
"""simple docstring"""


import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    BartForSequenceClassification,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    TapexTokenizer,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.17.0.dev0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

A_    							=					logging.getLogger(__name__)




@dataclass
class  __lowerCamelCase  :
				a__:      Optional[str]          =    field(
				    default='tab_fact'						,	metadata={'help': 'The name of the dataset to use (via the datasets library).'}			)
				a__:      Optional[str]          =    field(
				    default='tab_fact'						,	metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}						,	)
				a__:      int          =    field(
				    default=1024						,	metadata={
				        'help': (
				            'The maximum total input sequence length after tokenization. Sequences longer '
				            'than this will be truncated, sequences shorter will be padded.'
				        )
				    }						,	)
				a__:      bool          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}			)
				a__:      bool          =    field(
				    default=lowerCAmelCase						,	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.'
				        )
				    }						,	)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'For debugging purposes or quicker training, truncate the number of training examples to this '
				            'value if set.'
				        )
				    }						,	)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
				            'value if set.'
				        )
				    }						,	)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'For debugging purposes or quicker training, truncate the number of prediction examples to this '
				            'value if set.'
				        )
				    }						,	)
				a__:      Optional[str]          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'A csv or a json file containing the training data.'}			)
				a__:      Optional[str]          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'A csv or a json file containing the validation data.'}			)
				a__:      Optional[str]          =    field(default=lowerCAmelCase						,	metadata={'help': 'A csv or a json file containing the test data.'}			)




				def        UpperCAmelCase__						(				self   ):
							if self.dataset_name is not None:
										pass
							elif self.train_file is None or self.validation_file is None:
										raise ValueError('''Need either a GLUE task, a training/validation file or a dataset name.'''   )
							else:
										lowerCamelCase_					=  self.train_file.split('''.'''   )[-1]
										assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
										lowerCamelCase_					=  self.validation_file.split('''.'''   )[-1]
										assert (
										    validation_extension == train_extension
										), "`validation_file` should have the same extension (csv or json) as `train_file`."




@dataclass
class  __lowerCamelCase  :
				a__:      str          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}			)
				a__:      Optional[str]          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'Pretrained config name or path if not the same as model_name'}			)
				a__:      Optional[str]          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}			)
				a__:      Optional[str]          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,	)
				a__:      bool          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}						,	)
				a__:      str          =    field(
				    default='main'						,	metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}						,	)
				a__:      bool          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
				            'with private models).'
				        )
				    }						,	)






def  lowercase						(	):
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			lowerCamelCase_					=  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.
						lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
			else:
						lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					=  parser.parse_args_into_dataclasses()

			# 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						)]	,)

			lowerCamelCase_					=  training_args.get_process_log_level()
			logger.setLevel(lowerCAmelCase__						)
			datasets.utils.logging.set_verbosity(lowerCAmelCase__						)
			transformers.utils.logging.set_verbosity(lowerCAmelCase__						)
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# 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}"						)
			logger.info(f"Training/evaluation parameters {training_args}"						)

			# Detecting last checkpoint.
			lowerCamelCase_					=  None
			if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
						lowerCamelCase_					=  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 and training_args.resume_from_checkpoint is 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.'''						)

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

			# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
			# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
			#
			# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
			#
			# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
			# single column. 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.
						lowerCamelCase_					=  load_dataset(
						    data_args.dataset_name	,data_args.dataset_config_name	,cache_dir=model_args.cache_dir						)
			else:
						# Loading a dataset from your local files.
						# CSV/JSON training and evaluation files are needed.
						lowerCamelCase_					=  {'''train''': data_args.train_file, '''validation''': data_args.validation_file}

						# Get the test dataset: you can provide your own CSV/JSON test file (see below)
						# when you use `do_predict` without specifying a GLUE benchmark task.
						if training_args.do_predict:
									if data_args.test_file is not None:
												lowerCamelCase_					=  data_args.train_file.split('''.'''						)[-1]
												lowerCamelCase_					=  data_args.test_file.split('''.'''						)[-1]
												assert (
												    test_extension == train_extension
												), "`test_file` should have the same extension (csv or json) as `train_file`."
												lowerCamelCase_					=  data_args.test_file
									else:
												raise ValueError('''Need either a GLUE task or a test file for `do_predict`.'''						)

						for key in data_files.keys():
									logger.info(f"load a local file for {key}: {data_files[key]}"						)

						if data_args.train_file.endswith('''.csv'''						):
									# Loading a dataset from local csv files
									lowerCamelCase_					=  load_dataset('''csv'''	,data_files=lowerCAmelCase__	,cache_dir=model_args.cache_dir						)
						else:
									# Loading a dataset from local json files
									lowerCamelCase_					=  load_dataset('''json'''	,data_files=lowerCAmelCase__	,cache_dir=model_args.cache_dir						)
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
			lowerCamelCase_					=  raw_datasets['''train'''].features['''label'''].names
			lowerCamelCase_					=  len(lowerCAmelCase__						)

			# Load pretrained model and tokenizer
			#
			# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			lowerCamelCase_					=  AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,num_labels=lowerCAmelCase__	,cache_dir=model_args.cache_dir	,revision=model_args.model_revision	,use_auth_token=True if model_args.use_auth_token else None	,)
			# load tapex tokenizer
			lowerCamelCase_					=  TapexTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,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	,add_prefix_space=lowerCAmelCase__	,)
			lowerCamelCase_					=  BartForSequenceClassification.from_pretrained(
			    model_args.model_name_or_path	,from_tf=bool('''.ckpt''' in model_args.model_name_or_path						)	,config=lowerCAmelCase__	,cache_dir=model_args.cache_dir	,revision=model_args.model_revision	,use_auth_token=True if model_args.use_auth_token else None	,)

			# Padding strategy
			if data_args.pad_to_max_length:
						lowerCamelCase_					=  '''max_length'''
			else:
						# We will pad later, dynamically at batch creation, to the max sequence length in each batch
						lowerCamelCase_					=  False

			# Some models have set the order of the labels to use, so let's make sure we do use it.
			lowerCamelCase_					=  {'''Refused''': 0, '''Entailed''': 1}
			lowerCamelCase_					=  {0: '''Refused''', 1: '''Entailed'''}

			if data_args.max_seq_length > tokenizer.model_max_length:
						logger.warning(
						    f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
						    f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."						)
			lowerCamelCase_					=  min(data_args.max_seq_length	,tokenizer.model_max_length						)

			def preprocess_tabfact_function(lowerCAmelCase__						):
						# Tokenize the texts
						def _convert_table_text_to_pandas(lowerCAmelCase__						):
									lowerCamelCase_					=  [_table_row.split('''#'''						) for _table_row in _table_text.strip('''\n'''						).split('''\n'''						)]
									lowerCamelCase_					=  pd.DataFrame.from_records(_table_content[1:]	,columns=_table_content[0]						)
									return _table_pd

						lowerCamelCase_					=  examples['''statement''']
						lowerCamelCase_					=  list(map(_convert_table_text_to_pandas	,examples['''table_text''']						)						)
						lowerCamelCase_					=  tokenizer(lowerCAmelCase__	,lowerCAmelCase__	,padding=lowerCAmelCase__	,max_length=lowerCAmelCase__	,truncation=lowerCAmelCase__						)

						lowerCamelCase_					=  examples['''label''']
						return result

			with training_args.main_process_first(desc='''dataset map pre-processing'''						):
						lowerCamelCase_					=  raw_datasets.map(
						    lowerCAmelCase__	,batched=lowerCAmelCase__	,load_from_cache_file=not data_args.overwrite_cache	,desc='''Running tokenizer on dataset'''	,)
			if training_args.do_train:
						if "train" not in raw_datasets:
									raise ValueError('''--do_train requires a train dataset'''						)
						lowerCamelCase_					=  raw_datasets['''train''']
						if data_args.max_train_samples is not None:
									lowerCamelCase_					=  train_dataset.select(range(data_args.max_train_samples						)						)

			if training_args.do_eval:
						if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
									raise ValueError('''--do_eval requires a validation dataset'''						)
						lowerCamelCase_					=  raw_datasets['''validation''']
						if data_args.max_eval_samples is not None:
									lowerCamelCase_					=  eval_dataset.select(range(data_args.max_eval_samples						)						)

			if training_args.do_predict or data_args.test_file is not None:
						if "test" not in raw_datasets and "test_matched" not in raw_datasets:
									raise ValueError('''--do_predict requires a test dataset'''						)
						lowerCamelCase_					=  raw_datasets['''test''']
						if data_args.max_predict_samples is not None:
									lowerCamelCase_					=  predict_dataset.select(range(data_args.max_predict_samples						)						)

    # Log a few random samples from the training set:
			if training_args.do_train:
						for index in random.sample(range(len(lowerCAmelCase__						)						)	,3						):
									logger.info(f"Sample {index} of the training set: {train_dataset[index]}."						)

    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
			def compute_metrics(lowerCAmelCase__						):
						lowerCamelCase_					=  p.predictions[0] if isinstance(p.predictions	,lowerCAmelCase__						) else p.predictions
						lowerCamelCase_					=  np.argmax(lowerCAmelCase__	,axis=1						)
						return {"accuracy": (preds == p.label_ids).astype(np.floataa						).mean().item()}

			# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
			if data_args.pad_to_max_length:
						lowerCamelCase_					=  default_data_collator
			elif training_args.fpaa:
						lowerCamelCase_					=  DataCollatorWithPadding(lowerCAmelCase__	,pad_to_multiple_of=8						)
			else:
						lowerCamelCase_					=  None

			# Initialize our Trainer
			lowerCamelCase_					=  Trainer(
			    model=lowerCAmelCase__	,args=lowerCAmelCase__	,train_dataset=train_dataset if training_args.do_train else None	,eval_dataset=eval_dataset if training_args.do_eval else None	,compute_metrics=lowerCAmelCase__	,tokenizer=lowerCAmelCase__	,data_collator=lowerCAmelCase__	,)

			# Training
			if training_args.do_train:
						lowerCamelCase_					=  None
						if training_args.resume_from_checkpoint is not None:
									lowerCamelCase_					=  training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									lowerCamelCase_					=  last_checkpoint
						lowerCamelCase_					=  trainer.train(resume_from_checkpoint=lowerCAmelCase__						)
						lowerCamelCase_					=  train_result.metrics
						lowerCamelCase_					=  (
						    data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCAmelCase__						)
						)
						lowerCamelCase_					=  min(lowerCAmelCase__	,len(lowerCAmelCase__						)						)

						trainer.save_model()  # Saves the tokenizer too for easy upload

						trainer.log_metrics('''train'''	,lowerCAmelCase__						)
						trainer.save_metrics('''train'''	,lowerCAmelCase__						)
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						logger.info('''*** Evaluate ***'''						)

						lowerCamelCase_					=  trainer.evaluate(eval_dataset=lowerCAmelCase__						)
						lowerCamelCase_					=  data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCAmelCase__						)
						lowerCamelCase_					=  min(lowerCAmelCase__	,len(lowerCAmelCase__						)						)

						trainer.log_metrics('''eval'''	,lowerCAmelCase__						)
						trainer.save_metrics('''eval'''	,lowerCAmelCase__						)

			if training_args.do_predict:
						logger.info('''*** Predict ***'''						)

						# Removing the `label` columns because it contains -1 and Trainer won't like that.
						lowerCamelCase_					=  predict_dataset.remove_columns('''label'''						)
						lowerCamelCase_					=  trainer.predict(lowerCAmelCase__	,metric_key_prefix='''predict'''						).predictions
						lowerCamelCase_					=  np.argmax(lowerCAmelCase__	,axis=1						)

						lowerCamelCase_					=  os.path.join(training_args.output_dir	,'''predict_results_tabfact.txt'''						)
						if trainer.is_world_process_zero():
									with open(lowerCAmelCase__	,'''w'''						) as writer:
												logger.info('''***** Predict Results *****'''						)
												writer.write('''index\tprediction\n'''						)
												for index, item in enumerate(lowerCAmelCase__						):
															lowerCamelCase_					=  label_list[item]
															writer.write(f"{index}\t{item}\n"						)

			lowerCamelCase_					=  {'''finetuned_from''': model_args.model_name_or_path, '''tasks''': '''text-classification'''}

			if training_args.push_to_hub:
						trainer.push_to_hub(**lowerCAmelCase__						)
			else:
						trainer.create_model_card(**lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
						main()





"""simple docstring"""





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:
		UpperCamelCase_       :    Dict						      =			None


UpperCamelCase_       :    int						      =			logging.get_logger(__name__)


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

UpperCamelCase_       :    List[Any]						      =			{
    '''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''',
    },
}

UpperCamelCase_       :    Optional[Any]						      =			{
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
UpperCamelCase_       :    str						      =			['''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 __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					VOCAB_FILES_NAMES
			snake_case						   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case						   =					PRETRAINED_VOCAB_FILES_MAP
			snake_case						   =					["input_ids", "attention_mask"]
			snake_case						   =					MBartTokenizer

			snake_case						   =					[]
			snake_case						   =					[]




			def __init__(					self    :       List[str]							, _snake_case    :       Tuple=None							, _snake_case    :       int=None							, _snake_case    :       List[Any]="<s>"							, _snake_case    :       Tuple="</s>"							, _snake_case    :       str="</s>"							, _snake_case    :       List[Any]="<s>"							, _snake_case    :       Dict="<unk>"							, _snake_case    :       str="<pad>"							, _snake_case    :       Any="<mask>"							, _snake_case    :       int=None							, _snake_case    :       Optional[int]=None							, _snake_case    :       Any=None							, **_snake_case    :       Union[str, Any]							, )   ->			List[str]:







								"""simple docstring"""
								# Mask token behave like a normal word, i.e. include the space before it
								A_								=    AddedToken(_snake_case							, lstrip=_snake_case							, rstrip=_snake_case						) if isinstance(_snake_case							, _snake_case						) else mask_token

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

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

								A_								=    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}						)
								A_								=    {
								    lang_code: self.convert_tokens_to_ids(_snake_case						) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

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




			@property
			def   lowerCamelCase__  (					self    :       Dict						)   ->			str:







								"""simple docstring"""
								return self._src_lang




			@src_lang.setter
			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       str						)   ->			None:







								"""simple docstring"""
								A_								=    new_src_lang
								self.set_src_lang_special_tokens(self._src_lang						)




			def   lowerCamelCase__  (					self    :       Optional[Any]							, _snake_case    :       List[int]							, _snake_case    :       Optional[List[int]] = None						)   ->			List[int]:







								"""simple docstring"""
								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   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       List[int]							, _snake_case    :       Optional[List[int]] = None						)   ->			List[int]:







								"""simple docstring"""
								A_								=    [self.sep_token_id]
								A_								=    [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   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       str							, _snake_case    :       str							, _snake_case    :       Optional[str]							, _snake_case    :       Optional[str]							, **_snake_case    :       Optional[int]						)   ->			str:







								"""simple docstring"""
								if src_lang is None or tgt_lang is None:
													raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
								A_								=    src_lang
								A_								=    self(_snake_case							, add_special_tokens=_snake_case							, return_tensors=_snake_case							, **_snake_case						)
								A_								=    self.convert_tokens_to_ids(_snake_case						)
								A_								=    tgt_lang_id
								return inputs




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       List[str]							, _snake_case    :       str = "en_XX"							, _snake_case    :       Optional[List[str]] = None							, _snake_case    :       str = "ro_RO"							, **_snake_case    :       str							, )   ->			BatchEncoding:







								"""simple docstring"""
								A_								=    src_lang
								A_								=    tgt_lang
								return super().prepare_seqaseq_batch(_snake_case							, _snake_case							, **_snake_case						)




			def   lowerCamelCase__  (					self    :       Any						)   ->			Optional[Any]:







								"""simple docstring"""
								return self.set_src_lang_special_tokens(self.src_lang						)




			def   lowerCamelCase__  (					self    :       Dict						)   ->			List[Any]:







								"""simple docstring"""
								return self.set_tgt_lang_special_tokens(self.tgt_lang						)




			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       List[str]						)   ->			None:







								"""simple docstring"""
								A_								=    self.convert_tokens_to_ids(_snake_case						)
								A_								=    []
								A_								=    [self.eos_token_id, self.cur_lang_code]

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

								A_								=    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   lowerCamelCase__  (					self    :       List[str]							, _snake_case    :       str						)   ->			None:







								"""simple docstring"""
								A_								=    self.convert_tokens_to_ids(_snake_case						)
								A_								=    []
								A_								=    [self.eos_token_id, self.cur_lang_code]

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

								A_								=    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   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       str							, _snake_case    :       Optional[str] = None						)   ->			Tuple[str]:







								"""simple docstring"""
								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(_snake_case						):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory.'						)
													return
								A_								=    os.path.join(
								    _snake_case							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

								return (out_vocab_file,)




