






import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__snake_case      :      Union[str, Any]             =       logging.getLogger(__name__)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   Any						)					->				str:

						"""simple docstring"""



						lowerCAmelCase__        =  np.argmax(UpperCamelCase_      ,    axis=1						)
						return np.sum(outputs == labels						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict						)					->				List[str]:

						"""simple docstring"""



						with open(UpperCamelCase_      ,    encoding='utf_8'						) as f:
												lowerCAmelCase__        =  csv.reader(UpperCamelCase_						)
												lowerCAmelCase__        =  []
												next(UpperCamelCase_						)  # skip the first line
												for line in tqdm(UpperCamelCase_						):
																		output.append((' '.join(line[1:5]						), line[5], line[6], int(line[-1]						) - 1)						)
						return output







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   List[str]						)					->				List[Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  []
						for dataset in encoded_datasets:
												lowerCAmelCase__        =  len(UpperCamelCase_						)
												lowerCAmelCase__        =  np.zeros((n_batch, 2, input_len)      ,    dtype=np.intaa						)
												lowerCAmelCase__        =  np.zeros((n_batch, 2)      ,    dtype=np.intaa						)
												lowerCAmelCase__        =  np.full((n_batch, 2, input_len)      ,    fill_value=-100      ,    dtype=np.intaa						)
												lowerCAmelCase__        =  np.zeros((n_batch,)      ,    dtype=np.intaa						)
												for (
												    i,
												    (story, conta, conta, mc_label),
												) in enumerate(UpperCamelCase_						):
																		lowerCAmelCase__        =  [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																		lowerCAmelCase__        =  [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																		lowerCAmelCase__        =  with_conta
																		lowerCAmelCase__        =  with_conta
																		lowerCAmelCase__        =  len(UpperCamelCase_						) - 1
																		lowerCAmelCase__        =  len(UpperCamelCase_						) - 1
																		lowerCAmelCase__        =  with_conta
																		lowerCAmelCase__        =  with_conta
																		lowerCAmelCase__        =  mc_label
												lowerCAmelCase__        =  (input_ids, mc_token_ids, lm_labels, mc_labels)
												tensor_datasets.append(tuple(torch.tensor(UpperCamelCase_						) for t in all_inputs						)						)
						return tensor_datasets







def 		_UpperCamelCase				(      )					->				Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  argparse.ArgumentParser()
						parser.add_argument('--model_name'      ,    type=UpperCamelCase_      ,    default='openai-gpt'      ,    help='pretrained model name'						)
						parser.add_argument('--do_train'      ,    action='store_true'      ,    help='Whether to run training.'						)
						parser.add_argument('--do_eval'      ,    action='store_true'      ,    help='Whether to run eval on the dev set.'						)
						parser.add_argument(
						    '--output_dir'      ,    default=UpperCamelCase_      ,    type=UpperCamelCase_      ,    required=UpperCamelCase_      ,    help='The output directory where the model predictions and checkpoints will be written.'      ,    )
						parser.add_argument('--train_dataset'      ,    type=UpperCamelCase_      ,    default=''						)
						parser.add_argument('--eval_dataset'      ,    type=UpperCamelCase_      ,    default=''						)
						parser.add_argument('--seed'      ,    type=UpperCamelCase_      ,    default=42						)
						parser.add_argument('--num_train_epochs'      ,    type=UpperCamelCase_      ,    default=3						)
						parser.add_argument('--train_batch_size'      ,    type=UpperCamelCase_      ,    default=8						)
						parser.add_argument('--eval_batch_size'      ,    type=UpperCamelCase_      ,    default=16						)
						parser.add_argument('--adam_epsilon'      ,    default=1e-8      ,    type=UpperCamelCase_      ,    help='Epsilon for Adam optimizer.'						)
						parser.add_argument('--max_grad_norm'      ,    type=UpperCamelCase_      ,    default=1						)
						parser.add_argument(
						    '--max_steps'      ,    default=-1      ,    type=UpperCamelCase_      ,    help=(
						        'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
						    )      ,    )
						parser.add_argument(
						    '--gradient_accumulation_steps'      ,    type=UpperCamelCase_      ,    default=1      ,    help='Number of updates steps to accumulate before                        performing a backward/update pass.'      ,    )
						parser.add_argument('--learning_rate'      ,    type=UpperCamelCase_      ,    default=6.25e-5						)
						parser.add_argument('--warmup_steps'      ,    default=0      ,    type=UpperCamelCase_      ,    help='Linear warmup over warmup_steps.'						)
						parser.add_argument('--lr_schedule'      ,    type=UpperCamelCase_      ,    default='warmup_linear'						)
						parser.add_argument('--weight_decay'      ,    type=UpperCamelCase_      ,    default=0.01						)
						parser.add_argument('--lm_coef'      ,    type=UpperCamelCase_      ,    default=0.9						)
						parser.add_argument('--n_valid'      ,    type=UpperCamelCase_      ,    default=374						)

						parser.add_argument('--server_ip'      ,    type=UpperCamelCase_      ,    default=''      ,    help='Can be used for distant debugging.'						)
						parser.add_argument('--server_port'      ,    type=UpperCamelCase_      ,    default=''      ,    help='Can be used for distant debugging.'						)
						lowerCAmelCase__        =  parser.parse_args()
						print(UpperCamelCase_						)

						if args.server_ip and args.server_port:
												# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
												import ptvsd

												print('Waiting for debugger attach'						)
												ptvsd.enable_attach(address=(args.server_ip, args.server_port)      ,    redirect_output=UpperCamelCase_						)
												ptvsd.wait_for_attach()

						random.seed(args.seed						)
						np.random.seed(args.seed						)
						torch.manual_seed(args.seed						)
						torch.cuda.manual_seed_all(args.seed						)

						lowerCAmelCase__        =  torch.device('cuda' if torch.cuda.is_available() else 'cpu'						)
						lowerCAmelCase__        =  torch.cuda.device_count()
						logger.info('device: {}, n_gpu {}'.format(UpperCamelCase_      ,    UpperCamelCase_						)						)

						if not args.do_train and not args.do_eval:
												raise ValueError('At least one of `do_train` or `do_eval` must be True.'						)

						if not os.path.exists(args.output_dir						):
												os.makedirs(args.output_dir						)

						# Load tokenizer and model
						# This loading functions also add new tokens and embeddings called `special tokens`
						# These new embeddings will be fine-tuned on the RocStories dataset
						lowerCAmelCase__        =  ['_start_', '_delimiter_', '_classify_']
						lowerCAmelCase__        =  OpenAIGPTTokenizer.from_pretrained(args.model_name						)
						tokenizer.add_tokens(UpperCamelCase_						)
						lowerCAmelCase__        =  tokenizer.convert_tokens_to_ids(UpperCamelCase_						)
						lowerCAmelCase__        =  OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name						)
						model.resize_token_embeddings(len(UpperCamelCase_						)						)
						model.to(UpperCamelCase_						)

						# Load and encode the datasets
						def tokenize_and_encode(UpperCamelCase_    :   str						):
												if isinstance(UpperCamelCase_      ,    UpperCamelCase_						):
																		return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(UpperCamelCase_						)						)
												elif isinstance(UpperCamelCase_      ,    UpperCamelCase_						):
																		return obj
												return [tokenize_and_encode(UpperCamelCase_						) for o in obj]

						logger.info('Encoding dataset...'						)
						lowerCAmelCase__        =  load_rocstories_dataset(args.train_dataset						)
						lowerCAmelCase__        =  load_rocstories_dataset(args.eval_dataset						)
						lowerCAmelCase__        =  (train_dataset, eval_dataset)
						lowerCAmelCase__        =  tokenize_and_encode(UpperCamelCase_						)

						# Compute the max input length for the Transformer
						lowerCAmelCase__        =  model.config.n_positions // 2 - 2
						lowerCAmelCase__        =  max(
						    len(story[:max_length]						) + max(len(conta[:max_length]						)      ,    len(conta[:max_length]						)						) + 3
						    for dataset in encoded_datasets
						    for story, conta, conta, _ in dataset						)
						lowerCAmelCase__        =  min(UpperCamelCase_      ,    model.config.n_positions						)  # Max size of input for the pre-trained model

						# Prepare inputs tensors and dataloaders
						lowerCAmelCase__        =  pre_process_datasets(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    *UpperCamelCase_						)
						lowerCAmelCase__  ,    lowerCAmelCase__        =  tensor_datasets[0], tensor_datasets[1]

						lowerCAmelCase__        =  TensorDataset(*UpperCamelCase_						)
						lowerCAmelCase__        =  RandomSampler(UpperCamelCase_						)
						lowerCAmelCase__        =  DataLoader(UpperCamelCase_      ,    sampler=UpperCamelCase_      ,    batch_size=args.train_batch_size						)

						lowerCAmelCase__        =  TensorDataset(*UpperCamelCase_						)
						lowerCAmelCase__        =  SequentialSampler(UpperCamelCase_						)
						lowerCAmelCase__        =  DataLoader(UpperCamelCase_      ,    sampler=UpperCamelCase_      ,    batch_size=args.eval_batch_size						)

						# Prepare optimizer
						if args.do_train:
												if args.max_steps > 0:
																		lowerCAmelCase__        =  args.max_steps
																		lowerCAmelCase__        =  args.max_steps // (len(UpperCamelCase_						) // args.gradient_accumulation_steps) + 1
												else:
																		lowerCAmelCase__        =  len(UpperCamelCase_						) // args.gradient_accumulation_steps * args.num_train_epochs

												lowerCAmelCase__        =  list(model.named_parameters()						)
												lowerCAmelCase__        =  ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
												lowerCAmelCase__        =  [
												    {
												        'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay						)],
												        'weight_decay': args.weight_decay,
												    },
												    {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay						)], 'weight_decay': 0.0},
												]
												lowerCAmelCase__        =  AdamW(UpperCamelCase_      ,    lr=args.learning_rate      ,    eps=args.adam_epsilon						)
												lowerCAmelCase__        =  get_linear_schedule_with_warmup(
												    UpperCamelCase_      ,    num_warmup_steps=args.warmup_steps      ,    num_training_steps=UpperCamelCase_						)

						if args.do_train:
												lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__        =  0, 0, None
												model.train()
												for _ in trange(int(args.num_train_epochs						)      ,    desc='Epoch'						):
																		lowerCAmelCase__        =  0
																		lowerCAmelCase__        =  0
																		lowerCAmelCase__        =  tqdm(UpperCamelCase_      ,    desc='Training'						)
																		for step, batch in enumerate(UpperCamelCase_						):
																								lowerCAmelCase__        =  tuple(t.to(UpperCamelCase_						) for t in batch						)
																								lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__        =  batch
																								lowerCAmelCase__        =  model(UpperCamelCase_      ,    mc_token_ids=UpperCamelCase_      ,    lm_labels=UpperCamelCase_      ,    mc_labels=UpperCamelCase_						)
																								lowerCAmelCase__        =  args.lm_coef * losses[0] + losses[1]
																								loss.backward()
																								optimizer.step()
																								scheduler.step()
																								optimizer.zero_grad()
																								tr_loss += loss.item()
																								lowerCAmelCase__        =  (
																								    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
																								)
																								nb_tr_steps += 1
																								lowerCAmelCase__        =  'Training loss: {:.2e} lr: {:.2e}'.format(UpperCamelCase_      ,    scheduler.get_lr()[0]						)

    # Save a trained model
						if args.do_train:
												# Save a trained model, configuration and tokenizer
												lowerCAmelCase__        =  model.module if hasattr(UpperCamelCase_      ,    'module'						) else model  # Only save the model itself

												# If we save using the predefined names, we can load using `from_pretrained`
												lowerCAmelCase__        =  os.path.join(args.output_dir      ,    UpperCamelCase_						)
												lowerCAmelCase__        =  os.path.join(args.output_dir      ,    UpperCamelCase_						)

												torch.save(model_to_save.state_dict()      ,    UpperCamelCase_						)
												model_to_save.config.to_json_file(UpperCamelCase_						)
												tokenizer.save_vocabulary(args.output_dir						)

												# Load a trained model and vocabulary that you have fine-tuned
												lowerCAmelCase__        =  OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir						)
												lowerCAmelCase__        =  OpenAIGPTTokenizer.from_pretrained(args.output_dir						)
												model.to(UpperCamelCase_						)

						if args.do_eval:
												model.eval()
												lowerCAmelCase__  ,    lowerCAmelCase__        =  0, 0
												lowerCAmelCase__  ,    lowerCAmelCase__        =  0, 0
												for batch in tqdm(UpperCamelCase_      ,    desc='Evaluating'						):
																		lowerCAmelCase__        =  tuple(t.to(UpperCamelCase_						) for t in batch						)
																		lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__        =  batch
																		with torch.no_grad():
																								lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__        =  model(
																								    UpperCamelCase_      ,    mc_token_ids=UpperCamelCase_      ,    lm_labels=UpperCamelCase_      ,    mc_labels=UpperCamelCase_						)

																		lowerCAmelCase__        =  mc_logits.detach().cpu().numpy()
																		lowerCAmelCase__        =  mc_labels.to('cpu'						).numpy()
																		lowerCAmelCase__        =  accuracy(UpperCamelCase_      ,    UpperCamelCase_						)

																		eval_loss += mc_loss.mean().item()
																		eval_accuracy += tmp_eval_accuracy

																		nb_eval_examples += input_ids.size(0						)
																		nb_eval_steps += 1

												lowerCAmelCase__        =  eval_loss / nb_eval_steps
												lowerCAmelCase__        =  eval_accuracy / nb_eval_examples
												lowerCAmelCase__        =  tr_loss / nb_tr_steps if args.do_train else None
												lowerCAmelCase__        =  {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

												lowerCAmelCase__        =  os.path.join(args.output_dir      ,    'eval_results.txt'						)
												with open(UpperCamelCase_      ,    'w'						) as writer:
																		logger.info('***** Eval results *****'						)
																		for key in sorted(result.keys()						):
																								logger.info('  %s = %s'      ,    UpperCamelCase_      ,    str(result[key]						)						)
																								writer.write('%s = %s\n' % (key, str(result[key]						))						)


if __name__ == "__main__":
	main()













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


__snake_case      :      Tuple             =       """sshleifer/mar_enro_6_3_student"""


class       __SCREAMING_SNAKE_CASE   (				__lowercase):






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												super().setUp()

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






						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												MarianMTModel.from_pretrained(_UpperCamelCase				)







						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  {
												    '$MAX_LEN': 64,
												    '$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
												lowerCAmelCase__        =  (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'				)[1].strip()
												lowerCAmelCase__        =  bash_script.replace('\\\n'       ,  ''				).strip().replace('"$@"'       ,  ''				)
												for k, v in env_vars_to_replace.items():
																		lowerCAmelCase__        =  bash_script.replace(_UpperCamelCase       ,  str(_UpperCamelCase				)				)
												lowerCAmelCase__        =  self.get_auto_remove_tmp_dir()

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

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

												# Check metrics
												lowerCAmelCase__        =  load_json(model.metrics_save_path				)
												lowerCAmelCase__        =  metrics['val'][0]
												lowerCAmelCase__        =  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}"]       ,  _UpperCamelCase				)

												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
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  map_location='cpu'				)
												lowerCAmelCase__        =  '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:
																		lowerCAmelCase__        =  {os.path.basename(_UpperCamelCase				) 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   (				__lowercase):






						@timeout_decorator.timeout(6_00				)
						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  F"{self.test_file_dir_str}/test_data/wmt_en_ro"
												lowerCAmelCase__        =  {
												    '--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
												lowerCAmelCase__        =  (
												    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'				)[1].strip()
												)
												lowerCAmelCase__        =  bash_script.replace('\\\n'       ,  ''				).strip().replace('"$@"'       ,  ''				)
												lowerCAmelCase__        =  bash_script.replace('--fp16 '       ,  ' '				)

												for k, v in env_vars_to_replace.items():
																		lowerCAmelCase__        =  bash_script.replace(_UpperCamelCase       ,  str(_UpperCamelCase				)				)
												lowerCAmelCase__        =  self.get_auto_remove_tmp_dir()
												lowerCAmelCase__        =  bash_script.replace('--fp16'       ,  ''				)
												lowerCAmelCase__        =  6
												lowerCAmelCase__        =  (
												    ['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(_UpperCamelCase       ,  'argv'       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  argparse.ArgumentParser()
																		lowerCAmelCase__        =  pl.Trainer.add_argparse_args(_UpperCamelCase				)
																		lowerCAmelCase__        =  SummarizationDistiller.add_model_specific_args(_UpperCamelCase       ,  os.getcwd()				)
																		lowerCAmelCase__        =  parser.parse_args()
																		# assert args.gpus == gpus THIS BREAKS for multi_gpu

																		lowerCAmelCase__        =  distill_main(_UpperCamelCase				)

												# Check metrics
												lowerCAmelCase__        =  load_json(model.metrics_save_path				)
												lowerCAmelCase__        =  metrics['val'][0]
												lowerCAmelCase__        =  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}"]       ,  _UpperCamelCase				)

												# check lightning ckpt can be loaded and has a reasonable statedict
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  map_location='cpu'				)
												lowerCAmelCase__        =  '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:
																		lowerCAmelCase__        =  {os.path.basename(_UpperCamelCase				) 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





