


import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


_lowercase							:   List[str]						=						logging.get_logger(__name__)





def 	_lowerCAmelCase					( UpperCamelCase__:  List[str]				,       UpperCamelCase__:  Any    )			-> Dict:





	"""simple docstring"""







	A             =					set()
	A             =					[]

	def parse_line(UpperCamelCase__:  Optional[Any]    ):
		for line in fp:
			if isinstance(_snake_case				,       _snake_case    ):
				A             =					line.decode("""UTF-8"""    )
			if "warnings summary (final)" in line:
				continue
			# This means we are outside the body of a warning
			elif not line.startswith(""" """    ):
				# process a single warning and move it to `selected_warnings`.
				if len(_snake_case    ) > 0:
					A             =					"""\n""".join(_snake_case    )
					# Only keep the warnings specified in `targets`
					if any(f': {x}: ' in warning for x in targets    ):
						selected_warnings.add(_snake_case    )
					buffer.clear()
				continue
			else:
				A             =					line.strip()
				buffer.append(_snake_case    )

	if from_gh:
		for filename in os.listdir(_snake_case    ):
			A             =					os.path.join(_snake_case				,       _snake_case    )
			if not os.path.isdir(_snake_case    ):
				# read the file
				if filename != "warnings.txt":
					continue
				with open(_snake_case    ) as fp:
					parse_line(_snake_case    )
	else:
		try:
			with zipfile.ZipFile(_snake_case    ) as z:
				for filename in z.namelist():
					if not os.path.isdir(_snake_case    ):
						# read the file
						if filename != "warnings.txt":
							continue
						with z.open(_snake_case    ) as fp:
							parse_line(_snake_case    )
		except Exception:
			logger.warning(
			    f'{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'    )

	return selected_warnings





def 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  List[str]    )			-> Dict:





	"""simple docstring"""







	A             =					set()

	A             =					[os.path.join(_snake_case				,       _snake_case    ) for p in os.listdir(_snake_case    ) if (p.endswith(""".zip"""    ) or from_gh)]
	for p in paths:
		selected_warnings.update(extract_warnings_from_single_artifact(_snake_case				,       _snake_case    )    )

	return selected_warnings


if __name__ == "__main__":





				def 	_lowerCAmelCase					( UpperCamelCase__:  int    )			-> Dict:





					"""simple docstring"""







					return values.split(""","""    )

				_lowercase							:   Dict						=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
				parser.add_argument(
				    "--output_dir",
				    type=str,
				    required=True,
				    help="Where to store the downloaded artifacts and other result files.",
				)
				parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
				# optional parameters
				parser.add_argument(
				    "--targets",
				    default="DeprecationWarning,UserWarning,FutureWarning",
				    type=list_str,
				    help="Comma-separated list of target warning(s) which we want to extract.",
				)
				parser.add_argument(
				    "--from_gh",
				    action="store_true",
				    help="If running from a GitHub action workflow and collecting warnings from its artifacts.",
				)

				_lowercase							:   int						=						parser.parse_args()

				_lowercase							:   Optional[Any]						=						args.from_gh
				if from_gh:
								# The artifacts have to be downloaded using `actions/download-artifact@v3`
								pass
				else:
								os.makedirs(args.output_dir, exist_ok=True)

								# get download links
								_lowercase							:   List[str]						=						get_artifacts_links(args.workflow_run_id, token=args.token)
								with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
												json.dump(artifacts, fp, ensure_ascii=False, indent=4)

								# download artifacts
								for idx, (name, url) in enumerate(artifacts.items()):
												print(name)
												print(url)
												print("=" * 80)
												download_artifact(name, url, args.output_dir, args.token)
												# Be gentle to GitHub
												time.sleep(1)

    # extract warnings from artifacts
				_lowercase							:   Optional[int]						=						extract_warnings(args.output_dir, args.targets)
				_lowercase							:   Optional[int]						=						sorted(selected_warnings)
				with open(os.path.join(args.output_dir, "selected_warnings.json"), "w", encoding="UTF-8") as fp:
								json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)









"""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],
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           ]
       }
       # fmt: on

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

