




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 							__SCREAMING_SNAKE_CASE  (			snake_case_						):

						'''simple docstring'''


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


lowercase_	:				List[Any]  				= logging.getLogger(__name__)


def 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_						):

						'''simple docstring'''


						if metric == "rouge2":
												_UpperCAmelCase													=     "{val_avg_rouge2:.4f}-{step_count}"
						elif metric == "bleu":
												_UpperCAmelCase													=     "{val_avg_bleu:.4f}-{step_count}"
						elif metric == "em":
												_UpperCAmelCase													=     "{val_avg_em:.4f}-{step_count}"
						elif metric == "loss":
												_UpperCAmelCase													=     "{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."						)

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

						return checkpoint_callback


def 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_						):

						'''simple docstring'''


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






class     __lowerCAmelCase (       pl.Callback ):







			def UpperCamelCase			(       self    :			Any    ,    snake_case__    :			Dict    ,    snake_case__    :			Optional[int]      ):




									"""simple docstring"""


									_UpperCAmelCase													=     {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 UpperCamelCase			(       self    :			Optional[Any]    ,    snake_case__    :			pl.Trainer    ,    snake_case__    :			pl.LightningModule    ,    snake_case__    :			str    ,    snake_case__    :			List[str]=True      ):




									"""simple docstring"""


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

									if not save_generations:
															return

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







			@rank_zero_only
			def UpperCamelCase			(       self    :			Optional[Any]    ,    snake_case__    :			str    ,    snake_case__    :			List[Any]      ):




									"""simple docstring"""


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

									_UpperCAmelCase													=     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 UpperCamelCase			(       self    :			List[str]    ,    snake_case__    :			pl.Trainer    ,    snake_case__    :			pl.LightningModule      ):




									"""simple docstring"""


									save_json(pl_module.metrics    ,    pl_module.metrics_save_path      )
									return self._write_logs(_UpperCamelCase    ,    _UpperCamelCase    ,    "test"      )







			@rank_zero_only
			def UpperCamelCase			(       self    :			Union[str, Any]    ,    snake_case__    :			pl.Trainer    ,    snake_case__    :			List[str]      ):




									"""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")











def _a				(	lowerCamelCase:   int = 2_00 )	->						int:







      '''simple docstring'''






      __A          =				[1, 2, 5, 10, 20, 50, 1_00, 2_00]
      __A          =				[0] * (pence + 1)
      __A          =				1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(lowerCamelCase					,				pence + 1					,				1 ):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73682





