"""simple docstring"""







import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
				from transformers import WhisperFeatureExtractor

if is_torch_available():
				import torch

SCREAMING_SNAKE_CASE									=						random.Random()


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase=1.0     ,__UpperCAmelCase=None     ,__UpperCAmelCase=None			):






	"""simple docstring"""





	if rng is None:
		_lowercase :    List[str]							=							global_rng

	_lowercase :    List[Any]							=							[]
	for batch_idx in range(shape[0]			):
		values.append([]			)
		for _ in range(shape[1]			):
			values[-1].append(rng.random() * scale			)

	return values



@require_torch
@require_torchaudio
class  _lowerCamelCase			(unittest.TestCase     ):







				def __init__( self    :       Dict			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       List[Any]=7			,					lowerCamelCase_    :       Any=4_0_0			,					lowerCamelCase_    :       int=2_0_0_0			,					lowerCamelCase_    :       str=1_0			,					lowerCamelCase_    :       List[str]=1_6_0			,					lowerCamelCase_    :       Optional[Any]=8			,					lowerCamelCase_    :       Optional[int]=0.0			,					lowerCamelCase_    :       Union[str, Any]=4_0_0_0			,					lowerCamelCase_    :       Union[str, Any]=False			,					lowerCamelCase_    :       Optional[Any]=True			,					):

					"""simple docstring"""






					_lowercase :    str							=							parent
					_lowercase :    List[Any]							=							batch_size
					_lowercase :    Union[str, Any]							=							min_seq_length
					_lowercase :    Dict							=							max_seq_length
					_lowercase :    List[str]							=							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					_lowercase :    str							=							padding_value
					_lowercase :    Dict							=							sampling_rate
					_lowercase :    Any							=							return_attention_mask
					_lowercase :    Union[str, Any]							=							do_normalize
					_lowercase :    Optional[Any]							=							feature_size
					_lowercase :    Optional[Any]							=							chunk_length
					_lowercase :    List[Any]							=							hop_length







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					return {
					    "feature_size": self.feature_size,
					    "hop_length": self.hop_length,
					    "chunk_length": self.chunk_length,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "return_attention_mask": self.return_attention_mask,
					    "do_normalize": self.do_normalize,
					}







				def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       Any=False			,					lowerCamelCase_    :       Union[str, Any]=False						):

					"""simple docstring"""






					def _flatten(lowerCamelCase_    :       str						):
						return list(itertools.chain(*lowerCamelCase_						)						)

					if equal_length:
						_lowercase :    Optional[Any]							=							[floats_list((self.max_seq_length, self.feature_size)						) for _ in range(self.batch_size						)]
					else:
						# make sure that inputs increase in size
						_lowercase :    Optional[int]							=							[
						    floats_list((x, self.feature_size)						)
						    for x in range(self.min_seq_length			,					self.max_seq_length			,					self.seq_length_diff						)
						]
					if numpify:
						_lowercase :    List[str]							=							[np.asarray(lowerCamelCase_						) for x in speech_inputs]
					return speech_inputs



@require_torch
@require_torchaudio
class  _lowerCamelCase			(__lowerCamelCase						,							unittest.TestCase     ):
				_snake_case     =  WhisperFeatureExtractor if is_speech_available() else None







				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					_lowercase :    str							=							WhisperFeatureExtractionTester(self						)







				def 							__UpperCAmelCase					( self    :       Optional[int]						):

					"""simple docstring"""






					_lowercase :    List[Any]							=							self.feature_extraction_class(**self.feat_extract_dict						)

					with tempfile.TemporaryDirectory() as tmpdirname:
						_lowercase :    Optional[Any]							=							feat_extract_first.save_pretrained(lowerCamelCase_						)[0]
						check_json_file_has_correct_format(lowerCamelCase_						)
						_lowercase :    Optional[int]							=							self.feature_extraction_class.from_pretrained(lowerCamelCase_						)

					_lowercase :    str							=							feat_extract_first.to_dict()
					_lowercase :    Optional[int]							=							feat_extract_second.to_dict()
					_lowercase :    Tuple							=							feat_extract_first.mel_filters
					_lowercase :    int							=							feat_extract_second.mel_filters
					self.assertTrue(np.allclose(lowerCamelCase_			,					lowerCamelCase_						)						)
					self.assertEqual(lowerCamelCase_			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					_lowercase :    Any							=							self.feature_extraction_class(**self.feat_extract_dict						)

					with tempfile.TemporaryDirectory() as tmpdirname:
						_lowercase :    Dict							=							os.path.join(lowerCamelCase_			,					'feat_extract.json'						)
						feat_extract_first.to_json_file(lowerCamelCase_						)
						_lowercase :    Tuple							=							self.feature_extraction_class.from_json_file(lowerCamelCase_						)

					_lowercase :    Tuple							=							feat_extract_first.to_dict()
					_lowercase :    List[str]							=							feat_extract_second.to_dict()
					_lowercase :    List[str]							=							feat_extract_first.mel_filters
					_lowercase :    Tuple							=							feat_extract_second.mel_filters
					self.assertTrue(np.allclose(lowerCamelCase_			,					lowerCamelCase_						)						)
					self.assertEqual(lowerCamelCase_			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					_lowercase :    Any							=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					# create three inputs of length 800, 1000, and 1200
					_lowercase :    Union[str, Any]							=							[floats_list((1, x)						)[0] for x in range(8_0_0			,					1_4_0_0			,					2_0_0						)]
					_lowercase :    Optional[Any]							=							[np.asarray(lowerCamelCase_						) for speech_input in speech_inputs]

					# Test feature size
					_lowercase :    List[Any]							=							feature_extractor(lowerCamelCase_			,					padding='max_length'			,					return_tensors='np'						).input_features
					self.assertTrue(input_features.ndim == 3						)
					self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames						)
					self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size						)

					# Test not batched input
					_lowercase :    str							=							feature_extractor(speech_inputs[0]			,					return_tensors='np'						).input_features
					_lowercase :    List[str]							=							feature_extractor(np_speech_inputs[0]			,					return_tensors='np'						).input_features
					self.assertTrue(np.allclose(lowerCamelCase_			,					lowerCamelCase_			,					atol=1E-3						)						)

					# Test batched
					_lowercase :    Optional[Any]							=							feature_extractor(lowerCamelCase_			,					return_tensors='np'						).input_features
					_lowercase :    str							=							feature_extractor(lowerCamelCase_			,					return_tensors='np'						).input_features
					for enc_seq_a, enc_seq_a in zip(lowerCamelCase_			,					lowerCamelCase_						):
						self.assertTrue(np.allclose(lowerCamelCase_			,					lowerCamelCase_			,					atol=1E-3						)						)

					# Test 2-D numpy arrays are batched.
					_lowercase :    List[Any]							=							[floats_list((1, x)						)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
					_lowercase :    Optional[int]							=							np.asarray(lowerCamelCase_						)
					_lowercase :    int							=							feature_extractor(lowerCamelCase_			,					return_tensors='np'						).input_features
					_lowercase :    Optional[Any]							=							feature_extractor(lowerCamelCase_			,					return_tensors='np'						).input_features
					for enc_seq_a, enc_seq_a in zip(lowerCamelCase_			,					lowerCamelCase_						):
						self.assertTrue(np.allclose(lowerCamelCase_			,					lowerCamelCase_			,					atol=1E-3						)						)

					# Test truncation required
					_lowercase :    Optional[int]							=							[floats_list((1, x)						)[0] for x in range(2_0_0			,					(feature_extractor.n_samples + 5_0_0)			,					2_0_0						)]
					_lowercase :    int							=							[np.asarray(lowerCamelCase_						) for speech_input in speech_inputs]

					_lowercase :    List[Any]							=							[x[: feature_extractor.n_samples] for x in speech_inputs]
					_lowercase :    Optional[int]							=							[np.asarray(lowerCamelCase_						) for speech_input in speech_inputs_truncated]

					_lowercase :    Union[str, Any]							=							feature_extractor(lowerCamelCase_			,					return_tensors='np'						).input_features
					_lowercase :    Optional[int]							=							feature_extractor(lowerCamelCase_			,					return_tensors='np'						).input_features
					for enc_seq_a, enc_seq_a in zip(lowerCamelCase_			,					lowerCamelCase_						):
						self.assertTrue(np.allclose(lowerCamelCase_			,					lowerCamelCase_			,					atol=1E-3						)						)







				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					import torch

					_lowercase :    Optional[int]							=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					_lowercase :    Any							=							np.random.rand(1_0_0			,					3_2						).astype(np.floataa						)
					_lowercase :    str							=							np_speech_inputs.tolist()

					for inputs in [py_speech_inputs, np_speech_inputs]:
						_lowercase :    str							=							feature_extractor.pad([{'input_features': inputs}]			,					return_tensors='np'						)
						self.assertTrue(np_processed.input_features.dtype == np.floataa						)
						_lowercase :    List[str]							=							feature_extractor.pad([{'input_features': inputs}]			,					return_tensors='pt'						)
						self.assertTrue(pt_processed.input_features.dtype == torch.floataa						)







				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       Dict						):

					"""simple docstring"""






					_lowercase :    List[Any]							=							load_dataset('hf-internal-testing/librispeech_asr_dummy'			,					'clean'			,					split='validation'						)
					# automatic decoding with librispeech
					_lowercase :    List[str]							=							ds.sort('id'						).select(range(lowerCamelCase_						)						)[:num_samples]['audio']

					return [x["array"] for x in speech_samples]







				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					_lowercase :    List[str]							=							torch.tensor(
					    [
					        0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
					        0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
					        0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
					        -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
					    ]						)
					# fmt: on

					_lowercase :    Optional[Any]							=							self._load_datasamples(1						)
					_lowercase :    str							=							WhisperFeatureExtractor()
					_lowercase :    Tuple							=							feature_extractor(lowerCamelCase_			,					return_tensors='pt'						).input_features
					self.assertEqual(input_features.shape			,					(1, 8_0, 3_0_0_0)						)
					self.assertTrue(torch.allclose(input_features[0, 0, :3_0]			,					lowerCamelCase_			,					atol=1E-4						)						)







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    Optional[Any]							=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					_lowercase :    Union[str, Any]							=							self._load_datasamples(1						)[0]
					_lowercase :    str							=							((audio - audio.min()) / (audio.max() - audio.min())) * 6_5_5_3_5  # Rescale to [0, 65535] to show issue
					_lowercase :    List[Any]							=							feat_extract.zero_mean_unit_var_norm([audio]			,					attention_mask=lowerCamelCase_						)[0]

					self.assertTrue(np.all(np.mean(lowerCamelCase_						) < 1E-3						)						)
					self.assertTrue(np.all(np.abs(np.var(lowerCamelCase_						) - 1						) < 1E-3						)						)




"""simple docstring"""







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


SCREAMING_SNAKE_CASE									=						get_tests_dir('fixtures/spiece.model')



@require_sentencepiece
@require_tokenizers
class  _lowerCamelCase			(__lowerCamelCase						,							unittest.TestCase     ):
				_snake_case     =  DebertaVaTokenizer
				_snake_case     =  DebertaVaTokenizerFast
				_snake_case     =  True
				_snake_case     =  True







				def 							__UpperCAmelCase					( self    :       List[Any]						):

					"""simple docstring"""






					super().setUp()

					# We have a SentencePiece fixture for testing
					_lowercase :    Dict							=							DebertaVaTokenizer(lowerCamelCase_			,					unk_token='<unk>'						)
					tokenizer.save_pretrained(self.tmpdirname						)







				def 							__UpperCAmelCase					( self    :       Dict			,					lowerCamelCase_    :       int						):

					"""simple docstring"""






					_lowercase :    Optional[Any]							=							'this is a test'
					_lowercase :    int							=							'this is a test'
					return input_text, output_text







				def 							__UpperCAmelCase					( self    :       List[Any]						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							'<pad>'
					_lowercase :    List[Any]							=							0

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







				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							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(lowerCamelCase_						)			,					3_0_0_0_1						)







				def 							__UpperCAmelCase					( self    :       str						):

					"""simple docstring"""






					self.assertEqual(self.get_tokenizer().vocab_size			,					3_0_0_0_0						)







				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






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

					_lowercase :    Optional[int]							=							DebertaVaTokenizer(lowerCamelCase_			,					do_lower_case=lowerCamelCase_						)
					_lowercase :    Dict							=							tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)						)

					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)

					_lowercase :    int							=							DebertaVaTokenizerFast(lowerCamelCase_			,					do_lower_case=lowerCamelCase_						)
					_lowercase :    Dict							=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)						)

					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)







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

					"""simple docstring"""






					pass







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






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

					_lowercase :    Union[str, Any]							=							DebertaVaTokenizer(lowerCamelCase_			,					split_by_punct=lowerCamelCase_						)
					_lowercase :    Optional[int]							=							tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)						)

					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)

					_lowercase :    Optional[int]							=							DebertaVaTokenizerFast(lowerCamelCase_			,					split_by_punct=lowerCamelCase_						)
					_lowercase :    Optional[int]							=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)						)

					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






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

					_lowercase :    Union[str, Any]							=							DebertaVaTokenizer(lowerCamelCase_			,					do_lower_case=lowerCamelCase_			,					split_by_punct=lowerCamelCase_						)
					_lowercase :    int							=							tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)

					_lowercase :    Dict							=							DebertaVaTokenizerFast(lowerCamelCase_			,					do_lower_case=lowerCamelCase_			,					split_by_punct=lowerCamelCase_						)
					_lowercase :    List[Any]							=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Union[str, Any]						):

					"""simple docstring"""






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

					_lowercase :    Union[str, Any]							=							DebertaVaTokenizer(lowerCamelCase_			,					do_lower_case=lowerCamelCase_			,					split_by_punct=lowerCamelCase_						)
					_lowercase :    Optional[int]							=							tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)						)

					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)

					_lowercase :    Union[str, Any]							=							DebertaVaTokenizerFast(lowerCamelCase_			,					do_lower_case=lowerCamelCase_			,					split_by_punct=lowerCamelCase_						)
					_lowercase :    Dict							=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)						)

					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






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

					_lowercase :    Optional[int]							=							DebertaVaTokenizer(lowerCamelCase_			,					do_lower_case=lowerCamelCase_			,					split_by_punct=lowerCamelCase_						)
					_lowercase :    Dict							=							tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)						)

					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)

					_lowercase :    Tuple							=							DebertaVaTokenizerFast(lowerCamelCase_			,					do_lower_case=lowerCamelCase_			,					split_by_punct=lowerCamelCase_						)
					_lowercase :    Any							=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)						)

					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






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

					_lowercase :    Dict							=							DebertaVaTokenizer(lowerCamelCase_			,					do_lower_case=lowerCamelCase_			,					split_by_punct=lowerCamelCase_						)
					_lowercase :    Dict							=							tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)						)

					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)

					_lowercase :    str							=							DebertaVaTokenizerFast(lowerCamelCase_			,					do_lower_case=lowerCamelCase_			,					split_by_punct=lowerCamelCase_						)
					_lowercase :    Dict							=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)						)

					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Union[str, Any]						):

					"""simple docstring"""






					_lowercase :    int							=							self.get_tokenizer()
					_lowercase :    str							=							self.get_rust_tokenizer()

					_lowercase :    Optional[int]							=							'I was born in 92000, and this is falsé.'

					_lowercase :    List[str]							=							tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)						)
					_lowercase :    Optional[int]							=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)

					_lowercase :    List[str]							=							tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)
					_lowercase :    List[Any]							=							rust_tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)

					_lowercase :    List[Any]							=							self.get_rust_tokenizer()
					_lowercase :    Tuple							=							tokenizer.encode(lowerCamelCase_						)
					_lowercase :    int							=							rust_tokenizer.encode(lowerCamelCase_						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							'This is a test'
					_lowercase :    Tuple							=							[1_3, 1, 4_3_9_8, 2_5, 2_1, 1_2_8_9]
					_lowercase :    Optional[Any]							=							['▁', 'T', 'his', '▁is', '▁a', '▁test']
					_lowercase :    str							=							['▁', '<unk>', 'his', '▁is', '▁a', '▁test']

					_lowercase :    List[Any]							=							DebertaVaTokenizer(lowerCamelCase_			,					keep_accents=lowerCamelCase_						)
					_lowercase :    Optional[Any]							=							DebertaVaTokenizerFast(lowerCamelCase_			,					keep_accents=lowerCamelCase_						)

					_lowercase :    Union[str, Any]							=							tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)
					_lowercase :    Optional[Any]							=							tokenizer.tokenize(lowerCamelCase_						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)
					_lowercase :    str							=							tokenizer.convert_ids_to_tokens(lowerCamelCase_						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)

					_lowercase :    int							=							rust_tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)
					_lowercase :    int							=							rust_tokenizer.tokenize(lowerCamelCase_						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)
					_lowercase :    int							=							rust_tokenizer.convert_ids_to_tokens(lowerCamelCase_						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)

					# fmt: off
					_lowercase :    str							=							'I was born in 92000, and this is falsé.'
					_lowercase :    str							=							[1_3, 1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]
					_lowercase :    str							=							['▁', 'I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.', ]
					_lowercase :    Optional[int]							=							['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
					# fmt: on

					_lowercase :    Tuple							=							tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)
					_lowercase :    List[str]							=							tokenizer.tokenize(lowerCamelCase_						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)
					_lowercase :    Union[str, Any]							=							tokenizer.convert_ids_to_tokens(lowerCamelCase_						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)

					_lowercase :    List[str]							=							rust_tokenizer.encode(lowerCamelCase_			,					add_special_tokens=lowerCamelCase_						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)
					_lowercase :    Any							=							rust_tokenizer.tokenize(lowerCamelCase_						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)
					_lowercase :    str							=							rust_tokenizer.convert_ids_to_tokens(lowerCamelCase_						)
					self.assertListEqual(lowerCamelCase_			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Union[str, Any]						):

					"""simple docstring"""






					_lowercase :    Optional[Any]							=							DebertaVaTokenizer(lowerCamelCase_						)

					_lowercase :    str							=							tokenizer.encode('sequence builders'						)
					_lowercase :    str							=							tokenizer.encode('multi-sequence build'						)

					_lowercase :    Tuple							=							tokenizer.build_inputs_with_special_tokens(lowerCamelCase_						)
					_lowercase :    Union[str, Any]							=							tokenizer.build_inputs_with_special_tokens(lowerCamelCase_			,					lowerCamelCase_						)

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







				@slow
				def 							__UpperCAmelCase					( self    :       Union[str, Any]						):

					"""simple docstring"""






					_lowercase :    Any							=							{'input_ids': [[1, 3_9_8_6_7, 3_6, 1_9_3_9_0, 4_8_6, 2_7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 6_0_6_8_5, 1_2_2_5, 7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 9_3_6_7, 1_6_8_9_9, 1_8, 1_5_9_3_7, 5_3, 5_9_4, 7_7_3, 1_8, 1_6_2_8_7, 3_0_4_6_5, 3_6, 1_5_9_3_7, 6, 4_1_1_3_9, 3_8, 3_6_9_7_9, 6_0_7_6_3, 1_9_1, 6, 3_4_1_3_2, 9_9, 6, 5_0_5_3_8, 3_9_0, 4_3_2_3_0, 6, 3_4_1_3_2, 2_7_7_9, 2_0_8_5_0, 1_4, 6_9_9, 1_0_7_2, 1_1_9_4, 3_6, 3_8_2, 1_0_9_0_1, 5_3, 7, 6_9_9, 1_0_7_2, 2_0_8_4, 3_6, 2_0_4_2_2, 6_3_0, 5_3, 1_9, 1_0_5, 3_0_4_9, 1_8_9_6, 1_0_5_3, 1_6_8_9_9, 1_5_0_6, 1_1, 3_7_9_7_8, 4_2_4_3, 7, 1_2_3_7, 3_1_8_6_9, 2_0_0, 1_6_5_6_6, 6_5_4, 6, 3_5_0_5_2, 8_1_4_3_6, 7, 5_5_6_3_0, 1_3_5_9_3, 4, 2], [1, 2_6, 1_5_0_1_1, 1_3, 6_6_7, 8, 1_0_5_3, 1_8, 2_3_6_1_1, 1_2_3_7, 7_2_3_5_6, 1_2_8_2_0, 3_4, 1_0_4_1_3_4, 1_2_0_9, 3_5, 1_3_3_1_3, 6_6_2_7, 2_1, 2_0_2, 3_4_7, 7, 1_6_4, 2_3_9_9, 1_1, 4_6, 4_4_8_5, 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, 1_2_3_2, 2_8_6_4, 1_5_7_8_5, 1_4_9_5_1, 1_0_5, 5, 8_5_8_1, 1_2_5_0, 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=lowerCamelCase_			,					model_name='microsoft/deberta-v2-xlarge'			,					revision='ad6e42c1532ddf3a15c39246b63f5559d558b670'			,					)



