
"""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


_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     ,						_lowerCamelCase     ,						_lowerCamelCase=None     ,						_lowerCamelCase="base"     ,						_lowerCamelCase=None     ,						_lowerCamelCase=None     ,						_lowerCamelCase=None     ,						**_lowerCamelCase     ,						) ->       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(_lowerCamelCase					)
										__lowercase						      =					0
										__lowercase						      =					Path(self.hparams.output_dir					)
										__lowercase						      =					self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
													__lowercase						      =					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=_lowerCamelCase     ,						**_lowerCamelCase     ,						)
										else:
													__lowercase						      =					config

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

										if tokenizer is None:
													__lowercase						      =					AutoTokenizer.from_pretrained(
													    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,						cache_dir=_lowerCamelCase     ,						)
										else:
													__lowercase						      =					tokenizer
										__lowercase						      =					MODEL_MODES[mode]
										if model is None:
													__lowercase						      =					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=_lowerCamelCase     ,						)
										else:
													__lowercase						      =					model
							def      SCREAMING_SNAKE_CASE	(    self     ,						*_lowerCamelCase     ,						**_lowerCamelCase					) ->       Dict:




										'''simple docstring'''
										__lowercase						      =					self.model_type.from_pretrained(*_lowerCamelCase     ,						**_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''simple docstring'''
										__lowercase						      =					arg_to_scheduler[self.hparams.lr_scheduler]
										__lowercase						      =					get_schedule_func(
										    self.opt     ,						num_warmup_steps=self.hparams.warmup_steps     ,						num_training_steps=self.total_steps()					)
										__lowercase						      =					{"scheduler": scheduler, "interval": "step", "frequency": 1}
										return scheduler
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




										'''simple docstring'''
										__lowercase						      =					self.model
										__lowercase						      =					["bias", "LayerNorm.weight"]
										__lowercase						      =					[
										    {
										        "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:
													__lowercase						      =					Adafactor(
													    _lowerCamelCase     ,						lr=self.hparams.learning_rate     ,						scale_parameter=_lowerCamelCase     ,						relative_step=_lowerCamelCase					)

										else:
													__lowercase						      =					AdamW(
													    _lowerCamelCase     ,						lr=self.hparams.learning_rate     ,						eps=self.hparams.adam_epsilon					)
										__lowercase						      =					optimizer

										__lowercase						      =					self.get_lr_scheduler()

										return [optimizer], [scheduler]
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Dict:




										'''simple docstring'''
										return self.validation_step(_lowerCamelCase     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Tuple:




										'''simple docstring'''
										return self.validation_end(_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       int:




										'''simple docstring'''
										__lowercase						      =					max(1     ,						self.hparams.gpus					)  # TODO: consider num_tpu_cores
										__lowercase						      =					self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Optional[int]:




										'''simple docstring'''
										if stage == "test":
													__lowercase						      =					len(self.test_dataloader().dataset					)
										else:
													__lowercase						      =					self.get_dataloader("train"     ,						self.hparams.train_batch_size     ,						shuffle=_lowerCamelCase					)
													__lowercase						      =					len(self.train_dataloader().dataset					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase = False					) ->       int:




										'''simple docstring'''
										raise NotImplementedError("You must implement this for your task"					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''simple docstring'''
										return self.train_loader
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Optional[Any]:




										'''simple docstring'''
										return self.get_dataloader("dev"     ,						self.hparams.eval_batch_size     ,						shuffle=_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Any:




										'''simple docstring'''
										return self.get_dataloader("test"     ,						self.hparams.eval_batch_size     ,						shuffle=_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       int:




										'''simple docstring'''
										return os.path.join(
										    self.hparams.data_dir     ,						"cached_{}_{}_{}".format(
										        _lowerCamelCase     ,						list(filter(_lowerCamelCase     ,						self.hparams.model_name_or_path.split("/"					)					)					).pop()     ,						str(self.hparams.max_seq_length					)     ,						)     ,						)
							@pl.utilities.rank_zero_only
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       None:




										'''simple docstring'''
										__lowercase						      =					self.output_dir.joinpath("best_tfmr"					)
										__lowercase						      =					self.step_count
										self.model.save_pretrained(_lowerCamelCase					)
										self.tokenizer.save_pretrained(_lowerCamelCase					)





							@staticmethod
							def      SCREAMING_SNAKE_CASE	(    _lowerCamelCase     ,						_lowerCamelCase					) ->       Union[str, Any]:




										'''simple docstring'''
										parser.add_argument(
										    "--model_name_or_path"     ,						default=_lowerCamelCase     ,						type=_lowerCamelCase     ,						required=_lowerCamelCase     ,						help="Path to pretrained model or model identifier from huggingface.co/models"     ,						)
										parser.add_argument(
										    "--config_name"     ,						default=""     ,						type=_lowerCamelCase     ,						help="Pretrained config name or path if not the same as model_name"					)
										parser.add_argument(
										    "--tokenizer_name"     ,						default=_lowerCamelCase     ,						type=_lowerCamelCase     ,						help="Pretrained tokenizer name or path if not the same as model_name"     ,						)
										parser.add_argument(
										    "--cache_dir"     ,						default=""     ,						type=_lowerCamelCase     ,						help="Where do you want to store the pre-trained models downloaded from huggingface.co"     ,						)
										parser.add_argument(
										    "--encoder_layerdrop"     ,						type=_lowerCamelCase     ,						help="Encoder layer dropout probability (Optional). Goes into model.config"     ,						)
										parser.add_argument(
										    "--decoder_layerdrop"     ,						type=_lowerCamelCase     ,						help="Decoder layer dropout probability (Optional). Goes into model.config"     ,						)
										parser.add_argument(
										    "--dropout"     ,						type=_lowerCamelCase     ,						help="Dropout probability (Optional). Goes into model.config"     ,						)
										parser.add_argument(
										    "--attention_dropout"     ,						type=_lowerCamelCase     ,						help="Attention dropout probability (Optional). Goes into model.config"     ,						)
										parser.add_argument("--learning_rate"     ,						default=5e-5     ,						type=_lowerCamelCase     ,						help="The initial learning rate for Adam."					)
										parser.add_argument(
										    "--lr_scheduler"     ,						default="linear"     ,						choices=_lowerCamelCase     ,						metavar=_lowerCamelCase     ,						type=_lowerCamelCase     ,						help="Learning rate scheduler"     ,						)
										parser.add_argument("--weight_decay"     ,						default=0.0     ,						type=_lowerCamelCase     ,						help="Weight decay if we apply some."					)
										parser.add_argument("--adam_epsilon"     ,						default=1e-8     ,						type=_lowerCamelCase     ,						help="Epsilon for Adam optimizer."					)
										parser.add_argument("--warmup_steps"     ,						default=0     ,						type=_lowerCamelCase     ,						help="Linear warmup over warmup_steps."					)
										parser.add_argument("--num_workers"     ,						default=4     ,						type=_lowerCamelCase     ,						help="kwarg passed to DataLoader"					)
										parser.add_argument("--num_train_epochs"     ,						dest="max_epochs"     ,						default=3     ,						type=_lowerCamelCase					)
										parser.add_argument("--train_batch_size"     ,						default=32     ,						type=_lowerCamelCase					)
										parser.add_argument("--eval_batch_size"     ,						default=32     ,						type=_lowerCamelCase					)
										parser.add_argument("--adafactor"     ,						action="store_true"					)





class 				__a    (   pl.Callback					):



							'''simple docstring'''







							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       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 				__a    (   pl.Callback					):



							'''simple docstring'''







							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Any:




										'''simple docstring'''
										__lowercase						      =					trainer.lr_schedulers[0]["scheduler"]
										__lowercase						      =					{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
										pl_module.logger.log_metrics(_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Union[str, Any]:




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





							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Any:




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





def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->None:
			#  To allow all pl args uncomment the following line
			#  parser = pl.Trainer.add_argparse_args(parser)
			parser.add_argument(
			    "--output_dir" ,      default=__magic_name__ ,      type=__magic_name__ ,      required=__magic_name__ ,      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=__magic_name__ ,      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=__magic_name__			)
			parser.add_argument("--max_grad_norm" ,      dest="gradient_clip_val" ,      default=1.0 ,      type=__magic_name__ ,      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=__magic_name__ ,      default=1 ,      help="Number of updates steps to accumulate before performing a backward/update pass." ,      )
			parser.add_argument("--seed" ,      type=__magic_name__ ,      default=4_2 ,      help="random seed for initialization"			)
			parser.add_argument(
			    "--data_dir" ,      default=__magic_name__ ,      type=__magic_name__ ,      required=__magic_name__ ,      help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." ,      )







def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__=None ,      __magic_name__=True ,      __magic_name__=None ,      __magic_name__=[] ,      __magic_name__=None ,      __magic_name__=None ,      **__magic_name__ ,      )	->Optional[int]:
			pl.seed_everything(args.seed			)

			# init model
			__lowercase						      =					Path(model.hparams.output_dir			)
			odir.mkdir(exist_ok=__magic_name__			)

			# add custom checkpoints
			if checkpoint_callback is None:
						__lowercase						      =					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(__magic_name__			)
			if logging_callback is None:
						__lowercase						      =					LoggingCallback()

			__lowercase						      =					{}

			# TODO: remove with PyTorch 1.6 since pl uses native amp
			if args.fpaa:
						__lowercase						      =					1_6
						# train_params["amp_level"] = args.fp16_opt_level

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

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

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

			if args.do_train:
						trainer.fit(__magic_name__			)

			return trainer







"""simple docstring"""






from scipy.constants import g



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->float:
			if mass < 0:
						# handling of negative values of mass
						raise ValueError("The mass of a body cannot be negative"			)
			if height < 0:
						# handling of negative values of height
						raise ValueError("The height above the ground cannot be negative"			)
			return mass * g * height


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

					testmod(name='''potential_energy''')





