





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__(__snake_case							)				->      List[Any]:







	'''simple docstring'''







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


_a 						=      logging.getLogger(__name__)





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







	'''simple docstring'''







	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}'''
	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=__snake_case       ,filename=__snake_case       ,monitor=F'val_{metric}'       ,mode='''max'''       ,save_top_k=3       ,every_n_epochs=1       ,)
	return checkpoint_callback





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      str:







	'''simple docstring'''







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






class 					__A    (    pl.Callback ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				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 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase=True							):



				'''simple docstring'''







				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 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				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 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				save_json(pl_module.metrics       ,  pl_module.metrics_save_path							)
				return self._write_logs(__lowerCAmelCase       ,  __lowerCAmelCase       ,  '''test'''							)





			@rank_zero_only
			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''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")










import numpy as np

from transformers import Pipeline





def lowerCAmelCase__(__snake_case							)				->      Tuple:







	'''simple docstring'''







	lowerCamelCase__								=		np.max(__snake_case       ,axis=-1       ,keepdims=__snake_case							)
	lowerCamelCase__								=		np.exp(outputs - maxes							)
	return shifted_exp / shifted_exp.sum(axis=-1       ,keepdims=__snake_case							)






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		{}
				if "second_text" in kwargs:
					lowerCamelCase__								=		kwargs['''second_text''']
				return preprocess_kwargs, {}, {}





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=None							):



				'''simple docstring'''







				return self.tokenizer(__lowerCAmelCase       ,  text_pair=__lowerCAmelCase       ,  return_tensors=self.framework							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.model(**__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		model_outputs.logits[0].numpy()
				lowerCamelCase__								=		softmax(__lowerCAmelCase							)

				lowerCamelCase__								=		np.argmax(__lowerCAmelCase							)
				lowerCamelCase__								=		self.model.config.idalabel[best_class]
				lowerCamelCase__								=		probabilities[best_class].item()
				lowerCamelCase__								=		logits.tolist()
				return {"label": label, "score": score, "logits": logits}



