


"""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__	(lowerCAmelCase_       ):




					'''simple docstring'''


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


a__    :							Optional[int]            =					logging.getLogger(__name__)




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					if metric == "rouge2":
										__SCREAMING_SNAKE_CASE						       =    "{val_avg_rouge2:.4f}-{step_count}"
					elif metric == "bleu":
										__SCREAMING_SNAKE_CASE						       =    "{val_avg_bleu:.4f}-{step_count}"
					elif metric == "em":
										__SCREAMING_SNAKE_CASE						       =    "{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						       =    ModelCheckpoint(
					    dirpath=lowerCAmelCase_  ,						filename=lowerCAmelCase_  ,						monitor=f"""val_{metric}"""  ,						mode="max"  ,						save_top_k=3  ,						every_n_epochs=1  ,						)
					return checkpoint_callback




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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




class 			UpperCamelCase_     (			pl.Callback):

	"""simple docstring"""





	def   UpperCAmelCase_				(			self      :    List[Any]      ,      UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    int       )	->     str:
						__SCREAMING_SNAKE_CASE						       =    {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      :    str      ,      UpperCAmelCase__      :    pl.Trainer      ,      UpperCAmelCase__      :    pl.LightningModule      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Any=True       )	->     None:
						logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""       )
						__SCREAMING_SNAKE_CASE						       =    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						       =    Path(pl_module.hparams.output_dir       )
						if type_path == "test":
											__SCREAMING_SNAKE_CASE						       =    od / "test_results.txt"
											__SCREAMING_SNAKE_CASE						       =    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						       =    od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
											__SCREAMING_SNAKE_CASE						       =    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
																__SCREAMING_SNAKE_CASE						       =    metrics[key]
																if isinstance(UpperCAmelCase__      ,      torch.Tensor       ):
																					__SCREAMING_SNAKE_CASE						       =    val.item()
																__SCREAMING_SNAKE_CASE						       =    F"""{key}: {val:.6f}\n"""
																writer.write(UpperCAmelCase__       )

						if not save_generations:
											return

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




	@rank_zero_only
	def   UpperCAmelCase_				(			self      :    int      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    int       )	->     Tuple:
						try:
											__SCREAMING_SNAKE_CASE						       =    pl_module.model.model.num_parameters()
						except AttributeError:
											__SCREAMING_SNAKE_CASE						       =    pl_module.model.num_parameters()

						__SCREAMING_SNAKE_CASE						       =    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      :    Optional[int]      ,      UpperCAmelCase__      :    pl.Trainer      ,      UpperCAmelCase__      :    pl.LightningModule       )	->     List[Any]:
						save_json(pl_module.metrics      ,      pl_module.metrics_save_path       )
						return self._write_logs(UpperCAmelCase__      ,      UpperCAmelCase__      ,      "test"       )




	@rank_zero_only
	def   UpperCAmelCase_				(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    pl.Trainer      ,      UpperCAmelCase__      :    Optional[int]       )	->     Dict:
						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 __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return math.sqrt(sum(pow(a - b  ,						2       ) for a, b in zip(lowerCAmelCase_  ,						lowerCAmelCase_       )       )       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					if dataset.ndim != value_array.ndim:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Wrong input data's dimensions... "
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(lowerCAmelCase_       )

					try:
										if dataset.shape[1] != value_array.shape[1]:
															__SCREAMING_SNAKE_CASE						       =    (
															    "Wrong input data's shape... "
															    f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
															)
															raise ValueError(lowerCAmelCase_       )
					except IndexError:
										if dataset.ndim != value_array.ndim:
															raise TypeError("Wrong shape"       )

					if dataset.dtype != value_array.dtype:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Input data have different datatype... "
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    []

					for value in value_array:
										__SCREAMING_SNAKE_CASE						       =    euclidean(lowerCAmelCase_  ,						dataset[0]       )
										__SCREAMING_SNAKE_CASE						       =    dataset[0].tolist()

										for dataset_value in dataset[1:]:
															__SCREAMING_SNAKE_CASE						       =    euclidean(lowerCAmelCase_  ,						lowerCAmelCase_       )

															if dist > temp_dist:
																				__SCREAMING_SNAKE_CASE						       =    temp_dist
																				__SCREAMING_SNAKE_CASE						       =    dataset_value.tolist()

										answer.append([vector, dist]       )

					return answer




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return np.dot(lowerCAmelCase_  ,						lowerCAmelCase_       ) / (norm(lowerCAmelCase_       ) * norm(lowerCAmelCase_       ))


if __name__ == "__main__":
		import doctest

		doctest.testmod()
