


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   	=  logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

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


class 	a__						(    pl.LightningModule     ):

       def __init__( self   :  Tuple   ,				lowerCamelCase_   :  List[Any]   ,				lowerCamelCase_   :  str=None   ,				lowerCamelCase_   :  Tuple="base"   ,				lowerCamelCase_   :  int=None   ,				lowerCamelCase_   :  Optional[Any]=None   ,				lowerCamelCase_   :  Any=None   ,				**lowerCamelCase_   :  Optional[Any]   ,				):
            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_     )
            a_     :  int				      =     0
            a_     :  Optional[int]				      =     Path(self.hparams.output_dir     )
            a_     :  Dict				      =     self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                 a_     :  int				      =     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:
                 a_     :  Union[str, Any]				      =     config

            a_     :  str				      =     ("""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:
                 a_     :  Dict				      =     AutoTokenizer.from_pretrained(
                     self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   ,				cache_dir=A_   ,				)
            else:
                 a_     :  Tuple				      =     tokenizer
            a_     :  Union[str, Any]				      =     MODEL_MODES[mode]
            if model is None:
                 a_     :  Union[str, Any]				      =     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:
                 a_     :  Optional[Any]				      =     model

       def     UpperCAmelCase( self   :  List[Any]   ,				*lowerCamelCase_   :  Optional[Any]   ,				**lowerCamelCase_   :  List[Any]     ):
            a_     :  Union[str, Any]				      =     self.model_type.from_pretrained(*A_   ,				**A_     )

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

       def     UpperCAmelCase( self   :  Optional[int]     ):
            a_     :  Union[str, Any]				      =     self.model
            a_     :  Optional[Any]				      =     ["""bias""", """LayerNorm.weight"""]
            a_     :  str				      =     [
                {
                    """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_     :  Optional[int]				      =     Adafactor(
                     A_   ,				lr=self.hparams.learning_rate   ,				scale_parameter=A_   ,				relative_step=A_     )

            else:
                 a_     :  Tuple				      =     AdamW(
                     A_   ,				lr=self.hparams.learning_rate   ,				eps=self.hparams.adam_epsilon     )
            a_     :  List[str]				      =     optimizer

            a_     :  Tuple				      =     self.get_lr_scheduler()

            return [optimizer], [scheduler]

       def     UpperCAmelCase( self   :  List[Any]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Optional[int]     ):
            return self.validation_step(A_   ,				A_     )

       def     UpperCAmelCase( self   :  Dict   ,				lowerCamelCase_   :  Optional[Any]     ):
            return self.validation_end(A_     )

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  Tuple				      =     max(1   ,				self.hparams.gpus     )  # TODO: consider num_tpu_cores
            a_     :  Optional[int]				      =     self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
            return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

       def     UpperCAmelCase( self   :  Any   ,				lowerCamelCase_   :  Any     ):
            if mode == "test":
                 a_     :  Optional[Any]				      =     len(self.test_dataloader().dataset     )
            else:
                 a_     :  Any				      =     self.get_dataloader("""train"""   ,				self.hparams.train_batch_size   ,				shuffle=A_     )
                 a_     :  int				      =     len(self.train_dataloader().dataset     )

       def     UpperCAmelCase( self   :  Union[str, Any]   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  Dict   ,				lowerCamelCase_   :  Optional[Any] = False     ):
            raise NotImplementedError("""You must implement this for your task"""     )

       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            return self.train_loader

       def     UpperCAmelCase( self   :  Dict     ):
            return self.get_dataloader("""dev"""   ,				self.hparams.eval_batch_size   ,				shuffle=A_     )

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            return self.get_dataloader("""test"""   ,				self.hparams.eval_batch_size   ,				shuffle=A_     )

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  Union[str, Any]     ):
            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     UpperCAmelCase( self   :  List[Any]   ,				lowerCamelCase_   :  List[Any]     ):
            a_     :  Any				      =     self.output_dir.joinpath("""best_tfmr"""     )
            a_     :  int				      =     self.step_count
            self.model.save_pretrained(A_     )
            self.tokenizer.save_pretrained(A_     )

       @staticmethod
       def     UpperCAmelCase( lowerCamelCase_   :  str   ,				lowerCamelCase_   :  List[Any]     ):
            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 	a__						(    pl.Callback     ):

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  List[str]     ):
            a_     :  Optional[Any]				      =     trainer.lr_schedulers[0]["""scheduler"""]
            a_     :  str				      =     {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()     )}
            pl_module.logger.log_metrics(A_     )

       def     UpperCAmelCase( self   :  Dict   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  List[Any]     ):
            rank_zero_info("""***** Validation results *****"""     )
            a_     :  Union[str, Any]				      =     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     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  str     ):
            rank_zero_info("""***** Test results *****"""     )
            a_     :  Union[str, Any]				      =     trainer.callback_metrics
            # Log and save results to file
            a_     :  Optional[Any]				      =     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 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     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=4_2	,	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 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase=None	,	__UpperCamelCase=True	,	__UpperCamelCase=[]	,	__UpperCamelCase=None	,	__UpperCamelCase=None	,	**__UpperCamelCase	,	):
     pl.seed_everything(args.seed			)

     # init model
     a_     :  Union[str, Any]				      =     Path(model.hparams.output_dir			)
     odir.mkdir(exist_ok=__UpperCamelCase			)

     # add custom checkpoints
     if checkpoint_callback is None:
          a_     :  Union[str, 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_     :  List[Any]				      =     LoggingCallback()

     a_     :  Optional[int]				      =     {}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          a_     :  Optional[Any]				      =     1_6
          a_     :  Any				      =     args.fpaa_opt_level

     if args.gpus > 1:
          a_     :  Optional[Any]				      =     """ddp"""

     a_     :  Dict				      =     args.accumulate_grad_batches
     a_     :  Dict				      =     extra_train_kwargs.get("""accelerator"""	,	__UpperCamelCase			)
     a_     :  Optional[Any]				      =     extra_train_kwargs.get("""profiler"""	,	__UpperCamelCase			)

     a_     :  Union[str, Any]				      =     pl.Trainer.from_argparse_args(
         __UpperCamelCase	,	weights_summary=__UpperCamelCase	,	callbacks=[logging_callback] + extra_callbacks	,	logger=__UpperCamelCase	,	checkpoint_callback=__UpperCamelCase	,	**__UpperCamelCase	,	)

     if args.do_train:
          trainer.fit(__UpperCamelCase			)

     return trainer





def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if not isinstance(lowercase       ,			lowercase					):
				UpperCamelCase            =	(
				    'operation can not be conducted on a object of type '
				    f'''{type(lowercase					).__name__}'''
				)
				raise TypeError(lowercase					)
		UpperCamelCase            =	''
		for _ in range(0       ,			32					):
				bit_string += str(number % 2					)
				UpperCamelCase            =	number >> 1
		return bit_string


def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if number < 0:
				raise ValueError('the value of input must be positive'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('Input value must be a \'int\' type'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''					)
		UpperCamelCase            =	0
		# iterator over [1 to 32],since we are dealing with 32 bit integer
		for _ in range(1       ,			33					):
				# left shift the bits by unity
				UpperCamelCase            =	result << 1
				# get the end bit
				UpperCamelCase            =	number % 2
				# right shift the bits by unity
				UpperCamelCase            =	number >> 1
				# add that bit to our ans
				UpperCamelCase            =	result | end_bit
		return get_reverse_bit_string(lowercase					)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
