"""simple docstring"""







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									=						logging.getLogger(__name__)



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  "sequence-classification"







				def __init__( self    :       Optional[int]			,					lowerCamelCase_    :       List[Any]						):

					"""simple docstring"""






					if type(lowerCamelCase_						) == dict:
						_lowercase :    Optional[int]							=							Namespace(**lowerCamelCase_						)
					_lowercase :    Dict							=							glue_output_modes[hparams.task]
					_lowercase :    List[str]							=							glue_tasks_num_labels[hparams.task]

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







				def 							__UpperCAmelCase					( self    :       Dict			,					**lowerCamelCase_    :       Union[str, Any]						):

					"""simple docstring"""






					return self.model(**lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       List[str]						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

					_lowercase :    int							=							self(**lowerCamelCase_						)
					_lowercase :    Any							=							outputs[0]

					_lowercase :    Union[str, Any]							=							self.trainer.lr_schedulers[0]['scheduler']
					_lowercase :    Optional[Any]							=							{'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
					return {"loss": loss, "log": tensorboard_logs}







				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					_lowercase :    List[Any]							=							self.hparams
					_lowercase :    List[str]							=							processors[args.task]()
					_lowercase :    Any							=							processor.get_labels()

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







				def 							__UpperCAmelCase					( self    :       List[Any]			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       bool = False						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							'dev' if mode == 'test' else mode

					_lowercase :    int							=							self._feature_file(lowerCamelCase_						)
					logger.info('Loading features from cached file %s'			,					lowerCamelCase_						)
					_lowercase :    Tuple							=							torch.load(lowerCamelCase_						)
					_lowercase :    str							=							torch.tensor([f.input_ids for f in features]			,					dtype=torch.long						)
					_lowercase :    str							=							torch.tensor([f.attention_mask for f in features]			,					dtype=torch.long						)
					_lowercase :    Any							=							torch.tensor([f.token_type_ids for f in features]			,					dtype=torch.long						)
					if self.hparams.glue_output_mode == "classification":
						_lowercase :    List[Any]							=							torch.tensor([f.label for f in features]			,					dtype=torch.long						)
					elif self.hparams.glue_output_mode == "regression":
						_lowercase :    Tuple							=							torch.tensor([f.label for f in features]			,					dtype=torch.float						)

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







				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       Dict						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

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

					_lowercase :    Optional[int]							=							self(**lowerCamelCase_						)
					_lowercase				,      _lowercase :    str							=							outputs[:2]
					_lowercase :    Dict							=							logits.detach().cpu().numpy()
					_lowercase :    List[Any]							=							inputs['labels'].detach().cpu().numpy()

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







				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       Any						):

					"""simple docstring"""






					_lowercase :    List[str]							=							torch.stack([x['val_loss'] for x in outputs]						).mean().detach().cpu().item()
					_lowercase :    List[str]							=							np.concatenate([x['pred'] for x in outputs]			,					axis=0						)

					if self.hparams.glue_output_mode == "classification":
						_lowercase :    List[Any]							=							np.argmax(lowerCamelCase_			,					axis=1						)
					elif self.hparams.glue_output_mode == "regression":
						_lowercase :    Optional[Any]							=							np.squeeze(lowerCamelCase_						)

					_lowercase :    List[str]							=							np.concatenate([x['target'] for x in outputs]			,					axis=0						)
					_lowercase :    Dict							=							[[] for _ in range(out_label_ids.shape[0]						)]
					_lowercase :    Union[str, Any]							=							[[] for _ in range(out_label_ids.shape[0]						)]

					_lowercase :    Optional[int]							=							{**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task			,					lowerCamelCase_			,					lowerCamelCase_						)}

					_lowercase :    Any							=							dict(results.items()						)
					_lowercase :    List[str]							=							results
					return ret, preds_list, out_label_list







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       list						):

					"""simple docstring"""






					_lowercase				,      _lowercase				,      _lowercase :    str							=							self._eval_end(lowerCamelCase_						)
					_lowercase :    List[str]							=							ret['log']
					return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       Dict						):

					"""simple docstring"""






					_lowercase				,      _lowercase				,      _lowercase :    Optional[int]							=							self._eval_end(lowerCamelCase_						)
					_lowercase :    List[str]							=							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					( lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       Any						):

					"""simple docstring"""






					BaseTransformer.add_model_specific_args(lowerCamelCase_			,					lowerCamelCase_						)
					parser.add_argument(
					    '--max_seq_length'			,					default=1_2_8			,					type=lowerCamelCase_			,					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=lowerCamelCase_			,					required=lowerCamelCase_			,					help='The GLUE task to run'			,					)
					parser.add_argument(
					    '--gpus'			,					default=0			,					type=lowerCamelCase_			,					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(		):






	"""simple docstring"""





	_lowercase :    Optional[int]							=							argparse.ArgumentParser()
	add_generic_args(__UpperCAmelCase     ,os.getcwd()			)
	_lowercase :    List[str]							=							GLUETransformer.add_model_specific_args(__UpperCAmelCase     ,os.getcwd()			)
	_lowercase :    Dict							=							parser.parse_args()

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

	_lowercase :    List[Any]							=							GLUETransformer(__UpperCAmelCase			)
	_lowercase :    List[Any]							=							generic_train(__UpperCAmelCase     ,__UpperCAmelCase			)

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


if __name__ == "__main__":
						main()




"""simple docstring"""







from __future__ import annotations



class  _lowerCamelCase			:







				def __init__( self    :       Any			,					lowerCamelCase_    :       list[list[int]]						):

					"""simple docstring"""






					_lowercase :    Tuple							=							TypeError(
					    'Matrices must be formed from a list of zero or more lists containing at '
					    'least one and the same number of values, each of which must be of type '
					    'int or float.'						)
					if len(lowerCamelCase_						) != 0:
						_lowercase :    Dict							=							len(rows[0]						)
						if cols == 0:
							raise error
						for row in rows:
							if len(lowerCamelCase_						) != cols:
								raise error
							for value in row:
								if not isinstance(lowerCamelCase_			,					(int, float)						):
									raise error
						_lowercase :    int							=							rows
					else:
						_lowercase :    Optional[Any]							=							[]







				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					return [[row[i] for row in self.rows] for i in range(len(self.rows[0]						)						)]







				@property
				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					return len(self.rows						)







				@property
				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					return len(self.rows[0]						)







				@property
				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					return (self.num_rows, self.num_columns)







				@property
				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					return self.order[0] == self.order[1]







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							[
					    [0 if column_num != row_num else 1 for column_num in range(self.num_rows						)]
					    for row_num in range(self.num_rows						)
					]
					return Matrix(lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					if not self.is_square:
						return 0
					if self.order == (0, 0):
						return 1
					if self.order == (1, 1):
						return int(self.rows[0][0]						)
					if self.order == (2, 2):
						return int(
						    (self.rows[0][0] * self.rows[1][1])
						    - (self.rows[0][1] * self.rows[1][0])						)
					else:
						return sum(
						    self.rows[0][column] * self.cofactors().rows[0][column]
						    for column in range(self.num_columns						)						)







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

					"""simple docstring"""






					return bool(self.determinant()						)







				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       int						):

					"""simple docstring"""






					_lowercase :    List[str]							=							[
					    [
					        self.rows[other_row][other_column]
					        for other_column in range(self.num_columns						)
					        if other_column != column
					    ]
					    for other_row in range(self.num_rows						)
					    if other_row != row
					]
					return Matrix(lowerCamelCase_						).determinant()







				def 							__UpperCAmelCase					( self    :       str			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       int						):

					"""simple docstring"""






					if (row + column) % 2 == 0:
						return self.get_minor(lowerCamelCase_			,					lowerCamelCase_						)
					return -1 * self.get_minor(lowerCamelCase_			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					return Matrix(
					    [
					        [self.get_minor(lowerCamelCase_			,					lowerCamelCase_						) for column in range(self.num_columns						)]
					        for row in range(self.num_rows						)
					    ]						)







				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					return Matrix(
					    [
					        [
					            self.minors().rows[row][column]
					            if (row + column) % 2 == 0
					            else self.minors().rows[row][column] * -1
					            for column in range(self.minors().num_columns						)
					        ]
					        for row in range(self.minors().num_rows						)
					    ]						)







				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					_lowercase :    str							=							[
					    [self.cofactors().rows[column][row] for column in range(self.num_columns						)]
					    for row in range(self.num_rows						)
					]
					return Matrix(lowerCamelCase_						)







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

					"""simple docstring"""






					_lowercase :    Dict							=							self.determinant()
					if not determinant:
						raise TypeError('Only matrices with a non-zero determinant have an inverse'						)
					return self.adjugate() * (1 / determinant)







				def __repr__( self    :       List[str]						):

					"""simple docstring"""






					return str(self.rows						)







				def __str__( self    :       str						):

					"""simple docstring"""






					if self.num_rows == 0:
						return "[]"
					if self.num_rows == 1:
						return "[[" + ". ".join(str(self.rows[0]						)						) + "]]"
					return (
					    "["
					    + "\n ".join(
					        [
					            '[' + '. '.join([str(lowerCamelCase_						) for value in row]						) + '.]'
					            for row in self.rows
					        ]						)
					    + "]"
					)







				def 							__UpperCAmelCase					( self    :       Union[str, Any]			,					lowerCamelCase_    :       list[int]			,					lowerCamelCase_    :       int | None = None						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							TypeError('Row must be a list containing all ints and/or floats'						)
					if not isinstance(lowerCamelCase_			,					lowerCamelCase_						):
						raise type_error
					for value in row:
						if not isinstance(lowerCamelCase_			,					(int, float)						):
							raise type_error
					if len(lowerCamelCase_						) != self.num_columns:
						raise ValueError(
						    'Row must be equal in length to the other rows in the matrix'						)
					if position is None:
						self.rows.append(lowerCamelCase_						)
					else:
						_lowercase :    Any							=							self.rows[0:position] + [row] + self.rows[position:]







				def 							__UpperCAmelCase					( self    :       str			,					lowerCamelCase_    :       list[int]			,					lowerCamelCase_    :       int | None = None						):

					"""simple docstring"""






					_lowercase :    Tuple							=							TypeError(
					    'Column must be a list containing all ints and/or floats'						)
					if not isinstance(lowerCamelCase_			,					lowerCamelCase_						):
						raise type_error
					for value in column:
						if not isinstance(lowerCamelCase_			,					(int, float)						):
							raise type_error
					if len(lowerCamelCase_						) != self.num_rows:
						raise ValueError(
						    'Column must be equal in length to the other columns in the matrix'						)
					if position is None:
						_lowercase :    List[Any]							=							[self.rows[i] + [column[i]] for i in range(self.num_rows						)]
					else:
						_lowercase :    Any							=							[
						    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
						    for i in range(self.num_rows						)
						]







				def __eq__( self    :       Union[str, Any]			,					lowerCamelCase_    :       object						):

					"""simple docstring"""






					if not isinstance(lowerCamelCase_			,					lowerCamelCase_						):
						return NotImplemented
					return self.rows == other.rows







				def __ne__( self    :       Any			,					lowerCamelCase_    :       object						):

					"""simple docstring"""






					return not self == other







				def __neg__( self    :       int						):

					"""simple docstring"""






					return self * -1







				def __add__( self    :       Optional[int]			,					lowerCamelCase_    :       Matrix						):

					"""simple docstring"""






					if self.order != other.order:
						raise ValueError('Addition requires matrices of the same order'						)
					return Matrix(
					    [
					        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns						)]
					        for i in range(self.num_rows						)
					    ]						)







				def __sub__( self    :       str			,					lowerCamelCase_    :       Matrix						):

					"""simple docstring"""






					if self.order != other.order:
						raise ValueError('Subtraction requires matrices of the same order'						)
					return Matrix(
					    [
					        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns						)]
					        for i in range(self.num_rows						)
					    ]						)







				def __mul__( self    :       Optional[Any]			,					lowerCamelCase_    :       Matrix | int | float						):

					"""simple docstring"""






					if isinstance(lowerCamelCase_			,					(int, float)						):
						return Matrix(
						    [[int(element * other						) for element in row] for row in self.rows]						)
					elif isinstance(lowerCamelCase_			,					lowerCamelCase_						):
						if self.num_columns != other.num_rows:
							raise ValueError(
							    'The number of columns in the first matrix must '
							    'be equal to the number of rows in the second'						)
						return Matrix(
						    [
						        [Matrix.dot_product(lowerCamelCase_			,					lowerCamelCase_						) for column in other.columns()]
						        for row in self.rows
						    ]						)
					else:
						raise TypeError(
						    'A Matrix can only be multiplied by an int, float, or another matrix'						)







				def __pow__( self    :       Optional[Any]			,					lowerCamelCase_    :       int						):

					"""simple docstring"""






					if not isinstance(lowerCamelCase_			,					lowerCamelCase_						):
						raise TypeError('A Matrix can only be raised to the power of an int'						)
					if not self.is_square:
						raise ValueError('Only square matrices can be raised to a power'						)
					if other == 0:
						return self.identity()
					if other < 0:
						if self.is_invertable():
							return self.inverse() ** (-other)
						raise ValueError(
						    'Only invertable matrices can be raised to a negative power'						)
					_lowercase :    str							=							self
					for _ in range(other - 1						):
						result *= self
					return result







				@classmethod
				def 							__UpperCAmelCase					( cls    :       str			,					lowerCamelCase_    :       list[int]			,					lowerCamelCase_    :       list[int]						):

					"""simple docstring"""






					return sum(row[i] * column[i] for i in range(len(lowerCamelCase_						)						)						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



