


"""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      lowercase			(				A_       )->      int:
  '''simple docstring'''







  a						:		Any      							=			filter(lambda A_       : p.requires_grad  ,   model.parameters()       )
  a						:		Optional[int]      							=			sum([np.prod(p.size()       ) for p in model_parameters]       )
  return params


__lowercase    					=      logging.getLogger(__name__)


def      lowercase			(				A_  ,   A_       )->      Dict:
  '''simple docstring'''







  if metric == "rouge2":
    a						:		Union[str, Any]      							=			"{val_avg_rouge2:.4f}-{step_count}"
  elif metric == "bleu":
    a						:		Any      							=			"{val_avg_bleu:.4f}-{step_count}"
  elif metric == "em":
    a						:		Optional[int]      							=			"{val_avg_em:.4f}-{step_count}"
  elif metric == "loss":
    a						:		Optional[Any]      							=			"{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						:		Union[str, Any]      							=			ModelCheckpoint(
      dirpath=A_  ,   filename=A_  ,   monitor=F'''val_{metric}'''  ,   mode="max"  ,   save_top_k=1  ,   every_n_epochs=1  ,   )

  return checkpoint_callback


def      lowercase			(				A_  ,   A_       )->      List[str]:
  '''simple docstring'''







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







class 						_A  (  pl.Callback  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    str				, __UpperCAmelCase				:    List[str]				, __UpperCAmelCase				:    int):
      a						:		Optional[Any]      							=			{f'''lr_group_{i}''': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
      pl_module.logger.log_metrics(__UpperCAmelCase)






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

      if not save_generations:
        return

      if "preds" in metrics:
        a						:		Any      							=			"\n".join(metrics["preds"])
        generations_file.open("w+").write(__UpperCAmelCase)






    @rank_zero_only
    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    Dict				, __UpperCAmelCase				:    Dict):
      try:
        a						:		Optional[Any]      							=			pl_module.model.model.num_parameters()
      except AttributeError:
        a						:		Any      							=			pl_module.model.num_parameters()

      a						:		Dict      							=			count_trainable_parameters(__UpperCAmelCase)
      # 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 	__snake_case							(					self				:    str				, __UpperCAmelCase				:    pl.Trainer				, __UpperCAmelCase				:    pl.LightningModule):
      save_json(pl_module.metrics				, pl_module.metrics_save_path)
      return self._write_logs(__UpperCAmelCase				, __UpperCAmelCase				, "test")






    @rank_zero_only
    def 	__snake_case							(					self				:    Dict				, __UpperCAmelCase				:    pl.Trainer				, __UpperCAmelCase				:    Union[str, Any]):
      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"""







import pytest

from datasets import inspect_metric, list_metrics, load_metric


@pytest.fixture
def      lowercase			(				A_       )->      List[Any]:
  '''simple docstring'''







  monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"  ,   set()       )


@pytest.fixture
def      lowercase			(				A_       )->      Tuple:
  '''simple docstring'''







  class 						_A  :







      """simple docstring"""




      def __init__(					self				:    str				, __UpperCAmelCase				:    int):
        a						:		List[Any]      							=			metric_id






  class 						_A  :







      """simple docstring"""




      UpperCAmelCase       :							Union[str, Any]	=       [MetricMock(_a  ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]






      def 	__snake_case							(					self				:    List[str]):
        return self._metrics






  monkeypatch.setattr("datasets.inspect.huggingface_hub"  ,   HfhMock()       )







@pytest.mark.parametrize(
    "func, args"  ,   [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]       )
def      lowercase			(				A_  ,   A_  ,   A_  ,   A_  ,   A_       )->      Any:
  '''simple docstring'''







  if "tmp_path" in args:
    a						:		Union[str, Any]      							=			tuple(arg if arg != "tmp_path" else tmp_path for arg in args       )
  with pytest.warns(A_  ,   match="https://huggingface.co/docs/evaluate"       ):
    func(*A_       )






