





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      (    a) ->List[str]:
					lowerCamelCase__       =       filter(lambda a: p.requires_grad,     model.parameters())
					lowerCamelCase__       =       sum([np.prod(p.size()) for p in model_parameters])
					return params


A_												=     logging.getLogger(__name__)





def    __UpperCamelCase      (    a,     a) ->Union[str, Any]:
					if metric == "rouge2":
										lowerCamelCase__       =       "{val_avg_rouge2:.4f}-{step_count}"
					elif metric == "bleu":
										lowerCamelCase__       =       "{val_avg_bleu:.4f}-{step_count}"
					elif metric == "em":
										lowerCamelCase__       =       "{val_avg_em:.4f}-{step_count}"
					elif metric == "loss":
										lowerCamelCase__       =       "{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.")

					lowerCamelCase__       =       ModelCheckpoint(
					    dirpath=a,     filename=a,     monitor=f"val_{metric}",     mode="max",     save_top_k=1,     every_n_epochs=1,     )

					return checkpoint_callback





def    __UpperCamelCase      (    a,     a) ->str:
					return EarlyStopping(
					    monitor=f"val_{metric}",     mode="min" if "loss" in metric else "max",     patience=a,     verbose=a,     )







class SCREAMING_SNAKE_CASE_ (					pl.Callback   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       {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 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase=True	):
										logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"	)
										lowerCamelCase__       =       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
										lowerCamelCase__       =       Path(pl_module.hparams.output_dir	)
										if type_path == "test":
															lowerCamelCase__       =       od / "test_results.txt"
															lowerCamelCase__       =       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.
															lowerCamelCase__       =       od / F"{type_path}_results/{trainer.global_step:05d}.txt"
															lowerCamelCase__       =       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
																				lowerCamelCase__       =       metrics[key]
																				if isinstance(_lowerCAmelCase	,  torch.Tensor	):
																									lowerCamelCase__       =       val.item()
																				lowerCamelCase__       =       F"{key}: {val:.6f}\n"
																				writer.write(_lowerCAmelCase	)

										if not save_generations:
															return

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





					@rank_zero_only
					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										try:
															lowerCamelCase__       =       pl_module.model.model.num_parameters()
										except AttributeError:
															lowerCamelCase__       =       pl_module.model.num_parameters()

										lowerCamelCase__       =       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 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										save_json(pl_module.metrics	,  pl_module.metrics_save_path	)
										return self._write_logs(_lowerCAmelCase	,  _lowerCAmelCase	,  "test"	)






					@rank_zero_only
					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										save_json(pl_module.metrics	,  pl_module.metrics_save_path	)
										# Uncommenting this will save val generations
										# return self._write_logs(trainer, pl_module, "valid")








from typing import TYPE_CHECKING

from ...utils import _LazyModule


A_												=     {"tokenization_byt5": ["ByT5Tokenizer"]}


if TYPE_CHECKING:
						from .tokenization_byta import ByTaTokenizer
else:
						import sys

						A_												=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

