






'''simple docstring'''




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


__lowerCamelCase   :					str   							=					getLogger(__name__)


__lowerCamelCase   :					Any   							=					'''cuda''' if torch.cuda.is_available() else '''cpu'''

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__ = 8     ,__magic_name__ = DEFAULT_DEVICE     ,__magic_name__=False     ,__magic_name__="summarization"     ,__magic_name__=None     ,**__magic_name__     ,)->    Dict:



			"""simple docstring"""



			snake_case_				:					int   =       Path(__magic_name__						).open("w"     ,encoding="utf-8"						)
			snake_case_				:					Union[str, Any]   =       str(__magic_name__						)
			snake_case_				:					Any   =       AutoModelForSeqaSeqLM.from_pretrained(__magic_name__						).to(__magic_name__						)
			if fpaa:
						snake_case_				:					Union[str, Any]   =       model.half()

			snake_case_				:					Optional[Any]   =       AutoTokenizer.from_pretrained(__magic_name__						)
			logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''						)  # if this is wrong, check config.model_type.

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

def 	__UpperCAmelCase       ( )->    Optional[int]:



			"""simple docstring"""



			return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"						)

def 	__UpperCAmelCase       ( __magic_name__=True						)->    Dict:



			"""simple docstring"""



			snake_case_				:					str   =       argparse.ArgumentParser()
			parser.add_argument("model_name"     ,type=__magic_name__     ,help="like facebook/bart-large-cnn,t5-base, etc."						)
			parser.add_argument("input_path"     ,type=__magic_name__     ,help="like cnn_dm/test.source"						)
			parser.add_argument("save_path"     ,type=__magic_name__     ,help="where to save summaries"						)
			parser.add_argument("--reference_path"     ,type=__magic_name__     ,required=__magic_name__     ,help="like cnn_dm/test.target"						)
			parser.add_argument("--score_path"     ,type=__magic_name__     ,required=__magic_name__     ,default="metrics.json"     ,help="where to save metrics"						)
			parser.add_argument("--device"     ,type=__magic_name__     ,required=__magic_name__     ,default=__magic_name__     ,help="cuda, cuda:1, cpu etc."						)
			parser.add_argument(
			    "--prefix"     ,type=__magic_name__     ,required=__magic_name__     ,default=__magic_name__     ,help="will be added to the begininng of src examples"						)
			parser.add_argument("--task"     ,type=__magic_name__     ,default="summarization"     ,help="used for task_specific_params + metrics"						)
			parser.add_argument("--bs"     ,type=__magic_name__     ,default=8     ,required=__magic_name__     ,help="batch size"						)
			parser.add_argument(
			    "--n_obs"     ,type=__magic_name__     ,default=-1     ,required=__magic_name__     ,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=__magic_name__     ,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
			snake_case_				:					List[Any]   =       parser.parse_known_args()
			snake_case_				:					List[Any]   =       parse_numeric_n_bool_cl_kwargs(__magic_name__						)
			if parsed_args and verbose:
						print(F'''parsed the following generate kwargs: {parsed_args}'''						)
			snake_case_				:					Dict   =       [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
			if args.n_obs > 0:
						snake_case_				:					Dict   =       examples[: args.n_obs]
			Path(args.save_path						).parent.mkdir(exist_ok=__magic_name__						)

			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"						)

			snake_case_				:					int   =       generate_summaries_or_translations(
			    __magic_name__     ,args.save_path     ,args.model_name     ,batch_size=args.bs     ,device=args.device     ,fpaa=args.fpaa     ,task=args.task     ,prefix=args.prefix     ,**__magic_name__     ,)

			if args.reference_path is None:
						return {}

			# Compute scores
			snake_case_				:					Dict   =       calculate_bleu if "translation" in args.task else calculate_rouge
			snake_case_				:					str   =       [x.rstrip() for x in open(args.save_path						).readlines()]
			snake_case_				:					List[str]   =       [x.rstrip() for x in open(args.reference_path						).readlines()][: len(__magic_name__						)]
			snake_case_				:					dict   =       score_fn(__magic_name__     ,__magic_name__						)
			scores.update(__magic_name__						)

			if args.dump_args:
						scores.update(__magic_name__						)
			if args.info:
						snake_case_				:					Union[str, Any]   =       args.info

			if verbose:
						print(__magic_name__						)

			if args.score_path is not None:
						json.dump(__magic_name__     ,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)












'''simple docstring'''




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():
		__lowerCamelCase   :					Optional[Any]   							=					get_tests_dir('''fixtures/test_sentencepiece.model''')


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

__lowerCamelCase   :					str   							=					128022
__lowerCamelCase   :					List[Any]   							=					128028







@require_sentencepiece
class    A_	(a_						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	MaMaaaTokenizer
							a__   		=	False
							a__   		=	False
							a__   		=	True

							def 							_A	(		self			:Union[str, Any]				)				->  List[str]:


										'''simple docstring'''




										super().setUp()

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

										snake_case_				:					Union[str, Any]   =       MaMaaaTokenizer.from_pretrained(self.tmpdirname				)
										tokenizer.save_pretrained(self.tmpdirname				)

							def 							_A	(		self			:List[Any]       ,							**lowerCAmelCase__			:List[Any]				)				->  str:


										'''simple docstring'''




										return MaMaaaTokenizer.from_pretrained(self.tmpdirname       ,							**lowerCAmelCase__				)

							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:Any				)				->  Optional[int]:


										'''simple docstring'''




										return (
										    "This is a test",
										    "This is a test",
										)

							def 							_A	(		self			:List[str]				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					str   =       "</s>"
										snake_case_				:					Union[str, Any]   =       0

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

							def 							_A	(		self			:Union[str, Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       self.get_tokenizer()
										snake_case_				:					Any   =       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(lowerCAmelCase__				)       ,							tokenizer.vocab_size + len(tokenizer.get_added_vocab()				)				)

							@unittest.skip("Skip this test while all models are still to be uploaded."				)
							def 							_A	(		self			:List[Any]				)				->  Union[str, Any]:


										'''simple docstring'''




										pass

							def 							_A	(		self			:Optional[int]				)				->  int:


										'''simple docstring'''




										snake_case_				:					int   =       self.get_tokenizer()

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

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

										snake_case_				:					Any   =       tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]				)
										self.assertListEqual(lowerCAmelCase__       ,							["▁This", "▁is", "▁a", "▁t", "est"]				)

										snake_case_				:					Any   =       tokenizer.convert_tokens_to_string(lowerCAmelCase__				)
										self.assertEqual(lowerCAmelCase__       ,							"This is a test"				)






							@slow
							def 							_A	(		self			:Any				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					int   =       {"input_ids": [[128_022, 110_108, 397, 11, 38_272, 2_247, 124_811, 285, 18_105, 1_586, 207, 7, 39_534, 4_428, 397, 1_019, 18_105, 1_586, 207, 7, 41_337, 16_786, 241, 7, 20_214, 17, 125_690, 10_398, 7, 44_378, 58_069, 68_342, 7_798, 7_343, 11, 299, 33_310, 4, 158, 37_350, 94_077, 4_569, 299, 33_310, 90, 4, 52_840, 290, 4, 31_270, 112, 299, 682, 4, 52_840, 39_953, 14_079, 193, 52_519, 90_894, 17_894, 120_697, 11, 40_445, 551, 17, 1_019, 52_519, 90_894, 17_756, 963, 11, 40_445, 480, 17, 9_792, 1_120, 5_173, 1_393, 6_240, 16_786, 241, 120_996, 28, 1_245, 1_393, 118_240, 11_123, 1_019, 93_612, 2_691, 10_618, 98_058, 120_409, 1_928, 279, 4, 40_683, 367, 178, 207, 1_019, 103, 103_121, 506, 65_296, 5, 2], [128_022, 21_217, 367, 117, 125_450, 128, 719, 7, 7_308, 40, 93_612, 12_669, 1_116, 16_704, 71, 17_785, 3_699, 15_592, 35, 144, 9_584, 241, 11_943, 713, 950, 799, 2_247, 88_427, 150, 149, 118_813, 120_706, 1_019, 106_906, 81_518, 28, 1_224, 22_799, 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], [128_022, 1_658, 123_311, 5_155, 5_578, 4_722, 279, 14_947, 2_366, 1_120, 1_197, 14, 1_348, 9_232, 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=lowerCAmelCase__       ,							model_name="facebook/m2m100_418M"       ,							revision="c168bae485c864188cf9aa0e4108b0b6934dc91e"       ,							)







@require_torch
@require_sentencepiece
@require_tokenizers
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							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, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2]

							@classmethod
							def 							_A	(		cls			:str				)				->  int:


										'''simple docstring'''




										snake_case_				:					MaMaaaTokenizer   =       MaMaaaTokenizer.from_pretrained(
										    cls.checkpoint_name       ,							src_lang="en"       ,							tgt_lang="fr"				)
										snake_case_				:					List[str]   =       1
										return cls

							def 							_A	(		self			:Tuple				)				->  Union[str, Any]:


										'''simple docstring'''




										self.assertEqual(self.tokenizer.get_lang_id("ar"				)       ,							128_006				)
										self.assertEqual(self.tokenizer.get_lang_id("en"				)       ,							128_022				)
										self.assertEqual(self.tokenizer.get_lang_id("ro"				)       ,							128_076				)
										self.assertEqual(self.tokenizer.get_lang_id("mr"				)       ,							128_063				)

							def 							_A	(		self			:Optional[int]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.tokenizer.get_vocab()
										self.assertEqual(len(lowerCAmelCase__				)       ,							self.tokenizer.vocab_size				)
										self.assertEqual(vocab["<unk>"]       ,							3				)
										self.assertIn(self.tokenizer.get_lang_token("en"				)       ,							lowerCAmelCase__				)

							def 							_A	(		self			:Any				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					List[str]   =       "en"
										snake_case_				:					Dict   =       self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
										self.assertListEqual(self.expected_src_tokens       ,							lowerCAmelCase__				)

							def 							_A	(		self			:Union[str, Any]				)				->  Dict:


										'''simple docstring'''




										self.assertIn(lowerCAmelCase__       ,							self.tokenizer.all_special_ids				)
										# fmt: off
										snake_case_				:					Dict   =       [FR_CODE, 5_364, 82, 8_642, 4, 294, 47, 8, 14_028, 136, 3_286, 9_706, 6, 90_797, 6, 144_012, 162, 88_128, 30_061, 5, 2]
										# fmt: on
										snake_case_				:					List[str]   =       self.tokenizer.decode(lowerCAmelCase__       ,							skip_special_tokens=lowerCAmelCase__				)
										snake_case_				:					str   =       self.tokenizer.decode(generated_ids[1:]       ,							skip_special_tokens=lowerCAmelCase__				)
										self.assertEqual(lowerCAmelCase__       ,							lowerCAmelCase__				)
										self.assertNotIn(self.tokenizer.eos_token       ,							lowerCAmelCase__				)

							def 							_A	(		self			:Tuple				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       tempfile.mkdtemp()
										snake_case_				:					int   =       self.tokenizer.lang_token_to_id
										self.tokenizer.save_pretrained(lowerCAmelCase__				)
										snake_case_				:					List[str]   =       MaMaaaTokenizer.from_pretrained(lowerCAmelCase__				)
										self.assertDictEqual(new_tok.lang_token_to_id       ,							lowerCAmelCase__				)

							@require_torch
							def 							_A	(		self			:Optional[Any]				)				->  str:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       "en"
										snake_case_				:					Tuple   =       "fr"

										snake_case_				:					Optional[int]   =       self.tokenizer(self.src_text       ,							text_target=self.tgt_text       ,							padding=lowerCAmelCase__       ,							return_tensors="pt"				)

										snake_case_				:					Dict   =       shift_tokens_right(
										    batch["labels"]       ,							self.tokenizer.pad_token_id       ,							self.tokenizer.eos_token_id				)

										for k in batch:
													snake_case_				:					str   =       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 							_A	(		self			:Optional[Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					List[str]   =       "mr"
										self.assertListEqual(self.tokenizer.prefix_tokens       ,							[self.tokenizer.get_lang_id("mr"				)]				)
										self.assertListEqual(self.tokenizer.suffix_tokens       ,							[self.tokenizer.eos_token_id]				)

										snake_case_				:					int   =       "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 							_A	(		self			:str				)				->  int:


										'''simple docstring'''




										snake_case_				:					Dict   =       "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				)]				)

										snake_case_				:					Tuple   =       "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 							_A	(		self			:Optional[Any]				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       self.tokenizer._build_translation_inputs("A test"       ,							return_tensors="pt"       ,							src_lang="en"       ,							tgt_lang="ar"				)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        # en_XX, A, test, EOS
										        "input_ids": [[128_022, 58, 4_183, 2]],
										        "attention_mask": [[1, 1, 1, 1]],
										        # ar_AR
										        "forced_bos_token_id": 128_006,
										    }       ,							)




