






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     (       a_					)     ->						int:
					__UpperCamelCase      :					Optional[Any]       		=filter(lambda a_					: p.requires_grad							,model.parameters()					)
					__UpperCamelCase      :					Tuple       		=sum([np.prod(p.size()					) for p in model_parameters]					)
					return params


A_       :Tuple       =						logging.getLogger(__name__)






def 							A     (       a_							,a_					)     ->						str:
					if metric == "rouge2":
										__UpperCamelCase      :					str       		='{val_avg_rouge2:.4f}-{step_count}'
					elif metric == "bleu":
										__UpperCamelCase      :					Optional[int]       		='{val_avg_bleu:.4f}-{step_count}'
					elif metric == "em":
										__UpperCamelCase      :					Optional[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.'					)

					__UpperCamelCase      :					Optional[Any]       		=ModelCheckpoint(
					    dirpath=a_							,filename=a_							,monitor=F'val_{metric}'							,mode='max'							,save_top_k=3							,every_n_epochs=1							,)
					return checkpoint_callback






def 							A     (       a_							,a_					)     ->						List[Any]:
					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       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					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(lowerCamelCase__    )


				@rank_zero_only
				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__=True    ):




									"""simple docstring"""

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

									if not save_generations:
														return

									if "preds" in metrics:
														__UpperCamelCase      :					List[str]       		='\n'.join(metrics['preds']    )
														generations_file.open('w+'    ).write(lowerCamelCase__    )


				@rank_zero_only
				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__    ):




									"""simple docstring"""

									try:
														__UpperCamelCase      :					Optional[Any]       		=pl_module.model.model.num_parameters()
									except AttributeError:
														__UpperCamelCase      :					Optional[Any]       		=pl_module.model.num_parameters()

									__UpperCamelCase      :					Optional[Any]       		=count_trainable_parameters(lowerCamelCase__    )
									# 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							,  lowerCamelCase__							,  lowerCamelCase__    ):




									"""simple docstring"""

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







				@rank_zero_only
				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__    ):




									"""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 sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

lowerCamelCase											=       'python tqdm regex requests packaging filelock numpy tokenizers'.split()
if sys.version_info < (3, 7):
 pkgs_to_check_at_runtime.append('dataclasses')
if sys.version_info < (3, 8):
 pkgs_to_check_at_runtime.append('importlib_metadata')

for pkg in pkgs_to_check_at_runtime:
 if pkg in deps:
  if pkg == "tokenizers":
   # must be loaded here, or else tqdm check may fail
   from .utils import is_tokenizers_available

   if not is_tokenizers_available():
    continue  # not required, check version only if installed

  require_version_core(deps[pkg])
 else:
  raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      str=None     ):
       '''simple docstring'''





       require_version(deps[pkg]		,			SCREAMING_SNAKE_CASE__     )


