


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      a	(							SCREAMING_SNAKE_CASE_      :		str       ):






       """simple docstring"""





       UpperCamelCase    :	Dict   	=		filter(lambda SCREAMING_SNAKE_CASE_       : p.requires_grad							,			model.parameters()       )
       UpperCamelCase    :	int   	=		sum([np.prod(p.size()       ) for p in model_parameters]       )
       return params


__UpperCAmelCase : List[str] 			=				logging.getLogger(__name__)


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Dict       ):






       """simple docstring"""





       if metric == "rouge2":
              UpperCamelCase    :	str   	=		'''{val_avg_rouge2:.4f}-{step_count}'''
       elif metric == "bleu":
              UpperCamelCase    :	Optional[Any]   	=		'''{val_avg_bleu:.4f}-{step_count}'''
       elif metric == "em":
              UpperCamelCase    :	int   	=		'''{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.'''       )

       UpperCamelCase    :	Tuple   	=		ModelCheckpoint(
           dirpath=SCREAMING_SNAKE_CASE_							,			filename=SCREAMING_SNAKE_CASE_							,			monitor=F"""val_{metric}"""							,			mode='''max'''							,			save_top_k=3							,			every_n_epochs=1							,			)
       return checkpoint_callback


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		List[Any]       ):






       """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    UpperCAmelCase_	(							pl.Callback):
       '''simple docstring'''






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		{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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=True						):




              """simple docstring"""




              logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""						)
              UpperCamelCase    :	Union[str, Any]   	=		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
              UpperCamelCase    :	List[str]   	=		Path(pl_module.hparams.output_dir						)
              if type_path == "test":
                     UpperCamelCase    :	Union[str, Any]   	=		od / '''test_results.txt'''
                     UpperCamelCase    :	Any   	=		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.
                     UpperCamelCase    :	Optional[int]   	=		od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
                     UpperCamelCase    :	Tuple   	=		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
                            UpperCamelCase    :	Any   	=		metrics[key]
                            if isinstance(__SCREAMING_SNAKE_CASE ,					torch.Tensor						):
                                   UpperCamelCase    :	Any   	=		val.item()
                            UpperCamelCase    :	Dict   	=		f"""{key}: {val:.6f}\n"""
                            writer.write(__SCREAMING_SNAKE_CASE						)

              if not save_generations:
                     return

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






       @rank_zero_only
       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              try:
                     UpperCamelCase    :	Optional[int]   	=		pl_module.model.model.num_parameters()
              except AttributeError:
                     UpperCamelCase    :	Dict   	=		pl_module.model.num_parameters()

              UpperCamelCase    :	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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """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")





import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class    UpperCAmelCase_	(							_a):
       '''simple docstring'''






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              with open(__SCREAMING_SNAKE_CASE ,					encoding='''utf-8'''						) as input_file:
                     UpperCamelCase    :	str   	=		re.compile(R'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''						)
                     UpperCamelCase    :	Optional[int]   	=		input_file.read()
                     UpperCamelCase    :	Union[str, Any]   	=		regexp.search(__SCREAMING_SNAKE_CASE						)

              return match






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              with open(__SCREAMING_SNAKE_CASE ,					encoding='''utf-8'''						) as input_file:
                     UpperCamelCase    :	Optional[int]   	=		re.compile(R'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()''' ,					re.DOTALL						)
                     UpperCamelCase    :	Tuple   	=		input_file.read()
                     # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
                     UpperCamelCase    :	Dict   	=		regexp.finditer(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	int   	=		[match for match in matches if match is not None and match.group(1						) is not None]
              return matches[0] if matches else None






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		Path('''./datasets'''						)
              UpperCamelCase    :	Tuple   	=		list(dataset_paths.absolute().glob('''**/*.py'''						)						)

              for dataset in dataset_files:
                     if self._no_encoding_on_file_open(str(__SCREAMING_SNAKE_CASE						)						):
                            raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		Path('''./datasets'''						)
              UpperCamelCase    :	Tuple   	=		list(dataset_paths.absolute().glob('''**/*.py'''						)						)

              for dataset in dataset_files:
                     if self._no_print_statements(str(__SCREAMING_SNAKE_CASE						)						):
                            raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""						)

