






from __future__ import annotations

from typing import Any





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   Any		,     __A:   int		,     __A:   int		,     __A:   float = 0   )    ->				None:
												_A							,_A								= row, column
												_A								= [[default_value for c in range(__A   )] for r in range(__A   )]

							def __str__(  self:   int   )    ->				str:
												_A								= f"""Matrix consist of {self.row} rows and {self.column} columns\n"""

												# Make string identifier
												_A								= 0
												for row_vector in self.array:
																	for obj in row_vector:
																						_A								= max(__A		,     len(str(__A   )   )   )
												_A								= f"""%{max_element_length}s"""

												# Make string and return
												def single_line(__A:   list[float]   ) -> str:
																	nonlocal string_format_identifier
																	_A								= '''['''
																	line += ", ".join(string_format_identifier % (obj,) for obj in row_vector   )
																	line += "]"
																	return line

												s += "\n".join(single_line(__A   ) for row_vector in self.array   )
												return s

							def __repr__(  self:   List[str]   )    ->				str:
												return str(self   )

							def       __A (  self:   str		,     __A:   tuple[int, int]   )    ->				bool:
												if not (isinstance(__A		,     (list, tuple)   ) and len(__A   ) == 2):
																	return False
												elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
																	return False
												else:
																	return True

							def __getitem__(  self:   int		,     __A:   tuple[int, int]   )    ->				Any:
												assert self.validate_indicies(__A   )
												return self.array[loc[0]][loc[1]]

							def __setitem__(  self:   Any		,     __A:   tuple[int, int]		,     __A:   float   )    ->				None:
												assert self.validate_indicies(__A   )
												_A								= value

							def __add__(  self:   int		,     __A:   Matrix   )    ->				Matrix:
												assert isinstance(__A		,     __A   )
												assert self.row == another.row and self.column == another.column

												# Add
												_A								= Matrix(self.row		,     self.column   )
												for r in range(self.row   ):
																	for c in range(self.column   ):
																						_A								= self[r, c] + another[r, c]
												return result

							def __neg__(  self:   Dict   )    ->				Matrix:
												_A								= Matrix(self.row		,     self.column   )
												for r in range(self.row   ):
																	for c in range(self.column   ):
																						_A								= -self[r, c]
												return result

							def __sub__(  self:   List[Any]		,     __A:   Matrix   )    ->				Matrix:
												return self + (-another)

							def __mul__(  self:   List[str]		,     __A:   int | float | Matrix   )    ->				Matrix:

												if isinstance(__A		,     (int, float)   ):  # Scalar multiplication
																	_A								= Matrix(self.row		,     self.column   )
																	for r in range(self.row   ):
																						for c in range(self.column   ):
																											_A								= self[r, c] * another
																	return result
												elif isinstance(__A		,     __A   ):  # Matrix multiplication
																	assert self.column == another.row
																	_A								= Matrix(self.row		,     another.column   )
																	for r in range(self.row   ):
																						for c in range(another.column   ):
																											for i in range(self.column   ):
																																result[r, c] += self[r, i] * another[i, c]
																	return result
												else:
																	_A								= f"""Unsupported type given for another ({type(__A   )})"""
																	raise TypeError(__A   )

							def       __A (  self:   int   )    ->				Matrix:
												_A								= Matrix(self.column		,     self.row   )
												for r in range(self.row   ):
																	for c in range(self.column   ):
																						_A								= self[r, c]
												return result



							def       __A (  self:   List[str]		,     __A:   Matrix		,     __A:   Matrix   )    ->				Any:
												assert isinstance(__A		,     __A   ) and isinstance(__A		,     __A   )
												assert self.row == self.column == u.row == v.row  # u, v should be column vector
												assert u.column == v.column == 1  # u, v should be column vector

												# Calculate
												_A								= v.transpose()
												_A								= (v_t * self * u)[0, 0] + 1
												if numerator_factor == 0:
																	return None  # It's not invertable
												return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":




				def      __A					(     ):

									'''simple docstring'''







									_A								= Matrix(3					,			3					,			0 )
									for i in range(3 ):
														_A								= 1
									print(f"""a^(-1) is {ainv}""" )
									# u, v
									_A								= Matrix(3					,			1					,			0 )
									_A							,_A							,_A								= 1, 2, -3
									_A								= Matrix(3					,			1					,			0 )
									_A							,_A							,_A								= 4, -2, 5
									print(f"""u is {u}""" )
									print(f"""v is {v}""" )
									print(f"""uv^T is {u * v.transpose()}""" )
									# Sherman Morrison
									print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(_lowercase					,			_lowercase )}""" )




				def      __A					(     ):

									'''simple docstring'''







									import doctest

									doctest.testmod()


				testa()











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


__A      =				logging.getLogger(__name__)





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"sequence-classification"

							def __init__(  self:   str		,     __A:   Union[str, Any]   )    ->				List[str]:
												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       __A (  self:   Optional[Any]		,     **__A:   Union[str, Any]   )    ->				Optional[int]:
												return self.model(**__A   )

							def       __A (  self:   Any		,     __A:   Union[str, Any]		,     __A:   int   )    ->				Optional[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       __A (  self:   List[str]   )    ->				Dict:
												_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       __A (  self:   List[str]		,     __A:   str		,     __A:   int		,     __A:   bool = False   )    ->				DataLoader:
												_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       __A (  self:   List[str]		,     __A:   str		,     __A:   Tuple   )    ->				str:
												_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       __A (  self:   str		,     __A:   Dict   )    ->				tuple:
												_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       __A (  self:   Any		,     __A:   list   )    ->				dict:
												_A							,_A							,_A								= self._eval_end(__A   )
												_A								= ret['''log''']
												return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

							def       __A (  self:   int		,     __A:   Union[str, Any]   )    ->				dict:
												_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       __A (  __A:   Optional[Any]		,     __A:   Optional[Any]   )    ->				Optional[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      __A					(     ):

					'''simple docstring'''







					_A								= argparse.ArgumentParser()
					add_generic_args(_lowercase					,			os.getcwd() )
					_A								= GLUETransformer.add_model_specific_args(_lowercase					,			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(_lowercase )
					_A								= generic_train(_lowercase					,			_lowercase )

					# 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=_lowercase ) )
										_A								= model.load_from_checkpoint(checkpoints[-1] )
										return trainer.test(_lowercase )


if __name__ == "__main__":
				main()



