






import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


__A      =				'sshleifer/mar_enro_6_3_student'





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							def       __A (  self:   Tuple   )    ->				Dict:
												super().setUp()

												_A								= cached_path(
												    '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''		,     extract_compressed_file=__A		,     )
												_A								= f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""

							@slow
							@require_torch_gpu
							def       __A (  self:   Any   )    ->				List[Any]:
												MarianMTModel.from_pretrained(__A   )



							@slow
							@require_torch_gpu
							def       __A (  self:   Tuple   )    ->				Optional[Any]:
												_A								= {
												    '''$MAX_LEN''': 64,
												    '''$BS''': 64,
												    '''$GAS''': 1,
												    '''$ENRO_DIR''': self.data_dir,
												    '''facebook/mbart-large-cc25''': MARIAN_MODEL,
												    # "val_check_interval=0.25": "val_check_interval=1.0",
												    '''--learning_rate=3e-5''': '''--learning_rate 3e-4''',
												    '''--num_train_epochs 6''': '''--num_train_epochs 1''',
												}

												# Clean up bash script
												_A								= (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''   )[1].strip()
												_A								= bash_script.replace('''\\\n'''		,     ''''''   ).strip().replace('''"$@"'''		,     ''''''   )
												for k, v in env_vars_to_replace.items():
																	_A								= bash_script.replace(__A		,     str(__A   )   )
												_A								= self.get_auto_remove_tmp_dir()

												# bash_script = bash_script.replace("--fp16 ", "")
												_A								= f"""
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        """.split()
												# XXX: args.gpus > 1 : handle multi_gpu in the future

												_A								= ['''finetune.py'''] + bash_script.split() + args
												with patch.object(__A		,     '''argv'''		,     __A   ):
																	_A								= argparse.ArgumentParser()
																	_A								= pl.Trainer.add_argparse_args(__A   )
																	_A								= SummarizationModule.add_model_specific_args(__A		,     os.getcwd()   )
																	_A								= parser.parse_args()
																	_A								= main(__A   )

												# Check metrics
												_A								= load_json(model.metrics_save_path   )
												_A								= metrics['''val'''][0]
												_A								= metrics['''val'''][-1]
												self.assertEqual(len(metrics['''val''']   )		,     (args.max_epochs / args.val_check_interval)   )
												assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]		,     __A   )

												self.assertGreater(last_step_stats['''val_avg_gen_time''']		,     0.01   )
												# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
												self.assertLessEqual(last_step_stats['''val_avg_gen_time''']		,     1.0   )

												# test learning requirements:

												# 1. BLEU improves over the course of training by more than 2 pts
												self.assertGreater(last_step_stats['''val_avg_bleu'''] - first_step_stats['''val_avg_bleu''']		,     2   )

												# 2. BLEU finishes above 17
												self.assertGreater(last_step_stats['''val_avg_bleu''']		,     17   )

												# 3. test BLEU and val BLEU within ~1.1 pt.
												self.assertLess(abs(metrics['''val'''][-1]['''val_avg_bleu'''] - metrics['''test'''][-1]['''test_avg_bleu''']   )		,     1.1   )

												# check lightning ckpt can be loaded and has a reasonable statedict
												_A								= os.listdir(__A   )
												_A								= [x for x in contents if x.endswith('''.ckpt'''   )][0]
												_A								= os.path.join(args.output_dir		,     __A   )
												_A								= torch.load(__A		,     map_location='''cpu'''   )
												_A								= '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
												assert expected_key in ckpt["state_dict"]
												assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

												# TODO: turn on args.do_predict when PL bug fixed.
												if args.do_predict:
																	_A								= {os.path.basename(__A   ) for p in contents}
																	assert "test_generations.txt" in contents
																	assert "test_results.txt" in contents
																	# assert len(metrics["val"]) ==  desired_n_evals
																	assert len(metrics['''test''']   ) == 1





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							@timeout_decorator.timeout(6_00   )
							@slow
							@require_torch_gpu
							def       __A (  self:   List[str]   )    ->				Union[str, Any]:
												_A								= f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
												_A								= {
												    '''--fp16_opt_level=O1''': '''''',
												    '''$MAX_LEN''': 1_28,
												    '''$BS''': 16,
												    '''$GAS''': 1,
												    '''$ENRO_DIR''': data_dir,
												    '''$m''': '''sshleifer/student_marian_en_ro_6_1''',
												    '''val_check_interval=0.25''': '''val_check_interval=1.0''',
												}

												# Clean up bash script
												_A								= (
												    (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''   )[1].strip()
												)
												_A								= bash_script.replace('''\\\n'''		,     ''''''   ).strip().replace('''"$@"'''		,     ''''''   )
												_A								= bash_script.replace('''--fp16 '''		,     ''' '''   )

												for k, v in env_vars_to_replace.items():
																	_A								= bash_script.replace(__A		,     str(__A   )   )
												_A								= self.get_auto_remove_tmp_dir()
												_A								= bash_script.replace('''--fp16'''		,     ''''''   )
												_A								= 6
												_A								= (
												    ['''distillation.py''']
												    + bash_script.split()
												    + [
												        f"""--output_dir={output_dir}""",
												        '''--gpus=1''',
												        '''--learning_rate=1e-3''',
												        f"""--num_train_epochs={epochs}""",
												        '''--warmup_steps=10''',
												        '''--val_check_interval=1.0''',
												        '''--do_predict''',
												    ]
												)
												with patch.object(__A		,     '''argv'''		,     __A   ):
																	_A								= argparse.ArgumentParser()
																	_A								= pl.Trainer.add_argparse_args(__A   )
																	_A								= SummarizationDistiller.add_model_specific_args(__A		,     os.getcwd()   )
																	_A								= parser.parse_args()
																	# assert args.gpus == gpus THIS BREAKS for multi_gpu

																	_A								= distill_main(__A   )

												# Check metrics
												_A								= load_json(model.metrics_save_path   )
												_A								= metrics['''val'''][0]
												_A								= metrics['''val'''][-1]
												assert len(metrics['''val''']   ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

												assert last_step_stats["val_avg_gen_time"] >= 0.01

												assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
												assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
												assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]		,     __A   )

												# check lightning ckpt can be loaded and has a reasonable statedict
												_A								= os.listdir(__A   )
												_A								= [x for x in contents if x.endswith('''.ckpt'''   )][0]
												_A								= os.path.join(args.output_dir		,     __A   )
												_A								= torch.load(__A		,     map_location='''cpu'''   )
												_A								= '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
												assert expected_key in ckpt["state_dict"]
												assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

												# TODO: turn on args.do_predict when PL bug fixed.
												if args.do_predict:
																	_A								= {os.path.basename(__A   ) for p in contents}
																	assert "test_generations.txt" in contents
																	assert "test_results.txt" in contents
																	# assert len(metrics["val"]) ==  desired_n_evals
																	assert len(metrics['''test''']   ) == 1











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()



