

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

lowerCAmelCase							:  Any   	=						random.Random()







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=1.0				,	_UpperCAmelCase=None				,	_UpperCAmelCase=None    ):
						if rng is None:
												SCREAMING_SNAKE_CASE_: Tuple  		=       global_rng

						SCREAMING_SNAKE_CASE_: Dict  		=       []
						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   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def __init__( self  :     Dict   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Optional[Any]=7   ,						lowerCAmelCase__  :     Tuple=400   ,						lowerCAmelCase__  :     int=2000   ,						lowerCAmelCase__  :     int=10   ,						lowerCAmelCase__  :     Any=160   ,						lowerCAmelCase__  :     Optional[Any]=8   ,						lowerCAmelCase__  :     Any=0.0   ,						lowerCAmelCase__  :     int=4000   ,						lowerCAmelCase__  :     str=False   ,						lowerCAmelCase__  :     Union[str, Any]=True   ,						):
									SCREAMING_SNAKE_CASE_: str  		=       parent
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       batch_size
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       min_seq_length
									SCREAMING_SNAKE_CASE_: str  		=       max_seq_length
									SCREAMING_SNAKE_CASE_: int  		=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       padding_value
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       sampling_rate
									SCREAMING_SNAKE_CASE_: List[str]  		=       return_attention_mask
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       do_normalize
									SCREAMING_SNAKE_CASE_: int  		=       feature_size
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       chunk_length
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       hop_length



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									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        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Optional[int]=False   ,						lowerCAmelCase__  :     Dict=False):
									def _flatten(lowerCAmelCase__  :     int):
															return list(itertools.chain(*lowerCAmelCase__))

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







@require_torch
@require_torchaudio
class   __lowercase				( UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							int										=     WhisperFeatureExtractor if is_speech_available() else None



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       WhisperFeatureExtractionTester(self)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.feature_extraction_class(**self.feat_extract_dict)

									with tempfile.TemporaryDirectory() as tmpdirname:
															SCREAMING_SNAKE_CASE_: str  		=       feat_extract_first.save_pretrained(lowerCAmelCase__)[0]
															check_json_file_has_correct_format(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Tuple  		=       self.feature_extraction_class.from_pretrained(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Dict  		=       feat_extract_first.to_dict()
									SCREAMING_SNAKE_CASE_: List[Any]  		=       feat_extract_second.to_dict()
									SCREAMING_SNAKE_CASE_: Dict  		=       feat_extract_first.mel_filters
									SCREAMING_SNAKE_CASE_: Any  		=       feat_extract_second.mel_filters
									self.assertTrue(np.allclose(lowerCAmelCase__   ,						lowerCAmelCase__))
									self.assertEqual(lowerCAmelCase__   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Any  		=       self.feature_extraction_class(**self.feat_extract_dict)

									with tempfile.TemporaryDirectory() as tmpdirname:
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       os.path.join(lowerCAmelCase__   ,						"feat_extract.json")
															feat_extract_first.to_json_file(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.feature_extraction_class.from_json_file(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Dict  		=       feat_extract_first.to_dict()
									SCREAMING_SNAKE_CASE_: Dict  		=       feat_extract_second.to_dict()
									SCREAMING_SNAKE_CASE_: Any  		=       feat_extract_first.mel_filters
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       feat_extract_second.mel_filters
									self.assertTrue(np.allclose(lowerCAmelCase__   ,						lowerCAmelCase__))
									self.assertEqual(lowerCAmelCase__   ,						lowerCAmelCase__)



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

									# Test feature size
									SCREAMING_SNAKE_CASE_: List[str]  		=       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
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       feature_extractor(speech_inputs[0]   ,						return_tensors="np").input_features
									SCREAMING_SNAKE_CASE_: Dict  		=       feature_extractor(np_speech_inputs[0]   ,						return_tensors="np").input_features
									self.assertTrue(np.allclose(lowerCAmelCase__   ,						lowerCAmelCase__   ,						atol=1E-3))

									# Test batched
									SCREAMING_SNAKE_CASE_: int  		=       feature_extractor(lowerCAmelCase__   ,						return_tensors="np").input_features
									SCREAMING_SNAKE_CASE_: 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))

									# Test 2-D numpy arrays are batched.
									SCREAMING_SNAKE_CASE_: int  		=       [floats_list((1, x))[0] for x in (800, 800, 800)]
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       np.asarray(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: str  		=       feature_extractor(lowerCAmelCase__   ,						return_tensors="np").input_features
									SCREAMING_SNAKE_CASE_: Union[str, 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
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [floats_list((1, x))[0] for x in range(200   ,						(feature_extractor.n_samples + 500)   ,						200)]
									SCREAMING_SNAKE_CASE_: List[Any]  		=       [np.asarray(lowerCAmelCase__) for speech_input in speech_inputs]

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [x[: feature_extractor.n_samples] for x in speech_inputs]
									SCREAMING_SNAKE_CASE_: Tuple  		=       [np.asarray(lowerCAmelCase__) for speech_input in speech_inputs_truncated]

									SCREAMING_SNAKE_CASE_: Any  		=       feature_extractor(lowerCAmelCase__   ,						return_tensors="np").input_features
									SCREAMING_SNAKE_CASE_: 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))



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									import torch

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       np.random.rand(100   ,						32).astype(np.floataa)
									SCREAMING_SNAKE_CASE_: Any  		=       np_speech_inputs.tolist()

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       load_dataset("hf-internal-testing/librispeech_asr_dummy"   ,						"clean"   ,						split="validation")
									# automatic decoding with librispeech
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       ds.sort("id").select(range(lowerCAmelCase__))[:num_samples]["audio"]

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



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									# fmt: off
									SCREAMING_SNAKE_CASE_: 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

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self._load_datasamples(1)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       WhisperFeatureExtractor()
									SCREAMING_SNAKE_CASE_: str  		=       feature_extractor(lowerCAmelCase__   ,						return_tensors="pt").input_features
									self.assertEqual(input_features.shape   ,						(1, 80, 3000))
									self.assertTrue(torch.allclose(input_features[0, 0, :30]   ,						lowerCAmelCase__   ,						atol=1E-4))




			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									SCREAMING_SNAKE_CASE_: Dict  		=       self._load_datasamples(1)[0]
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
									SCREAMING_SNAKE_CASE_: int  		=       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))









import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase							:  Optional[Any]   	=						get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
@require_tokenizers
class   __lowercase				( UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[Any]										=     XGLMTokenizer
			_UpperCAmelCase   :							List[Any]										=     XGLMTokenizerFast
			_UpperCAmelCase   :							Optional[int]										=     True
			_UpperCAmelCase   :							Tuple										=     True



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									super().setUp()

									# We have a SentencePiece fixture for testing
									SCREAMING_SNAKE_CASE_: List[Any]  		=       XGLMTokenizer(lowerCAmelCase__   ,						keep_accents=lowerCAmelCase__)
									tokenizer.save_pretrained(self.tmpdirname)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       "<pad>"
									SCREAMING_SNAKE_CASE_: int  		=       1

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



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       list(self.get_tokenizer().get_vocab().keys())

									self.assertEqual(vocab_keys[0]   ,						"<s>")
									self.assertEqual(vocab_keys[1]   ,						"<pad>")
									self.assertEqual(len(lowerCAmelCase__)   ,						1008)



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									self.assertEqual(self.get_tokenizer().vocab_size   ,						1008)



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       XGLMTokenizer(lowerCAmelCase__   ,						keep_accents=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       tokenizer.tokenize("This is a test")
									self.assertListEqual(lowerCAmelCase__   ,						["▁This", "▁is", "▁a", "▁t", "est"])

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(lowerCAmelCase__)   ,						[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]   ,						)

									SCREAMING_SNAKE_CASE_: List[str]  		=       tokenizer.tokenize("I was born in 92000, and this is falsé.")
									self.assertListEqual(
									    lowerCAmelCase__   ,						[
									        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",
									        ".",
									    ]   ,						)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       tokenizer.convert_tokens_to_ids(lowerCAmelCase__)
									self.assertListEqual(
									    lowerCAmelCase__   ,						[
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									    ]   ,						)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       tokenizer.convert_ids_to_tokens(lowerCAmelCase__)
									self.assertListEqual(
									    lowerCAmelCase__   ,						[
									        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        _SCREAMING_SNAKE_CASE			( self  :     Any):
									return XGLMTokenizer.from_pretrained("facebook/xglm-564M")



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									with tempfile.NamedTemporaryFile() as f:
															shutil.copyfile(lowerCAmelCase__   ,						f.name)
															SCREAMING_SNAKE_CASE_: Tuple  		=       XGLMTokenizer(f.name   ,						keep_accents=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: List[str]  		=       pickle.dumps(lowerCAmelCase__)
									pickle.loads(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									if not self.test_rust_tokenizer:
															return

									SCREAMING_SNAKE_CASE_: Dict  		=       self.get_tokenizer()
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.get_rust_tokenizer()

									SCREAMING_SNAKE_CASE_: Any  		=       "I was born in 92000, and this is falsé."

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       tokenizer.tokenize(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       rust_tokenizer.tokenize(lowerCAmelCase__)
									self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[str]  		=       tokenizer.encode(lowerCAmelCase__   ,						add_special_tokens=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       rust_tokenizer.encode(lowerCAmelCase__   ,						add_special_tokens=lowerCAmelCase__)
									self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_rust_tokenizer()
									SCREAMING_SNAKE_CASE_: str  		=       tokenizer.encode(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       rust_tokenizer.encode(lowerCAmelCase__)
									self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__)



			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Dict  		=       "Hello World!"
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [2, 3_1227, 4447, 35]

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



			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       (
									    "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"
									)
									# fmt: off
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 7_1630, 2_8085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 1_3675, 377, 652, 7580, 1_0341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 20_2277, 1_7892, 33, 60, 87, 4, 3234, 157, 61, 2667, 5_2376, 19, 88, 23, 735]
									# fmt: on

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




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									# fmt: off
									SCREAMING_SNAKE_CASE_: str  		=       {
									    "input_ids": [[2, 10_8825, 1163, 15, 8_8010, 473, 1_5898, 157, 1_3672, 1857, 312, 8, 23_8021, 1163, 53, 1_3672, 1857, 312, 8, 5_3283, 18_2396, 8, 1_8566, 16, 3_6733, 4101, 8, 230, 24_4017, 12_2553, 7, 15, 13_2597, 4, 293, 1_2511, 7610, 4, 3414, 13_2597, 9, 4, 3_2361, 362, 4, 734, 2_8512, 3_2569, 18, 4, 3_2361, 2_6096, 1_4982, 73, 1_8715, 2_1433, 23_5261, 15, 492, 1_2427, 16, 53, 1_8715, 2_1433, 6_5454, 15, 2_3659, 563, 16, 278, 597, 2843, 595, 7931, 18_2396, 6_4186, 22, 886, 595, 13_2981, 53, 2_5540, 3449, 4_3982, 3_9901, 5951, 878, 330, 4, 2_7694, 8_0269, 312, 53, 6517, 1_1780, 611, 2_0408, 5], [2, 6, 13_2597, 67, 4_2897, 33, 592, 8, 16_3729, 2_5540, 361, 13_6997, 10_9514, 17_3230, 7, 501, 60, 10_2913, 196, 5631, 235, 6_3243, 473, 6, 23_1757, 74, 5277, 7905, 53, 3095, 3_7317, 22, 454, 18_3874, 5], [2, 268, 3_1298, 4_6530, 6, 13_2935, 4_3831, 7, 597, 32, 24, 3688, 9865, 5]],
									    "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, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
									}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=lowerCAmelCase__   ,						model_name="facebook/xglm-564M"   ,						padding=lowerCAmelCase__   ,						)






