






import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
				import torch
				from torch import nn

				from transformers import (
				    Adafactor,
				    AdamW,
				    get_constant_schedule,
				    get_constant_schedule_with_warmup,
				    get_cosine_schedule_with_warmup,
				    get_cosine_with_hard_restarts_schedule_with_warmup,
				    get_inverse_sqrt_schedule,
				    get_linear_schedule_with_warmup,
				    get_polynomial_decay_schedule_with_warmup,
				)




def      __A					(     _lowercase					,			_lowercase=10 ):

					'''simple docstring'''







					_A								= []
					for _ in range(_lowercase ):
										lrs.append(scheduler.get_lr()[0] )
										scheduler.step()
					return lrs




def      __A					(     _lowercase					,			_lowercase=10 ):

					'''simple docstring'''







					_A								= []
					for step in range(_lowercase ):
										lrs.append(scheduler.get_lr()[0] )
										scheduler.step()
										if step == num_steps // 2:
															with tempfile.TemporaryDirectory() as tmpdirname:
																				_A								= os.path.join(_lowercase					,			'''schedule.bin''' )
																				torch.save(scheduler.state_dict()					,			_lowercase )

																				_A								= torch.load(_lowercase )
																				scheduler.load_state_dict(_lowercase )
					return lrs





@require_torch
class 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							def       __A (  self:   str		,     __A:   Optional[int]		,     __A:   Optional[int]		,     __A:   Optional[Any]   )    ->				Optional[int]:
												self.assertEqual(len(__A   )		,     len(__A   )   )
												for a, b in zip(__A		,     __A   ):
																	self.assertAlmostEqual(__A		,     __A		,     delta=__A   )

							def       __A (  self:   Union[str, Any]   )    ->				Union[str, Any]:
												_A								= torch.tensor([0.1, -0.2, -0.1]		,     requires_grad=__A   )
												_A								= torch.tensor([0.4, 0.2, -0.5]   )
												_A								= nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												_A								= AdamW(params=[w]		,     lr=2e-1		,     weight_decay=0.0   )
												for _ in range(1_00   ):
																	_A								= criterion(__A		,     __A   )
																	loss.backward()
																	optimizer.step()
																	w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																	w.grad.zero_()
												self.assertListAlmostEqual(w.tolist()		,     [0.4, 0.2, -0.5]		,     tol=1e-2   )



							def       __A (  self:   Optional[Any]   )    ->				List[Any]:
												_A								= torch.tensor([0.1, -0.2, -0.1]		,     requires_grad=__A   )
												_A								= torch.tensor([0.4, 0.2, -0.5]   )
												_A								= nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												_A								= Adafactor(
												    params=[w]		,     lr=1e-2		,     eps=(1e-30, 1e-3)		,     clip_threshold=1.0		,     decay_rate=-0.8		,     betaa=__A		,     weight_decay=0.0		,     relative_step=__A		,     scale_parameter=__A		,     warmup_init=__A		,     )
												for _ in range(10_00   ):
																	_A								= criterion(__A		,     __A   )
																	loss.backward()
																	optimizer.step()
																	w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																	w.grad.zero_()
												self.assertListAlmostEqual(w.tolist()		,     [0.4, 0.2, -0.5]		,     tol=1e-2   )





@require_torch
class 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							A_						  =					nn.Linear(50  ,	50					) if is_torch_available() else None
							A_						  =					AdamW(m.parameters()  ,	lr=10.0					) if is_torch_available() else None
							A_						  =					10

							def       __A (  self:   Optional[Any]		,     __A:   List[str]		,     __A:   Optional[Any]		,     __A:   Optional[int]		,     __A:   List[Any]=None   )    ->				List[Any]:
												self.assertEqual(len(__A   )		,     len(__A   )   )
												for a, b in zip(__A		,     __A   ):
																	self.assertAlmostEqual(__A		,     __A		,     delta=__A		,     msg=__A   )



							def       __A (  self:   Tuple   )    ->				List[Any]:
												_A								= {'''num_warmup_steps''': 2, '''num_training_steps''': 10}
												# schedulers doct format
												# function: (sched_args_dict, expected_learning_rates)
												_A								= {
												    get_constant_schedule: ({}, [10.0] * self.num_steps),
												    get_constant_schedule_with_warmup: (
												        {'''num_warmup_steps''': 4},
												        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
												    ),
												    get_linear_schedule_with_warmup: (
												        {**common_kwargs},
												        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
												    ),
												    get_cosine_schedule_with_warmup: (
												        {**common_kwargs},
												        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
												    ),
												    get_cosine_with_hard_restarts_schedule_with_warmup: (
												        {**common_kwargs, '''num_cycles''': 2},
												        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
												    ),
												    get_polynomial_decay_schedule_with_warmup: (
												        {**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
												        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
												    ),
												    get_inverse_sqrt_schedule: (
												        {'''num_warmup_steps''': 2},
												        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
												    ),
												}

												for scheduler_func, data in scheds.items():
																	_A							,_A								= data

																	_A								= scheduler_func(self.optimizer		,     **__A   )
																	self.assertEqual(len([scheduler.get_lr()[0]]   )		,     1   )
																	_A								= unwrap_schedule(__A		,     self.num_steps   )
																	self.assertListAlmostEqual(
																	    __A		,     __A		,     tol=1e-2		,     msg=f"""failed for {scheduler_func} in normal scheduler"""		,     )

																	_A								= scheduler_func(self.optimizer		,     **__A   )
																	if scheduler_func.__name__ != "get_constant_schedule":
																						LambdaScheduleWrapper.wrap_scheduler(__A   )  # wrap to test picklability of the schedule
																	_A								= unwrap_and_save_reload_schedule(__A		,     self.num_steps   )
																	self.assertListEqual(__A		,     __A		,     msg=f"""failed for {scheduler_func} in save and reload"""   )





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   Optional[int]		,     __A:   Tuple   )    ->				Optional[Any]:
												_A								= fn

							def __call__(  self:   int		,     *__A:   Tuple		,     **__A:   Optional[Any]   )    ->				Optional[int]:
												return self.fn(*__A		,     **__A   )



							@classmethod
							def       __A (  self:   Tuple		,     __A:   Dict   )    ->				List[str]:
												_A								= list(map(self		,     scheduler.lr_lambdas   )   )











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()



