




'''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


lowerCAmelCase_			:     Tuple							    =     logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowerCAmelCase_			:     Optional[int]							    =     {
    '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
lowerCAmelCase_			:     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
}
lowerCAmelCase_			:     Optional[int]							    =     sorted(arg_to_scheduler.keys())
lowerCAmelCase_			:     Dict							    =     '{' + ', '.join(arg_to_scheduler_choices) + '}'







class     SCREAMING_SNAKE_CASE						(	pl.LightningModule							):






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


           '''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_    :       List[str]    	=     0
           a_    :       Any    	=     Path(self.hparams.output_dir					)
           a_    :       Any    	=     self.hparams.cache_dir if self.hparams.cache_dir else None
           if config is None:
                a_    :       Dict    	=     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_    :       PretrainedConfig    	=     config

           a_    :       List[Any]    	=     ("""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_    :       str    	=     AutoTokenizer.from_pretrained(
                    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path	,	cache_dir=lowercase__	,	)
           else:
                a_    :       PreTrainedTokenizer    	=     tokenizer
           a_    :       Any    	=     MODEL_MODES[mode]
           if model is None:
                a_    :       int    	=     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_    :       Dict    	=     model






      def        lowercase_	(   self      :     Tuple	,	*lowercase__      :     str	,	**lowercase__      :     Optional[int]					):


           '''simple docstring'''

           a_    :       List[str]    	=     self.model_type.from_pretrained(*lowercase__	,	**lowercase__					)






      def        lowercase_	(   self      :     List[Any]					):


           '''simple docstring'''

           a_    :       int    	=     arg_to_scheduler[self.hparams.lr_scheduler]
           a_    :       Tuple    	=     get_schedule_func(
               self.opt	,	num_warmup_steps=self.hparams.warmup_steps	,	num_training_steps=self.total_steps()					)
           a_    :       List[Any]    	=     {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
           return scheduler






      def        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

           a_    :       Any    	=     self.model
           a_    :       Any    	=     ["""bias""", """LayerNorm.weight"""]
           a_    :       Any    	=     [
               {
                   """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_    :       List[Any]    	=     Adafactor(
                    lowercase__	,	lr=self.hparams.learning_rate	,	scale_parameter=lowercase__	,	relative_step=lowercase__					)

           else:
                a_    :       Any    	=     AdamW(
                    lowercase__	,	lr=self.hparams.learning_rate	,	eps=self.hparams.adam_epsilon					)
           a_    :       Dict    	=     optimizer

           a_    :       List[str]    	=     self.get_lr_scheduler()

           return [optimizer], [scheduler]






      def        lowercase_	(   self      :     int	,	lowercase__      :     Union[str, Any]	,	lowercase__      :     Optional[Any]					):


           '''simple docstring'''

           return self.validation_step(lowercase__	,	lowercase__					)






      def        lowercase_	(   self      :     Tuple	,	lowercase__      :     Tuple					):


           '''simple docstring'''

           return self.validation_end(lowercase__					)






      def        lowercase_	(   self      :     List[str]					):


           '''simple docstring'''

           a_    :       Optional[int]    	=     max(1	,	self.hparams.gpus					)  # TODO: consider num_tpu_cores
           a_    :       List[Any]    	=     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      :     List[Any]	,	lowercase__      :     List[Any]					):


           '''simple docstring'''

           if stage == "test":
                a_    :       Optional[int]    	=     len(self.test_dataloader().dataset					)
           else:
                a_    :       Optional[Any]    	=     self.get_dataloader("""train"""	,	self.hparams.train_batch_size	,	shuffle=lowercase__					)
                a_    :       Union[str, Any]    	=     len(self.train_dataloader().dataset					)






      def        lowercase_	(   self      :     Optional[int]	,	lowercase__      :     str	,	lowercase__      :     int	,	lowercase__      :     bool = False					):


           '''simple docstring'''

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






      def        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

           return self.train_loader






      def        lowercase_	(   self      :     Optional[Any]					):


           '''simple docstring'''

           return self.get_dataloader("""dev"""	,	self.hparams.eval_batch_size	,	shuffle=lowercase__					)






      def        lowercase_	(   self      :     Optional[int]					):


           '''simple docstring'''

           return self.get_dataloader("""test"""	,	self.hparams.eval_batch_size	,	shuffle=lowercase__					)






      def        lowercase_	(   self      :     Dict	,	lowercase__      :     Optional[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        lowercase_	(   self      :     Dict	,	lowercase__      :     Dict[str, Any]					):


           '''simple docstring'''

           a_    :       str    	=     self.output_dir.joinpath("""best_tfmr"""					)
           a_    :       Tuple    	=     self.step_count
           self.model.save_pretrained(lowercase__					)
           self.tokenizer.save_pretrained(lowercase__					)






      @staticmethod
      def        lowercase_	(   lowercase__      :     Tuple	,	lowercase__      :     Dict					):


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







class     SCREAMING_SNAKE_CASE						(	pl.Callback							):






      def        lowercase_	(   self      :     Any	,	lowercase__      :     Optional[int]	,	lowercase__      :     Optional[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     SCREAMING_SNAKE_CASE						(	pl.Callback							):






      def        lowercase_	(   self      :     Tuple	,	lowercase__      :     int	,	lowercase__      :     Optional[int]					):


           '''simple docstring'''

           a_    :       int    	=     trainer.lr_schedulers[0]["""scheduler"""]
           a_    :       Any    	=     {F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
           pl_module.logger.log_metrics(lowercase__					)






      def        lowercase_	(   self      :     Any	,	lowercase__      :     pl.Trainer	,	lowercase__      :     pl.LightningModule					):


           '''simple docstring'''

           rank_zero_info("""***** Validation results *****"""					)
           a_    :       int    	=     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        lowercase_	(   self      :     Tuple	,	lowercase__      :     pl.Trainer	,	lowercase__      :     pl.LightningModule					):


           '''simple docstring'''

           rank_zero_info("""***** Test results *****"""					)
           a_    :       Dict    	=     trainer.callback_metrics
           # Log and save results to file
           a_    :       int    	=     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    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				List[str]   ,						UpperCamelCase__						:				int							):




     """simple docstring"""


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



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				BaseTransformer   ,						UpperCamelCase__						:				argparse.Namespace   ,						UpperCamelCase__						:				Union[str, Any]=None   ,						UpperCamelCase__						:				List[Any]=True   ,						UpperCamelCase__						:				Union[str, Any]=None   ,						UpperCamelCase__						:				List[Any]=[]   ,						UpperCamelCase__						:				Optional[Any]=None   ,						UpperCamelCase__						:				str=None   ,						**UpperCamelCase__						:				Dict   ,						):




     """simple docstring"""


     pl.seed_everything(args.seed							)

     # init model
     a_    :       List[str]    	=     Path(model.hparams.output_dir							)
     odir.mkdir(exist_ok=UpperCamelCase__							)

     # add custom checkpoints
     if checkpoint_callback is None:
          a_    :       List[Any]    	=     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(UpperCamelCase__							)
     if logging_callback is None:
          a_    :       Optional[Any]    	=     LoggingCallback()

     a_    :       Optional[int]    	=     {}

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

     if args.gpus > 1:
          a_    :       Any    	=     """auto"""  # "ddp"
          a_    :       str    	=     """ddp"""

     a_    :       Optional[Any]    	=     args.accumulate_grad_batches
     a_    :       List[str]    	=     None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
     a_    :       Dict    	=     """auto"""

     a_    :       List[Any]    	=     pl.Trainer.from_argparse_args(
         UpperCamelCase__   ,						weights_summary=UpperCamelCase__   ,						callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]   ,						logger=UpperCamelCase__   ,						**UpperCamelCase__   ,						)

     if args.do_train:
          trainer.fit(UpperCamelCase__							)

     return trainer






'''simple docstring'''


from .stack import Stack



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str							):




     """simple docstring"""


     a_    :       Stack[str]    	=     Stack()
     a_    :       Tuple    	=     {"""(""": """)""", """[""": """]""", """{""": """}"""}
     for bracket in parentheses:
          if bracket in bracket_pairs:
               stack.push(UpperCamelCase__							)
          elif bracket in (")", "]", "}"):
               if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                    return False
     return stack.is_empty()


if __name__ == "__main__":
 from doctest import testmod

 testmod()

 lowerCAmelCase_			:     Any							    =     ['((()))', '((())', '(()))']
 print('Balanced parentheses demonstration:\n')
 for example in examples:
  lowerCAmelCase_			:     Optional[Any]							    =     '' if balanced_parentheses(example) else 'not '
  print(f"{example} is {not_str}balanced")
