



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


lowercase_						=       logging.getLogger(__name__)
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Any       						="""sequence-classification"""




				def __init__(  self   :  Tuple,   __lowercase   :  Optional[int]  ):
							if type(__lowercase  ) == dict:
										lowercase__   					=			Namespace(**__lowercase  )
							lowercase__   					=			glue_output_modes[hparams.task]
							lowercase__   					=			glue_tasks_num_labels[hparams.task]

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




				def 							A__ (  self   :  str,   **__lowercase   :  Any  ):
							return self.model(**__lowercase  )




				def 							A__ (  self   :  Optional[int],   __lowercase   :  str,   __lowercase   :  Any  ):
							lowercase__   					=			{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

							lowercase__   					=			self(**__lowercase  )
							lowercase__   					=			outputs[0]

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




				def 							A__ (  self   :  Union[str, Any]  ):
							lowercase__   					=			self.hparams
							lowercase__   					=			processors[args.task]()
							lowercase__   					=			processor.get_labels()

							for mode in ["train", "dev"]:
										lowercase__   					=			self._feature_file(__lowercase  )
										if os.path.exists(__lowercase  ) and not args.overwrite_cache:
													logger.info("Loading features from cached file %s",   __lowercase  )
										else:
													logger.info("Creating features from dataset file at %s",   args.data_dir  )
													lowercase__   					=			(
													    processor.get_dev_examples(args.data_dir  )
													    if mode == "dev"
													    else processor.get_train_examples(args.data_dir  )
													)
													lowercase__   					=			convert_examples_to_features(
													    __lowercase,   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",   __lowercase  )
													torch.save(__lowercase,   __lowercase  )




				def 							A__ (  self   :  Optional[Any],   __lowercase   :  str,   __lowercase   :  int,   __lowercase   :  bool = False  ):
							lowercase__   					=			"dev" if mode == "test" else mode

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

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




				def 							A__ (  self   :  str,   __lowercase   :  Tuple,   __lowercase   :  Any  ):
							lowercase__   					=			{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

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

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




				def 							A__ (  self   :  Union[str, Any],   __lowercase   :  List[Any]  ):
							lowercase__   					=			torch.stack([x["val_loss"] for x in outputs]  ).mean().detach().cpu().item()
							lowercase__   					=			np.concatenate([x["pred"] for x in outputs],   axis=0  )

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

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

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

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




				def 							A__ (  self   :  List[Any],   __lowercase   :  list  ):
							lowercase__					,    lowercase__					,    lowercase__   					=			self._eval_end(__lowercase  )
							lowercase__   					=			ret["log"]
							return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




				def 							A__ (  self   :  Tuple,   __lowercase   :  str  ):
							lowercase__					,    lowercase__					,    lowercase__   					=			self._eval_end(__lowercase  )
							lowercase__   					=			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 							A__ (  __lowercase   :  int,   __lowercase   :  Dict  ):
							BaseTransformer.add_model_specific_args(__lowercase,   __lowercase  )
							parser.add_argument(
							    "--max_seq_length",   default=128,   type=__lowercase,   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=__lowercase,   required=__lowercase,   help="The GLUE task to run",   )
							parser.add_argument(
							    "--gpus",   default=0,   type=__lowercase,   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 __lowerCAmelCase	(       ):
			lowercase__   					=			argparse.ArgumentParser()
			add_generic_args(SCREAMING_SNAKE_CASE_  ,					os.getcwd()					)
			lowercase__   					=			GLUETransformer.add_model_specific_args(SCREAMING_SNAKE_CASE_  ,					os.getcwd()					)
			lowercase__   					=			parser.parse_args()

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

			lowercase__   					=			GLUETransformer(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			generic_train(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

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


if __name__ == "__main__":
							main()









from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
lowercase_						=       2_9979_2458

# Symbols
lowercase_			,      lowercase_			,      lowercase_			,      lowercase_						=       symbols("""ct x y z""")



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if velocity > c:
						raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!"					)
			elif velocity < 1:
						# Usually the speed should be much higher than 1 (c order of magnitude)
						raise ValueError("Speed must be greater than or equal to 1!"					)

			return velocity / c



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE_					) ** 2					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return np.array(
			    [
			        [gamma(SCREAMING_SNAKE_CASE_					), -gamma(SCREAMING_SNAKE_CASE_					) * beta(SCREAMING_SNAKE_CASE_					), 0, 0],
			        [-gamma(SCREAMING_SNAKE_CASE_					) * beta(SCREAMING_SNAKE_CASE_					), gamma(SCREAMING_SNAKE_CASE_					), 0, 0],
			        [0, 0, 1, 0],
			        [0, 0, 0, 1],
			    ]					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_ = None					):
			# Ensure event is not empty
			if event is None:
						lowercase__   					=			np.array([ct, x, y, z]					)  # Symbolic four vector
			else:
						event[0] *= c  # x0 is ct (speed of light * time)

			return transformation_matrix(SCREAMING_SNAKE_CASE_					) @ event


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Example of symbolic vector:
							lowercase_						=       transform(2997_9245)
							print("""Example of four vector: """)
							print(F'ct\' = {four_vector[0]}')
							print(F'x\' = {four_vector[1]}')
							print(F'y\' = {four_vector[2]}')
							print(F'z\' = {four_vector[3]}')

							# Substitute symbols with numerical values
							lowercase_						=       {ct: c, x: 1, y: 1, z: 1}
							lowercase_						=       [four_vector[i].subs(sub_dict) for i in range(4)]

							print(F'\n{numerical_vector}')




