

'''simple docstring'''
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


lowerCamelCase :      List[Any]					 =     "sshleifer/mar_enro_6_3_student"







class 							A__    (       A__  ):





  def 						A  (		self      :	Tuple )	->    Tuple:





       '''simple docstring'''






       super().setUp()

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





  @slow
  @require_torch_gpu
  def 						A  (		self      :	List[Any] )	->    str:





       '''simple docstring'''






       MarianMTModel.from_pretrained(_a )





  @slow
  @require_torch_gpu
  def 						A  (		self      :	int )	->    Union[str, Any]:





       '''simple docstring'''






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

       # bash_script = bash_script.replace("--fp16 ", "")
       _SCREAMING_SNAKE_CASE							    =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

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

       # Check metrics
       _SCREAMING_SNAKE_CASE							    =load_json(model.metrics_save_path )
       _SCREAMING_SNAKE_CASE							    =metrics['val'][0]
       _SCREAMING_SNAKE_CASE							    =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
       _SCREAMING_SNAKE_CASE							    =os.listdir(_a )
       _SCREAMING_SNAKE_CASE							    =[x for x in contents if x.endswith('.ckpt' )][0]
       _SCREAMING_SNAKE_CASE							    =os.path.join(args.output_dir							,					_a )
       _SCREAMING_SNAKE_CASE							    =torch.load(_a							,					map_location='cpu' )
       _SCREAMING_SNAKE_CASE							    ='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:
            _SCREAMING_SNAKE_CASE							    ={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 							A__    (       A__  ):





  @timeout_decorator.timeout(600 )
  @slow
  @require_torch_gpu
  def 						A  (		self      :	List[str] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =f"{self.test_file_dir_str}/test_data/wmt_en_ro"
       _SCREAMING_SNAKE_CASE							    ={
           '--fp16_opt_level=O1': '',
           '$MAX_LEN': 128,
           '$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
       _SCREAMING_SNAKE_CASE							    =(
           (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py' )[1].strip()
       )
       _SCREAMING_SNAKE_CASE							    =bash_script.replace('\\\n'							,					'' ).strip().replace('"$@"'							,					'' )
       _SCREAMING_SNAKE_CASE							    =bash_script.replace('--fp16 '							,					' ' )

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

            _SCREAMING_SNAKE_CASE							    =distill_main(_a )

       # Check metrics
       _SCREAMING_SNAKE_CASE							    =load_json(model.metrics_save_path )
       _SCREAMING_SNAKE_CASE							    =metrics['val'][0]
       _SCREAMING_SNAKE_CASE							    =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
       _SCREAMING_SNAKE_CASE							    =os.listdir(_a )
       _SCREAMING_SNAKE_CASE							    =[x for x in contents if x.endswith('.ckpt' )][0]
       _SCREAMING_SNAKE_CASE							    =os.path.join(args.output_dir							,					_a )
       _SCREAMING_SNAKE_CASE							    =torch.load(_a							,					map_location='cpu' )
       _SCREAMING_SNAKE_CASE							    ='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:
            _SCREAMING_SNAKE_CASE							    ={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



'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def        _lowerCAmelCase					(						)      ->			Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =ArgumentParser(
         description=(
             'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
         )				)

     # Optional arguments for the launch helper
     parser.add_argument('--num_cores'							, type=_UpperCamelCase							, default=1							, help='Number of TPU cores to use (1 or 8).'				)

     # positional
     parser.add_argument(
         'training_script'							, type=_UpperCamelCase							, help=(
             'The full path to the single TPU training '
             'program/script to be launched in parallel, '
             'followed by all the arguments for the '
             'training script'
         )							, )

     # rest from the training program
     parser.add_argument('training_script_args'							, nargs=_UpperCamelCase				)

     return parser.parse_args()


def        _lowerCAmelCase					(						)      ->			Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =parse_args()

     # Import training_script as a module.
     _SCREAMING_SNAKE_CASE							    =Path(args.training_script				)
     sys.path.append(str(script_fpath.parent.resolve()				)				)
     _SCREAMING_SNAKE_CASE							    =script_fpath.stem
     _SCREAMING_SNAKE_CASE							    =importlib.import_module(_UpperCamelCase				)

     # Patch sys.argv
     _SCREAMING_SNAKE_CASE							    =[args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores				)]

     xmp.spawn(mod._mp_fn							, args=()							, nprocs=args.num_cores				)


if __name__ == "__main__":
     main()
