




'''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_(     snake_case							:	List[Any]					):

							'''simple docstring'''






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


_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			logging.getLogger(__name__)


def 					UpperCamelCase_(     snake_case							:	List[str]							,    snake_case							:	Tuple					):

							'''simple docstring'''






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

							snake_case_      =				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 					UpperCamelCase_(     snake_case							:	Dict							,    snake_case							:	List[Any]					):

							'''simple docstring'''






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




class 			_snake_case		(     pl.Callback							):




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__							)    ->    List[str]:

										'''simple docstring'''




										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(a__							)




			@rank_zero_only
			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__    ,							a__=True							)    ->    None:

										'''simple docstring'''




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

										if not save_generations:
																	return

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




			@rank_zero_only
			def        lowerCAmelCase__    ( self    ,							a__    ,							a__							)    ->    Optional[Any]:

										'''simple docstring'''




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

										snake_case_      =				count_trainable_parameters(a__							)
										# 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        lowerCAmelCase__    ( self    ,							a__    ,							a__							)    ->    int:

										'''simple docstring'''




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




			@rank_zero_only
			def        lowerCAmelCase__    ( self    ,							a__    ,							a__							)    ->    List[Any]:

										'''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")










'''simple docstring'''





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin




class 			_snake_case		(     unittest.TestCase						,						lowercase_							):




			def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				load_tool("text-classification"							)
										self.tool.setup()
										snake_case_      =				load_tool("text-classification"    ,							remote=a__							)




			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										snake_case_      =				self.tool("That's quite cool"    ,							["positive", "negative"]							)
										self.assertEqual(a__    ,							"positive"							)




			def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				self.remote_tool("That's quite cool"    ,							["positive", "negative"]							)
										self.assertEqual(a__    ,							"positive"							)




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										snake_case_      =				self.tool(text="That's quite cool"    ,							labels=["positive", "negative"]							)
										self.assertEqual(a__    ,							"positive"							)




			def        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




										snake_case_      =				self.remote_tool(text="That's quite cool"    ,							labels=["positive", "negative"]							)
										self.assertEqual(a__    ,							"positive"							)




