






'''simple docstring'''







import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_A			:    List[Any]					  =16
_A			:    List[str]					  =32





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase = 16       ,    UpperCamelCase = "bert-base-cased"   )   -> Union[str, Any]:
							lowerCamelCase__      :			Optional[int] 						= AutoTokenizer.from_pretrained(UpperCamelCase   )
							lowerCamelCase__      :			Tuple 						= load_dataset("""glue"""       ,    """mrpc"""   )

							def tokenize_function(UpperCamelCase   ):
														# max_length=None => use the model max length (it's actually the default)
														lowerCamelCase__      :			Optional[Any] 						= tokenizer(examples["""sentence1"""]       ,    examples["""sentence2"""]       ,    truncation=UpperCamelCase       ,    max_length=UpperCamelCase   )
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							lowerCamelCase__      :			Dict 						= datasets.map(
							    UpperCamelCase       ,    batched=UpperCamelCase       ,    remove_columns=["""idx""", """sentence1""", """sentence2"""]       ,    load_from_cache_file=UpperCamelCase   )

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							lowerCamelCase__      :			str 						= tokenized_datasets.rename_column("""label"""       ,    """labels"""   )

							def collate_fn(UpperCamelCase   ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(UpperCamelCase       ,    padding="""max_length"""       ,    max_length=128       ,    return_tensors="""pt"""   )
														return tokenizer.pad(UpperCamelCase       ,    padding="""longest"""       ,    return_tensors="""pt"""   )

							# Instantiate dataloaders.
							lowerCamelCase__      :			int 						= DataLoader(
							    tokenized_datasets["""train"""]       ,    shuffle=UpperCamelCase       ,    collate_fn=UpperCamelCase       ,    batch_size=UpperCamelCase   )
							lowerCamelCase__      :			List[str] 						= DataLoader(
							    tokenized_datasets["""validation"""]       ,    shuffle=UpperCamelCase       ,    collate_fn=UpperCamelCase       ,    batch_size=UpperCamelCase   )

							return train_dataloader, eval_dataloader





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> str:
							model.eval()
							lowerCamelCase__      :			Any 						= 0
							for step, batch in enumerate(UpperCamelCase   ):
														# We could avoid this line since we set the accelerator with `device_placement=True`.
														batch.to(accelerator.device   )
														with torch.no_grad():
																					lowerCamelCase__      :			Tuple 						= model(**UpperCamelCase   )
														lowerCamelCase__      :			Tuple 						= outputs.logits.argmax(dim=-1   )
														# It is slightly faster to call this once, than multiple times
														lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= accelerator.gather(
														    (predictions, batch["""labels"""])   )  # If we are in a multiprocess environment, the last batch has duplicates
														if accelerator.use_distributed:
																					if step == len(UpperCamelCase   ) - 1:
																												lowerCamelCase__      :			Optional[Any] 						= predictions[: len(eval_dataloader.dataset   ) - samples_seen]
																												lowerCamelCase__      :			List[str] 						= references[: len(eval_dataloader.dataset   ) - samples_seen]
																					else:
																												samples_seen += references.shape[0]
														metric.add_batch(
														    predictions=UpperCamelCase       ,    references=UpperCamelCase       ,    )

							lowerCamelCase__      :			Optional[Any] 						= metric.compute()
							return eval_metric["accuracy"]





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> int:
							# Initialize accelerator
							lowerCamelCase__      :			Union[str, Any] 						= Accelerator()

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							lowerCamelCase__      :			Union[str, Any] 						= config["""lr"""]
							lowerCamelCase__      :			str 						= int(config["""num_epochs"""]   )
							lowerCamelCase__      :			Tuple 						= int(config["""seed"""]   )
							lowerCamelCase__      :			List[Any] 						= int(config["""batch_size"""]   )
							lowerCamelCase__      :			Dict 						= args.model_name_or_path

							set_seed(UpperCamelCase   )
							lowerCamelCase__						,	lowerCamelCase__      :			Optional[int] 						= get_dataloaders(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							lowerCamelCase__      :			str 						= AutoModelForSequenceClassification.from_pretrained(UpperCamelCase       ,    return_dict=UpperCamelCase   )

							# Instantiate optimizer
							lowerCamelCase__      :			Dict 						= (
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							lowerCamelCase__      :			Union[str, Any] 						= optimizer_cls(params=model.parameters()       ,    lr=UpperCamelCase   )

							if accelerator.state.deepspeed_plugin is not None:
														lowerCamelCase__      :			Dict 						= accelerator.state.deepspeed_plugin.deepspeed_config[
														    """gradient_accumulation_steps"""
														]
							else:
														lowerCamelCase__      :			Any 						= 1
							lowerCamelCase__      :			Any 						= (len(UpperCamelCase   ) * num_epochs) // gradient_accumulation_steps

							# Instantiate scheduler
							if (
							    accelerator.state.deepspeed_plugin is None
							    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
							):
														lowerCamelCase__      :			List[str] 						= get_linear_schedule_with_warmup(
														    optimizer=UpperCamelCase       ,    num_warmup_steps=0       ,    num_training_steps=UpperCamelCase       ,    )
							else:
														lowerCamelCase__      :			Union[str, Any] 						= DummyScheduler(UpperCamelCase       ,    total_num_steps=UpperCamelCase       ,    warmup_num_steps=0   )

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.
							lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Tuple 						= accelerator.prepare(
							    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )

							# We need to keep track of how many total steps we have iterated over
							lowerCamelCase__      :			str 						= 0
							# We also need to keep track of the stating epoch so files are named properly
							lowerCamelCase__      :			Any 						= 0
							lowerCamelCase__      :			List[Any] 						= evaluate.load("""glue"""       ,    """mrpc"""   )
							lowerCamelCase__      :			Union[str, Any] 						= num_epochs

							if args.partial_train_epoch is not None:
														lowerCamelCase__      :			List[Any] 						= args.partial_train_epoch

							if args.resume_from_checkpoint:
														accelerator.load_state(args.resume_from_checkpoint   )
														lowerCamelCase__      :			Dict 						= args.resume_from_checkpoint.split("""epoch_"""   )[1]
														lowerCamelCase__      :			Union[str, Any] 						= """"""
														for char in epoch_string:
																					if char.isdigit():
																												state_epoch_num += char
																					else:
																												break
														lowerCamelCase__      :			Union[str, Any] 						= int(UpperCamelCase   ) + 1
														lowerCamelCase__      :			Dict 						= evaluation_loop(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )
														accelerator.print("""resumed checkpoint performance:"""       ,    UpperCamelCase   )
														accelerator.print("""resumed checkpoint's scheduler's lr:"""       ,    lr_scheduler.get_lr()[0]   )
														accelerator.print("""resumed optimizers's lr:"""       ,    optimizer.param_groups[0]["""lr"""]   )
														with open(os.path.join(args.output_dir       ,    f'''state_{starting_epoch-1}.json'''   )       ,    """r"""   ) as f:
																					lowerCamelCase__      :			str 						= json.load(UpperCamelCase   )
																					assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
																					assert (
																					    resumed_state["lr"] == lr_scheduler.get_lr()[0]
																					), "Scheduler learning rate mismatch, loading from checkpoint failed"
																					assert (
																					    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
																					), "Optimizer learning rate mismatch, loading from checkpoint failed"
																					assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
																					return

    # Now we train the model
							lowerCamelCase__      :			Optional[Any] 						= {}
							for epoch in range(UpperCamelCase       ,    UpperCamelCase   ):
														model.train()
														for step, batch in enumerate(UpperCamelCase   ):
																					lowerCamelCase__      :			str 						= model(**UpperCamelCase   )
																					lowerCamelCase__      :			List[str] 						= outputs.loss
																					lowerCamelCase__      :			List[Any] 						= loss / gradient_accumulation_steps
																					accelerator.backward(UpperCamelCase   )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

																					overall_step += 1
														lowerCamelCase__      :			Optional[int] 						= f'''epoch_{epoch}'''
														lowerCamelCase__      :			List[str] 						= os.path.join(args.output_dir       ,    UpperCamelCase   )
														accelerator.save_state(UpperCamelCase   )
														lowerCamelCase__      :			Optional[Any] 						= evaluation_loop(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )
														lowerCamelCase__      :			Optional[Any] 						= accuracy
														lowerCamelCase__      :			List[Any] 						= lr_scheduler.get_lr()[0]
														lowerCamelCase__      :			Dict 						= optimizer.param_groups[0]["""lr"""]
														lowerCamelCase__      :			Optional[int] 						= epoch
														lowerCamelCase__      :			Union[str, Any] 						= overall_step
														accelerator.print(f'''epoch {epoch}:'''       ,    UpperCamelCase   )

														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					with open(os.path.join(args.output_dir       ,    f'''state_{epoch}.json'''   )       ,    """w"""   ) as f:
																												json.dump(UpperCamelCase       ,    UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ ()   -> Union[str, Any]:
							lowerCamelCase__      :			str 						= argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""   )
							parser.add_argument(
							    """--model_name_or_path"""       ,    type=UpperCamelCase       ,    default="""bert-base-cased"""       ,    help="""Path to pretrained model or model identifier from huggingface.co/models."""       ,    required=UpperCamelCase       ,    )
							parser.add_argument(
							    """--output_dir"""       ,    type=UpperCamelCase       ,    default="""."""       ,    help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""       ,    )
							parser.add_argument(
							    """--resume_from_checkpoint"""       ,    type=UpperCamelCase       ,    default=UpperCamelCase       ,    help="""If the training should continue from a checkpoint folder."""       ,    )
							parser.add_argument(
							    """--partial_train_epoch"""       ,    type=UpperCamelCase       ,    default=UpperCamelCase       ,    help="""If passed, the training will stop after this number of epochs."""       ,    )
							parser.add_argument(
							    """--num_epochs"""       ,    type=UpperCamelCase       ,    default=2       ,    help="""Number of train epochs."""       ,    )
							lowerCamelCase__      :			Union[str, Any] 						= parser.parse_args()
							lowerCamelCase__      :			Tuple 						= {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

							training_function(UpperCamelCase       ,    UpperCamelCase   )


if __name__ == "__main__":
			main()









'''simple docstring'''







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

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
			from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


_A			:    int					  =get_tests_dir('''fixtures/test_sentencepiece.model''')

_A			:    Tuple					  ={'''target_lang''': '''fi''', '''source_lang''': '''en'''}
_A			:    int					  ='''>>zh<<'''
_A			:    Dict					  ='''Helsinki-NLP/'''

if is_torch_available():
			_A			:    List[Any]					  ='''pt'''
elif is_tf_available():
			_A			:    Optional[int]					  ='''tf'''
else:
			_A			:    Dict					  ='''jax'''

@require_sentencepiece
class _lowercase			(       _lowercase       ,   unittest.TestCase	):
							a          = MarianTokenizer
							a          = False
							a          = True




							def 	lowerCamelCase_       (							self:       List[str]  ):
														super().setUp()
														lowerCamelCase__      :			List[Any] 						= ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
														lowerCamelCase__      :			Optional[Any] 						= dict(zip(UpperCamelCase__    ,							range(len(UpperCamelCase__  )  )  )  )
														lowerCamelCase__      :			Optional[int] 						= Path(self.tmpdirname  )
														save_json(UpperCamelCase__    ,							save_dir / VOCAB_FILES_NAMES["""vocab"""]  )
														save_json(UpperCamelCase__    ,							save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]  )
														if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
																					copyfile(UpperCamelCase__    ,							save_dir / VOCAB_FILES_NAMES["""source_spm"""]  )
																					copyfile(UpperCamelCase__    ,							save_dir / VOCAB_FILES_NAMES["""target_spm"""]  )

														lowerCamelCase__      :			Dict 						= MarianTokenizer.from_pretrained(self.tmpdirname  )
														tokenizer.save_pretrained(self.tmpdirname  )




							def 	lowerCamelCase_       (							self:       Optional[Any]    ,							**UpperCamelCase__:       Any  ):
														return MarianTokenizer.from_pretrained(self.tmpdirname    ,							**UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       List[str]  ):
														return (
														    "This is a test",
														    "This is a test",
														)




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			Any 						= """</s>"""
														lowerCamelCase__      :			List[Any] 						= 0

														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:       Union[str, Any]  ):
														lowerCamelCase__      :			int 						= list(self.get_tokenizer().get_vocab().keys()  )

														self.assertEqual(vocab_keys[0]    ,							"""</s>"""  )
														self.assertEqual(vocab_keys[1]    ,							"""<unk>"""  )
														self.assertEqual(vocab_keys[-1]    ,							"""<pad>"""  )
														self.assertEqual(len(UpperCamelCase__  )    ,							9  )




							def 	lowerCamelCase_       (							self:       int  ):
														self.assertEqual(self.get_tokenizer().vocab_size    ,							9  )




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			List[Any] 						= MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''  )
														lowerCamelCase__      :			Optional[int] 						= en_de_tokenizer(["""I am a small frog"""]    ,							return_tensors=UpperCamelCase__  )
														self.assertIsInstance(UpperCamelCase__    ,							UpperCamelCase__  )
														lowerCamelCase__      :			Optional[Any] 						= [38, 121, 14, 697, 38_848, 0]
														self.assertListEqual(UpperCamelCase__    ,							batch.input_ids[0]  )

														lowerCamelCase__      :			List[str] 						= tempfile.mkdtemp()
														en_de_tokenizer.save_pretrained(UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= [x.name for x in Path(UpperCamelCase__  ).glob("""*"""  )]
														self.assertIn("""source.spm"""    ,							UpperCamelCase__  )
														MarianTokenizer.from_pretrained(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			List[Any] 						= self.get_tokenizer()

														lowerCamelCase__      :			Any 						= tok(
														    ["""I am a small frog""" * 1_000, """I am a small frog"""]    ,							padding=UpperCamelCase__    ,							truncation=UpperCamelCase__    ,							return_tensors=UpperCamelCase__  )
														self.assertIsInstance(UpperCamelCase__    ,							UpperCamelCase__  )
														self.assertEqual(batch.input_ids.shape    ,							(2, 512)  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			str 						= self.get_tokenizer()
														lowerCamelCase__      :			Dict 						= tok(["""I am a tiny frog""", """I am a small frog"""]    ,							padding=UpperCamelCase__    ,							return_tensors=UpperCamelCase__  )
														self.assertIsInstance(UpperCamelCase__    ,							UpperCamelCase__  )
														self.assertEqual(batch_smaller.input_ids.shape    ,							(2, 10)  )




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														# fmt: off
														lowerCamelCase__      :			int 						= {"""input_ids""": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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="""Helsinki-NLP/opus-mt-en-de"""    ,							revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""    ,							decode_kwargs={"""use_source_tokenizer""": True}    ,							)




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Union[str, Any] 						= MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""  )

														lowerCamelCase__      :			str 						= """Tämä on testi"""
														lowerCamelCase__      :			Any 						= """This is a test"""

														lowerCamelCase__      :			int 						= [76, 7, 2_047, 2]
														lowerCamelCase__      :			List[str] 						= [69, 12, 11, 940, 2]

														lowerCamelCase__      :			Tuple 						= tokenizer(UpperCamelCase__  ).input_ids
														self.assertListEqual(UpperCamelCase__    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= tokenizer(text_target=UpperCamelCase__  ).input_ids
														self.assertListEqual(UpperCamelCase__    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Tuple 						= tokenizer.decode(UpperCamelCase__    ,							skip_special_tokens=UpperCamelCase__  )
														self.assertEqual(UpperCamelCase__    ,							UpperCamelCase__  )

