





'''simple docstring'''




import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
lowercase__       : List[Any]         =       logging.getLogger()






def 			_lowerCAmelCase     (    __snake_case    :					Path					,				__snake_case    :					list     )	->	List[str]:
     __A		:					Tuple						=			'\n'.join(__snake_case     )
     Path(__snake_case     ).open('w'     ).writelines(__snake_case     )


lowercase__       : Optional[Any]         =       '''patrickvonplaten/t5-tiny-random'''
lowercase__       : List[Any]         =       '''sshleifer/bart-tiny-random'''
lowercase__       : Optional[Any]         =       '''sshleifer/tiny-mbart'''

lowercase__       : Any         =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







class 					SCREAMING_SNAKE_CASE       (a__				):


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Dict						=			Path(self.get_auto_remove_tmp_dir()) / 'utest_input.source'
          __A		:					Optional[Any]						=			input_file_name.parent / 'utest_output.txt'
          assert not output_file_name.exists()
          __A		:					Optional[int]						=			[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
          _dump_articles(_UpperCAmelCase		,      _UpperCAmelCase)

          __A		:					Optional[int]						=			str(Path(self.get_auto_remove_tmp_dir()) / 'scores.json')
          __A		:					Dict						=			'translation_en_to_de' if model == T5_TINY else 'summarization'
          __A		:					Any						=			F'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

          with patch.object(_UpperCAmelCase		,      'argv'		,      _UpperCAmelCase):
               run_generate()
               assert Path(_UpperCAmelCase).exists()
               # os.remove(Path(output_file_name))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          self.run_eval_tester(_UpperCAmelCase)


     @parameterized.expand([BART_TINY, MBART_TINY])
     @slow
     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          self.run_eval_tester(_UpperCAmelCase)


     @parameterized.expand([T5_TINY, MBART_TINY])
     @slow
     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Tuple						=			Path(self.get_auto_remove_tmp_dir()) / 'utest_input.source'
          __A		:					str						=			input_file_name.parent / 'utest_output.txt'
          assert not output_file_name.exists()

          __A		:					int						=			{
              'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
              'de': [
                  'Maschinelles Lernen ist großartig, oder?',
                  'Ich esse gerne Bananen',
                  'Morgen ist wieder ein toller Tag!',
              ],
          }

          __A		:					Dict						=			Path(self.get_auto_remove_tmp_dir())
          __A		:					str						=			str(tmp_dir / 'scores.json')
          __A		:					int						=			str(tmp_dir / 'val.target')
          _dump_articles(_UpperCAmelCase		,      text['en'])
          _dump_articles(_UpperCAmelCase		,      text['de'])
          __A		:					Optional[int]						=			'translation_en_to_de' if model == T5_TINY else 'summarization'
          __A		:					int						=			F'\n            run_eval_search.py\n            {model}\n            {str(_UpperCAmelCase)}\n            {str(_UpperCAmelCase)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
          testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'])

          with patch.object(_UpperCAmelCase		,      'argv'		,      _UpperCAmelCase):
               with CaptureStdout() as cs:
                    run_search()
               __A		:					str						=			[' num_beams | length_penalty', model, 'Best score args']
               __A		:					List[Any]						=			['Info']
               if "translation" in task:
                    expected_strings.append('bleu')
               else:
                    expected_strings.extend(_UpperCAmelCase)
               for w in expected_strings:
                    assert w in cs.out
               for w in un_expected_strings:
                    assert w not in cs.out
               assert Path(_UpperCAmelCase).exists()
               os.remove(Path(_UpperCAmelCase))






import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    _A     =							filter(lambda _SCREAMING_SNAKE_CASE				: p.requires_grad							,     model.parameters()				)
    _A     =							sum([np.prod(p.size()				) for p in model_parameters]				)
    return params


__A							:			Union[str, Any]         =			logging.getLogger(__name__)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    if metric == "rouge2":
        _A     =							'{val_avg_rouge2:.4f}-{step_count}'
    elif metric == "bleu":
        _A     =							'{val_avg_bleu:.4f}-{step_count}'
    elif metric == "em":
        _A     =							'{val_avg_em:.4f}-{step_count}'
    elif metric == "loss":
        _A     =							'{val_avg_loss:.4f}-{step_count}'
    else:
        raise NotImplementedError(
            F"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"
            ' function.'				)

    _A     =							ModelCheckpoint(
        dirpath=_SCREAMING_SNAKE_CASE							,     filename=_SCREAMING_SNAKE_CASE							,     monitor=F"val_{metric}"							,     mode='max'							,     save_top_k=1							,     every_n_epochs=1							,     )

    return checkpoint_callback





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




    return EarlyStopping(
        monitor=F"val_{metric}"							,     mode='min' if 'loss' in metric else 'max'							,     patience=_SCREAMING_SNAKE_CASE							,     verbose=_SCREAMING_SNAKE_CASE							,     )







class      lowerCamelCase(						pl.Callback	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         _A     =							{F"lr_group_{i}": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
         pl_module.logger.log_metrics(snake_case_							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_=True							):
         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"							)
         _A     =							trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}							)
         # Log results
         _A     =							Path(pl_module.hparams.output_dir							)
         if type_path == "test":
             _A     =							od / 'test_results.txt'
             _A     =							od / 'test_generations.txt'
         else:
             # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
             # If people want this it will be easy enough to add back.
             _A     =							od / F"{type_path}_results/{trainer.global_step:05d}.txt"
             _A     =							od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
             results_file.parent.mkdir(exist_ok=snake_case_							)
             generations_file.parent.mkdir(exist_ok=snake_case_							)
         with open(snake_case_				,     'a+'							) as writer:
             for key in sorted(snake_case_							):
                 if key in ["log", "progress_bar", "preds"]:
                     continue
                 _A     =							metrics[key]
                 if isinstance(snake_case_				,     torch.Tensor							):
                     _A     =							val.item()
                 _A     =							F"{key}: {val:.6f}\n"
                 writer.write(snake_case_							)

         if not save_generations:
             return

         if "preds" in metrics:
             _A     =							'\n'.join(metrics['preds']							)
             generations_file.open('w+'							).write(snake_case_							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         try:
             _A     =							pl_module.model.model.num_parameters()
         except AttributeError:
             _A     =							pl_module.model.num_parameters()

         _A     =							count_trainable_parameters(snake_case_							)
         # mp stands for million parameters
         trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         save_json(pl_module.metrics				,     pl_module.metrics_save_path							)
         return self._write_logs(snake_case_				,     snake_case_				,     'test'							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         save_json(pl_module.metrics				,     pl_module.metrics_save_path							)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")





