





import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
				import torch


UpperCAmelCase						:   str			  =     random.Random()







def  _SCREAMING_SNAKE_CASE				(   a					,						a=1.0					,						a=None					,						a=None  )      ->      Optional[int]:
					if rng is None:
										__A	:						Dict       =				global_rng

					__A	:						Union[str, 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
class 						_A(					unittest.TestCase   ):


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A=7     ,					_A=400     ,					_A=2000     ,					_A=1     ,					_A=0.0     ,					_A=16000     ,					_A=True     ,					_A=80     ,					_A=16     ,					_A=64     ,					_A="hann_window"     ,					_A=80     ,					_A=7600     ,					_A=1e-1_0     ,					_A=True     ,					):
									__A	:						Optional[Any]       =				parent
									__A	:						Any       =				batch_size
									__A	:						Tuple       =				min_seq_length
									__A	:						str       =				max_seq_length
									__A	:						Union[str, Any]       =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									__A	:						List[str]       =				feature_size
									__A	:						int       =				padding_value
									__A	:						Optional[Any]       =				sampling_rate
									__A	:						Any       =				do_normalize
									__A	:						List[str]       =				num_mel_bins
									__A	:						int       =				hop_length
									__A	:						Tuple       =				win_length
									__A	:						Union[str, Any]       =				win_function
									__A	:						Union[str, Any]       =				fmin
									__A	:						int       =				fmax
									__A	:						Union[str, Any]       =				mel_floor
									__A	:						Optional[int]       =				return_attention_mask



				def 					UpperCAmelCase_		(			self     ):
									return {
									    "feature_size": self.feature_size,
									    "padding_value": self.padding_value,
									    "sampling_rate": self.sampling_rate,
									    "do_normalize": self.do_normalize,
									    "num_mel_bins": self.num_mel_bins,
									    "hop_length": self.hop_length,
									    "win_length": self.win_length,
									    "win_function": self.win_function,
									    "fmin": self.fmin,
									    "fmax": self.fmax,
									    "mel_floor": self.mel_floor,
									    "return_attention_mask": self.return_attention_mask,
									}



				def 					UpperCAmelCase_		(			self     ,					_A=False     ,					_A=False     ):
									def _flatten(_A     ):
														return list(itertools.chain(*_A     )     )

									if equal_length:
														__A	:						int       =				floats_list((self.batch_size, self.max_seq_length)     )
									else:
														# make sure that inputs increase in size
														__A	:						int       =				[
														    _flatten(floats_list((x, self.feature_size)     )     )
														    for x in range(self.min_seq_length     ,					self.max_seq_length     ,					self.seq_length_diff     )
														]

									if numpify:
														__A	:						Optional[int]       =				[np.asarray(_A     ) for x in speech_inputs]

									return speech_inputs





				def 					UpperCAmelCase_		(			self     ,					_A=False     ,					_A=False     ):
									if equal_length:
														__A	:						List[Any]       =				[floats_list((self.max_seq_length, self.num_mel_bins)     ) for _ in range(self.batch_size     )]
									else:
														# make sure that inputs increase in size
														__A	:						Dict       =				[
														    floats_list((x, self.num_mel_bins)     )
														    for x in range(self.min_seq_length     ,					self.max_seq_length     ,					self.seq_length_diff     )
														]

									if numpify:
														__A	:						Optional[Any]       =				[np.asarray(_A     ) for x in speech_inputs]

									return speech_inputs





@require_torch
class 						_A(					snake_case__				, unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  Optional[Any]					    =	SpeechTaFeatureExtractor



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Tuple       =				SpeechTaFeatureExtractionTester(self     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									self.assertTrue(np.all(np.mean(_A     ,					axis=0     ) < 1e-3     )     )
									self.assertTrue(np.all(np.abs(np.var(_A     ,					axis=0     ) - 1     ) < 1e-3     )     )



				def 					UpperCAmelCase_		(			self     ):
									# Tests that all call wrap to encode_plus and batch_encode_plus
									__A	:						Any       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
									# create three inputs of length 800, 1000, and 1200
									__A	:						Dict       =				[floats_list((1, x)     )[0] for x in range(800     ,					1400     ,					200     )]
									__A	:						Optional[Any]       =				[np.asarray(_A     ) for speech_input in speech_inputs]

									# Test not batched input
									__A	:						List[str]       =				feat_extract(speech_inputs[0]     ,					return_tensors='np'     ).input_values
									__A	:						Any       =				feat_extract(np_speech_inputs[0]     ,					return_tensors='np'     ).input_values
									self.assertTrue(np.allclose(_A     ,					_A     ,					atol=1e-3     )     )

									# Test batched
									__A	:						List[str]       =				feat_extract(_A     ,					return_tensors='np'     ).input_values
									__A	:						int       =				feat_extract(_A     ,					return_tensors='np'     ).input_values
									for enc_seq_a, enc_seq_a in zip(_A     ,					_A     ):
														self.assertTrue(np.allclose(_A     ,					_A     ,					atol=1e-3     )     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
									__A	:						Union[str, Any]       =				[floats_list((1, x)     )[0] for x in range(800     ,					1400     ,					200     )]

									__A	:						Union[str, Any]       =				['longest', 'max_length', 'do_not_pad']
									__A	:						List[str]       =				[None, 1600, None]
									for max_length, padding in zip(_A     ,					_A     ):
														__A	:						Tuple       =				feat_extract(_A     ,					padding=_A     ,					max_length=_A     ,					return_tensors='np'     )
														__A	:						Dict       =				processed.input_values

														self._check_zero_mean_unit_variance(input_values[0][:800]     )
														self.assertTrue(input_values[0][800:].sum() < 1e-6     )
														self._check_zero_mean_unit_variance(input_values[1][:1000]     )
														self.assertTrue(input_values[0][1000:].sum() < 1e-6     )
														self._check_zero_mean_unit_variance(input_values[2][:1200]     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Any       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
									__A	:						int       =				range(800     ,					1400     ,					200     )
									__A	:						Optional[Any]       =				[floats_list((1, x)     )[0] for x in lengths]

									__A	:						Any       =				['longest', 'max_length', 'do_not_pad']
									__A	:						int       =				[None, 1600, None]

									for max_length, padding in zip(_A     ,					_A     ):
														__A	:						List[str]       =				feat_extract(_A     ,					max_length=_A     ,					padding=_A     )
														__A	:						Optional[int]       =				processed.input_values

														self._check_zero_mean_unit_variance(input_values[0][:800]     )
														self._check_zero_mean_unit_variance(input_values[1][:1000]     )
														self._check_zero_mean_unit_variance(input_values[2][:1200]     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[str]       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
									__A	:						int       =				[floats_list((1, x)     )[0] for x in range(800     ,					1400     ,					200     )]
									__A	:						Dict       =				feat_extract(
									    _A     ,					truncation=_A     ,					max_length=1000     ,					padding='max_length'     ,					return_tensors='np'     )
									__A	:						Union[str, Any]       =				processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]     )
									self._check_zero_mean_unit_variance(input_values[1]     )
									self._check_zero_mean_unit_variance(input_values[2]     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Any       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
									__A	:						int       =				[floats_list((1, x)     )[0] for x in range(800     ,					1400     ,					200     )]
									__A	:						Tuple       =				feat_extract(
									    _A     ,					truncation=_A     ,					max_length=1000     ,					padding='longest'     ,					return_tensors='np'     )
									__A	:						Tuple       =				processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]     )
									self._check_zero_mean_unit_variance(input_values[1, :1000]     )
									self._check_zero_mean_unit_variance(input_values[2]     )

									# make sure that if max_length < longest -> then pad to max_length
									self.assertTrue(input_values.shape == (3, 1000)     )

									__A	:						List[str]       =				[floats_list((1, x)     )[0] for x in range(800     ,					1400     ,					200     )]
									__A	:						str       =				feat_extract(
									    _A     ,					truncation=_A     ,					max_length=2000     ,					padding='longest'     ,					return_tensors='np'     )
									__A	:						List[Any]       =				processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]     )
									self._check_zero_mean_unit_variance(input_values[1, :1000]     )
									self._check_zero_mean_unit_variance(input_values[2]     )

									# make sure that if max_length > longest -> then pad to longest
									self.assertTrue(input_values.shape == (3, 1200)     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Any       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
									__A	:						List[str]       =				np.random.rand(100     ).astype(np.floataa     )
									__A	:						Optional[int]       =				np_speech_inputs.tolist()

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



				def 					UpperCAmelCase_		(			self     ):
									# Tests that all call wrap to encode_plus and batch_encode_plus
									__A	:						Optional[int]       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
									# create three inputs of length 800, 1000, and 1200
									__A	:						Union[str, Any]       =				[floats_list((1, x)     )[0] for x in range(800     ,					1400     ,					200     )]
									__A	:						List[str]       =				[np.asarray(_A     ) for speech_input in speech_inputs]

									# Test feature size
									__A	:						Dict       =				feature_extractor(audio_target=_A     ,					padding=_A     ,					return_tensors='np'     ).input_values
									self.assertTrue(input_values.ndim == 3     )
									self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins     )

									# Test not batched input
									__A	:						Dict       =				feature_extractor(speech_inputs[0]     ,					return_tensors='np'     ).input_values
									__A	:						List[str]       =				feature_extractor(np_speech_inputs[0]     ,					return_tensors='np'     ).input_values
									self.assertTrue(np.allclose(_A     ,					_A     ,					atol=1e-3     )     )

									# Test batched
									__A	:						Union[str, Any]       =				feature_extractor(_A     ,					return_tensors='np'     ).input_values
									__A	:						Any       =				feature_extractor(_A     ,					return_tensors='np'     ).input_values
									for enc_seq_a, enc_seq_a in zip(_A     ,					_A     ):
														self.assertTrue(np.allclose(_A     ,					_A     ,					atol=1e-3     )     )

									# Test 2-D numpy arrays are batched.
									__A	:						Union[str, Any]       =				[floats_list((1, x)     )[0] for x in (800, 800, 800)]
									__A	:						Union[str, Any]       =				np.asarray(_A     )
									__A	:						Optional[int]       =				feature_extractor(_A     ,					return_tensors='np'     ).input_values
									__A	:						List[Any]       =				feature_extractor(_A     ,					return_tensors='np'     ).input_values
									for enc_seq_a, enc_seq_a in zip(_A     ,					_A     ):
														self.assertTrue(np.allclose(_A     ,					_A     ,					atol=1e-3     )     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Dict       =				self.feat_extract_tester.prepare_inputs_for_target()
									__A	:						str       =				self.feature_extraction_class(**self.feat_extract_dict     )
									__A	:						Any       =				feat_extract.model_input_names[0]

									__A	:						Any       =				BatchFeature({input_name: speech_inputs}     )

									self.assertTrue(all(len(_A     ) == len(_A     ) for x, y in zip(_A     ,					processed_features[input_name]     )     )     )

									__A	:						int       =				self.feat_extract_tester.prepare_inputs_for_target(equal_length=_A     )
									__A	:						int       =				BatchFeature({input_name: speech_inputs}     ,					tensor_type='np'     )

									__A	:						Dict       =				processed_features[input_name]

									if len(batch_features_input.shape     ) < 3:
														__A	:						Tuple       =				batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]     ), self.feat_extract_tester.num_mel_bins)     )



				@require_torch
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Tuple       =				self.feat_extract_tester.prepare_inputs_for_target(equal_length=_A     )
									__A	:						Optional[int]       =				self.feature_extraction_class(**self.feat_extract_dict     )
									__A	:						Union[str, Any]       =				feat_extract.model_input_names[0]

									__A	:						Dict       =				BatchFeature({input_name: speech_inputs}     ,					tensor_type='pt'     )

									__A	:						str       =				processed_features[input_name]

									if len(batch_features_input.shape     ) < 3:
														__A	:						Tuple       =				batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]     ), self.feat_extract_tester.num_mel_bins)     )



				@require_torch
				def 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				self.feature_extraction_class(**self.feat_extract_dict     )
									__A	:						Optional[Any]       =				self.feat_extract_tester.prepare_inputs_for_target()
									__A	:						int       =				feat_extract.model_input_names[0]

									__A	:						Optional[Any]       =				BatchFeature({input_name: speech_inputs}     )

									__A	:						Union[str, Any]       =				feat_extract.num_mel_bins  # hack!

									__A	:						Dict       =				feat_extract.pad(_A     ,					padding='longest'     ,					return_tensors='np'     )[input_name]
									__A	:						str       =				feat_extract.pad(_A     ,					padding='longest'     ,					return_tensors='pt'     )[input_name]

									self.assertTrue(abs(input_np.astype(np.floataa     ).sum() - input_pt.numpy().astype(np.floataa     ).sum()     ) < 1e-2     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Dict       =				self.feat_extract_dict
									__A	:						str       =				True
									__A	:						Union[str, Any]       =				self.feature_extraction_class(**_A     )
									__A	:						str       =				self.feat_extract_tester.prepare_inputs_for_target()
									__A	:						Optional[int]       =				[len(_A     ) for x in speech_inputs]
									__A	:						Any       =				feat_extract.model_input_names[0]

									__A	:						int       =				BatchFeature({input_name: speech_inputs}     )

									__A	:						Union[str, Any]       =				feat_extract.num_mel_bins  # hack!

									__A	:						Dict       =				feat_extract.pad(_A     ,					padding='longest'     ,					return_tensors='np'     )
									self.assertIn('attention_mask'     ,					_A     )
									self.assertListEqual(list(processed.attention_mask.shape     )     ,					list(processed[input_name].shape[:2]     )     )
									self.assertListEqual(processed.attention_mask.sum(-1     ).tolist()     ,					_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Union[str, Any]       =				self.feat_extract_dict
									__A	:						List[Any]       =				True
									__A	:						Optional[int]       =				self.feature_extraction_class(**_A     )
									__A	:						int       =				self.feat_extract_tester.prepare_inputs_for_target()
									__A	:						Union[str, Any]       =				[len(_A     ) for x in speech_inputs]
									__A	:						Union[str, Any]       =				feat_extract.model_input_names[0]

									__A	:						str       =				BatchFeature({input_name: speech_inputs}     )
									__A	:						Dict       =				min(_A     )

									__A	:						List[Any]       =				feat_extract.num_mel_bins  # hack!

									__A	:						Optional[Any]       =				feat_extract.pad(
									    _A     ,					padding='max_length'     ,					max_length=_A     ,					truncation=_A     ,					return_tensors='np'     )
									self.assertIn('attention_mask'     ,					_A     )
									self.assertListEqual(
									    list(processed_pad.attention_mask.shape     )     ,					[processed_pad[input_name].shape[0], max_length]     )
									self.assertListEqual(
									    processed_pad.attention_mask[:, :max_length].sum(-1     ).tolist()     ,					[max_length for x in speech_inputs]     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									from datasets import load_dataset

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

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



				def 					UpperCAmelCase_		(			self     ):
									# fmt: off
									__A	:						Optional[Any]       =				torch.tensor(
									    [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
									     3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
									     2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
									     4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
									     7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
									     4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]     )
									# fmt: on

									__A	:						Any       =				self._load_datasamples(1     )
									__A	:						Tuple       =				SpeechTaFeatureExtractor()
									__A	:						str       =				feature_extractor(_A     ,					return_tensors='pt'     ).input_values
									self.assertEquals(input_values.shape     ,					(1, 93680)     )
									self.assertTrue(torch.allclose(input_values[0, :30]     ,					_A     ,					atol=1e-6     )     )





				def 					UpperCAmelCase_		(			self     ):
									# fmt: off
									__A	:						Optional[int]       =				torch.tensor(
									    [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
									     -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
									     -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
									     -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8]     )
									# fmt: on

									__A	:						List[str]       =				self._load_datasamples(1     )
									__A	:						Optional[Any]       =				SpeechTaFeatureExtractor()
									__A	:						Optional[int]       =				feature_extractor(audio_target=_A     ,					return_tensors='pt'     ).input_values
									self.assertEquals(input_values.shape     ,					(1, 366, 80)     )
									self.assertTrue(torch.allclose(input_values[0, 0, :30]     ,					_A     ,					atol=1e-4     )     )











import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase						:   List[str]			  =     '''▁'''

UpperCAmelCase						:   Optional[Any]			  =     get_tests_dir('''fixtures/test_sentencepiece.model''')





@require_sentencepiece
class 						_A(					snake_case__				, unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  Optional[int]					    =	BertGenerationTokenizer
				UpperCamelCase    :  str					    =	False
				UpperCamelCase    :  Tuple					    =	True



				def 					UpperCAmelCase_		(			self     ):
									super().setUp()

									__A	:						Tuple       =				BertGenerationTokenizer(_A     ,					keep_accents=_A     )
									tokenizer.save_pretrained(self.tmpdirname     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						str       =				'<s>'
									__A	:						str       =				1

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



				def 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				list(self.get_tokenizer().get_vocab().keys()     )

									self.assertEqual(vocab_keys[0]     ,					'<unk>'     )
									self.assertEqual(vocab_keys[1]     ,					'<s>'     )
									self.assertEqual(vocab_keys[-1]     ,					'<pad>'     )
									self.assertEqual(len(_A     )     ,					1002     )



				def 					UpperCAmelCase_		(			self     ):
									self.assertEqual(self.get_tokenizer().vocab_size     ,					1000     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						str       =				BertGenerationTokenizer(_A     ,					keep_accents=_A     )

									__A	:						Dict       =				tokenizer.tokenize('This is a test'     )
									self.assertListEqual(_A     ,					['▁This', '▁is', '▁a', '▁t', 'est']     )

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(_A     )     ,					[285, 46, 10, 170, 382]     ,					)

									__A	:						int       =				tokenizer.tokenize('I was born in 92000, and this is falsé.'     )
									self.assertListEqual(
									    _A     ,					[
									        SPIECE_UNDERLINE + 'I',
									        SPIECE_UNDERLINE + 'was',
									        SPIECE_UNDERLINE + 'b',
									        'or',
									        'n',
									        SPIECE_UNDERLINE + 'in',
									        SPIECE_UNDERLINE + '',
									        '9',
									        '2',
									        '0',
									        '0',
									        '0',
									        ',',
									        SPIECE_UNDERLINE + 'and',
									        SPIECE_UNDERLINE + 'this',
									        SPIECE_UNDERLINE + 'is',
									        SPIECE_UNDERLINE + 'f',
									        'al',
									        's',
									        'é',
									        '.',
									    ]     ,					)
									__A	:						Dict       =				tokenizer.convert_tokens_to_ids(_A     )
									self.assertListEqual(
									    _A     ,					[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]     ,					)

									__A	:						Optional[int]       =				tokenizer.convert_ids_to_tokens(_A     )
									self.assertListEqual(
									    _A     ,					[
									        SPIECE_UNDERLINE + 'I',
									        SPIECE_UNDERLINE + 'was',
									        SPIECE_UNDERLINE + 'b',
									        'or',
									        'n',
									        SPIECE_UNDERLINE + 'in',
									        SPIECE_UNDERLINE + '',
									        '<unk>',
									        '2',
									        '0',
									        '0',
									        '0',
									        ',',
									        SPIECE_UNDERLINE + 'and',
									        SPIECE_UNDERLINE + 'this',
									        SPIECE_UNDERLINE + 'is',
									        SPIECE_UNDERLINE + 'f',
									        'al',
									        's',
									        '<unk>',
									        '.',
									    ]     ,					)



				@cached_property
				def 					UpperCAmelCase_		(			self     ):
									return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'     )



				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				'Hello World!'
									__A	:						Optional[Any]       =				[18536, 2260, 101]

									self.assertListEqual(_A     ,					self.big_tokenizer.encode(_A     )     )



				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Dict       =				(
									    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
									    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
									)
									__A	:						int       =				[
									    871,
									    419,
									    358,
									    946,
									    991,
									    2521,
									    452,
									    358,
									    1357,
									    387,
									    7751,
									    3536,
									    112,
									    985,
									    456,
									    126,
									    865,
									    938,
									    5400,
									    5734,
									    458,
									    1368,
									    467,
									    786,
									    2462,
									    5246,
									    1159,
									    633,
									    865,
									    4519,
									    457,
									    582,
									    852,
									    2557,
									    427,
									    916,
									    508,
									    405,
									    34324,
									    497,
									    391,
									    408,
									    11342,
									    1244,
									    385,
									    100,
									    938,
									    985,
									    456,
									    574,
									    362,
									    12597,
									    3200,
									    3129,
									    1172,
									]

									self.assertListEqual(_A     ,					self.big_tokenizer.encode(_A     )     )



				@require_torch
				@slow
				def 					UpperCAmelCase_		(			self     ):
									import torch

									from transformers import BertGenerationConfig, BertGenerationEncoder

									# Build sequence
									__A	:						Tuple       =				list(self.big_tokenizer.get_vocab().keys()     )[:10]
									__A	:						List[Any]       =				' '.join(_A     )
									__A	:						Union[str, Any]       =				self.big_tokenizer.encode_plus(_A     ,					return_tensors='pt'     ,					return_token_type_ids=_A     )
									__A	:						Optional[Any]       =				self.big_tokenizer.batch_encode_plus(
									    [sequence + ' ' + sequence]     ,					return_tensors='pt'     ,					return_token_type_ids=_A     )

									__A	:						int       =				BertGenerationConfig()
									__A	:						List[str]       =				BertGenerationEncoder(_A     )

									assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

									with torch.no_grad():
														model(**_A     )
														model(**_A     )





				@slow
				def 					UpperCAmelCase_		(			self     ):
									# fmt: off
									__A	:						str       =				{'input_ids': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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], [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=_A     ,					model_name='google/bert_for_seq_generation_L-24_bbc_encoder'     ,					revision='c817d1fd1be2ffa69431227a1fe320544943d4db'     ,					)




