





'''simple docstring'''




import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


__UpperCAmelCase            =       logging.getLogger(__name__)
__UpperCAmelCase            =       list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__UpperCAmelCase            =       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class      UpperCamelCase__					:




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={
					        '''help''': (
					            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
					        )
					    }						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(lowercase_			)}						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={
					        '''help''': (
					            '''Override some existing default config settings when a model is trained from scratch. Example: '''
					            '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
					        )
					    }						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''main'''						,					metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={
					        '''help''': (
					            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
					            '''with private models).'''
					        )
					    }						,					)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
									raise ValueError(
									    """--config_overrides can't be used in combination with --config_name or --model_name_or_path"""						)







@dataclass
class      UpperCamelCase__					:




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=lowercase_						,					metadata={'''help''': '''The input training data file (a text file).'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=5						,					metadata={
					        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
					    }						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={
					        '''help''': (
					            '''The maximum total input sequence length after tokenization. Sequences longer '''
					            '''than this will be truncated. Default to the max input length of the model.'''
					        )
					    }						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''The number of processes to use for the preprocessing.'''}						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=0.15						,					metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={
					        '''help''': (
					            '''Whether to pad all samples to `max_seq_length`. '''
					            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
					        )
					    }						,					)


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							if self.train_file is not None:
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.train_file.split("""."""						)[-1]
									assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
							if self.validation_file is not None:
									SCREAMING_SNAKE_CASE :				Tuple		=			self.validation_file.split("""."""						)[-1]
									assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."







def 		__A		(  lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		with open(UpperCAmelCase__			,			"""r"""			,			encoding="""utf-8"""	) as f:
				SCREAMING_SNAKE_CASE :				List[str]		=			[json.loads(UpperCAmelCase__	) for line in f.read().splitlines() if (len(UpperCAmelCase__	) > 0 and not line.isspace())]
		assert len(UpperCAmelCase__	) == len(UpperCAmelCase__	)

		SCREAMING_SNAKE_CASE :				List[str]		=			{c: dataset[c] for c in dataset.column_names}
		SCREAMING_SNAKE_CASE :				str		=			refs
		return Dataset.from_dict(UpperCAmelCase__	)



def 		__A		(  ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				int		=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
		if len(sys.argv	) == 2 and sys.argv[1].endswith(""".json"""	):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				List[Any]		=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
		else:
				SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				List[str]		=			parser.parse_args_into_dataclasses()

		# Detecting last checkpoint.
		SCREAMING_SNAKE_CASE :				List[Any]		=			None
		if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
				SCREAMING_SNAKE_CASE :				Tuple		=			get_last_checkpoint(training_args.output_dir	)
				if last_checkpoint is None and len(os.listdir(training_args.output_dir	)	) > 0:
						raise ValueError(
						    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
						    """Use --overwrite_output_dir to overcome."""	)
				elif last_checkpoint is not None:
						logger.info(
						    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
						    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""	)

    # Setup logging
		logging.basicConfig(
		    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""			,			datefmt="""%m/%d/%Y %H:%M:%S"""			,			handlers=[logging.StreamHandler(sys.stdout	)]			,			)
		logger.setLevel(logging.INFO if is_main_process(training_args.local_rank	) else logging.WARN	)

		# Log on each process the small summary:
		logger.warning(
		    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
		    + f'''distributed training: {bool(training_args.local_rank != -1	)}, 16-bits training: {training_args.fpaa}'''	)
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank	):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info("""Training/evaluation parameters %s"""			,			UpperCAmelCase__	)

		# Set seed before initializing model.
		set_seed(training_args.seed	)

		# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
		# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
		# (the dataset will be downloaded automatically from the datasets Hub).
		#
		# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
		# 'text' is found. You can easily tweak this behavior (see below).
		#
		# In distributed training, the load_dataset function guarantee that only one local process can concurrently
		# download the dataset.
		if data_args.dataset_name is not None:
				# Downloading and loading a dataset from the hub.
				SCREAMING_SNAKE_CASE :				Tuple		=			load_dataset(data_args.dataset_name			,			data_args.dataset_config_name	)
				if "validation" not in datasets.keys():
						SCREAMING_SNAKE_CASE :				Optional[int]		=			load_dataset(
						    data_args.dataset_name			,			data_args.dataset_config_name			,			split=f'''train[:{data_args.validation_split_percentage}%]'''			,			)
						SCREAMING_SNAKE_CASE :				int		=			load_dataset(
						    data_args.dataset_name			,			data_args.dataset_config_name			,			split=f'''train[{data_args.validation_split_percentage}%:]'''			,			)
		else:
				SCREAMING_SNAKE_CASE :				List[str]		=			{}
				if data_args.train_file is not None:
						SCREAMING_SNAKE_CASE :				Tuple		=			data_args.train_file
				if data_args.validation_file is not None:
						SCREAMING_SNAKE_CASE :				List[str]		=			data_args.validation_file
				SCREAMING_SNAKE_CASE :				str		=			data_args.train_file.split("""."""	)[-1]
				if extension == "txt":
						SCREAMING_SNAKE_CASE :				List[Any]		=			"""text"""
				SCREAMING_SNAKE_CASE :				Optional[int]		=			load_dataset(UpperCAmelCase__			,			data_files=UpperCAmelCase__	)
		# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
		# https://huggingface.co/docs/datasets/loading_datasets.html.

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		SCREAMING_SNAKE_CASE :				int		=			{
		    """cache_dir""": model_args.cache_dir,
		    """revision""": model_args.model_revision,
		    """use_auth_token""": True if model_args.use_auth_token else None,
		}
		if model_args.config_name:
				SCREAMING_SNAKE_CASE :				List[str]		=			AutoConfig.from_pretrained(model_args.config_name			,			**UpperCAmelCase__	)
		elif model_args.model_name_or_path:
				SCREAMING_SNAKE_CASE :				Any		=			AutoConfig.from_pretrained(model_args.model_name_or_path			,			**UpperCAmelCase__	)
		else:
				SCREAMING_SNAKE_CASE :				Tuple		=			CONFIG_MAPPING[model_args.model_type]()
				logger.warning("""You are instantiating a new config instance from scratch."""	)
				if model_args.config_overrides is not None:
						logger.info(f'''Overriding config: {model_args.config_overrides}'''	)
						config.update_from_string(model_args.config_overrides	)
						logger.info(f'''New config: {config}'''	)

		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			{
		    """cache_dir""": model_args.cache_dir,
		    """use_fast""": model_args.use_fast_tokenizer,
		    """revision""": model_args.model_revision,
		    """use_auth_token""": True if model_args.use_auth_token else None,
		}
		if model_args.tokenizer_name:
				SCREAMING_SNAKE_CASE :				Any		=			AutoTokenizer.from_pretrained(model_args.tokenizer_name			,			**UpperCAmelCase__	)
		elif model_args.model_name_or_path:
				SCREAMING_SNAKE_CASE :				List[Any]		=			AutoTokenizer.from_pretrained(model_args.model_name_or_path			,			**UpperCAmelCase__	)
		else:
				raise ValueError(
				    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
				    """You can do it from another script, save it, and load it from here, using --tokenizer_name."""	)

		if model_args.model_name_or_path:
				SCREAMING_SNAKE_CASE :				Optional[int]		=			AutoModelForMaskedLM.from_pretrained(
				    model_args.model_name_or_path			,			from_tf=bool(""".ckpt""" in model_args.model_name_or_path	)			,			config=UpperCAmelCase__			,			cache_dir=model_args.cache_dir			,			revision=model_args.model_revision			,			use_auth_token=True if model_args.use_auth_token else None			,			)
		else:
				logger.info("""Training new model from scratch"""	)
				SCREAMING_SNAKE_CASE :				Any		=			AutoModelForMaskedLM.from_config(UpperCAmelCase__	)

		model.resize_token_embeddings(len(UpperCAmelCase__	)	)

		# Preprocessing the datasets.
		# First we tokenize all the texts.
		if training_args.do_train:
				SCREAMING_SNAKE_CASE :				Any		=			datasets["""train"""].column_names
		else:
				SCREAMING_SNAKE_CASE :				Dict		=			datasets["""validation"""].column_names
		SCREAMING_SNAKE_CASE :				Tuple		=			"""text""" if """text""" in column_names else column_names[0]

		SCREAMING_SNAKE_CASE :				List[str]		=			"""max_length""" if data_args.pad_to_max_length else False

		def tokenize_function(lowerCamelCase_	):
				# Remove empty lines
				SCREAMING_SNAKE_CASE :				Dict		=			[line for line in examples["""text"""] if len(UpperCAmelCase__	) > 0 and not line.isspace()]
				return tokenizer(examples["""text"""]			,			padding=UpperCAmelCase__			,			truncation=UpperCAmelCase__			,			max_length=data_args.max_seq_length	)

		SCREAMING_SNAKE_CASE :				Optional[Any]		=			datasets.map(
		    UpperCAmelCase__			,			batched=UpperCAmelCase__			,			num_proc=data_args.preprocessing_num_workers			,			remove_columns=[text_column_name]			,			load_from_cache_file=not data_args.overwrite_cache			,			)

		# Add the chinese references if provided
		if data_args.train_ref_file is not None:
				SCREAMING_SNAKE_CASE :				Tuple		=			add_chinese_references(tokenized_datasets["""train"""]			,			data_args.train_ref_file	)
		if data_args.validation_ref_file is not None:
				SCREAMING_SNAKE_CASE :				int		=			add_chinese_references(
				    tokenized_datasets["""validation"""]			,			data_args.validation_ref_file	)
		# If we have ref files, need to avoid it removed by trainer
		SCREAMING_SNAKE_CASE :				Dict		=			data_args.train_ref_file or data_args.validation_ref_file
		if has_ref:
				SCREAMING_SNAKE_CASE :				Any		=			False

		# Data collator
		# This one will take care of randomly masking the tokens.
		SCREAMING_SNAKE_CASE :				Tuple		=			DataCollatorForWholeWordMask(tokenizer=UpperCAmelCase__			,			mlm_probability=data_args.mlm_probability	)

		# Initialize our Trainer
		SCREAMING_SNAKE_CASE :				Optional[Any]		=			Trainer(
		    model=UpperCAmelCase__			,			args=UpperCAmelCase__			,			train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None			,			eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None			,			tokenizer=UpperCAmelCase__			,			data_collator=UpperCAmelCase__			,			)

		# Training
		if training_args.do_train:
				if last_checkpoint is not None:
						SCREAMING_SNAKE_CASE :				Dict		=			last_checkpoint
				elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path	):
						SCREAMING_SNAKE_CASE :				str		=			model_args.model_name_or_path
				else:
						SCREAMING_SNAKE_CASE :				Tuple		=			None
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			trainer.train(resume_from_checkpoint=UpperCAmelCase__	)
				trainer.save_model()  # Saves the tokenizer too for easy upload

				SCREAMING_SNAKE_CASE :				Dict		=			os.path.join(training_args.output_dir			,			"""train_results.txt"""	)
				if trainer.is_world_process_zero():
						with open(UpperCAmelCase__			,			"""w"""	) as writer:
								logger.info("""***** Train results *****"""	)
								for key, value in sorted(train_result.metrics.items()	):
										logger.info(f'''  {key} = {value}'''	)
										writer.write(f'''{key} = {value}\n'''	)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
						trainer.state.save_to_json(os.path.join(training_args.output_dir			,			"""trainer_state.json"""	)	)

    # Evaluation
		SCREAMING_SNAKE_CASE :				Any		=			{}
		if training_args.do_eval:
				logger.info("""*** Evaluate ***"""	)

				SCREAMING_SNAKE_CASE :				Optional[Any]		=			trainer.evaluate()

				SCREAMING_SNAKE_CASE :				Optional[int]		=			math.exp(eval_output["""eval_loss"""]	)
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			perplexity

				SCREAMING_SNAKE_CASE :				Tuple		=			os.path.join(training_args.output_dir			,			"""eval_results_mlm_wwm.txt"""	)
				if trainer.is_world_process_zero():
						with open(UpperCAmelCase__			,			"""w"""	) as writer:
								logger.info("""***** Eval results *****"""	)
								for key, value in sorted(results.items()	):
										logger.info(f'''  {key} = {value}'''	)
										writer.write(f'''{key} = {value}\n'''	)

		return results



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		main()


if __name__ == "__main__":
							main()





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_mbart import MBartTokenizer
else:
		lowercase   =	None


lowercase   =	logging.get_logger(__name__)


lowercase   =	{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase   =	{
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

lowercase   =	{
    '''facebook/mbart-large-en-ro''': 1_0_2_4,
    '''facebook/mbart-large-cc25''': 1_0_2_4,
}

# fmt: off
lowercase   =	['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE           =     PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE           =     ['''input_ids''', '''attention_mask''']
					SCREAMING_SNAKE_CASE           =     MBartTokenizer

					SCREAMING_SNAKE_CASE           =     []
					SCREAMING_SNAKE_CASE           =     []

					def __init__(						self       :  Any ,       __UpperCamelCase       :  Optional[int]=None ,       __UpperCamelCase       :  int=None ,       __UpperCamelCase       :  Dict="<s>" ,       __UpperCamelCase       :  Tuple="</s>" ,       __UpperCamelCase       :  Union[str, Any]="</s>" ,       __UpperCamelCase       :  Optional[Any]="<s>" ,       __UpperCamelCase       :  Optional[Any]="<unk>" ,       __UpperCamelCase       :  Dict="<pad>" ,       __UpperCamelCase       :  Dict="<mask>" ,       __UpperCamelCase       :  Any=None ,       __UpperCamelCase       :  Optional[int]=None ,       __UpperCamelCase       :  Optional[int]=None ,       **__UpperCamelCase       :  str ,       ):
												# Mask token behave like a normal word, i.e. include the space before it
												lowerCamelCase_					=       AddedToken(__UpperCamelCase ,       lstrip=__UpperCamelCase ,       rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase ,       __UpperCamelCase ) else mask_token

												super().__init__(
												    vocab_file=__UpperCamelCase ,       tokenizer_file=__UpperCamelCase ,       bos_token=__UpperCamelCase ,       eos_token=__UpperCamelCase ,       sep_token=__UpperCamelCase ,       cls_token=__UpperCamelCase ,       unk_token=__UpperCamelCase ,       pad_token=__UpperCamelCase ,       mask_token=__UpperCamelCase ,       src_lang=__UpperCamelCase ,       tgt_lang=__UpperCamelCase ,       additional_special_tokens=__UpperCamelCase ,       **__UpperCamelCase ,       )

												lowerCamelCase_					=       vocab_file
												lowerCamelCase_					=       False if not self.vocab_file else True

												lowerCamelCase_					=       FAIRSEQ_LANGUAGE_CODES.copy()

												if additional_special_tokens is not None:
																			# Only add those special tokens if they are not already there.
																			_additional_special_tokens.extend(
																			    [t for t in additional_special_tokens if t not in _additional_special_tokens] )

												self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
												lowerCamelCase_					=       {
												    lang_code: self.convert_tokens_to_ids(__UpperCamelCase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
												}

												lowerCamelCase_					=       src_lang if src_lang is not None else """en_XX"""
												lowerCamelCase_					=       self.convert_tokens_to_ids(self._src_lang )
												lowerCamelCase_					=       tgt_lang
												self.set_src_lang_special_tokens(self._src_lang )

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self._src_lang

					@src_lang.setter
					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  str ):
												lowerCamelCase_					=       new_src_lang
												self.set_src_lang_special_tokens(self._src_lang )

					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):
												if token_ids_a is None:
																			return self.prefix_tokens + token_ids_a + self.suffix_tokens
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):
												lowerCamelCase_					=       [self.sep_token_id]
												lowerCamelCase_					=       [self.cls_token_id]

												if token_ids_a is None:
																			return len(cls + token_ids_a + sep ) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]

					def  lowercase__   (						self       :  int ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Optional[str] ,       __UpperCamelCase       :  Optional[str] ,       **__UpperCamelCase       :  Dict ):
												if src_lang is None or tgt_lang is None:
																			raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
												lowerCamelCase_					=       src_lang
												lowerCamelCase_					=       self(__UpperCamelCase ,       add_special_tokens=__UpperCamelCase ,       return_tensors=__UpperCamelCase ,       **__UpperCamelCase )
												lowerCamelCase_					=       self.convert_tokens_to_ids(__UpperCamelCase )
												lowerCamelCase_					=       tgt_lang_id
												return inputs

					def  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  str = "en_XX" ,       __UpperCamelCase       :  Optional[List[str]] = None ,       __UpperCamelCase       :  str = "ro_RO" ,       **__UpperCamelCase       :  Optional[int] ,       ):
												lowerCamelCase_					=       src_lang
												lowerCamelCase_					=       tgt_lang
												return super().prepare_seqaseq_batch(__UpperCamelCase ,       __UpperCamelCase ,       **__UpperCamelCase )

					def  lowercase__   (						self       :  int ):
												return self.set_src_lang_special_tokens(self.src_lang )

					def  lowercase__   (						self       :  Dict ):
												return self.set_tgt_lang_special_tokens(self.tgt_lang )

					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  Dict ):
												lowerCamelCase_					=       self.convert_tokens_to_ids(__UpperCamelCase )
												lowerCamelCase_					=       []
												lowerCamelCase_					=       [self.eos_token_id, self.cur_lang_code]

												lowerCamelCase_					=       self.convert_ids_to_tokens(self.prefix_tokens )
												lowerCamelCase_					=       self.convert_ids_to_tokens(self.suffix_tokens )

												lowerCamelCase_					=       processors.TemplateProcessing(
												    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str ,       pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str ,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,       self.prefix_tokens + self.suffix_tokens ) ) ,       )

					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  str ):
												lowerCamelCase_					=       self.convert_tokens_to_ids(__UpperCamelCase )
												lowerCamelCase_					=       []
												lowerCamelCase_					=       [self.eos_token_id, self.cur_lang_code]

												lowerCamelCase_					=       self.convert_ids_to_tokens(self.prefix_tokens )
												lowerCamelCase_					=       self.convert_ids_to_tokens(self.suffix_tokens )

												lowerCamelCase_					=       processors.TemplateProcessing(
												    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str ,       pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str ,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,       self.prefix_tokens + self.suffix_tokens ) ) ,       )



					def  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Optional[str] = None ):
												if not self.can_save_slow_tokenizer:
																			raise ValueError(
																			    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																			    """tokenizer.""" )

												if not os.path.isdir(__UpperCamelCase ):
																			logger.error(F'''Vocabulary path ({save_directory}) should be a directory.''' )
																			return
												lowerCamelCase_					=       os.path.join(
												    __UpperCamelCase ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

												if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCamelCase ):
																			copyfile(self.vocab_file ,       __UpperCamelCase )

												return (out_vocab_file,)





