import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


A						=			logging.get_logger(__name__)





class     lowerCAmelCase__     (				__snake_case			):


		'''simple docstring'''






		lowerCAmelCase_     =   ['input_values', 'attention_mask']
		def __init__(       self     :     Dict     ,  snake_case__     :     int = 1     ,  snake_case__     :     int = 1_6_0_0_0     ,  snake_case__     :     float = 0.0     ,  snake_case__     :     bool = False     ,  snake_case__     :     int = 8_0     ,  snake_case__     :     int = 1_6     ,  snake_case__     :     int = 6_4     ,  snake_case__     :     str = "hann_window"     ,  snake_case__     :     float = 1.0     ,  snake_case__     :     float = 8_0     ,  snake_case__     :     float = 7_6_0_0     ,  snake_case__     :     float = 1e-10     ,  snake_case__     :     int = 2     ,  snake_case__     :     bool = True     ,  **snake_case__     :     Union[str, Any]     ,  )      ->	List[Any]:
				super().__init__(feature_size=_SCREAMING_SNAKE_CASE     ,  sampling_rate=_SCREAMING_SNAKE_CASE     ,  padding_value=_SCREAMING_SNAKE_CASE     ,  **_SCREAMING_SNAKE_CASE		)
				_lowerCamelCase				   =  do_normalize
				_lowerCamelCase				   =  return_attention_mask

				_lowerCamelCase				   =  num_mel_bins
				_lowerCamelCase				   =  hop_length
				_lowerCamelCase				   =  win_length
				_lowerCamelCase				   =  win_function
				_lowerCamelCase				   =  frame_signal_scale
				_lowerCamelCase				   =  fmin
				_lowerCamelCase				   =  fmax
				_lowerCamelCase				   =  mel_floor
				_lowerCamelCase				   =  reduction_factor

				_lowerCamelCase				   =  win_length * sampling_rate // 1_0_0_0
				_lowerCamelCase				   =  hop_length * sampling_rate // 1_0_0_0
				_lowerCamelCase				   =  optimal_fft_length(self.sample_size		)
				_lowerCamelCase				   =  (self.n_fft // 2) + 1

				_lowerCamelCase				   =  window_function(window_length=self.sample_size     ,  name=self.win_function     ,  periodic=_SCREAMING_SNAKE_CASE		)

				_lowerCamelCase				   =  mel_filter_bank(
				    num_frequency_bins=self.n_freqs     ,  num_mel_filters=self.num_mel_bins     ,  min_frequency=self.fmin     ,  max_frequency=self.fmax     ,  sampling_rate=self.sampling_rate     ,  norm='slaney'     ,  mel_scale='slaney'     ,  )

				if frame_signal_scale != 1.0:
						warnings.warn(
						    'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'     ,  _SCREAMING_SNAKE_CASE     ,  )
				if reduction_factor != 2.0:
						warnings.warn(
						    'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'     ,  _SCREAMING_SNAKE_CASE     ,  )
		@staticmethod
		# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
		def   _snake_case       (       snake_case__     :     List[np.ndarray]     ,  snake_case__     :     List[np.ndarray]     ,  snake_case__     :     float = 0.0		)      ->	Tuple:




				if attention_mask is not None:
						_lowerCamelCase				   =  np.array(_SCREAMING_SNAKE_CASE     ,  np.intaa		)
						_lowerCamelCase				   =  []

						for vector, length in zip(_SCREAMING_SNAKE_CASE     ,  attention_mask.sum(-1		)		):
								_lowerCamelCase				   =  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7		)
								if length < normed_slice.shape[0]:
										_lowerCamelCase				   =  padding_value

								normed_input_values.append(_SCREAMING_SNAKE_CASE		)
				else:
						_lowerCamelCase				   =  [(x - x.mean()) / np.sqrt(x.var() + 1e-7		) for x in input_values]

				return normed_input_values
		def   _snake_case       (       self     :     Any     ,  snake_case__     :     np.ndarray     ,  )      ->	int:
				_lowerCamelCase				   =  spectrogram(
				    _SCREAMING_SNAKE_CASE     ,  window=self.window     ,  frame_length=self.sample_size     ,  hop_length=self.sample_stride     ,  fft_length=self.n_fft     ,  mel_filters=self.mel_filters     ,  mel_floor=self.mel_floor     ,  log_mel='log10'     ,  )
				return log_mel_spec.T
		def __call__(       self     :     Optional[Any]     ,  snake_case__     :     Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None     ,  snake_case__     :     Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None     ,  snake_case__     :     Union[bool, str, PaddingStrategy] = False     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     bool = False     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[Union[str, TensorType]] = None     ,  snake_case__     :     Optional[int] = None     ,  **snake_case__     :     List[str]     ,  )      ->	int:




				if audio is None and audio_target is None:
						raise ValueError('You must provide either `audio` or `audio_target` values.'		)

				if sampling_rate is not None:
						if sampling_rate != self.sampling_rate:
								raise ValueError(
								    f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
								    f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
								    f""" {self.sampling_rate} and not {sampling_rate}."""		)
				else:
						logger.warning(
						    'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
						    'Failing to do so can result in silent errors that might be hard to debug.'		)

				if audio is not None:
						_lowerCamelCase				   =  self._process_audio(
						    _SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE     ,  **_SCREAMING_SNAKE_CASE     ,  )
				else:
						_lowerCamelCase				   =  None

				if audio_target is not None:
						_lowerCamelCase				   =  self._process_audio(
						    _SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE     ,  _SCREAMING_SNAKE_CASE     ,  **_SCREAMING_SNAKE_CASE     ,  )

						if inputs is None:
								return inputs_target
						else:
								_lowerCamelCase				   =  inputs_target['input_values']
								_lowerCamelCase				   =  inputs_target.get('attention_mask'		)
								if decoder_attention_mask is not None:
										_lowerCamelCase				   =  decoder_attention_mask

				return inputs
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,  snake_case__     :     bool = False     ,  snake_case__     :     Union[bool, str, PaddingStrategy] = False     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     bool = False     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[Union[str, TensorType]] = None     ,  **snake_case__     :     Union[str, Any]     ,  )      ->	List[str]:
				_lowerCamelCase				   =  isinstance(_SCREAMING_SNAKE_CASE     ,  np.ndarray		) and len(speech.shape		) > 1
				if is_batched_numpy and len(speech.shape		) > 2:
						raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""		)
				_lowerCamelCase				   =  is_batched_numpy or (
				    isinstance(_SCREAMING_SNAKE_CASE     ,  (list, tuple)		) and (isinstance(speech[0]     ,  (np.ndarray, tuple, list)		))
				)

				if is_batched:
						_lowerCamelCase				   =  [np.asarray(_SCREAMING_SNAKE_CASE     ,  dtype=np.floataa		) for speech in speech]
				elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE     ,  np.ndarray		):
						_lowerCamelCase				   =  np.asarray(_SCREAMING_SNAKE_CASE     ,  dtype=np.floataa		)
				elif isinstance(_SCREAMING_SNAKE_CASE     ,  np.ndarray		) and speech.dtype is np.dtype(np.floataa		):
						_lowerCamelCase				   =  speech.astype(np.floataa		)

				# always return batch
				if not is_batched:
						_lowerCamelCase				   =  [speech]

				# needed to make pad() work on spectrogram inputs
				_lowerCamelCase				   =  self.feature_size

				# convert into correct format for padding
				if is_target:
						_lowerCamelCase				   =  [self._extract_mel_features(_SCREAMING_SNAKE_CASE		) for waveform in speech]
						_lowerCamelCase				   =  BatchFeature({'input_values': features}		)
						_lowerCamelCase				   =  self.num_mel_bins
				else:
						_lowerCamelCase				   =  BatchFeature({'input_values': speech}		)

				_lowerCamelCase				   =  self.pad(
				    _SCREAMING_SNAKE_CASE     ,  padding=_SCREAMING_SNAKE_CASE     ,  max_length=_SCREAMING_SNAKE_CASE     ,  truncation=_SCREAMING_SNAKE_CASE     ,  pad_to_multiple_of=_SCREAMING_SNAKE_CASE     ,  return_attention_mask=_SCREAMING_SNAKE_CASE     ,  **_SCREAMING_SNAKE_CASE     ,  )

				_lowerCamelCase				   =  feature_size_hack

				# convert input values to correct format
				_lowerCamelCase				   =  padded_inputs['input_values']
				if not isinstance(input_values[0]     ,  np.ndarray		):
						_lowerCamelCase				   =  [np.asarray(_SCREAMING_SNAKE_CASE     ,  dtype=np.floataa		) for array in input_values]
				elif (
				    not isinstance(_SCREAMING_SNAKE_CASE     ,  np.ndarray		)
				    and isinstance(input_values[0]     ,  np.ndarray		)
				    and input_values[0].dtype is np.dtype(np.floataa		)
				):
						_lowerCamelCase				   =  [array.astype(np.floataa		) for array in input_values]
				elif isinstance(_SCREAMING_SNAKE_CASE     ,  np.ndarray		) and input_values.dtype is np.dtype(np.floataa		):
						_lowerCamelCase				   =  input_values.astype(np.floataa		)

				# convert attention_mask to correct format
				_lowerCamelCase				   =  padded_inputs.get('attention_mask'		)
				if attention_mask is not None:
						_lowerCamelCase				   =  [np.asarray(_SCREAMING_SNAKE_CASE     ,  dtype=np.intaa		) for array in attention_mask]

				# zero-mean and unit-variance normalization
				if not is_target and self.do_normalize:
						_lowerCamelCase				   =  (
						    attention_mask
						    if self._get_padding_strategies(_SCREAMING_SNAKE_CASE     ,  max_length=_SCREAMING_SNAKE_CASE		) is not PaddingStrategy.DO_NOT_PAD
						    else None
						)
						_lowerCamelCase				   =  self.zero_mean_unit_var_norm(
						    padded_inputs['input_values']     ,  attention_mask=_SCREAMING_SNAKE_CASE     ,  padding_value=self.padding_value		)

				if return_tensors is not None:
						_lowerCamelCase				   =  padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE		)

				return padded_inputs



		def   _snake_case       (       self     :     Dict		)      ->	Optional[Any]:
				_lowerCamelCase				   =  super().to_dict()

				# Don't serialize these as they are derived from the other properties.
				_lowerCamelCase				   =  ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
				for name in names:
						if name in output:
								del output[name]

				return output







import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__magic_name__   :  Optional[int]        =							get_tests_dir('''fixtures/spiece.model''')





@require_sentencepiece
@require_tokenizers
class 					A__     (     __snake_case   ,    unittest.TestCase ):

     '''simple docstring'''






     snake_case__					=					DebertaVaTokenizer
     snake_case__					=					DebertaVaTokenizerFast
     snake_case__					=					True
     snake_case__					=					True







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           super().setUp()

           # We have a SentencePiece fixture for testing
           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						unk_token='<unk>' )
           tokenizer.save_pretrained(self.tmpdirname )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  'this is a test'
           UpperCamelCase					       =  'this is a test'
           return input_text, output_text







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  '<pad>'
           UpperCamelCase					       =  0

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE )   ,						_SCREAMING_SNAKE_CASE )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE )   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  list(self.get_tokenizer().get_vocab().keys() )
           self.assertEqual(vocab_keys[0]   ,						'<pad>' )
           self.assertEqual(vocab_keys[1]   ,						'<unk>' )
           self.assertEqual(vocab_keys[-1]   ,						'[PAD]' )
           self.assertEqual(len(_SCREAMING_SNAKE_CASE )   ,						3_0001 )







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           self.assertEqual(self.get_tokenizer().vocab_size   ,						3_0000 )







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  ' \tHeLLo!how  \n Are yoU?  '
           UpperCamelCase					       =  ['▁hello', '!', 'how', '▁are', '▁you', '?']
           # fmt: on

           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  DebertaVaTokenizerFast(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     @unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.' )
     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           pass







     @unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.' )
     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  'I was born in 92000, and this is falsé.'
           UpperCamelCase					       =  ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
           # fmt: on

           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  DebertaVaTokenizerFast(_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  'I was born in 92000, and this is falsé.'
           UpperCamelCase					       =  ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
           # fmt: on

           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  DebertaVaTokenizerFast(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  'I was born in 92000, and this is falsé.'
           UpperCamelCase					       =  ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
           # fmt: on

           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  DebertaVaTokenizerFast(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  'I was born in 92000, and this is falsé.'
           UpperCamelCase					       =  ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
           # fmt: on

           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  DebertaVaTokenizerFast(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  ' \tHeLLo!how  \n Are yoU?  '
           UpperCamelCase					       =  ['▁', '<unk>', 'e', '<unk>', 'o', '!', 'how', '▁', '<unk>', 're', '▁yo', '<unk>', '?']
           # fmt: on

           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  DebertaVaTokenizerFast(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           UpperCamelCase					       =  self.get_tokenizer()
           UpperCamelCase					       =  self.get_rust_tokenizer()

           UpperCamelCase					       =  'I was born in 92000, and this is falsé.'

           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  self.get_rust_tokenizer()
           UpperCamelCase					       =  tokenizer.encode(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.encode(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           UpperCamelCase					       =  'This is a test'
           UpperCamelCase					       =  [13, 1, 4398, 25, 21, 1289]
           UpperCamelCase					       =  ['▁', 'T', 'his', '▁is', '▁a', '▁test']
           UpperCamelCase					       =  ['▁', '<unk>', 'his', '▁is', '▁a', '▁test']

           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						keep_accents=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  DebertaVaTokenizerFast(_SCREAMING_SNAKE_CASE   ,						keep_accents=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           # fmt: off
           UpperCamelCase					       =  'I was born in 92000, and this is falsé.'
           UpperCamelCase					       =  [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
           UpperCamelCase					       =  ['▁', 'I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.', ]
           UpperCamelCase					       =  ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
           # fmt: on

           UpperCamelCase					       =  tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  tokenizer.encode('sequence builders' )
           UpperCamelCase					       =  tokenizer.encode('multi-sequence build' )

           UpperCamelCase					       =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]   ,						_SCREAMING_SNAKE_CASE )
           self.assertEqual(
               [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id]   ,						_SCREAMING_SNAKE_CASE   ,						)







     @slow
     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  {'input_ids': [[1, 3_9867, 36, 1_9390, 486, 27, 3_5052, 8_1436, 18, 6_0685, 1225, 7, 3_5052, 8_1436, 18, 9367, 1_6899, 18, 1_5937, 53, 594, 773, 18, 1_6287, 3_0465, 36, 1_5937, 6, 4_1139, 38, 3_6979, 6_0763, 191, 6, 3_4132, 99, 6, 5_0538, 390, 4_3230, 6, 3_4132, 2779, 2_0850, 14, 699, 1072, 1194, 36, 382, 1_0901, 53, 7, 699, 1072, 2084, 36, 2_0422, 630, 53, 19, 105, 3049, 1896, 1053, 1_6899, 1506, 11, 3_7978, 4243, 7, 1237, 3_1869, 200, 1_6566, 654, 6, 3_5052, 8_1436, 7, 5_5630, 1_3593, 4, 2], [1, 26, 1_5011, 13, 667, 8, 1053, 18, 2_3611, 1237, 7_2356, 1_2820, 34, 10_4134, 1209, 35, 1_3313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 1_5785, 1_4951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=_SCREAMING_SNAKE_CASE   ,						model_name='microsoft/deberta-v2-xlarge'   ,						revision='ad6e42c1532ddf3a15c39246b63f5559d558b670'   ,						)



