





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



class 				__snake_case  (     pl.LightningModule						):


    '''simple docstring'''







    def __init__(					self      ,							A_      ,							A_=None      ,							A_="base"      ,							A_=None      ,							A_=None      ,							A_=None      ,							**A_      ,							):





        '''simple docstring'''

        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_    )
        SCREAMING_SNAKE_CASE__       							= 0
        SCREAMING_SNAKE_CASE__       							= Path(self.hparams.output_dir    )
        SCREAMING_SNAKE_CASE__       							= self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
            SCREAMING_SNAKE_CASE__       							= 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:
            SCREAMING_SNAKE_CASE__       							= config

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



    def    lowercase_				(					self      ,							*A_      ,							**A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.model_type.from_pretrained(*A_      ,							**A_    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

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



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.model
        SCREAMING_SNAKE_CASE__       							= ['''bias''', '''LayerNorm.weight''']
        SCREAMING_SNAKE_CASE__       							= [
            {
                '''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:
            SCREAMING_SNAKE_CASE__       							= Adafactor(
                A_      ,							lr=self.hparams.learning_rate      ,							scale_parameter=A_      ,							relative_step=A_    )

        else:
            SCREAMING_SNAKE_CASE__       							= AdamW(
                A_      ,							lr=self.hparams.learning_rate      ,							eps=self.hparams.adam_epsilon    )
        SCREAMING_SNAKE_CASE__       							= optimizer

        SCREAMING_SNAKE_CASE__       							= self.get_lr_scheduler()

        return [optimizer], [scheduler]



    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        return self.validation_step(A_      ,							A_    )



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        return self.validation_end(A_    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

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



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

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



    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_ = False    ):





        '''simple docstring'''

        raise NotImplementedError('''You must implement this for your task'''    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        return self.train_loader



    def    lowercase_				(					self    ):





        '''simple docstring'''

        return self.get_dataloader('''dev'''      ,							self.hparams.eval_batch_size      ,							shuffle=A_    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        return self.get_dataloader('''test'''      ,							self.hparams.eval_batch_size      ,							shuffle=A_    )



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        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    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.output_dir.joinpath('''best_tfmr'''    )
        SCREAMING_SNAKE_CASE__       							= self.step_count
        self.model.save_pretrained(A_    )
        self.tokenizer.save_pretrained(A_    )




    @staticmethod
    def    lowercase_				(					A_      ,							A_    ):





        '''simple docstring'''

        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=32      ,							type=A_    )
        parser.add_argument('''--eval_batch_size'''      ,							default=32      ,							type=A_    )
        parser.add_argument('''--adafactor'''      ,							action='''store_true'''    )





class 				__snake_case  (     pl.Callback						):


    '''simple docstring'''







    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

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



    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        rank_zero_info('''***** Validation results *****'''    )
        SCREAMING_SNAKE_CASE__       							= 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    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        rank_zero_info('''***** Test results *****'''    )
        SCREAMING_SNAKE_CASE__       							= trainer.callback_metrics
        # Log and save results to file
        SCREAMING_SNAKE_CASE__       							= 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        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	None:
    #  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=4_2   ,       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        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_=None   ,       lowerCAmelCase_=True   ,       lowerCAmelCase_=[]   ,       lowerCAmelCase_=None   ,       lowerCAmelCase_=None   ,       **lowerCAmelCase_   ,       )							->	Dict:
    pl.seed_everything(args.seed  )

    # init model
    SCREAMING_SNAKE_CASE__       							= Path(model.hparams.output_dir  )
    odir.mkdir(exist_ok=lowerCAmelCase_  )

    # add custom checkpoints
    if checkpoint_callback is None:
        SCREAMING_SNAKE_CASE__       							= 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:
        SCREAMING_SNAKE_CASE__       							= LoggingCallback()

    SCREAMING_SNAKE_CASE__       							= {}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        SCREAMING_SNAKE_CASE__       							= 1_6
        SCREAMING_SNAKE_CASE__       							= args.fpaa_opt_level

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

    SCREAMING_SNAKE_CASE__       							= args.accumulate_grad_batches
    SCREAMING_SNAKE_CASE__       							= extra_train_kwargs.get('''accelerator'''   ,       lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= extra_train_kwargs.get('''profiler'''   ,       lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__       							= 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 io
import json
import subprocess


_A				:							str							= [
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

_A				:							int							= 8


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Optional[Any]:
    SCREAMING_SNAKE_CASE__       							= {}
    for src, tgt in pairs:
        SCREAMING_SNAKE_CASE__       							= f'''{src}-{tgt}'''
        SCREAMING_SNAKE_CASE__       							= f'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
        SCREAMING_SNAKE_CASE__       							= subprocess.run(lowerCAmelCase_   ,       stdout=subprocess.PIPE  ).stdout.decode('''utf-8'''  ).splitlines()
        SCREAMING_SNAKE_CASE__       							= f'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
        SCREAMING_SNAKE_CASE__       							= subprocess.run(lowerCAmelCase_   ,       stdout=subprocess.PIPE  ).stdout.decode('''utf-8'''  ).splitlines()
        SCREAMING_SNAKE_CASE__       							= {'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
    return text


_A				:							Optional[Any]							= get_all_data(pairs, n_objs)
_A				:							Optional[Any]							= """./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
  _A				:							int							= json.dump(text, f, indent=2, ensure_ascii=False)
