  ):






									"""simple docstring"""


									if index in self.special_tokens_decoder:
																a_	    = self.special_tokens_decoder[index]
									elif index in self.added_tokens_decoder:
																a_	    = self.added_tokens_decoder[index]
									else:
																a_	    = chr(index - self._num_special_tokens  )
									return token







		def       _a					( self      ,  UpperCamelCase__  ):






									"""simple docstring"""


									a_	    = b''
									for token in tokens:
																if token in self.special_tokens_decoder:
																							a_	    = self.special_tokens_decoder[token].encode('utf-8'  )
																elif token in self.added_tokens_decoder:
																							a_	    = self.special_tokens_decoder[token].encode('utf-8'  )
																elif token in self.special_tokens_encoder:
																							a_	    = token.encode('utf-8'  )
																elif token in self.added_tokens_encoder:
																							a_	    = token.encode('utf-8'  )
																else:
																							a_	    = bytes([ord(UpperCamelCase__  )]  )
																bstring += tok_string
									a_	    = bstring.decode('utf-8'      ,  errors='replace'  )
									return string







		def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__ = None  ):






									"""simple docstring"""


									return ()












'''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      __SCREAMING_SNAKE_CASE					(unittest.TestCase					):


  """simple docstring"""


  def       _a					( self  ):






         """simple docstring"""


         a_	    = '<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(' '  )
         a_	    = dict(zip(UpperCamelCase__      ,  range(len(UpperCamelCase__  )  )  )  )

         a_	    = {
             'pad_token': '<pad>',
             'unk_token': '<unk>',
             'bos_token': '<s>',
             'eos_token': '</s>',
         }
         a_	    = {
             'feature_size': 1,
             'padding_value': 0.0,
             'sampling_rate': 16_000,
             'return_attention_mask': False,
             'do_normalize': True,
         }

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

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







  def       _a					( self      ,  **UpperCamelCase__  ):






         """simple docstring"""


         a_	    = self.add_kwargs_tokens_map.copy()
         kwargs.update(UpperCamelCase__  )
         return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,  **UpperCamelCase__  )







  def       _a					( self      ,  **UpperCamelCase__  ):






         """simple docstring"""


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







  def       _a					( self  ):






         """simple docstring"""


         shutil.rmtree(self.tmpdirname  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_tokenizer()
         a_	    = self.get_feature_extractor()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

         processor.save_pretrained(self.tmpdirname  )
         a_	    = WavaVecaProcessor.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       _a					( self  ):






         """simple docstring"""


         a_	    = WavaVecaProcessor(tokenizer=self.get_tokenizer()      ,  feature_extractor=self.get_feature_extractor()  )
         processor.save_pretrained(self.tmpdirname  )

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

         a_	    = WavaVecaProcessor.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       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

         a_	    = floats_list((3, 1_000)  )

         a_	    = feature_extractor(UpperCamelCase__      ,  return_tensors='np'  )
         a_	    = 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       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

         a_	    = 'This is a test string'

         a_	    = processor(text=UpperCamelCase__  )

         a_	    = tokenizer(UpperCamelCase__  )

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







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

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

         a_	    = processor.batch_decode(UpperCamelCase__  )
         a_	    = tokenizer.batch_decode(UpperCamelCase__  )

         self.assertListEqual(UpperCamelCase__      ,  UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(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'      ,  )




