




'''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 __magic_name__(						pl.LightningModule      ):






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



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

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

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






						def       __lowerCAmelCase(   self						:						List[str]					,   *__UpperCamelCase						:						Dict					,   **__UpperCamelCase						:						Tuple       ):



									'''simple docstring'''






									snake_case__          =						self.model_type.from_pretrained(*__UpperCamelCase					,   **__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






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






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



									'''simple docstring'''






									snake_case__          =						self.model
									snake_case__          =						["""bias""", """LayerNorm.weight"""]
									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:
												snake_case__          =						Adafactor(
												    __UpperCamelCase					,   lr=self.hparams.learning_rate					,   scale_parameter=__UpperCamelCase					,   relative_step=__UpperCamelCase       )

									else:
												snake_case__          =						AdamW(
												    __UpperCamelCase					,   lr=self.hparams.learning_rate					,   eps=self.hparams.adam_epsilon       )
									snake_case__          =						optimizer

									snake_case__          =						self.get_lr_scheduler()

									return [optimizer], [scheduler]






						def       __lowerCAmelCase(   self						:						Optional[Any]					,   __UpperCamelCase						:						Any					,   __UpperCamelCase						:						Dict       ):



									'''simple docstring'''






									return self.validation_step(__UpperCamelCase					,   __UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Union[str, Any]					,   __UpperCamelCase						:						Optional[Any]       ):



									'''simple docstring'''






									return self.validation_end(__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									snake_case__          =						max(1					,   self.hparams.gpus       )  # TODO: consider num_tpu_cores
									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       __lowerCAmelCase(   self						:						Optional[Any]					,   __UpperCamelCase						:						List[Any]       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						Any					,   __UpperCamelCase						:						str					,   __UpperCamelCase						:						int					,   __UpperCamelCase						:						bool = False       ):



									'''simple docstring'''






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






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



									'''simple docstring'''






									return self.train_loader






						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						List[Any]					,   __UpperCamelCase						:						str       ):



									'''simple docstring'''






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






						@pl.utilities.rank_zero_only
						def       __lowerCAmelCase(   self						:						List[str]					,   __UpperCamelCase						:						Dict[str, Any]       ):



									'''simple docstring'''






									snake_case__          =						self.output_dir.joinpath("""best_tfmr"""       )
									snake_case__          =						self.step_count
									self.model.save_pretrained(__UpperCamelCase       )
									self.tokenizer.save_pretrained(__UpperCamelCase       )







						@staticmethod
						def       __lowerCAmelCase(   __UpperCamelCase						:						Union[str, Any]					,   __UpperCamelCase						:						str       ):



									'''simple docstring'''






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







class __magic_name__(						pl.Callback      ):






						def       __lowerCAmelCase(   self						:						Optional[int]					,   __UpperCamelCase						:						Optional[int]					,   __UpperCamelCase						:						int       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						pl.Trainer					,   __UpperCamelCase						:						pl.LightningModule       ):



									'''simple docstring'''






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







						def       __lowerCAmelCase(   self						:						Union[str, Any]					,   __UpperCamelCase						:						pl.Trainer					,   __UpperCamelCase						:						pl.LightningModule       ):



									'''simple docstring'''






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







def    snake_case__     (  a       ,      a	)			->       None:





			'''simple docstring'''






			parser.add_argument(
			    """--output_dir"""       ,      default=a       ,      type=a       ,      required=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=42       ,      help="""random seed for initialization"""	)
			parser.add_argument(
			    """--data_dir"""       ,      default=a       ,      type=a       ,      required=a       ,      help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""       ,      )




def    snake_case__     (  a       ,      a       ,      a=None       ,      a=True       ,      a=[]       ,      a=None       ,      a=None       ,      **a       ,      )			->       int:





			'''simple docstring'''






			pl.seed_everything(args.seed	)

			# init model
			snake_case__          =						Path(model.hparams.output_dir	)
			odir.mkdir(exist_ok=a	)

			# add custom checkpoints
			if checkpoint_callback is None:
						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(a	)
			if logging_callback is None:
						snake_case__          =						LoggingCallback()

			snake_case__          =						{}

			# TODO: remove with PyTorch 1.6 since pl uses native amp
			if args.fpaa:
						snake_case__          =						16
						snake_case__          =						args.fpaa_opt_level

			if args.gpus > 1:
						snake_case__          =						"""ddp"""

			snake_case__          =						args.accumulate_grad_batches
			snake_case__          =						extra_train_kwargs.get("""accelerator"""       ,      a	)
			snake_case__          =						extra_train_kwargs.get("""profiler"""       ,      a	)

			snake_case__          =						pl.Trainer.from_argparse_args(
			    a       ,      weights_summary=a       ,      callbacks=[logging_callback] + extra_callbacks       ,      logger=a       ,      checkpoint_callback=a       ,      **a       ,      )

			if args.do_train:
						trainer.fit(a	)

			return trainer
lowerCAmelCase      :				Union[str, Any]     			=1000







def     A__	(       __A						):







    '''simple docstring'''
    return x * x







def     A__	(       __A ,			__A ,			__A ,			__A = 4						):







    '''simple docstring'''
    assert callable(
        __A						), F"""the function(object) passed should be callable your input : {function}"""
    assert isinstance(__A ,			(float, int)						), F"""a should be float or integer your input : {a}"""
    assert isinstance(function(__A						) ,			(float, int)						), (
        "the function should return integer or float return type of your function, "
        F"""{type(__A						)}"""
    )
    assert isinstance(__A ,			(float, int)						), F"""b should be float or integer your input : {b}"""
    assert (
        isinstance(__A ,			__A						) and precision > 0
    ), F"""precision should be positive integer your input : {precision}"""

    # just applying the formula of simpson for approximate integration written in
    # mentioned article in first comment of this file and above this function

    _lowerCamelCase  :		Optional[Any]			    =    (b - a) / N_STEPS
    _lowerCamelCase  :		str			    =    function(__A						) + function(__A						)

    for i in range(1 ,			__A						):
        _lowerCamelCase  :		str			    =    a + h * i
        result += function(__A						) * (4 if i % 2 else 2)

    result *= h / 3
    return round(__A ,			__A						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




