






'''simple docstring'''







import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_A			:    str					  =logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

_A			:    Dict					  ={
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_A			:    Optional[int]					  ={
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_A			:    Optional[int]					  =sorted(arg_to_scheduler.keys())
_A			:    Optional[Any]					  ='''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''

class _lowercase			(       pl.LightningModule	):




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       argparse.Namespace    ,							UpperCamelCase__:       Dict=None    ,							UpperCamelCase__:       List[Any]="base"    ,							UpperCamelCase__:       Optional[Any]=None    ,							UpperCamelCase__:       Union[str, Any]=None    ,							UpperCamelCase__:       Any=None    ,							**UpperCamelCase__:       str    ,							):
														super().__init__()
														# TODO: move to self.save_hyperparameters()
														# self.save_hyperparameters()
														# can also expand arguments into trainer signature for easier reading

														self.save_hyperparameters(UpperCamelCase__  )
														lowerCamelCase__      :			List[Any] 						= 0
														lowerCamelCase__      :			Union[str, Any] 						= Path(self.hparams.output_dir  )
														lowerCamelCase__      :			Tuple 						= self.hparams.cache_dir if self.hparams.cache_dir else None
														if config is None:
																					lowerCamelCase__      :			Optional[Any] 						= AutoConfig.from_pretrained(
																					    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path    ,							**({"""num_labels""": num_labels} if num_labels is not None else {})    ,							cache_dir=UpperCamelCase__    ,							**UpperCamelCase__    ,							)
														else:
																					lowerCamelCase__      :			PretrainedConfig 						= config

														lowerCamelCase__      :			int 						= ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
														for p in extra_model_params:
																					if getattr(self.hparams    ,							UpperCamelCase__    ,							UpperCamelCase__  ):
																												assert hasattr(self.config    ,							UpperCamelCase__  ), F'''model config doesn\'t have a `{p}` attribute'''
																												setattr(self.config    ,							UpperCamelCase__    ,							getattr(self.hparams    ,							UpperCamelCase__  )  )

														if tokenizer is None:
																					lowerCamelCase__      :			Optional[int] 						= AutoTokenizer.from_pretrained(
																					    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,							cache_dir=UpperCamelCase__    ,							)
														else:
																					lowerCamelCase__      :			PreTrainedTokenizer 						= tokenizer
														lowerCamelCase__      :			List[Any] 						= MODEL_MODES[mode]
														if model is None:
																					lowerCamelCase__      :			Tuple 						= self.model_type.from_pretrained(
																					    self.hparams.model_name_or_path    ,							from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path  )    ,							config=self.config    ,							cache_dir=UpperCamelCase__    ,							)
														else:
																					lowerCamelCase__      :			int 						= model




							def 	lowerCamelCase_       (							self:       Any    ,							*UpperCamelCase__:       Tuple    ,							**UpperCamelCase__:       Tuple  ):
														lowerCamelCase__      :			int 						= self.model_type.from_pretrained(*UpperCamelCase__    ,							**UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			int 						= arg_to_scheduler[self.hparams.lr_scheduler]
														lowerCamelCase__      :			str 						= get_schedule_func(
														    self.opt    ,							num_warmup_steps=self.hparams.warmup_steps    ,							num_training_steps=self.total_steps()  )
														lowerCamelCase__      :			Dict 						= {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
														return scheduler




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			Any 						= self.model
														lowerCamelCase__      :			int 						= ["""bias""", """LayerNorm.weight"""]
														lowerCamelCase__      :			Union[str, Any] 						= [
														    {
														        """params""": [
														            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay  )
														        ],  # check this named paramters
														        """weight_decay""": self.hparams.weight_decay,
														    },
														    {
														        """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay  )],
														        """weight_decay""": 0.0,
														    },
														]
														if self.hparams.adafactor:
																					lowerCamelCase__      :			Union[str, Any] 						= Adafactor(
																					    UpperCamelCase__    ,							lr=self.hparams.learning_rate    ,							scale_parameter=UpperCamelCase__    ,							relative_step=UpperCamelCase__  )

														else:
																					lowerCamelCase__      :			Any 						= AdamW(
																					    UpperCamelCase__    ,							lr=self.hparams.learning_rate    ,							eps=self.hparams.adam_epsilon  )
														lowerCamelCase__      :			Tuple 						= optimizer

														lowerCamelCase__      :			Optional[int] 						= self.get_lr_scheduler()

														return [optimizer], [scheduler]




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       List[Any]  ):
														return self.validation_step(UpperCamelCase__    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[Any]    ,							UpperCamelCase__:       List[str]  ):
														return self.validation_end(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			Any 						= max(1    ,							self.hparams.gpus  )  # TODO: consider num_tpu_cores
														lowerCamelCase__      :			List[Any] 						= self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
														return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       List[Any]  ):
														if stage == "test":
																					lowerCamelCase__      :			Union[str, Any] 						= len(self.test_dataloader().dataset  )
														else:
																					lowerCamelCase__      :			Optional[Any] 						= self.get_dataloader("""train"""    ,							self.hparams.train_batch_size    ,							shuffle=UpperCamelCase__  )
																					lowerCamelCase__      :			Dict 						= len(self.train_dataloader().dataset  )




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       bool = False  ):
														raise NotImplementedError("""You must implement this for your task"""  )




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														return self.train_loader




							def 	lowerCamelCase_       (							self:       Any  ):
														return self.get_dataloader("""dev"""    ,							self.hparams.eval_batch_size    ,							shuffle=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Dict  ):
														return self.get_dataloader("""test"""    ,							self.hparams.eval_batch_size    ,							shuffle=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       Union[str, Any]  ):
														return os.path.join(
														    self.hparams.data_dir    ,							"""cached_{}_{}_{}""".format(
														        UpperCamelCase__    ,							list(filter(UpperCamelCase__    ,							self.hparams.model_name_or_path.split("""/"""  )  )  ).pop()    ,							str(self.hparams.max_seq_length  )    ,							)    ,							)




							@pl.utilities.rank_zero_only
							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       Dict[str, Any]  ):
														lowerCamelCase__      :			Optional[int] 						= self.output_dir.joinpath("""best_tfmr"""  )
														lowerCamelCase__      :			Optional[Any] 						= self.step_count
														self.model.save_pretrained(UpperCamelCase__  )
														self.tokenizer.save_pretrained(UpperCamelCase__  )




							@staticmethod
							def 	lowerCamelCase_       (							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Union[str, Any]  ):
														parser.add_argument(
														    """--model_name_or_path"""    ,							default=UpperCamelCase__    ,							type=UpperCamelCase__    ,							required=UpperCamelCase__    ,							help="""Path to pretrained model or model identifier from huggingface.co/models"""    ,							)
														parser.add_argument(
														    """--config_name"""    ,							default=""""""    ,							type=UpperCamelCase__    ,							help="""Pretrained config name or path if not the same as model_name"""  )
														parser.add_argument(
														    """--tokenizer_name"""    ,							default=UpperCamelCase__    ,							type=UpperCamelCase__    ,							help="""Pretrained tokenizer name or path if not the same as model_name"""    ,							)
														parser.add_argument(
														    """--cache_dir"""    ,							default=str(Path(UpperCamelCase__  ).parent / """test_run""" / """cache"""  )    ,							type=UpperCamelCase__    ,							help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""    ,							)
														parser.add_argument(
														    """--encoder_layerdrop"""    ,							type=UpperCamelCase__    ,							help="""Encoder layer dropout probability (Optional). Goes into model.config"""    ,							)
														parser.add_argument(
														    """--decoder_layerdrop"""    ,							type=UpperCamelCase__    ,							help="""Decoder layer dropout probability (Optional). Goes into model.config"""    ,							)
														parser.add_argument(
														    """--dropout"""    ,							type=UpperCamelCase__    ,							help="""Dropout probability (Optional). Goes into model.config"""    ,							)
														parser.add_argument(
														    """--attention_dropout"""    ,							type=UpperCamelCase__    ,							help="""Attention dropout probability (Optional). Goes into model.config"""    ,							)
														parser.add_argument("""--learning_rate"""    ,							default=5e-5    ,							type=UpperCamelCase__    ,							help="""The initial learning rate for Adam."""  )
														parser.add_argument(
														    """--lr_scheduler"""    ,							default="""linear"""    ,							choices=UpperCamelCase__    ,							metavar=UpperCamelCase__    ,							type=UpperCamelCase__    ,							help="""Learning rate scheduler"""    ,							)
														parser.add_argument("""--weight_decay"""    ,							default=0.0    ,							type=UpperCamelCase__    ,							help="""Weight decay if we apply some."""  )
														parser.add_argument("""--adam_epsilon"""    ,							default=1e-8    ,							type=UpperCamelCase__    ,							help="""Epsilon for Adam optimizer."""  )
														parser.add_argument("""--warmup_steps"""    ,							default=0    ,							type=UpperCamelCase__    ,							help="""Linear warmup over warmup_steps."""  )
														parser.add_argument("""--num_workers"""    ,							default=4    ,							type=UpperCamelCase__    ,							help="""kwarg passed to DataLoader"""  )
														parser.add_argument("""--num_train_epochs"""    ,							dest="""max_epochs"""    ,							default=3    ,							type=UpperCamelCase__  )
														parser.add_argument("""--train_batch_size"""    ,							default=32    ,							type=UpperCamelCase__  )
														parser.add_argument("""--eval_batch_size"""    ,							default=32    ,							type=UpperCamelCase__  )
														parser.add_argument("""--adafactor"""    ,							action="""store_true"""  )



class _lowercase			(       pl.Callback	):




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       int  ):
														if (
														    trainer.is_global_zero and trainer.global_rank == 0
														):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
																					pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.



class _lowercase			(       pl.Callback	):




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[Any]  ):
														# print(pl_module.model.rag)
														for name, param in pl_module.model.rag.named_parameters():
																					if param.grad is None:
																												print(UpperCamelCase__  )



class _lowercase			(       pl.Callback	):




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			int 						= trainer.lr_schedulers[0]["""scheduler"""]
														lowerCamelCase__      :			Optional[Any] 						= {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()  )}
														pl_module.logger.log_metrics(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       pl.Trainer    ,							UpperCamelCase__:       pl.LightningModule  ):
														rank_zero_info("""***** Validation results *****"""  )
														lowerCamelCase__      :			Dict 						= trainer.callback_metrics
														# Log results
														for key in sorted(UpperCamelCase__  ):
																					if key not in ["log", "progress_bar"]:
																												rank_zero_info("""{} = {}\n""".format(UpperCamelCase__    ,							str(metrics[key]  )  )  )




							def 	lowerCamelCase_       (							self:       Optional[Any]    ,							UpperCamelCase__:       pl.Trainer    ,							UpperCamelCase__:       pl.LightningModule  ):
														rank_zero_info("""***** Test results *****"""  )
														lowerCamelCase__      :			Dict 						= trainer.callback_metrics
														# Log and save results to file
														lowerCamelCase__      :			Optional[Any] 						= os.path.join(pl_module.hparams.output_dir    ,							"""test_results.txt"""  )
														with open(UpperCamelCase__    ,							"""w"""  ) as writer:
																					for key in sorted(UpperCamelCase__  ):
																												if key not in ["log", "progress_bar"]:
																																			rank_zero_info("""{} = {}\n""".format(UpperCamelCase__    ,							str(metrics[key]  )  )  )
																																			writer.write("""{} = {}\n""".format(UpperCamelCase__    ,							str(metrics[key]  )  )  )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> None:
							#  To allow all pl args uncomment the following line
							#  parser = pl.Trainer.add_argparse_args(parser)
							parser.add_argument(
							    """--output_dir"""       ,    default=str(Path(UpperCamelCase   ).parent / """test_run""" / """model_checkpoints"""   )       ,    type=UpperCamelCase       ,    help="""The output directory where the model predictions and checkpoints will be written."""       ,    )
							parser.add_argument(
							    """--fp16"""       ,    action="""store_true"""       ,    help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""       ,    )

							parser.add_argument(
							    """--fp16_opt_level"""       ,    type=UpperCamelCase       ,    default="""O2"""       ,    help=(
							        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
							        """See details at https://nvidia.github.io/apex/amp.html"""
							    )       ,    )
							parser.add_argument("""--n_tpu_cores"""       ,    dest="""tpu_cores"""       ,    type=UpperCamelCase   )
							parser.add_argument("""--max_grad_norm"""       ,    dest="""gradient_clip_val"""       ,    default=1.0       ,    type=UpperCamelCase       ,    help="""Max gradient norm"""   )
							parser.add_argument("""--do_train"""       ,    action="""store_true"""       ,    help="""Whether to run training."""   )
							parser.add_argument("""--do_predict"""       ,    action="""store_true"""       ,    help="""Whether to run predictions on the test set."""   )
							parser.add_argument(
							    """--gradient_accumulation_steps"""       ,    dest="""accumulate_grad_batches"""       ,    type=UpperCamelCase       ,    default=1       ,    help="""Number of updates steps to accumulate before performing a backward/update pass."""       ,    )
							parser.add_argument("""--seed"""       ,    type=UpperCamelCase       ,    default=42       ,    help="""random seed for initialization"""   )
							parser.add_argument(
							    """--data_dir"""       ,    default=str(Path(UpperCamelCase   ).parent / """test_run""" / """dummy-train-data"""   )       ,    type=UpperCamelCase       ,    help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""       ,    )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=None       ,    UpperCamelCase=True       ,    UpperCamelCase=[]       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    **UpperCamelCase       ,    )   -> Any:
							pl.seed_everything(args.seed   )

							# init model
							lowerCamelCase__      :			Optional[int] 						= Path(model.hparams.output_dir   )
							odir.mkdir(exist_ok=UpperCamelCase   )

							# add custom checkpoints
							if checkpoint_callback is None:
														lowerCamelCase__      :			Optional[int] 						= pl.callbacks.ModelCheckpoint(
														    filepath=args.output_dir       ,    prefix="""checkpoint"""       ,    monitor="""val_loss"""       ,    mode="""min"""       ,    save_top_k=1   )
							if early_stopping_callback:
														extra_callbacks.append(UpperCamelCase   )
							if logging_callback is None:
														lowerCamelCase__      :			Optional[Any] 						= LoggingCallback()

							lowerCamelCase__      :			Optional[Any] 						= {}

							if args.fpaa:
														lowerCamelCase__      :			Tuple 						= 16

							if args.gpus > 1:
														lowerCamelCase__      :			List[str] 						= """auto"""
														lowerCamelCase__      :			Any 						= """ddp"""

							lowerCamelCase__      :			List[str] 						= args.accumulate_grad_batches
							lowerCamelCase__      :			Optional[int] 						= None
							lowerCamelCase__      :			int 						= """auto"""

							lowerCamelCase__      :			Dict 						= pl.Trainer.from_argparse_args(
							    UpperCamelCase       ,    weights_summary=UpperCamelCase       ,    callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]       ,    logger=UpperCamelCase       ,    val_check_interval=1       ,    num_sanity_val_steps=2       ,    **UpperCamelCase       ,    )

							if args.do_train:
														trainer.fit(UpperCamelCase   )

							else:
														print("""RAG modeling tests with new set functions successfuly executed!"""   )
							return trainer









'''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__  )

