




'''simple docstring'''







import numpy as np





def 						snake_case_							(_a	:							np.array     ):
    return (2 / (1 + np.exp(-2 * vector     ))) - 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()










'''simple docstring'''







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     =logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

A     ={
    '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     ={
    '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     =sorted(arg_to_scheduler.keys())
A     ='{' + ', '.join(arg_to_scheduler_choices) + '}'


class 			_a  (	pl.LightningModule ):
       def __init__(	self						:	List[str]   ,				lowercase						:	argparse.Namespace   ,				lowercase						:	List[Any]=None   ,				lowercase						:	Dict="base"   ,				lowercase						:	Optional[int]=None   ,				lowercase						:	Dict=None   ,				lowercase						:	Tuple=None   ,				**lowercase						:	Optional[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				)
           UpperCAmelCase			       =		0
           UpperCAmelCase			       =		Path(self.hparams.output_dir				)
           UpperCAmelCase			       =		self.hparams.cache_dir if self.hparams.cache_dir else None
           if config is None:
               UpperCAmelCase			       =		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:
               UpperCAmelCase			       =		config

           UpperCAmelCase			       =		('''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:
               UpperCAmelCase			       =		AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   ,				cache_dir=lowercase   ,				)
           else:
               UpperCAmelCase			       =		tokenizer
           UpperCAmelCase			       =		MODEL_MODES[mode]
           if model is None:
               UpperCAmelCase			       =		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:
               UpperCAmelCase			       =		model
       def     A      (	self						:	List[Any]   ,				*lowercase						:	List[str]   ,				**lowercase						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_type.from_pretrained(*lowercase   ,				**lowercase				)
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

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



           '''simple docstring'''

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

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

           UpperCAmelCase			       =		self.get_lr_scheduler()

           return [optimizer], [scheduler]
       def     A      (	self						:	List[Any]   ,				lowercase						:	int   ,				lowercase						:	List[str]				):



           '''simple docstring'''

           return self.validation_step(lowercase   ,				lowercase				)
       def     A      (	self						:	List[Any]   ,				lowercase						:	Tuple				):



           '''simple docstring'''

           return self.validation_end(lowercase				)
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		max(1   ,				self.hparams.gpus				)  # TODO: consider num_tpu_cores
           UpperCAmelCase			       =		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						:	List[str]   ,				lowercase						:	Any				):



           '''simple docstring'''

           if stage == "test":
               UpperCAmelCase			       =		len(self.test_dataloader().dataset				)
           else:
               UpperCAmelCase			       =		self.get_dataloader('''train'''   ,				self.hparams.train_batch_size   ,				shuffle=lowercase				)
               UpperCAmelCase			       =		len(self.train_dataloader().dataset				)
       def     A      (	self						:	List[str]   ,				lowercase						:	str   ,				lowercase						:	int   ,				lowercase						:	bool = False				):



           '''simple docstring'''

           raise NotImplementedError('''You must implement this for your task'''				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           return self.train_loader
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           return self.get_dataloader('''dev'''   ,				self.hparams.eval_batch_size   ,				shuffle=lowercase				)
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           return self.get_dataloader('''test'''   ,				self.hparams.eval_batch_size   ,				shuffle=lowercase				)
       def     A      (	self						:	Any   ,				lowercase						:	Union[str, Any]				):



           '''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     A      (	self						:	List[str]   ,				lowercase						:	Dict[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.output_dir.joinpath('''best_tfmr'''				)
           UpperCAmelCase			       =		self.step_count
           self.model.save_pretrained(lowercase				)
           self.tokenizer.save_pretrained(lowercase				)







       @staticmethod
       def     A      (	lowercase						:	Optional[int]   ,				lowercase						:	List[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=str(Path(lowercase				).parent / '''test_run''' / '''cache'''				)   ,				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=32   ,				type=lowercase				)
           parser.add_argument('''--eval_batch_size'''   ,				default=32   ,				type=lowercase				)
           parser.add_argument('''--adafactor'''   ,				action='''store_true'''				)






class 			_a  (	pl.Callback ):
       def     A      (	self						:	Dict   ,				lowercase						:	Optional[Any]   ,				lowercase						:	List[Any]				):



           '''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 ):
       def     A      (	self						:	Optional[int]   ,				lowercase						:	Union[str, Any]   ,				lowercase						:	Any				):



           '''simple docstring'''

           for name, param in pl_module.model.rag.named_parameters():
               if param.grad is None:
                   print(lowercase				)






class 			_a  (	pl.Callback ):
       def     A      (	self						:	Optional[int]   ,				lowercase						:	Optional[int]   ,				lowercase						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		trainer.lr_schedulers[0]['''scheduler''']
           UpperCAmelCase			       =		{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
           pl_module.logger.log_metrics(lowercase				)
       def     A      (	self						:	Tuple   ,				lowercase						:	pl.Trainer   ,				lowercase						:	pl.LightningModule				):



           '''simple docstring'''

           rank_zero_info('''***** Validation results *****'''				)
           UpperCAmelCase			       =		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     A      (	self						:	Dict   ,				lowercase						:	pl.Trainer   ,				lowercase						:	pl.LightningModule				):



           '''simple docstring'''

           rank_zero_info('''***** Test results *****'''				)
           UpperCAmelCase			       =		trainer.callback_metrics
           # Log and save results to file
           UpperCAmelCase			       =		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_							(_a	:							int    ,   _a	:							Optional[Any]     ):
    #  To allow all pl args uncomment the following line
    #  parser = pl.Trainer.add_argparse_args(parser)
    parser.add_argument(
        '''--output_dir'''    ,   default=str(Path(_a     ).parent / '''test_run''' / '''model_checkpoints'''     )    ,   type=_a    ,   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=_a    ,   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=_a     )
    parser.add_argument('''--max_grad_norm'''    ,   dest='''gradient_clip_val'''    ,   default=1.0    ,   type=_a    ,   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=_a    ,   default=1    ,   help='''Number of updates steps to accumulate before performing a backward/update pass.'''    ,   )
    parser.add_argument('''--seed'''    ,   type=_a    ,   default=4_2    ,   help='''random seed for initialization'''     )
    parser.add_argument(
        '''--data_dir'''    ,   default=str(Path(_a     ).parent / '''test_run''' / '''dummy-train-data'''     )    ,   type=_a    ,   help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''    ,   )







def 						snake_case_							(_a	:							BaseTransformer    ,   _a	:							argparse.Namespace    ,   _a	:							List[Any]=None    ,   _a	:							Tuple=True    ,   _a	:							int=[]    ,   _a	:							Any=None    ,   _a	:							int=None    ,   **_a	:							Optional[Any]    ,   ):
    pl.seed_everything(args.seed     )

    # init model
    UpperCAmelCase			       =		Path(model.hparams.output_dir     )
    odir.mkdir(exist_ok=_a     )

    # add custom checkpoints
    if checkpoint_callback is None:
        UpperCAmelCase			       =		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(_a     )
    if logging_callback is None:
        UpperCAmelCase			       =		LoggingCallback()

    UpperCAmelCase			       =		{}

    if args.fpaa:
        UpperCAmelCase			       =		1_6

    if args.gpus > 1:
        UpperCAmelCase			       =		'''auto'''
        UpperCAmelCase			       =		'''ddp'''

    UpperCAmelCase			       =		args.accumulate_grad_batches
    UpperCAmelCase			       =		None
    UpperCAmelCase			       =		'''auto'''

    UpperCAmelCase			       =		pl.Trainer.from_argparse_args(
        _a    ,   weights_summary=_a    ,   callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]    ,   logger=_a    ,   val_check_interval=1    ,   num_sanity_val_steps=2    ,   **_a    ,   )

    if args.do_train:
        trainer.fit(_a     )

    else:
        print('''RAG modeling tests with new set functions successfuly executed!'''     )
    return trainer




