





import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


UpperCAmelCase__						:			Optional[int]									=re.compile(r'''\s+''')





def      _lowercase					(						_UpperCAmelCase				)						->  List[Any]:
				return {"hash": hashlib.mda(re.sub(_UpperCAmelCase			,      """"""			,      example["""content"""]				).encode("""utf-8"""				)				).hexdigest()}





def      _lowercase					(						_UpperCAmelCase				)						->  Optional[int]:
				lowerCamelCase	 =[len(_UpperCAmelCase				) for line in example["""content"""].splitlines()]
				return {"line_mean": np.mean(_UpperCAmelCase				), "line_max": max(_UpperCAmelCase				)}





def      _lowercase					(						_UpperCAmelCase				)						->  Optional[Any]:
				lowerCamelCase	 =np.mean([c.isalnum() for c in example["""content"""]]				)
				return {"alpha_frac": alpha_frac}





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  Union[str, Any]:
				if example["hash"] in uniques:
								uniques.remove(example["""hash"""]				)
								return True
				else:
								return False





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase=5				)						->  Optional[Any]:
				lowerCamelCase	 =["""auto-generated""", """autogenerated""", """automatically generated"""]
				lowerCamelCase	 =example["""content"""].splitlines()
				for _, line in zip(range(_UpperCAmelCase				)			,      _UpperCAmelCase				):
								for keyword in keywords:
												if keyword in line.lower():
																return {"autogenerated": True}
				else:
								return {"autogenerated": False}





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase=5			,      _UpperCAmelCase=0.0_5				)						->  Union[str, Any]:
				lowerCamelCase	 =["""unit tests""", """test file""", """configuration file"""]
				lowerCamelCase	 =example["""content"""].splitlines()
				lowerCamelCase	 =0
				lowerCamelCase	 =0
				# first test
				for _, line in zip(range(_UpperCAmelCase				)			,      _UpperCAmelCase				):
								for keyword in keywords:
												if keyword in line.lower():
																return {"config_or_test": True}
    # second test
				lowerCamelCase	 =example["""content"""].count("""\n"""				)
				lowerCamelCase	 =int(coeff * nlines				)
				for line in lines:
								count_config += line.lower().count("""config"""				)
								count_test += line.lower().count("""test"""				)
								if count_config > threshold or count_test > threshold:
												return {"config_or_test": True}
				return {"config_or_test": False}





def      _lowercase					(						_UpperCAmelCase				)						->  Optional[Any]:
				lowerCamelCase	 =["""def """, """class """, """for """, """while """]
				lowerCamelCase	 =example["""content"""].splitlines()
				for line in lines:
								for keyword in keywords:
												if keyword in line.lower():
																return {"has_no_keywords": False}
				return {"has_no_keywords": True}





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase=4				)						->  int:
				lowerCamelCase	 =example["""content"""].splitlines()
				lowerCamelCase	 =0
				for line in lines:
								counter += line.lower().count("""="""				)
								if counter > minimum:
												return {"has_few_assignments": False}
				return {"has_few_assignments": True}





def      _lowercase					(						_UpperCAmelCase				)						->  Any:
				lowerCamelCase	 =tokenizer(example["""content"""]			,      truncation=_UpperCAmelCase				)["""input_ids"""]
				lowerCamelCase	 =len(example["""content"""]				) / len(_UpperCAmelCase				)
				return {"ratio": ratio}





def      _lowercase					(						_UpperCAmelCase				)						->  List[Any]:
				lowerCamelCase	 ={}
				results.update(get_hash(_UpperCAmelCase				)				)
				results.update(line_stats(_UpperCAmelCase				)				)
				results.update(alpha_stats(_UpperCAmelCase				)				)
				results.update(char_token_ratio(_UpperCAmelCase				)				)
				results.update(is_autogenerated(_UpperCAmelCase				)				)
				results.update(is_config_or_test(_UpperCAmelCase				)				)
				results.update(has_no_keywords(_UpperCAmelCase				)				)
				results.update(has_few_assignments(_UpperCAmelCase				)				)
				return results





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				if not check_uniques(_UpperCAmelCase			,      _UpperCAmelCase				):
								return False
				elif example["autogenerated"]:
								return False
				elif example["line_max"] > args.line_max:
								return False
				elif example["line_mean"] > args.line_mean:
								return False
				elif example["alpha_frac"] < args.alpha_frac:
								return False
				elif example["ratio"] < args.min_token_ratio:
								return False
				elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
								return False
				elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
								return False
				elif example["has_few_assignments"]:
								return False
				else:
								return True





def      _lowercase					(						_UpperCAmelCase				)						->  Optional[Any]:
				with open(_UpperCAmelCase			,      """rb"""				) as f_in:
								with gzip.open(str(_UpperCAmelCase				) + """.gz"""			,      """wb"""			,      compresslevel=6				) as f_out:
												shutil.copyfileobj(_UpperCAmelCase			,      _UpperCAmelCase				)
				os.unlink(_UpperCAmelCase				)


# Settings
UpperCAmelCase__						:			Optional[int]									=HfArgumentParser(PreprocessingArguments)
UpperCAmelCase__						:			Optional[int]									=parser.parse_args()
if args.num_workers is None:
							UpperCAmelCase__						:			int									=multiprocessing.cpu_count()
UpperCAmelCase__						:			Any									=AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
UpperCAmelCase__						:			Union[str, Any]									=time.time()
UpperCAmelCase__						:			Union[str, Any]									=load_dataset(args.dataset_name, split='''train''')
print(F"Time to load dataset: {time.time()-t_start:.2f}")

# Run preprocessing
UpperCAmelCase__						:			Dict									=time.time()
UpperCAmelCase__						:			Union[str, Any]									=ds.map(preprocess, num_proc=args.num_workers)
print(F"Time to preprocess dataset: {time.time()-t_start:.2f}")

# Deduplicate hashes
UpperCAmelCase__						:			Optional[Any]									=set(ds.unique('''hash'''))
UpperCAmelCase__						:			List[str]									=len(uniques) / len(ds)
print(F"Fraction of duplicates: {1-frac:.2%}")

# Deduplicate data and apply heuristics
UpperCAmelCase__						:			str									=time.time()
UpperCAmelCase__						:			List[Any]									=ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(F"Time to filter dataset: {time.time()-t_start:.2f}")
print(F"Size of filtered dataset: {len(ds_filter)}")

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
							UpperCAmelCase__						:			List[str]									=time.time()
							UpperCAmelCase__				,UpperCAmelCase__						:			Union[str, Any]									=deduplicate_dataset(ds_filter, args.jaccard_threshold)
							print(F"Time to deduplicate dataset: {time.time()-t_start:.2f}")
							print(F"Size of deduplicate dataset: {len(ds_filter)}")

# Save data in batches of samples_per_file
UpperCAmelCase__						:			Tuple									=Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
							with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
														json.dump(duplicate_clusters, f)

UpperCAmelCase__						:			Optional[int]									=output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

UpperCAmelCase__						:			Optional[Any]									=time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
							UpperCAmelCase__						:			Union[str, Any]									=str(data_dir / F"file-{file_number+1:012}.json")
							UpperCAmelCase__						:			Tuple									=min(len(ds_filter), index + args.samples_per_file)
							ds_filter.select(list(range(index, end_index))).to_json(file_path)
							compress_file(file_path)




print(F"Time to save dataset: {time.time()-t_start:.2f}")












import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
							from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
							UpperCAmelCase__						:			Union[str, Any]									=get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
							from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCAmelCase__						:			Union[str, Any]									=12_80_22
UpperCAmelCase__						:			Optional[Any]									=12_80_28

@require_sentencepiece
class       __A     (						a    ,					unittest.TestCase    ):
				__A      =				MaMaaaTokenizer
				__A      =				False
				__A      =				False
				__A      =				True


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

								lowerCamelCase	 =["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
								lowerCamelCase	 =dict(zip(UpperCAmelCase_   , range(len(UpperCAmelCase_							)							)							)							)
								lowerCamelCase	 =Path(self.tmpdirname							)
								save_json(UpperCAmelCase_   , save_dir / VOCAB_FILES_NAMES["""vocab_file"""]							)
								if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
												copyfile(UpperCAmelCase_   , save_dir / VOCAB_FILES_NAMES["""spm_file"""]							)

								lowerCamelCase	 =MaMaaaTokenizer.from_pretrained(self.tmpdirname							)
								tokenizer.save_pretrained(self.tmpdirname							)


				def _snake_case       (		self   , **UpperCAmelCase_							):
								return MaMaaaTokenizer.from_pretrained(self.tmpdirname   , **UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								return (
								    "This is a test",
								    "This is a test",
								)


				def _snake_case       (		self							):
								lowerCamelCase	 ="""</s>"""
								lowerCamelCase	 =0

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


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_tokenizer()
								lowerCamelCase	 =list(tokenizer.get_vocab().keys()							)

								self.assertEqual(vocab_keys[0]   , """</s>"""							)
								self.assertEqual(vocab_keys[1]   , """<unk>"""							)
								self.assertEqual(vocab_keys[-1]   , """<s>"""							)
								self.assertEqual(len(UpperCAmelCase_							)   , tokenizer.vocab_size + len(tokenizer.get_added_vocab()							)							)


				@unittest.skip("""Skip this test while all models are still to be uploaded."""							)
				def _snake_case       (		self							):
								pass


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_tokenizer()

								lowerCamelCase	 =tokenizer.tokenize("""This is a test"""							)
								self.assertListEqual(UpperCAmelCase_   , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)   , [2, 3, 4, 5, 6]   , )

								lowerCamelCase	 =tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]							)
								self.assertListEqual(UpperCAmelCase_   , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

								lowerCamelCase	 =tokenizer.convert_tokens_to_string(UpperCAmelCase_							)
								self.assertEqual(UpperCAmelCase_   , """This is a test"""							)


				@slow
				def _snake_case       (		self							):
								# fmt: off
								lowerCamelCase	 ={"""input_ids""": [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 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], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=UpperCAmelCase_   , model_name="""facebook/m2m100_418M"""   , revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e"""   , )




@require_torch
@require_sentencepiece
@require_tokenizers
class       __A     (						unittest.TestCase    ):
				__A      =				"""facebook/m2m100_418M"""
				__A      =				[
				    """In my opinion, there are two levels of response from the French government.""",
				    """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
				]
				__A      =				[
				    """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
				    """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
				]

				# fmt: off
				__A      =				[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]


				@classmethod
				def _snake_case       (		cls							):
								lowerCamelCase	 =MaMaaaTokenizer.from_pretrained(
								    cls.checkpoint_name   , src_lang="""en"""   , tgt_lang="""fr"""							)
								lowerCamelCase	 =1
								return cls


				def _snake_case       (		self							):
								self.assertEqual(self.tokenizer.get_lang_id("""ar"""							)   , 128006							)
								self.assertEqual(self.tokenizer.get_lang_id("""en"""							)   , 128022							)
								self.assertEqual(self.tokenizer.get_lang_id("""ro"""							)   , 128076							)
								self.assertEqual(self.tokenizer.get_lang_id("""mr"""							)   , 128063							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.tokenizer.get_vocab()
								self.assertEqual(len(UpperCAmelCase_							)   , self.tokenizer.vocab_size							)
								self.assertEqual(vocab["""<unk>"""]   , 3							)
								self.assertIn(self.tokenizer.get_lang_token("""en"""							)   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 ="""en"""
								lowerCamelCase	 =self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
								self.assertListEqual(self.expected_src_tokens   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								self.assertIn(UpperCAmelCase_   , self.tokenizer.all_special_ids							)
								# fmt: off
								lowerCamelCase	 =[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
								# fmt: on
								lowerCamelCase	 =self.tokenizer.decode(UpperCAmelCase_   , skip_special_tokens=UpperCAmelCase_							)
								lowerCamelCase	 =self.tokenizer.decode(generated_ids[1:]   , skip_special_tokens=UpperCAmelCase_							)
								self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)
								self.assertNotIn(self.tokenizer.eos_token   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =tempfile.mkdtemp()
								lowerCamelCase	 =self.tokenizer.lang_token_to_id
								self.tokenizer.save_pretrained(UpperCAmelCase_							)
								lowerCamelCase	 =MaMaaaTokenizer.from_pretrained(UpperCAmelCase_							)
								self.assertDictEqual(new_tok.lang_token_to_id   , UpperCAmelCase_							)


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 ="""en"""
								lowerCamelCase	 ="""fr"""

								lowerCamelCase	 =self.tokenizer(self.src_text   , text_target=self.tgt_text   , padding=UpperCAmelCase_   , return_tensors="""pt"""							)

								lowerCamelCase	 =shift_tokens_right(
								    batch["""labels"""]   , self.tokenizer.pad_token_id   , self.tokenizer.eos_token_id							)

								for k in batch:
												lowerCamelCase	 =batch[k].tolist()
								# batch = {k: v.tolist() for k,v in batch.items()}
								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								# batch.decoder_inputs_ids[0][0] ==
								assert batch.input_ids[1][0] == EN_CODE
								assert batch.input_ids[1][-1] == 2
								assert batch.labels[1][0] == FR_CODE
								assert batch.labels[1][-1] == 2
								assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 ="""mr"""
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""mr"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)

								lowerCamelCase	 ="""zh"""
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""zh"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 ="""mr"""
								self.tokenizer._switch_to_target_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""mr"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)
								self.tokenizer._switch_to_input_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)

								lowerCamelCase	 ="""zh"""
								self.tokenizer._switch_to_target_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""zh"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)
								self.tokenizer._switch_to_input_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 =self.tokenizer._build_translation_inputs("""A test"""   , return_tensors="""pt"""   , src_lang="""en"""   , tgt_lang="""ar"""							)

								self.assertEqual(
								    nested_simplify(UpperCAmelCase_							)   , {
								        # en_XX, A, test, EOS
								        """input_ids""": [[128022, 58, 4183, 2]],
								        """attention_mask""": [[1, 1, 1, 1]],
								        # ar_AR
								        """forced_bos_token_id""": 128006,
								    }   , )





