





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  :    Dict						    =  'sshleifer/mar_enro_6_3_student'



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  def 				lowercase		(				self :		Union[str, Any]     )							->						int:
     super().setUp()

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






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






  @slow
  @require_torch_gpu
  def 				lowercase		(				self :		List[Any]     )							->						Dict:
     __lowerCAmelCase								=	{
         '$MAX_LEN': 6_4,
         '$BS': 6_4,
         '$GAS': 1,
         '$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(lowerCAmelCase_					,  str(lowerCAmelCase_     )     )
     __lowerCAmelCase								=	self.get_auto_remove_tmp_dir()

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

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

     # 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}"""]					,  lowerCAmelCase_     )

     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']					,  1_7     )

     # 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(lowerCAmelCase_     )
     __lowerCAmelCase								=	[x for x in contents if x.endswith('.ckpt'     )][0]
     __lowerCAmelCase								=	os.path.join(args.output_dir					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	torch.load(lowerCAmelCase_					,  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(lowerCAmelCase_     ) 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 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  @timeout_decorator.timeout(6_0_0     )
  @slow
  @require_torch_gpu
  def 				lowercase		(				self :		Optional[int]     )							->						int:
     __lowerCAmelCase								=	f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
     __lowerCAmelCase								=	{
         '--fp16_opt_level=O1': '',
         '$MAX_LEN': 1_2_8,
         '$BS': 1_6,
         '$GAS': 1,
         '$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(lowerCAmelCase_					,  str(lowerCAmelCase_     )     )
     __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(lowerCAmelCase_					,  'argv'					,  lowerCAmelCase_     ):
        __lowerCAmelCase								=	argparse.ArgumentParser()
        __lowerCAmelCase								=	pl.Trainer.add_argparse_args(lowerCAmelCase_     )
        __lowerCAmelCase								=	SummarizationDistiller.add_model_specific_args(lowerCAmelCase_					,  os.getcwd()     )
        __lowerCAmelCase								=	parser.parse_args()
        # assert args.gpus == gpus THIS BREAKS for multi_gpu

        __lowerCAmelCase								=	distill_main(lowerCAmelCase_     )

     # 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}"""]					,  lowerCAmelCase_     )

     # check lightning ckpt can be loaded and has a reasonable statedict
     __lowerCAmelCase								=	os.listdir(lowerCAmelCase_     )
     __lowerCAmelCase								=	[x for x in contents if x.endswith('.ckpt'     )][0]
     __lowerCAmelCase								=	os.path.join(args.output_dir					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	torch.load(lowerCAmelCase_					,  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(lowerCAmelCase_     ) 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








def   a_    ( lowerCAmelCase_      :			int	):
   if number < 0:
      raise ValueError('number must not be negative'	)
   return number & (number - 1) == 0


if __name__ == "__main__":
     import doctest

     doctest.testmod()

