
import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


SCREAMING_SNAKE_CASE				:List[Any]									=							logging.getLogger(__name__)





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	"sequence-classification"






	def __init__(			self   :					Tuple				,A   :					List[Any]					):
					if type(A					) == dict:
									__A       =					Namespace(**A					)
					__A       =					glue_output_modes[hparams.task]
					__A       =					glue_tasks_num_labels[hparams.task]

					super().__init__(A				,A				,self.mode					)






	def 							UpperCamelCase_    (			self   :					List[Any]				,**A   :					Optional[Any]					):
					return self.model(**A					)






	def 							UpperCamelCase_    (			self   :					Optional[Any]				,A   :					int				,A   :					Any					):
					__A       =					{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

					if self.config.model_type not in ["distilbert", "bart"]:
									__A       =					batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

					__A       =					self(**A					)
					__A       =					outputs[0]

					__A       =					self.trainer.lr_schedulers[0]["scheduler"]
					__A       =					{"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
					return {"loss": loss, "log": tensorboard_logs}






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					self.hparams
					__A       =					processors[args.task]()
					__A       =					processor.get_labels()

					for mode in ["train", "dev"]:
									__A       =					self._feature_file(A					)
									if os.path.exists(A					) and not args.overwrite_cache:
													logger.info("Loading features from cached file %s"				,A					)
									else:
													logger.info("Creating features from dataset file at %s"				,args.data_dir					)
													__A       =					(
													    processor.get_dev_examples(args.data_dir					)
													    if mode == "dev"
													    else processor.get_train_examples(args.data_dir					)
													)
													__A       =					convert_examples_to_features(
													    A				,self.tokenizer				,max_length=args.max_seq_length				,label_list=self.labels				,output_mode=args.glue_output_mode				,)
													logger.info("Saving features into cached file %s"				,A					)
													torch.save(A				,A					)






	def 							UpperCamelCase_    (			self   :					Union[str, Any]				,A   :					str				,A   :					int				,A   :					bool = False					):
					__A       =					"dev" if mode == "test" else mode

					__A       =					self._feature_file(A					)
					logger.info("Loading features from cached file %s"				,A					)
					__A       =					torch.load(A					)
					__A       =					torch.tensor([f.input_ids for f in features]				,dtype=torch.long					)
					__A       =					torch.tensor([f.attention_mask for f in features]				,dtype=torch.long					)
					__A       =					torch.tensor([f.token_type_ids for f in features]				,dtype=torch.long					)
					if self.hparams.glue_output_mode == "classification":
									__A       =					torch.tensor([f.label for f in features]				,dtype=torch.long					)
					elif self.hparams.glue_output_mode == "regression":
									__A       =					torch.tensor([f.label for f in features]				,dtype=torch.float					)

					return DataLoader(
					    TensorDataset(A				,A				,A				,A					)				,batch_size=A				,shuffle=A				,)






	def 							UpperCamelCase_    (			self   :					Optional[Any]				,A   :					Optional[Any]				,A   :					int					):
					__A       =					{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

					if self.config.model_type not in ["distilbert", "bart"]:
									__A       =					batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

					__A       =					self(**A					)
					__A     ,				__A       =					outputs[:2]
					__A       =					logits.detach().cpu().numpy()
					__A       =					inputs["labels"].detach().cpu().numpy()

					return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






	def 							UpperCamelCase_    (			self   :					Optional[Any]				,A   :					int					):
					__A       =					torch.stack([x["val_loss"] for x in outputs]					).mean().detach().cpu().item()
					__A       =					np.concatenate([x["pred"] for x in outputs]				,axis=0					)

					if self.hparams.glue_output_mode == "classification":
									__A       =					np.argmax(A				,axis=1					)
					elif self.hparams.glue_output_mode == "regression":
									__A       =					np.squeeze(A					)

					__A       =					np.concatenate([x["target"] for x in outputs]				,axis=0					)
					__A       =					[[] for _ in range(out_label_ids.shape[0]					)]
					__A       =					[[] for _ in range(out_label_ids.shape[0]					)]

					__A       =					{**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task				,A				,A					)}

					__A       =					dict(results.items()					)
					__A       =					results
					return ret, preds_list, out_label_list






	def 							UpperCamelCase_    (			self   :					Any				,A   :					list					):
					__A     ,				__A     ,				__A       =					self._eval_end(A					)
					__A       =					ret["log"]
					return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






	def 							UpperCamelCase_    (			self   :					List[str]				,A   :					Union[str, Any]					):
					__A     ,				__A     ,				__A       =					self._eval_end(A					)
					__A       =					ret["log"]
					# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
					return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







	@staticmethod
	def 							UpperCamelCase_    (			A   :					Any				,A   :					List[Any]					):
					BaseTransformer.add_model_specific_args(A				,A					)
					parser.add_argument(
					    "--max_seq_length"				,default=1_28				,type=A				,help=(
					        "The maximum total input sequence length after tokenization. Sequences longer "
					        "than this will be truncated, sequences shorter will be padded."
					    )				,)

					parser.add_argument(
					    "--task"				,default=""				,type=A				,required=A				,help="The GLUE task to run"				,)
					parser.add_argument(
					    "--gpus"				,default=0				,type=A				,help="The number of GPUs allocated for this, it is by default 0 meaning none"				,)

					parser.add_argument(
					    "--overwrite_cache"				,action="store_true"				,help="Overwrite the cached training and evaluation sets"					)

					return parser





def 		UpperCAmelCase     (      )						->      Any:







				"""simple docstring"""



				__A       =					argparse.ArgumentParser()
				add_generic_args(a_						,  os.getcwd()		)
				__A       =					GLUETransformer.add_model_specific_args(a_						,  os.getcwd()		)
				__A       =					parser.parse_args()

				# If output_dir not provided, a folder will be generated in pwd
				if args.output_dir is None:
								__A       =					os.path.join(
								    "./results"						,  F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S'		)}'''						,  )
								os.makedirs(args.output_dir		)

				__A       =					GLUETransformer(a_		)
				__A       =					generic_train(a_						,  a_		)

				# Optionally, predict on dev set and write to output_dir
				if args.do_predict:
								__A       =					sorted(glob.glob(os.path.join(args.output_dir						,  "checkpoint-epoch=*.ckpt"		)						,  recursive=a_		)		)
								__A       =					model.load_from_checkpoint(checkpoints[-1]		)
								return trainer.test(a_		)


if __name__ == "__main__":
					main()




from __future__ import annotations

import math


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[Any]				     =						u
		for i in range(1     , a ):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						temp * (u - i)
		return temp


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						int(input("enter the numbers of values: " ) )
		SCREAMING_SNAKE_CASE_				:list[list[float]]				     =						[]
		for _ in range(a ):
				y.append([] )
		for i in range(a ):
				for j in range(a ):
						y[i].append(a )
						SCREAMING_SNAKE_CASE_				:Any				     =						0

		print("enter the values of parameters in a list: " )
		SCREAMING_SNAKE_CASE_				:Dict				     =						list(map(a     , input().split() ) )

		print("enter the values of corresponding parameters: " )
		for i in range(a ):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						float(input() )

		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						int(input("enter the value to interpolate: " ) )
		SCREAMING_SNAKE_CASE_				:str				     =						(value - x[0]) / (x[1] - x[0])

		# for calculating forward difference table

		for i in range(1     , a ):
				for j in range(n - i ):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						y[j + 1][i - 1] - y[j][i - 1]

		SCREAMING_SNAKE_CASE_				:Tuple				     =						y[0][0]
		for i in range(1     , a ):
				summ += (ucal(a     , a ) * y[0][i]) / math.factorial(a )

		print(F"the value at {value} is {summ}" )


if __name__ == "__main__":
		main()






