

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_									=    logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowercase_									=    {
    "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_									=    {
    "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_									=    sorted(arg_to_scheduler.keys())
lowercase_									=    "{" + ", ".join(arg_to_scheduler_choices) + "}"


class 					A		(				pl.LightningModule					):







    """simple docstring"""





    def __init__( self					:			List[Any],lowercase_					:			argparse.Namespace,lowercase_					:			Tuple=None,lowercase_					:			str="base",lowercase_					:			Dict=None,lowercase_					:			Any=None,lowercase_					:			int=None,**lowercase_					:			Any,)->       int:





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

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

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


    def 		snake_case__	( self					:			Optional[int],*lowercase_					:			Dict,**lowercase_					:			Any						)->       List[Any]:





        '''simple docstring'''







        A__        =  self.model_type.from_pretrained(*lowercase_,**lowercase_						)


    def 		snake_case__	( self					:			Optional[Any]						)->       str:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			Tuple						)->       str:





        '''simple docstring'''







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

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

        A__        =  self.get_lr_scheduler()

        return [optimizer], [scheduler]


    def 		snake_case__	( self					:			Dict,lowercase_					:			List[str],lowercase_					:			Dict						)->       Tuple:





        '''simple docstring'''







        return self.validation_step(lowercase_,lowercase_						)


    def 		snake_case__	( self					:			int,lowercase_					:			Optional[Any]						)->       Optional[int]:





        '''simple docstring'''







        return self.validation_end(lowercase_						)


    def 		snake_case__	( self					:			Any						)->       int:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			Optional[int],lowercase_					:			int						)->       Any:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			Optional[int],lowercase_					:			str,lowercase_					:			int,lowercase_					:			bool = False						)->       Tuple:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			List[Any]						)->       int:





        '''simple docstring'''







        return self.train_loader


    def 		snake_case__	( self					:			str						)->       Any:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			Dict						)->       int:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			List[str],lowercase_					:			Tuple						)->       int:





        '''simple docstring'''







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


    @pl.utilities.rank_zero_only
    def 		snake_case__	( self					:			Optional[Any],lowercase_					:			Dict[str, Any]						)->       None:





        '''simple docstring'''







        A__        =  self.output_dir.joinpath('best_tfmr'						)
        A__        =  self.step_count
        self.model.save_pretrained(lowercase_						)
        self.tokenizer.save_pretrained(lowercase_						)


    @staticmethod
    def 		snake_case__	( lowercase_					:			Dict,lowercase_					:			Optional[Any]						)->       str:





        '''simple docstring'''







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




class 					A		(				pl.Callback					):







    """simple docstring"""





    def 		snake_case__	( self					:			Dict,lowercase_					:			Optional[int],lowercase_					:			Tuple						)->       Optional[int]:





        '''simple docstring'''







        if (
            trainer.is_global_zero and trainer.global_rank == 0
        ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
            pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.




class 					A		(				pl.Callback					):







    """simple docstring"""





    def 		snake_case__	( self					:			Any,lowercase_					:			int,lowercase_					:			Tuple						)->       List[str]:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			Tuple,lowercase_					:			pl.Trainer,lowercase_					:			pl.LightningModule						)->       Optional[Any]:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			Optional[Any],lowercase_					:			pl.Trainer,lowercase_					:			pl.LightningModule						)->       Dict:





        '''simple docstring'''







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




def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[Any]       ,     SCREAMING_SNAKE_CASE__     :      str )				-> None:





    '''simple docstring'''






    parser.add_argument(
        '--output_dir'       ,     default=SCREAMING_SNAKE_CASE__       ,     type=SCREAMING_SNAKE_CASE__       ,     required=SCREAMING_SNAKE_CASE__       ,     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=SCREAMING_SNAKE_CASE__       ,     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=SCREAMING_SNAKE_CASE__ )
    parser.add_argument('--max_grad_norm'       ,     dest='gradient_clip_val'       ,     default=1.0       ,     type=SCREAMING_SNAKE_CASE__       ,     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=SCREAMING_SNAKE_CASE__       ,     default=1       ,     help='Number of updates steps to accumulate before performing a backward/update pass.'       ,     )
    parser.add_argument('--seed'       ,     type=SCREAMING_SNAKE_CASE__       ,     default=42       ,     help='random seed for initialization' )
    parser.add_argument(
        '--data_dir'       ,     default=SCREAMING_SNAKE_CASE__       ,     type=SCREAMING_SNAKE_CASE__       ,     required=SCREAMING_SNAKE_CASE__       ,     help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'       ,     )

def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      BaseTransformer       ,     SCREAMING_SNAKE_CASE__     :      argparse.Namespace       ,     SCREAMING_SNAKE_CASE__     :      Optional[Any]=None       ,     SCREAMING_SNAKE_CASE__     :      List[str]=True       ,     SCREAMING_SNAKE_CASE__     :      Optional[int]=None       ,     SCREAMING_SNAKE_CASE__     :      Union[str, Any]=[]       ,     SCREAMING_SNAKE_CASE__     :      str=None       ,     SCREAMING_SNAKE_CASE__     :      Dict=None       ,     **SCREAMING_SNAKE_CASE__     :      List[str]       ,     )				-> Any:





    '''simple docstring'''






    pl.seed_everything(args.seed )

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

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

    A__        =  {}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        A__        =  16
        # train_params["amp_level"] = args.fp16_opt_level

    if args.gpus > 1:
        A__        =  'auto'  # "ddp"
        A__        =  'ddp'

    A__        =  args.accumulate_grad_batches
    A__        =  None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
    A__        =  'auto'

    A__        =  pl.Trainer.from_argparse_args(
        SCREAMING_SNAKE_CASE__       ,     weights_summary=SCREAMING_SNAKE_CASE__       ,     callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]       ,     logger=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       ,     )

    if args.do_train:
        trainer.fit(SCREAMING_SNAKE_CASE__ )

    return trainer




import unittest

from cer import CER


lowercase_									=    CER()


class 					A		(				unittest.TestCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			str						)->       Dict:





        '''simple docstring'''







        A__        =  ['White House']
        A__        =  ['white house']
        # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.1_818_181_818						) < 1E-6						)


    def 		snake_case__	( self					:			str						)->       Dict:





        '''simple docstring'''







        A__        =  ['were wolf']
        A__        =  ['werewolf']
        # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.1_111_111						) < 1E-6						)

        A__        =  ['werewolf']
        A__        =  ['weae     wolf']
        # S = 1, D = 1, I = 0, N = 8, CER = 0.25
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.25						) < 1E-6						)

        # consecutive whitespaces case 1
        A__        =  ['were wolf']
        A__        =  ['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.0						) < 1E-6						)

        # consecutive whitespaces case 2
        A__        =  ['were   wolf']
        A__        =  ['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.0						) < 1E-6						)


    def 		snake_case__	( self					:			int						)->       str:





        '''simple docstring'''







        A__        =  ['werewolf']
        A__        =  ['weaewolf']
        # S = 1, D = 0, I = 0, N = 8, CER = 0.125
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.125						) < 1E-6						)


    def 		snake_case__	( self					:			Optional[int]						)->       Optional[int]:





        '''simple docstring'''







        A__        =  ['werewolf']
        A__        =  ['wereawolf']
        # S = 0, D = 1, I = 0, N = 8, CER = 0.125
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.125						) < 1E-6						)


    def 		snake_case__	( self					:			List[Any]						)->       Optional[Any]:





        '''simple docstring'''







        A__        =  ['werewolf']
        A__        =  ['wereolf']
        # S = 0, D = 0, I = 1, N = 8, CER = 0.125
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.125						) < 1E-6						)


    def 		snake_case__	( self					:			str						)->       Tuple:





        '''simple docstring'''







        A__        =  ['werewolf']
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertEqual(lowercase_,0.0						)


    def 		snake_case__	( self					:			Optional[int]						)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  ['werewolf', 'I am your father']
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertEqual(lowercase_,0.0						)

        A__        =  ['werewolf', 'I am your father', 'doge']
        A__        =  ['werxwolf', 'I       am your father', 'doge']
        # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.03_571_428						) < 1E-6						)


    def 		snake_case__	( self					:			List[str]						)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  ['My hovercraft', 'is full of eels']
        A__        =  ['My hovercraft is full', ' of eels']
        # S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
        # whitespace at the front of " of eels" will be strip during preporcessing
        # so need to insert 2 whitespaces
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_,concatenate_texts=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.071_428						) < 1E-6						)


    def 		snake_case__	( self					:			Dict						)->       List[str]:





        '''simple docstring'''







        A__        =  ['我能吞下玻璃而不伤身体']
        A__        =  [' 能吞虾玻璃而 不霜身体啦']
        # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.4_545_454_545						) < 1E-6						)

        A__        =  ['我能吞下玻璃', '而不伤身体']
        A__        =  ['我    能 吞 下 玻 璃', '而不伤身体']
        # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.454_545_454_545						) < 1E-6						)

        A__        =  ['我能吞下玻璃而不伤身体']
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertFalse(lowercase_,0.0						)


    def 		snake_case__	( self					:			Any						)->       List[str]:





        '''simple docstring'''







        A__        =  ['']
        A__        =  ['Hypothesis']
        with self.assertRaises(lowercase_						):
            cer.compute(predictions=lowercase_,references=lowercase_						)


if __name__ == "__main__":
     unittest.main()

