


import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


_lowercase							:   Optional[Any]						=						{
    "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
    "gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}





def 	_lowerCAmelCase					( UpperCamelCase__:  Dict    )			-> Dict:





	"""simple docstring"""







	assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
	assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
	if args.mlm:
		assert os.path.isfile(args.token_counts    )
		assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
	else:
		assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

	assert args.teacher_type == args.student_type or (
	    args.student_type == "distilbert" and args.teacher_type == "bert"
	)
	assert os.path.isfile(args.student_config    )
	if args.student_pretrained_weights is not None:
		assert os.path.isfile(args.student_pretrained_weights    )

	if args.freeze_token_type_embds:
		assert args.student_type in ["roberta"]

	assert args.alpha_ce >= 0.0
	assert args.alpha_mlm >= 0.0
	assert args.alpha_clm >= 0.0
	assert args.alpha_mse >= 0.0
	assert args.alpha_cos >= 0.0
	assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[int]				,       UpperCamelCase__:  int    )			-> List[Any]:





	"""simple docstring"""







	if args.student_type == "roberta":
		A             =					False
	elif args.student_type == "gpt2":
		A             =					False





def 	_lowerCAmelCase					( UpperCamelCase__:  Tuple				,       UpperCamelCase__:  List[str]    )			-> int:





	"""simple docstring"""







	if args.student_type == "roberta":
		A             =					False





def 	_lowerCAmelCase					( )			-> List[Any]:





	"""simple docstring"""







	A             =					argparse.ArgumentParser(description="""Training"""    )
	parser.add_argument("""--force"""				,       action="""store_true"""				,       help="""Overwrite dump_path if it already exists."""    )

	parser.add_argument(
	    """--dump_path"""				,       type=_snake_case				,       required=_snake_case				,       help="""The output directory (log, checkpoints, parameters, etc.)"""    )
	parser.add_argument(
	    """--data_file"""				,       type=_snake_case				,       required=_snake_case				,       help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""				,       )

	parser.add_argument(
	    """--student_type"""				,       type=_snake_case				,       choices=["""distilbert""", """roberta""", """gpt2"""]				,       required=_snake_case				,       help="""The student type (DistilBERT, RoBERTa)."""				,       )
	parser.add_argument("""--student_config"""				,       type=_snake_case				,       required=_snake_case				,       help="""Path to the student configuration."""    )
	parser.add_argument(
	    """--student_pretrained_weights"""				,       default=_snake_case				,       type=_snake_case				,       help="""Load student initialization checkpoint."""    )

	parser.add_argument(
	    """--teacher_type"""				,       choices=["""bert""", """roberta""", """gpt2"""]				,       required=_snake_case				,       help="""Teacher type (BERT, RoBERTa)."""    )
	parser.add_argument("""--teacher_name"""				,       type=_snake_case				,       required=_snake_case				,       help="""The teacher model."""    )

	parser.add_argument("""--temperature"""				,       default=2.0				,       type=_snake_case				,       help="""Temperature for the softmax temperature."""    )
	parser.add_argument(
	    """--alpha_ce"""				,       default=0.5				,       type=_snake_case				,       help="""Linear weight for the distillation loss. Must be >=0."""    )
	parser.add_argument(
	    """--alpha_mlm"""				,       default=0.0				,       type=_snake_case				,       help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."""				,       )
	parser.add_argument("""--alpha_clm"""				,       default=0.5				,       type=_snake_case				,       help="""Linear weight for the CLM loss. Must be >=0."""    )
	parser.add_argument("""--alpha_mse"""				,       default=0.0				,       type=_snake_case				,       help="""Linear weight of the MSE loss. Must be >=0."""    )
	parser.add_argument(
	    """--alpha_cos"""				,       default=0.0				,       type=_snake_case				,       help="""Linear weight of the cosine embedding loss. Must be >=0."""    )

	parser.add_argument(
	    """--mlm"""				,       action="""store_true"""				,       help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."""    )
	parser.add_argument(
	    """--mlm_mask_prop"""				,       default=0.15				,       type=_snake_case				,       help="""Proportion of tokens for which we need to make a prediction."""				,       )
	parser.add_argument("""--word_mask"""				,       default=0.8				,       type=_snake_case				,       help="""Proportion of tokens to mask out."""    )
	parser.add_argument("""--word_keep"""				,       default=0.1				,       type=_snake_case				,       help="""Proportion of tokens to keep."""    )
	parser.add_argument("""--word_rand"""				,       default=0.1				,       type=_snake_case				,       help="""Proportion of tokens to randomly replace."""    )
	parser.add_argument(
	    """--mlm_smoothing"""				,       default=0.7				,       type=_snake_case				,       help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."""				,       )
	parser.add_argument("""--token_counts"""				,       type=_snake_case				,       help="""The token counts in the data_file for MLM."""    )

	parser.add_argument(
	    """--restrict_ce_to_mask"""				,       action="""store_true"""				,       help="""If true, compute the distillation loss only the [MLM] prediction distribution."""				,       )
	parser.add_argument(
	    """--freeze_pos_embs"""				,       action="""store_true"""				,       help="""Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only."""				,       )
	parser.add_argument(
	    """--freeze_token_type_embds"""				,       action="""store_true"""				,       help="""Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only."""				,       )

	parser.add_argument("""--n_epoch"""				,       type=_snake_case				,       default=3				,       help="""Number of pass on the whole dataset."""    )
	parser.add_argument("""--batch_size"""				,       type=_snake_case				,       default=5				,       help="""Batch size (for each process)."""    )
	parser.add_argument(
	    """--group_by_size"""				,       action="""store_false"""				,       help="""If true, group sequences that have similar length into the same batch. Default is true."""				,       )

	parser.add_argument(
	    """--gradient_accumulation_steps"""				,       type=_snake_case				,       default=50				,       help="""Gradient accumulation for larger training batches."""				,       )
	parser.add_argument("""--warmup_prop"""				,       default=0.05				,       type=_snake_case				,       help="""Linear warmup proportion."""    )
	parser.add_argument("""--weight_decay"""				,       default=0.0				,       type=_snake_case				,       help="""Weight decay if we apply some."""    )
	parser.add_argument("""--learning_rate"""				,       default=5e-4				,       type=_snake_case				,       help="""The initial learning rate for Adam."""    )
	parser.add_argument("""--adam_epsilon"""				,       default=1e-6				,       type=_snake_case				,       help="""Epsilon for Adam optimizer."""    )
	parser.add_argument("""--max_grad_norm"""				,       default=5.0				,       type=_snake_case				,       help="""Max gradient norm."""    )
	parser.add_argument("""--initializer_range"""				,       default=0.02				,       type=_snake_case				,       help="""Random initialization range."""    )

	parser.add_argument(
	    """--fp16"""				,       action="""store_true"""				,       help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""				,       )
	parser.add_argument(
	    """--fp16_opt_level"""				,       type=_snake_case				,       default="""O1"""				,       help=(
	        """For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']."""
	        """See details at https://nvidia.github.io/apex/amp.html"""
	    )				,       )
	parser.add_argument("""--n_gpu"""				,       type=_snake_case				,       default=1				,       help="""Number of GPUs in the node."""    )
	parser.add_argument("""--local_rank"""				,       type=_snake_case				,       default=-1				,       help="""Distributed training - Local rank"""    )
	parser.add_argument("""--seed"""				,       type=_snake_case				,       default=56				,       help="""Random seed"""    )

	parser.add_argument("""--log_interval"""				,       type=_snake_case				,       default=5_00				,       help="""Tensorboard logging interval."""    )
	parser.add_argument("""--checkpoint_interval"""				,       type=_snake_case				,       default=40_00				,       help="""Checkpoint interval."""    )
	A             =					parser.parse_args()
	sanity_checks(_snake_case    )

	# ARGS #
	init_gpu_params(_snake_case    )
	set_seed(_snake_case    )
	if args.is_master:
		if os.path.exists(args.dump_path    ):
			if not args.force:
				raise ValueError(
				    f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
				    """ itUse `--force` if you want to overwrite it"""    )
			else:
				shutil.rmtree(args.dump_path    )

		if not os.path.exists(args.dump_path    ):
			os.makedirs(args.dump_path    )
		logger.info(f'Experiment will be dumped and logged in {args.dump_path}'    )

		# SAVE PARAMS #
		logger.info(f'Param: {args}'    )
		with open(os.path.join(args.dump_path				,       """parameters.json"""    )				,       """w"""    ) as f:
			json.dump(vars(_snake_case    )				,       _snake_case				,       indent=4    )
		git_log(args.dump_path    )

	A       ,      A       ,      A             =					MODEL_CLASSES[args.student_type]
	A       ,      A       ,      A             =					MODEL_CLASSES[args.teacher_type]

	# TOKENIZER #
	A             =					teacher_tokenizer_class.from_pretrained(args.teacher_name    )
	A             =					{}
	for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
		A             =					tokenizer.all_special_tokens.index(_snake_case    )
		A             =					tokenizer.all_special_ids[idx]
	logger.info(f'Special tokens {special_tok_ids}'    )
	A             =					special_tok_ids
	A             =					tokenizer.max_model_input_sizes[args.teacher_name]

	# DATA LOADER #
	logger.info(f'Loading data from {args.data_file}'    )
	with open(args.data_file				,       """rb"""    ) as fp:
		A             =					pickle.load(_snake_case    )

	if args.mlm:
		logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)'    )
		with open(args.token_counts				,       """rb"""    ) as fp:
			A             =					pickle.load(_snake_case    )

		A             =					np.maximum(_snake_case				,       1    ) ** -args.mlm_smoothing
		for idx in special_tok_ids.values():
			A             =					0.0  # do not predict special tokens
		A             =					torch.from_numpy(_snake_case    )
	else:
		A             =					None

	A             =					LmSeqsDataset(params=_snake_case				,       data=_snake_case    )
	logger.info("""Data loader created."""    )

	# STUDENT #
	logger.info(f'Loading student config from {args.student_config}'    )
	A             =					student_config_class.from_pretrained(args.student_config    )
	A             =					True

	if args.student_pretrained_weights is not None:
		logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}'    )
		A             =					student_model_class.from_pretrained(args.student_pretrained_weights				,       config=_snake_case    )
	else:
		A             =					student_model_class(_snake_case    )

	if args.n_gpu > 0:
		student.to(f'cuda:{args.local_rank}'    )
	logger.info("""Student loaded."""    )

	# TEACHER #
	A             =					teacher_model_class.from_pretrained(args.teacher_name				,       output_hidden_states=_snake_case    )
	if args.n_gpu > 0:
		teacher.to(f'cuda:{args.local_rank}'    )
	logger.info(f'Teacher loaded from {args.teacher_name}.'    )

	# FREEZING #
	if args.freeze_pos_embs:
		freeze_pos_embeddings(_snake_case				,       _snake_case    )
	if args.freeze_token_type_embds:
		freeze_token_type_embeddings(_snake_case				,       _snake_case    )

	# SANITY CHECKS #
	assert student.config.vocab_size == teacher.config.vocab_size
	assert student.config.hidden_size == teacher.config.hidden_size
	assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
	if args.mlm:
		assert token_probs.size(0    ) == stu_architecture_config.vocab_size

	# DISTILLER #
	torch.cuda.empty_cache()
	A             =					Distiller(
	    params=_snake_case				,       dataset=_snake_case				,       token_probs=_snake_case				,       student=_snake_case				,       teacher=_snake_case    )
	distiller.train()
	logger.info("""Let\'s go get some drinks."""    )


if __name__ == "__main__":
				main()









"""simple docstring"""

import unittest

from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


a							      =     get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')





@require_sentencepiece
@require_tokenizers
class 							lowercase_			(	__lowerCAmelCase     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	List[str]     =      SpeechTaTokenizer
 UpperCAmelCase       :	Tuple     =      False
 UpperCAmelCase       :	Optional[int]     =      True

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

       # We have a SentencePiece fixture for testing
       _A     =  SpeechTaTokenizer(_UpperCAmelCase						)

       _A     =  AddedToken('<mask>'       ,				lstrip=_UpperCAmelCase       ,				rstrip=_UpperCAmelCase						)
       _A     =  mask_token
       tokenizer.add_special_tokens({'mask_token': mask_token}						)
       tokenizer.add_tokens(['<ctc_blank>']						)

       tokenizer.save_pretrained(self.tmpdirname						)

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Tuple						):
       _A     =  'this is a test'
       _A     =  'this is a test'
       return input_text, output_text

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			Optional[int]       ,				_UpperCAmelCase			:			Union[str, Any]=False       ,				_UpperCAmelCase			:			Dict=20       ,				_UpperCAmelCase			:			str=5						):
       _A  ,       _A     =  self.get_input_output_texts(_UpperCAmelCase						)
       _A     =  tokenizer.encode(_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase						)
       _A     =  tokenizer.decode(_UpperCAmelCase       ,				clean_up_tokenization_spaces=_UpperCAmelCase						)
       return text, ids

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       _A     =  '<pad>'
       _A     =  1

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

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       _A     =  list(self.get_tokenizer().get_vocab().keys()						)

       self.assertEqual(vocab_keys[0]       ,				'<s>'						)
       self.assertEqual(vocab_keys[1]       ,				'<pad>'						)
       self.assertEqual(vocab_keys[-4]       ,				'œ'						)
       self.assertEqual(vocab_keys[-2]       ,				'<mask>'						)
       self.assertEqual(vocab_keys[-1]       ,				'<ctc_blank>'						)
       self.assertEqual(len(_UpperCAmelCase						)       ,				81						)

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       self.assertEqual(self.get_tokenizer().vocab_size       ,				79						)

 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  self.get_tokenizers(do_lower_case=_UpperCAmelCase						)
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
                   _A     =  tokenizer.vocab_size
                   _A     =  len(_UpperCAmelCase						)

                   self.assertNotEqual(_UpperCAmelCase       ,				0						)

                   # We usually have added tokens from the start in tests because our vocab fixtures are
                   # smaller than the original vocabs - let's not assert this
                   # self.assertEqual(vocab_size, all_size)

                   _A     =  ['aaaaa bbbbbb', 'cccccccccdddddddd']
                   _A     =  tokenizer.add_tokens(_UpperCAmelCase						)
                   _A     =  tokenizer.vocab_size
                   _A     =  len(_UpperCAmelCase						)

                   self.assertNotEqual(_UpperCAmelCase       ,				0						)
                   self.assertEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)
                   self.assertEqual(_UpperCAmelCase       ,				len(_UpperCAmelCase						)						)
                   self.assertEqual(_UpperCAmelCase       ,				all_size + len(_UpperCAmelCase						)						)

                   _A     =  tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l'       ,				add_special_tokens=_UpperCAmelCase						)

                   self.assertGreaterEqual(len(_UpperCAmelCase						)       ,				4						)
                   self.assertGreater(tokens[0]       ,				tokenizer.vocab_size - 1						)
                   self.assertGreater(tokens[-3]       ,				tokenizer.vocab_size - 1						)

                   _A     =  {'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
                   _A     =  tokenizer.add_special_tokens(_UpperCAmelCase						)
                   _A     =  tokenizer.vocab_size
                   _A     =  len(_UpperCAmelCase						)

                   self.assertNotEqual(_UpperCAmelCase       ,				0						)
                   self.assertEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)
                   self.assertEqual(_UpperCAmelCase       ,				len(_UpperCAmelCase						)						)
                   self.assertEqual(_UpperCAmelCase       ,				all_size_a + len(_UpperCAmelCase						)						)

                   _A     =  tokenizer.encode(
                       '>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'       ,				add_special_tokens=_UpperCAmelCase						)

                   self.assertGreaterEqual(len(_UpperCAmelCase						)       ,				6						)
                   self.assertGreater(tokens[0]       ,				tokenizer.vocab_size - 1						)
                   self.assertGreater(tokens[0]       ,				tokens[1]						)
                   self.assertGreater(tokens[-3]       ,				tokenizer.vocab_size - 1						)
                   self.assertGreater(tokens[-3]       ,				tokens[-4]						)
                   self.assertEqual(tokens[0]       ,				tokenizer.eos_token_id						)
                   self.assertEqual(tokens[-3]       ,				tokenizer.pad_token_id						)

 def      lowerCAmelCase_				(					self			:			str						):
       pass

 def      lowerCAmelCase_				(					self			:			Any						):
       pass

 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  self.get_tokenizer()

       _A     =  tokenizer.tokenize('This is a test'						)
       # fmt: off
       self.assertListEqual(_UpperCAmelCase       ,				[SPIECE_UNDERLINE, 'T', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'a', SPIECE_UNDERLINE, 't', 'e', 's', 't']						)
       # fmt: on

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)       ,				[4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6]       ,				)

       _A     =  tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
       self.assertListEqual(
           _UpperCAmelCase       ,				[SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '92000', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.']						)

       _A     =  tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)
       # fmt: off
       self.assertListEqual(_UpperCAmelCase       ,				[4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26]						)
       # fmt: on

       _A     =  tokenizer.convert_ids_to_tokens(_UpperCAmelCase						)
       self.assertListEqual(
           _UpperCAmelCase       ,				[SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '<unk>', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.']						)



 @slow
 def      lowerCAmelCase_				(					self			:			List[Any]						):
       # Use custom sequence because this tokenizer does not handle numbers.
       _A     =  [
           'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
           'general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
           'Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '
           'models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.',
           'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
           'conditioning on both left and right context in all layers.',
           'The quick brown fox jumps over the lazy dog.',
       ]

       # fmt: off
       _A     =  {
           'input_ids': [
               [4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
               [4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
               [4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
           ],
           '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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
               [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
           ]
       }
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=_UpperCAmelCase       ,				model_name='microsoft/speecht5_asr'       ,				revision='c5ef64c71905caeccde0e4462ef3f9077224c524'       ,				sequences=_UpperCAmelCase       ,				)

