"""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      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





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


__lowerCamelCase							 =     logging.getLogger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""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}"""
							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=_lowerCamelCase				,			filename=_lowerCamelCase				,			monitor=F'''val_{metric}'''				,			mode='max'				,			save_top_k=3				,			every_n_epochs=1				,			)
							return checkpoint_callback






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





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


class 			UpperCamelCase__(	pl.Callback       ):





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Union[str, Any]:
													A__		   =							{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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=True   )    -> int:
													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=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
																											A__		   =							metrics[key]
																											if isinstance(lowerCAmelCase__						,torch.Tensor   ):
																																		A__		   =							val.item()
																											A__		   =							f'''{key}: {val:.6f}\n'''
																											writer.write(lowerCAmelCase__   )

													if not save_generations:
																				return

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





						@rank_zero_only
						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Optional[int]:






													try:
																				A__		   =							pl_module.model.model.num_parameters()
													except AttributeError:
																				A__		   =							pl_module.model.num_parameters()

													A__		   =							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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Optional[int]:
													save_json(pl_module.metrics						,pl_module.metrics_save_path   )
													return self._write_logs(lowerCAmelCase__						,lowerCAmelCase__						,'test'   )





						@rank_zero_only
						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Optional[int]:
													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 .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

UpperCamelCase__       :	Dict    = [
    '''python''',
    '''tqdm''',
    '''regex''',
    '''requests''',
    '''packaging''',
    '''filelock''',
    '''numpy''',
    '''tokenizers''',
    '''huggingface-hub''',
    '''safetensors''',
    '''accelerate''',
    '''pyyaml''',
]

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
												elif pkg == "accelerate":
																		# must be loaded here, or else tqdm check may fail
																		from .utils import is_accelerate_available

																		# Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
																		# Transformers with PyTorch
																		if not is_accelerate_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 				lowerCAmelCase_   (	_lowerCamelCase:   List[Any]       ,      _lowerCamelCase:   Optional[int]=None					):
		require_version(deps[pkg]       ,      _lowerCamelCase					)