
'''simple docstring'''




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_				(   snake_case__						):




    '''simple docstring'''



    A       :   Any					=						filter(lambda snake_case__						: p.requires_grad		,				model.parameters()						)
    A       :   Union[str, Any]					=						sum([np.prod(p.size()						) for p in model_parameters]						)
    return params


lowercase			:				List[Any]    		=			logging.getLogger(__name__)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    if metric == "rouge2":
        A       :   List[Any]					=						'''{val_avg_rouge2:.4f}-{step_count}'''
    elif metric == "bleu":
        A       :   Union[str, Any]					=						'''{val_avg_bleu:.4f}-{step_count}'''
    elif metric == "em":
        A       :   Any					=						'''{val_avg_em:.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       :   str					=						ModelCheckpoint(
        dirpath=snake_case__		,				filename=snake_case__		,				monitor=F'val_{metric}'		,				mode='''max'''		,				save_top_k=3		,				every_n_epochs=1		,				)
    return checkpoint_callback







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



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







class  A				(							pl.Callback						):





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         A       :   int					=						{F'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups	)}
         pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE	)





     @rank_zero_only
     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=True	) ->				None:






         """simple docstring"""






         logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'	)
         A       :   int					=						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       :   int					=						Path(pl_module.hparams.output_dir	)
         if type_path == "test":
             A       :   str					=						od / '''test_results.txt'''
             A       :   int					=						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       :   Any					=						od / F'{type_path}_results/{trainer.global_step:05d}.txt'
             A       :   Optional[int]					=						od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
             results_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE	)
             generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE	)
         with open(SCREAMING_SNAKE_CASE						,			'''a+'''	) as writer:
             for key in sorted(SCREAMING_SNAKE_CASE	):
                 if key in ["log", "progress_bar", "preds"]:
                     continue
                 A       :   str					=						metrics[key]
                 if isinstance(SCREAMING_SNAKE_CASE						,			torch.Tensor	):
                     A       :   str					=						val.item()
                 A       :   str					=						F'{key}: {val:.6f}\n'
                 writer.write(SCREAMING_SNAKE_CASE	)

         if not save_generations:
             return

         if "preds" in metrics:
             A       :   Union[str, Any]					=						'''\n'''.join(metrics['''preds''']	)
             generations_file.open('''w+'''	).write(SCREAMING_SNAKE_CASE	)





     @rank_zero_only
     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         try:
             A       :   Union[str, Any]					=						pl_module.model.model.num_parameters()
         except AttributeError:
             A       :   List[Any]					=						pl_module.model.num_parameters()

         A       :   int					=						count_trainable_parameters(SCREAMING_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						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         save_json(pl_module.metrics						,			pl_module.metrics_save_path	)
         return self._write_logs(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			'''test'''	)





     @rank_zero_only
     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Any:






         """simple docstring"""






         save_json(pl_module.metrics						,			pl_module.metrics_save_path	)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")






'''simple docstring'''




from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest





@require_tokenizers
class  A				(							__snake_case						):
     __magic_name__						=		DistilBertTokenizer
     __magic_name__						=		DistilBertTokenizerFast
     __magic_name__						=		True





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   List[Any]					=						DistilBertTokenizer.from_pretrained('''distilbert-base-uncased'''	)

         A       :   Dict					=						tokenizer.encode('''sequence builders'''						,			add_special_tokens=SCREAMING_SNAKE_CASE	)
         A       :   List[str]					=						tokenizer.encode('''multi-sequence build'''						,			add_special_tokens=SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]




