


"""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


snake_case__						:   Optional[Any]			 = '''sshleifer/mar_enro_6_3_student'''



class 						snake_case_(				a__	):






     def    lowerCamelCase__   (		self     :    List[Any] ):
           super().setUp()

           lowerCAmelCase    :		Optional[Any]				=      cached_path(
               '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''    , extract_compressed_file=UpperCamelCase_    , )
           lowerCAmelCase    :		List[Any]				=      F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''






     @slow
     @require_torch_gpu
     def    lowerCamelCase__   (		self     :    Any ):
           MarianMTModel.from_pretrained(UpperCamelCase_ )






     @slow
     @require_torch_gpu
     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           lowerCAmelCase    :		List[str]				=      {
               '''$MAX_LEN''': 6_4,
               '''$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    :		Tuple				=      (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py''' )[1].strip()
           lowerCAmelCase    :		Any				=      bash_script.replace('''\\\n'''    , '''''' ).strip().replace('''"$@"'''    , '''''' )
           for k, v in env_vars_to_replace.items():
                 lowerCAmelCase    :		Any				=      bash_script.replace(UpperCamelCase_    , str(UpperCamelCase_ ) )
           lowerCAmelCase    :		Optional[Any]				=      self.get_auto_remove_tmp_dir()

           # bash_script = bash_script.replace("--fp16 ", "")
           lowerCAmelCase    :		List[Any]				=      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    :		Union[str, Any]				=      ['''finetune.py'''] + bash_script.split() + args
           with patch.object(UpperCamelCase_    , '''argv'''    , UpperCamelCase_ ):
                 lowerCAmelCase    :		Dict				=      argparse.ArgumentParser()
                 lowerCAmelCase    :		Dict				=      pl.Trainer.add_argparse_args(UpperCamelCase_ )
                 lowerCAmelCase    :		Optional[int]				=      SummarizationModule.add_model_specific_args(UpperCamelCase_    , os.getcwd() )
                 lowerCAmelCase    :		List[Any]				=      parser.parse_args()
                 lowerCAmelCase    :		Union[str, Any]				=      main(UpperCamelCase_ )

           # Check metrics
           lowerCAmelCase    :		str				=      load_json(model.metrics_save_path )
           lowerCAmelCase    :		Any				=      metrics['''val'''][0]
           lowerCAmelCase    :		Optional[Any]				=      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''']    , 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    :		List[Any]				=      os.listdir(UpperCamelCase_ )
           lowerCAmelCase    :		Any				=      [x for x in contents if x.endswith('''.ckpt''' )][0]
           lowerCAmelCase    :		Optional[int]				=      os.path.join(args.output_dir    , UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      torch.load(UpperCamelCase_    , map_location='''cpu''' )
           lowerCAmelCase    :		List[str]				=      '''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    :		str				=      {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 						snake_case_(				a__	):






     @timeout_decorator.timeout(6_0_0 )
     @slow
     @require_torch_gpu
     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		Any				=      F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
           lowerCAmelCase    :		Any				=      {
               '''--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    :		Tuple				=      (
               (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py''' )[1].strip()
           )
           lowerCAmelCase    :		Union[str, Any]				=      bash_script.replace('''\\\n'''    , '''''' ).strip().replace('''"$@"'''    , '''''' )
           lowerCAmelCase    :		Dict				=      bash_script.replace('''--fp16 '''    , ''' ''' )

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

                 lowerCAmelCase    :		int				=      distill_main(UpperCamelCase_ )

           # Check metrics
           lowerCAmelCase    :		Dict				=      load_json(model.metrics_save_path )
           lowerCAmelCase    :		Optional[int]				=      metrics['''val'''][0]
           lowerCAmelCase    :		str				=      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    :		Dict				=      os.listdir(UpperCamelCase_ )
           lowerCAmelCase    :		List[str]				=      [x for x in contents if x.endswith('''.ckpt''' )][0]
           lowerCAmelCase    :		Dict				=      os.path.join(args.output_dir    , UpperCamelCase_ )
           lowerCAmelCase    :		Any				=      torch.load(UpperCamelCase_    , map_location='''cpu''' )
           lowerCAmelCase    :		Dict				=      '''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    :		Tuple				=      {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





def    __UpperCAmelCase (	__a      :			str	)			->     int:

					"""simple docstring"""

					assert column_title.isupper()
					_a	:    Optional[Any]         =		0
					_a	:    List[Any]         =		len(__a	) - 1
					_a	:    List[str]         =		0

					while index >= 0:
										_a	:    Dict         =		(ord(column_title[index]	) - 64) * pow(26		,__a	)
										answer += value
										power += 1
										index -= 1

					return answer


if __name__ == "__main__":
			from doctest import testmod

			testmod()



