
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


lowercase__						:int					    =       logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowercase__						: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
lowercase__						: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
}
lowercase__						:Dict					    =       sorted(arg_to_scheduler.keys())
lowercase__						:List[Any]					    =       "{" + ", ".join(arg_to_scheduler_choices) + "}"







class 							lowercase						(   pl.LightningModule						):




							def __init__(							self  ,A__  ,A__=None  ,A__="base"  ,A__=None  ,A__=None  ,A__=None  ,**A__  ,):
												super().__init__()
												# TODO: move to self.save_hyperparameters()
												# self.save_hyperparameters()
												# can also expand arguments into trainer signature for easier reading

												self.save_hyperparameters(A__)
												lowercase		      =		0
												lowercase		      =		Path(self.hparams.output_dir)
												lowercase		      =		self.hparams.cache_dir if self.hparams.cache_dir else None
												if config is None:
																	lowercase		      =		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=A__  ,**A__  ,)
												else:
																	lowercase		      =		config

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

												if tokenizer is None:
																	lowercase		      =		AutoTokenizer.from_pretrained(
																	    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,cache_dir=A__  ,)
												else:
																	lowercase		      =		tokenizer
												lowercase		      =		MODEL_MODES[mode]
												if model is None:
																	lowercase		      =		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=A__  ,)
												else:
																	lowercase		      =		model




							def 					A__ (							self  ,*A__  ,**A__):
												lowercase		      =		self.model_type.from_pretrained(*A__  ,**A__)




							def 					A__ (							self):
												lowercase		      =		arg_to_scheduler[self.hparams.lr_scheduler]
												lowercase		      =		get_schedule_func(
												    self.opt  ,num_warmup_steps=self.hparams.warmup_steps  ,num_training_steps=self.total_steps())
												lowercase		      =		{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
												return scheduler




							def 					A__ (							self):
												lowercase		      =		self.model
												lowercase		      =		['''bias''', '''LayerNorm.weight''']
												lowercase		      =		[
												    {
												        '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
												        '''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:
																	lowercase		      =		Adafactor(
																	    A__  ,lr=self.hparams.learning_rate  ,scale_parameter=A__  ,relative_step=A__)

												else:
																	lowercase		      =		AdamW(
																	    A__  ,lr=self.hparams.learning_rate  ,eps=self.hparams.adam_epsilon)
												lowercase		      =		optimizer

												lowercase		      =		self.get_lr_scheduler()

												return [optimizer], [scheduler]




							def 					A__ (							self  ,A__  ,A__):
												return self.validation_step(A__  ,A__)




							def 					A__ (							self  ,A__):
												return self.validation_end(A__)




							def 					A__ (							self):
												lowercase		      =		max(1  ,self.hparams.gpus)  # TODO: consider num_tpu_cores
												lowercase		      =		self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
												return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




							def 					A__ (							self  ,A__):
												if mode == "test":
																	lowercase		      =		len(self.test_dataloader().dataset)
												else:
																	lowercase		      =		self.get_dataloader('''train'''  ,self.hparams.train_batch_size  ,shuffle=A__)
																	lowercase		      =		len(self.train_dataloader().dataset)




							def 					A__ (							self  ,A__  ,A__  ,A__ = False):
												raise NotImplementedError('''You must implement this for your task''')




							def 					A__ (							self):
												return self.train_loader




							def 					A__ (							self):
												return self.get_dataloader('''dev'''  ,self.hparams.eval_batch_size  ,shuffle=A__)




							def 					A__ (							self):
												return self.get_dataloader('''test'''  ,self.hparams.eval_batch_size  ,shuffle=A__)




							def 					A__ (							self  ,A__):
												return os.path.join(
												    self.hparams.data_dir  ,'''cached_{}_{}_{}'''.format(
												        A__  ,list(filter(A__  ,self.hparams.model_name_or_path.split('''/'''))).pop()  ,str(self.hparams.max_seq_length)  ,)  ,)




							@pl.utilities.rank_zero_only
							def 					A__ (							self  ,A__):
												lowercase		      =		self.output_dir.joinpath('''best_tfmr''')
												lowercase		      =		self.step_count
												self.model.save_pretrained(A__)
												self.tokenizer.save_pretrained(A__)





							@staticmethod
							def 					A__ (							A__  ,A__):
												parser.add_argument(
												    '''--model_name_or_path'''  ,default=A__  ,type=A__  ,required=A__  ,help='''Path to pretrained model or model identifier from huggingface.co/models'''  ,)
												parser.add_argument(
												    '''--config_name'''  ,default=''''''  ,type=A__  ,help='''Pretrained config name or path if not the same as model_name''')
												parser.add_argument(
												    '''--tokenizer_name'''  ,default=A__  ,type=A__  ,help='''Pretrained tokenizer name or path if not the same as model_name'''  ,)
												parser.add_argument(
												    '''--cache_dir'''  ,default=''''''  ,type=A__  ,help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''  ,)
												parser.add_argument(
												    '''--encoder_layerdrop'''  ,type=A__  ,help='''Encoder layer dropout probability (Optional). Goes into model.config'''  ,)
												parser.add_argument(
												    '''--decoder_layerdrop'''  ,type=A__  ,help='''Decoder layer dropout probability (Optional). Goes into model.config'''  ,)
												parser.add_argument(
												    '''--dropout'''  ,type=A__  ,help='''Dropout probability (Optional). Goes into model.config'''  ,)
												parser.add_argument(
												    '''--attention_dropout'''  ,type=A__  ,help='''Attention dropout probability (Optional). Goes into model.config'''  ,)
												parser.add_argument('''--learning_rate'''  ,default=5E-5  ,type=A__  ,help='''The initial learning rate for Adam.''')
												parser.add_argument(
												    '''--lr_scheduler'''  ,default='''linear'''  ,choices=A__  ,metavar=A__  ,type=A__  ,help='''Learning rate scheduler'''  ,)
												parser.add_argument('''--weight_decay'''  ,default=0.0  ,type=A__  ,help='''Weight decay if we apply some.''')
												parser.add_argument('''--adam_epsilon'''  ,default=1E-8  ,type=A__  ,help='''Epsilon for Adam optimizer.''')
												parser.add_argument('''--warmup_steps'''  ,default=0  ,type=A__  ,help='''Linear warmup over warmup_steps.''')
												parser.add_argument('''--num_workers'''  ,default=4  ,type=A__  ,help='''kwarg passed to DataLoader''')
												parser.add_argument('''--num_train_epochs'''  ,dest='''max_epochs'''  ,default=3  ,type=A__)
												parser.add_argument('''--train_batch_size'''  ,default=3_2  ,type=A__)
												parser.add_argument('''--eval_batch_size'''  ,default=3_2  ,type=A__)
												parser.add_argument('''--adafactor'''  ,action='''store_true''')







class 							lowercase						(   pl.Callback						):




							def 					A__ (							self  ,A__  ,A__):
												lowercase		      =		trainer.lr_schedulers[0]['''scheduler''']
												lowercase		      =		{f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr())}
												pl_module.logger.log_metrics(A__)




							def 					A__ (							self  ,A__  ,A__):
												rank_zero_info('''***** Validation results *****''')
												lowercase		      =		trainer.callback_metrics
												# Log results
												for key in sorted(A__):
																	if key not in ["log", "progress_bar"]:
																						rank_zero_info('''{} = {}\n'''.format(A__  ,str(metrics[key])))





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



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__						):
					'''simple docstring'''
					#  To allow all pl args uncomment the following line
					#  parser = pl.Trainer.add_argparse_args(parser)
					parser.add_argument(
					    '''--output_dir'''					,     default=lowerCAmelCase__					,     type=lowerCAmelCase__					,     required=lowerCAmelCase__					,     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=lowerCAmelCase__					,     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=lowerCAmelCase__						)
					parser.add_argument('''--max_grad_norm'''					,     dest='''gradient_clip_val'''					,     default=1.0					,     type=lowerCAmelCase__					,     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=lowerCAmelCase__					,     default=1					,     help='''Number of updates steps to accumulate before performing a backward/update pass.'''					,     )
					parser.add_argument('''--seed'''					,     type=lowerCAmelCase__					,     default=42					,     help='''random seed for initialization'''						)
					parser.add_argument(
					    '''--data_dir'''					,     default=lowerCAmelCase__					,     type=lowerCAmelCase__					,     required=lowerCAmelCase__					,     help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''					,     )



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=None					,     lowerCAmelCase__=True					,     lowerCAmelCase__=[]					,     lowerCAmelCase__=None					,     lowerCAmelCase__=None					,     **lowerCAmelCase__					,     ):
					'''simple docstring'''
					pl.seed_everything(args.seed						)

					# init model
					lowercase		      =		Path(model.hparams.output_dir						)
					odir.mkdir(exist_ok=lowerCAmelCase__						)

					# add custom checkpoints
					if checkpoint_callback is None:
										lowercase		      =		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(lowerCAmelCase__						)
					if logging_callback is None:
										lowercase		      =		LoggingCallback()

					lowercase		      =		{}

					# TODO: remove with PyTorch 1.6 since pl uses native amp
					if args.fpaa:
										lowercase		      =		16
										lowercase		      =		args.fpaa_opt_level

					if args.gpus > 1:
										lowercase		      =		'''ddp'''

					lowercase		      =		args.accumulate_grad_batches
					lowercase		      =		extra_train_kwargs.get('''accelerator'''					,     lowerCAmelCase__						)
					lowercase		      =		extra_train_kwargs.get('''profiler'''					,     lowerCAmelCase__						)

					lowercase		      =		pl.Trainer.from_argparse_args(
					    lowerCAmelCase__					,     weights_summary=lowerCAmelCase__					,     callbacks=[logging_callback] + extra_callbacks					,     logger=lowerCAmelCase__					,     checkpoint_callback=lowerCAmelCase__					,     **lowerCAmelCase__					,     )

					if args.do_train:
										trainer.fit(lowerCAmelCase__						)

					return trainer






import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__						:Tuple					    =       logging.get_logger(__name__)

lowercase__						:List[str]					    =       {
    "src_vocab_file": "vocab-src.json",
    "tgt_vocab_file": "vocab-tgt.json",
    "merges_file": "merges.txt",
}

lowercase__						:List[str]					    =       {
    "src_vocab_file": {
        "stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json"
    },
    "tgt_vocab_file": {
        "stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json"
    },
    "merges_file": {"stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt"},
}

lowercase__						:Dict					    =       {"stas/tiny-wmt19-en-de": 1024}
lowercase__						:str					    =       {
    "stas/tiny-wmt19-en-de": {
        "langs": ["en", "de"],
        "model_max_length": 1024,
        "special_tokens_map_file": None,
        "full_tokenizer_file": None,
    }
}



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		set()
					lowercase		      =		word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)						)
										lowercase		      =		char
					return pairs



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		text.replace('''，'''					,     ''','''						)
					lowercase		      =		re.sub(R'''。\s*'''					,     '''. '''					,     lowerCAmelCase__						)
					lowercase		      =		text.replace('''、'''					,     ''','''						)
					lowercase		      =		text.replace('''”'''					,     '''"'''						)
					lowercase		      =		text.replace('''“'''					,     '''"'''						)
					lowercase		      =		text.replace('''∶'''					,     ''':'''						)
					lowercase		      =		text.replace('''：'''					,     ''':'''						)
					lowercase		      =		text.replace('''？'''					,     '''?'''						)
					lowercase		      =		text.replace('''《'''					,     '''"'''						)
					lowercase		      =		text.replace('''》'''					,     '''"'''						)
					lowercase		      =		text.replace('''）'''					,     ''')'''						)
					lowercase		      =		text.replace('''！'''					,     '''!'''						)
					lowercase		      =		text.replace('''（'''					,     '''('''						)
					lowercase		      =		text.replace('''；'''					,     ''';'''						)
					lowercase		      =		text.replace('''１'''					,     '''1'''						)
					lowercase		      =		text.replace('''」'''					,     '''"'''						)
					lowercase		      =		text.replace('''「'''					,     '''"'''						)
					lowercase		      =		text.replace('''０'''					,     '''0'''						)
					lowercase		      =		text.replace('''３'''					,     '''3'''						)
					lowercase		      =		text.replace('''２'''					,     '''2'''						)
					lowercase		      =		text.replace('''５'''					,     '''5'''						)
					lowercase		      =		text.replace('''６'''					,     '''6'''						)
					lowercase		      =		text.replace('''９'''					,     '''9'''						)
					lowercase		      =		text.replace('''７'''					,     '''7'''						)
					lowercase		      =		text.replace('''８'''					,     '''8'''						)
					lowercase		      =		text.replace('''４'''					,     '''4'''						)
					lowercase		      =		re.sub(R'''．\s*'''					,     '''. '''					,     lowerCAmelCase__						)
					lowercase		      =		text.replace('''～'''					,     '''~'''						)
					lowercase		      =		text.replace('''’'''					,     '''\''''						)
					lowercase		      =		text.replace('''…'''					,     '''...'''						)
					lowercase		      =		text.replace('''━'''					,     '''-'''						)
					lowercase		      =		text.replace('''〈'''					,     '''<'''						)
					lowercase		      =		text.replace('''〉'''					,     '''>'''						)
					lowercase		      =		text.replace('''【'''					,     '''['''						)
					lowercase		      =		text.replace('''】'''					,     ''']'''						)
					lowercase		      =		text.replace('''％'''					,     '''%'''						)
					return text



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		[]
					for char in text:
										lowercase		      =		unicodedata.category(lowerCAmelCase__						)
										if cat.startswith('''C'''						):
															continue
										output.append(lowerCAmelCase__						)
					return "".join(lowerCAmelCase__						)







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			Any			   =VOCAB_FILES_NAMES
							lowercase_			:			List[str]			   =PRETRAINED_VOCAB_FILES_MAP
							lowercase_			:			Any			   =PRETRAINED_INIT_CONFIGURATION
							lowercase_			:			List[str]			   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_			:			Optional[Any]			   =['''input_ids''', '''attention_mask''']




							def __init__(							self  ,A__=None  ,A__=None  ,A__=None  ,A__=None  ,A__=False  ,A__="<unk>"  ,A__="<s>"  ,A__="</s>"  ,A__="<pad>"  ,**A__  ,):
												super().__init__(
												    langs=A__  ,src_vocab_file=A__  ,tgt_vocab_file=A__  ,merges_file=A__  ,do_lower_case=A__  ,unk_token=A__  ,bos_token=A__  ,sep_token=A__  ,pad_token=A__  ,**A__  ,)

												try:
																	import sacremoses
												except ImportError:
																	raise ImportError(
																	    '''You need to install sacremoses to use XLMTokenizer. '''
																	    '''See https://pypi.org/project/sacremoses/ for installation.''')

												lowercase		      =		sacremoses

												lowercase		      =		src_vocab_file
												lowercase		      =		tgt_vocab_file
												lowercase		      =		merges_file
												lowercase		      =		do_lower_case

												# cache of sm.MosesPunctNormalizer instance
												lowercase		      =		{}
												# cache of sm.MosesTokenizer instance
												lowercase		      =		{}
												lowercase		      =		{}

												if langs and len(A__) == 2:
																	lowercase      ,		lowercase		      =		langs
												else:
																	raise ValueError(
																	    f'arg `langs` needs to be a list of 2 langs, e.g. [\'en\', \'ru\'], but got {langs}. '
																	    '''Usually that means that tokenizer can\'t find a mapping for the given model path '''
																	    '''in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer.''')

												with open(A__  ,encoding='''utf-8''') as src_vocab_handle:
																	lowercase		      =		json.load(A__)
												with open(A__  ,encoding='''utf-8''') as tgt_vocab_handle:
																	lowercase		      =		json.load(A__)
																	lowercase		      =		{v: k for k, v in tgt_vocab.items()}
												with open(A__  ,encoding='''utf-8''') as merges_handle:
																	lowercase		      =		merges_handle.read().split('''\n''')[:-1]
												lowercase		      =		[tuple(merge.split()[:2]) for merge in merges]
												lowercase		      =		dict(zip(A__  ,range(len(A__))))
												lowercase		      =		{}




							def 					A__ (							self):
												return self.get_src_vocab()




							@property
							def 					A__ (							self):
												return self.src_vocab_size




							def 					A__ (							self  ,A__  ,A__):
												if lang not in self.cache_moses_punct_normalizer:
																	lowercase		      =		self.sm.MosesPunctNormalizer(lang=A__)
																	lowercase		      =		punct_normalizer
												return self.cache_moses_punct_normalizer[lang].normalize(A__)




							def 					A__ (							self  ,A__  ,A__):
												if lang not in self.cache_moses_tokenizer:
																	lowercase		      =		self.sm.MosesTokenizer(lang=A__)
																	lowercase		      =		moses_tokenizer
												return self.cache_moses_tokenizer[lang].tokenize(
												    A__  ,aggressive_dash_splits=A__  ,return_str=A__  ,escape=A__)




							def 					A__ (							self  ,A__  ,A__):
												if lang not in self.cache_moses_detokenizer:
																	lowercase		      =		self.sm.MosesDetokenizer(lang=A__)
																	lowercase		      =		moses_detokenizer
												return self.cache_moses_detokenizer[lang].detokenize(A__)




							def 					A__ (							self  ,A__  ,A__):
												lowercase		      =		replace_unicode_punct(A__)
												lowercase		      =		self.moses_punct_norm(A__  ,A__)
												lowercase		      =		remove_non_printing_char(A__)
												return text




							@property
							def 					A__ (							self):
												return len(self.encoder)




							@property
							def 					A__ (							self):
												return len(self.decoder)




							def 					A__ (							self):
												return dict(self.encoder  ,**self.added_tokens_encoder)




							def 					A__ (							self):
												return dict(self.decoder  ,**self.added_tokens_decoder)




							def 					A__ (							self  ,A__):
												lowercase		      =		tuple(token[:-1]) + (token[-1] + '''</w>''',)
												if token in self.cache:
																	return self.cache[token]
												lowercase		      =		get_pairs(A__)

												if not pairs:
																	return token + "</w>"

												while True:
																	lowercase		      =		min(A__  ,key=lambda A__: self.bpe_ranks.get(A__  ,float('''inf''')))
																	if bigram not in self.bpe_ranks:
																						break
																	lowercase      ,		lowercase		      =		bigram
																	lowercase		      =		[]
																	lowercase		      =		0
																	while i < len(A__):
																						try:
																											lowercase		      =		word.index(A__  ,A__)
																						except ValueError:
																											new_word.extend(word[i:])
																											break
																						else:
																											new_word.extend(word[i:j])
																											lowercase		      =		j

																						if word[i] == first and i < len(A__) - 1 and word[i + 1] == second:
																											new_word.append(first + second)
																											i += 2
																						else:
																											new_word.append(word[i])
																											i += 1
																	lowercase		      =		tuple(A__)
																	lowercase		      =		new_word
																	if len(A__) == 1:
																						break
																	else:
																						lowercase		      =		get_pairs(A__)
												lowercase		      =		''' '''.join(A__)
												if word == "\n  </w>":
																	lowercase		      =		'''\n</w>'''
												lowercase		      =		word
												return word




							def 					A__ (							self  ,A__  ,A__="en"  ,A__=False):
												lowercase		      =		self.src_lang

												if self.do_lower_case:
																	lowercase		      =		text.lower()

												if bypass_tokenizer:
																	lowercase		      =		text.split()
												else:
																	lowercase		      =		self.moses_pipeline(A__  ,lang=A__)
																	lowercase		      =		self.moses_tokenize(A__  ,lang=A__)

												lowercase		      =		[]
												for token in text:
																	if token:
																						split_tokens.extend(list(self.bpe(A__).split(''' ''')))

												return split_tokens




							def 					A__ (							self  ,A__):
												return self.encoder.get(A__  ,self.encoder.get(self.unk_token))




							def 					A__ (							self  ,A__):
												return self.decoder.get(A__  ,self.unk_token)




							def 					A__ (							self  ,A__):
												lowercase		      =		[t.replace(''' '''  ,'''''').replace('''</w>'''  ,''' ''') for t in tokens]
												lowercase		      =		''''''.join(A__).split()
												# detokenize
												lowercase		      =		self.moses_detokenize(A__  ,self.tgt_lang)
												return text




							def 					A__ (							self  ,A__  ,A__ = None):
												lowercase		      =		[self.sep_token_id]

												# no bos used in fairseq
												if token_ids_a is None:
																	return token_ids_a + sep
												return token_ids_a + sep + token_ids_a + sep




							def 					A__ (							self  ,A__  ,A__ = None  ,A__ = False):

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=A__  ,token_ids_a=A__  ,already_has_special_tokens=A__)
												# no bos used in fairseq
												if token_ids_a is not None:
																	return ([0] * len(A__)) + [1] + ([0] * len(A__)) + [1]
												return ([0] * len(A__)) + [1]




							def 					A__ (							self  ,A__  ,A__ = None):
												lowercase		      =		[self.sep_token_id]

												# no bos used in fairseq
												if token_ids_a is None:
																	return len(token_ids_a + sep) * [0]
												return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]




							def 					A__ (							self  ,A__  ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return

												lowercase		      =		os.path.join(
												    A__  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''src_vocab_file'''])
												lowercase		      =		os.path.join(
												    A__  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''tgt_vocab_file'''])
												lowercase		      =		os.path.join(
												    A__  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''])

												with open(A__  ,'''w'''  ,encoding='''utf-8''') as f:
																	f.write(json.dumps(self.encoder  ,indent=2  ,sort_keys=A__  ,ensure_ascii=A__) + '''\n''')

												with open(A__  ,'''w'''  ,encoding='''utf-8''') as f:
																	lowercase		      =		{v: k for k, v in self.decoder.items()}
																	f.write(json.dumps(A__  ,indent=2  ,sort_keys=A__  ,ensure_ascii=A__) + '''\n''')

												lowercase		      =		0
												with open(A__  ,'''w'''  ,encoding='''utf-8''') as writer:
																	for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,key=lambda A__: kv[1]):
																						if index != token_index:
																											logger.warning(
																											    f'Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'
																											    ''' Please check that the tokenizer is not corrupted!''')
																											lowercase		      =		token_index
																						writer.write(''' '''.join(A__) + '''\n''')
																						index += 1

												return src_vocab_file, tgt_vocab_file, merges_file




							def __getstate__(							self):
												lowercase		      =		self.__dict__.copy()
												lowercase		      =		None
												return state





							def __setstate__(							self  ,A__):
												lowercase		      =		d

												try:
																	import sacremoses
												except ImportError:
																	raise ImportError(
																	    '''You need to install sacremoses to use XLMTokenizer. '''
																	    '''See https://pypi.org/project/sacremoses/ for installation.''')

												lowercase		      =		sacremoses




