import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_lowercase							:      int      							=getLogger(__name__)


_lowercase							:      Dict      							='''cuda''' if torch.cuda.is_available() else '''cpu'''



def A__  (			lowercase:     List[str],		lowercase:     str,		lowercase:     str,		lowercase:     int = 8,		lowercase:     str = DEFAULT_DEVICE,		lowercase:     Optional[Any]=False,		lowercase:     Union[str, Any]="summarization",		lowercase:     Any=None,		**lowercase:     str,		)				->		Dict:
       A						:     List[Any]         =Path(lowercase					).open('w',		encoding='utf-8'					)
       A						:     int         =str(lowercase					)
       A						:     Optional[Any]         =AutoModelForSeqaSeqLM.from_pretrained(lowercase					).to(lowercase					)
       if fpaa:
              A						:     List[str]         =model.half()

       A						:     Dict         =AutoTokenizer.from_pretrained(lowercase					)
       logger.info(F'Inferred tokenizer type: {tokenizer.__class__}'					)  # if this is wrong, check config.model_type.

       A						:     Any         =time.time()
       # update config with task specific params
       use_task_specific_params(lowercase,		lowercase					)
       if prefix is None:
              A						:     int         =prefix or getattr(model.config,		'prefix',		''					) or ''
       for examples_chunk in tqdm(list(chunks(lowercase,		lowercase					)					)					):
              A						:     Dict         =[prefix + text for text in examples_chunk]
              A						:     List[str]         =tokenizer(lowercase,		return_tensors='pt',		truncation=lowercase,		padding='longest'					).to(lowercase					)
              A						:     List[Any]         =model.generate(
                  input_ids=batch.input_ids,		attention_mask=batch.attention_mask,		**lowercase,		)
              A						:     Optional[int]         =tokenizer.batch_decode(lowercase,		skip_special_tokens=lowercase,		clean_up_tokenization_spaces=lowercase					)
              for hypothesis in dec:
                     fout.write(hypothesis + '\n'					)
                     fout.flush()
       fout.close()
       A						:     Dict         =int(time.time() - start_time					)  # seconds
       A						:     List[Any]         =len(lowercase					)
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs,		4					)}



def A__  (			)				->		List[Any]:
       return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'					)



def A__  (			lowercase:     Union[str, Any]=True					)				->		Any:
       A						:     Union[str, Any]         =argparse.ArgumentParser()
       parser.add_argument('model_name',		type=lowercase,		help='like facebook/bart-large-cnn,t5-base, etc.'					)
       parser.add_argument('input_path',		type=lowercase,		help='like cnn_dm/test.source'					)
       parser.add_argument('save_path',		type=lowercase,		help='where to save summaries'					)
       parser.add_argument('--reference_path',		type=lowercase,		required=lowercase,		help='like cnn_dm/test.target'					)
       parser.add_argument('--score_path',		type=lowercase,		required=lowercase,		default='metrics.json',		help='where to save metrics'					)
       parser.add_argument('--device',		type=lowercase,		required=lowercase,		default=lowercase,		help='cuda, cuda:1, cpu etc.'					)
       parser.add_argument(
           '--prefix',		type=lowercase,		required=lowercase,		default=lowercase,		help='will be added to the begininng of src examples'					)
       parser.add_argument('--task',		type=lowercase,		default='summarization',		help='used for task_specific_params + metrics'					)
       parser.add_argument('--bs',		type=lowercase,		default=8,		required=lowercase,		help='batch size'					)
       parser.add_argument(
           '--n_obs',		type=lowercase,		default=-1,		required=lowercase,		help='How many observations. Defaults to all.'					)
       parser.add_argument('--fp16',		action='store_true'					)
       parser.add_argument('--dump-args',		action='store_true',		help='print the custom hparams with the results'					)
       parser.add_argument(
           '--info',		nargs='?',		type=lowercase,		const=datetime_now(),		help=(
               'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
               ' lang=en-ru. If no value is passed, the current datetime string will be used.'
           ),		)
       # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
       A  ,  A						:     str         =parser.parse_known_args()
       A						:     Optional[int]         =parse_numeric_n_bool_cl_kwargs(lowercase					)
       if parsed_args and verbose:
              print(F'parsed the following generate kwargs: {parsed_args}'					)
       A						:     Optional[int]         =[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path					).readlines()]
       if args.n_obs > 0:
              A						:     List[str]         =examples[: args.n_obs]
       Path(args.save_path					).parent.mkdir(exist_ok=lowercase					)

       if args.reference_path is None and Path(args.score_path					).exists():
              warnings.warn(F'score_path {args.score_path} will be overwritten unless you type ctrl-c.'					)

       if args.device == "cpu" and args.fpaa:
              # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
              raise ValueError('Can\'t mix --fp16 and --device cpu'					)

       A						:     Tuple         =generate_summaries_or_translations(
           lowercase,		args.save_path,		args.model_name,		batch_size=args.bs,		device=args.device,		fpaa=args.fpaa,		task=args.task,		prefix=args.prefix,		**lowercase,		)

       if args.reference_path is None:
              return {}

       # Compute scores
       A						:     Tuple         =calculate_bleu if 'translation' in args.task else calculate_rouge
       A						:     Tuple         =[x.rstrip() for x in open(args.save_path					).readlines()]
       A						:     Any         =[x.rstrip() for x in open(args.reference_path					).readlines()][: len(lowercase					)]
       A						:     dict         =score_fn(lowercase,		lowercase					)
       scores.update(lowercase					)

       if args.dump_args:
              scores.update(lowercase					)
       if args.info:
              A						:     Tuple         =args.info

       if verbose:
              print(lowercase					)

       if args.score_path is not None:
              json.dump(lowercase,		open(args.score_path,		'w'					)					)

       return scores


if __name__ == "__main__":
    # Usage for MT:
    # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
    run_generate(verbose=True)







import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
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


_lowercase							:      Optional[int]      							=get_tests_dir('''fixtures/test_sentencepiece.model''')



@require_sentencepiece
@require_tokenizers
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     List[str]            =				XLMRobertaTokenizer
  lowercase		:     Dict            =				XLMRobertaTokenizerFast
  lowercase		:     str            =				True
  lowercase		:     Tuple            =				True







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Optional[Any]:
         super().setUp()

         # We have a SentencePiece fixture for testing
         A						:     List[str]         =XLMRobertaTokenizer(SCREAMING_SNAKE_CASE__      ,	keep_accents=SCREAMING_SNAKE_CASE__     )
         tokenizer.save_pretrained(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				List[Any]:
         A						:     List[str]         ='<pad>'
         A						:     int         =1

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Any:
         A						:     List[str]         =list(self.get_tokenizer().get_vocab().keys()     )

         self.assertEqual(vocab_keys[0]      ,	'<s>'     )
         self.assertEqual(vocab_keys[1]      ,	'<pad>'     )
         self.assertEqual(vocab_keys[-1]      ,	'<mask>'     )
         self.assertEqual(len(SCREAMING_SNAKE_CASE__     )      ,	10_02     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Dict:
         self.assertEqual(self.get_tokenizer().vocab_size      ,	10_02     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				str:
         A						:     Union[str, Any]         =XLMRobertaTokenizer(SCREAMING_SNAKE_CASE__      ,	keep_accents=SCREAMING_SNAKE_CASE__     )

         A						:     str         =tokenizer.tokenize('This is a test'     )
         self.assertListEqual(SCREAMING_SNAKE_CASE__      ,	['▁This', '▁is', '▁a', '▁t', 'est']     )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )      ,	[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]      ,	)

         A						:     Any         =tokenizer.tokenize('I was born in 92000, and this is falsé.'     )
         self.assertListEqual(
             SCREAMING_SNAKE_CASE__      ,	[
                 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						:     Tuple         =tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )
         self.assertListEqual(
             SCREAMING_SNAKE_CASE__      ,	[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
             ]      ,	)

         A						:     Union[str, Any]         =tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__     )
         self.assertListEqual(
             SCREAMING_SNAKE_CASE__      ,	[
                 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>',
                 '.',
             ]      ,	)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Optional[int]:
         if not self.test_slow_tokenizer:
                # as we don't have a slow version, we can't compare the outputs between slow and fast versions
                return

         A						:     Any         =(self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
                       A						:     List[Any]         =self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )
                       A						:     Dict         =self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )

                       A						:     str         =tempfile.mkdtemp()

                       A						:     Optional[int]         =tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__     )
                       A						:     Optional[Any]         =tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__     )

                       # Checks it save with the same files + the tokenizer.json file for the fast one
                       self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files     )     )
                       A						:     List[str]         =tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f     )
                       self.assertSequenceEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

                       # Checks everything loads correctly in the same way
                       A						:     Tuple         =tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__     )
                       A						:     Dict         =tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__     )

                       # Check special tokens are set accordingly on Rust and Python
                       for key in tokenizer_pp.special_tokens_map:
                              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )     )
                              # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                              # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                       shutil.rmtree(SCREAMING_SNAKE_CASE__     )

                       # Save tokenizer rust, legacy_format=True
                       A						:     Optional[int]         =tempfile.mkdtemp()

                       A						:     Optional[int]         =tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__      ,	legacy_format=SCREAMING_SNAKE_CASE__     )
                       A						:     List[Any]         =tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__     )

                       # Checks it save with the same files
                       self.assertSequenceEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

                       # Checks everything loads correctly in the same way
                       A						:     Tuple         =tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__     )
                       A						:     Tuple         =tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__     )

                       # Check special tokens are set accordingly on Rust and Python
                       for key in tokenizer_pp.special_tokens_map:
                              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )     )

                       shutil.rmtree(SCREAMING_SNAKE_CASE__     )

                       # Save tokenizer rust, legacy_format=False
                       A						:     List[Any]         =tempfile.mkdtemp()

                       A						:     Optional[int]         =tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__      ,	legacy_format=SCREAMING_SNAKE_CASE__     )
                       A						:     str         =tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__     )

                       # Checks it saved the tokenizer.json file
                       self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files     )     )

                       # Checks everything loads correctly in the same way
                       A						:     List[Any]         =tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__     )
                       A						:     List[Any]         =tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__     )

                       # Check special tokens are set accordingly on Rust and Python
                       for key in tokenizer_pp.special_tokens_map:
                              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )     )

                       shutil.rmtree(SCREAMING_SNAKE_CASE__     )







  @cached_property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Optional[int]:
         return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Any:
         with tempfile.NamedTemporaryFile() as f:
                shutil.copyfile(SCREAMING_SNAKE_CASE__      ,	f.name     )
                A						:     Optional[Any]         =XLMRobertaTokenizer(f.name      ,	keep_accents=SCREAMING_SNAKE_CASE__     )
                A						:     int         =pickle.dumps(SCREAMING_SNAKE_CASE__     )
         pickle.loads(SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				Union[str, Any]:
         if not self.test_rust_tokenizer:
                return

         A						:     Union[str, Any]         =self.get_tokenizer()
         A						:     int         =self.get_rust_tokenizer()

         A						:     List[str]         ='I was born in 92000, and this is falsé.'

         A						:     Union[str, Any]         =tokenizer.tokenize(SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         =rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__     )
         self.assertListEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         A						:     Any         =tokenizer.encode(SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__     )
         A						:     Tuple         =rust_tokenizer.encode(SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__     )
         self.assertListEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         A						:     Optional[Any]         =self.get_rust_tokenizer()
         A						:     int         =tokenizer.encode(SCREAMING_SNAKE_CASE__     )
         A						:     Dict         =rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     )
         self.assertListEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				List[str]:
         A						:     Any         ='Hello World!'
         A						:     Optional[Any]         =[0, 3_53_78, 66_61, 38, 2]
         # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
         # xlmr.eval()
         # xlmr.encode(symbols)

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







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				str:
         A						:     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'
         )
         A						:     int         =[
             0,
             32_93,
             83,
             10,
             45_52,
             49_89,
             79_86,
             6_78,
             10,
             59_15,
             1_11,
             17_94_59,
             12_48_50,
             4,
             60_44,
             2_37,
             12,
             6,
             5,
             6,
             4,
             67_80,
             7_05,
             15,
             13_88,
             44,
             3_78,
             1_01_14,
             7_11,
             1_52,
             20,
             6,
             5,
             2_23_76,
             6_42,
             12_21,
             1_51_90,
             3_41_53,
             4_50,
             56_08,
             9_59,
             11_19,
             5_77_02,
             1_36,
             1_86,
             47,
             10_98,
             2_93_67,
             47,
             # 4426, # What fairseq tokenizes from "<unk>": "_<"
             # 3678, # What fairseq tokenizes from "<unk>": "unk"
             # 2740, # What fairseq tokenizes from "<unk>": ">"
             3,  # What we tokenize from "<unk>": "<unk>"
             6,  # Residue from the tokenization: an extra sentencepiece underline
             4,
             60_44,
             2_37,
             62_84,
             5_09_01,
             5_28,
             31,
             90,
             34,
             9_27,
             2,
         ]
         # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
         # xlmr.eval()
         # xlmr.encode(symbols)

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







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Any:
         # fmt: off
         A						:     List[Any]         ={'input_ids': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 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], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 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]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=SCREAMING_SNAKE_CASE__      ,	model_name='xlm-roberta-base'      ,	revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'      ,	)






