

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 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   str					  =							filter(lambda _a: p.requires_grad				,      model.parameters())
	SCREAMING_SNAKE_CASE :   Dict					  =							sum([np.prod(p.size()) for p in model_parameters])
	return params


a_      =     logging.getLogger(__name__)

def 			lowerCamelCase__			(       _a				,      _a):




	if metric == "rouge2":
		SCREAMING_SNAKE_CASE :   Dict					  =							"{val_avg_rouge2:.4f}-{step_count}"
	elif metric == "bleu":
		SCREAMING_SNAKE_CASE :   Dict					  =							"{val_avg_bleu:.4f}-{step_count}"
	elif metric == "em":
		SCREAMING_SNAKE_CASE :   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.")

	SCREAMING_SNAKE_CASE :   Optional[int]					  =							ModelCheckpoint(
	    dirpath=_SCREAMING_SNAKE_CASE				,      filename=_SCREAMING_SNAKE_CASE				,      monitor=f"val_{metric}"				,      mode="max"				,      save_top_k=3				,      every_n_epochs=1				,      )
	return checkpoint_callback

def 			lowerCamelCase__			(       _a				,      _a):
	return EarlyStopping(
	    monitor=f"val_{metric}"				,      mode="min" if "loss" in metric else "max"				,      patience=_SCREAMING_SNAKE_CASE				,      verbose=_SCREAMING_SNAKE_CASE				,      )






class _UpperCamelCase				(				pl.Callback ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			Optional[int]   ,   a				:			Optional[int]						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							{F"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
				pl_module.logger.log_metrics(snake_case_						)
			@rank_zero_only
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			List[str]   ,   a				:			Any   ,   a				:			int   ,   a				:			List[str]=True						)				->							int:

				"""simple docstring"""

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

				if not save_generations:
					return

				if "preds" in metrics:
					SCREAMING_SNAKE_CASE :   Tuple					  =							"\n".join(metrics["preds"]						)
					generations_file.open("w+"						).write(snake_case_						)
			@rank_zero_only
			def 				__UpperCamelCase			(      self				:			List[str]   ,   a				:			Any   ,   a				:			Union[str, Any]						)				->							Tuple:

				"""simple docstring"""

				try:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pl_module.model.model.num_parameters()
				except AttributeError:
					SCREAMING_SNAKE_CASE :   List[str]					  =							pl_module.model.num_parameters()

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							count_trainable_parameters(snake_case_						)
				# 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				:			Any   ,   a				:			Optional[Any]   ,   a				:			int						)				->							Any:

				"""simple docstring"""

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






			@rank_zero_only
			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			List[str]   ,   a				:			Dict						)				->							Dict:

				"""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")






from collections import defaultdict
from math import ceil, sqrt





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 1_000_000							,     _SCREAMING_SNAKE_CASE = 10				)							-> int:




    """simple docstring"""




    _A     =							defaultdict(_SCREAMING_SNAKE_CASE				)

    for outer_width in range(3							,     (t_limit // 4) + 2				):
        if outer_width * outer_width > t_limit:
            _A     =							max(
                ceil(sqrt(outer_width * outer_width - t_limit				)				)							,     1				)
        else:
            _A     =							1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(_SCREAMING_SNAKE_CASE							,     outer_width - 1							,     2				):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10				)


if __name__ == "__main__":
 print(f"{solution() = }")





