


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					( UpperCamelCase__:  Tuple    )			-> Dict:





	"""simple docstring"""







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


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





def 	_lowerCAmelCase					( UpperCamelCase__:  Tuple				,       UpperCamelCase__:  int    )			-> Any:





	"""simple docstring"""







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

	A             =					ModelCheckpoint(
	    dirpath=UpperCamelCase__				,       filename=UpperCamelCase__				,       monitor=f'val_{metric}'				,       mode="""max"""				,       save_top_k=1				,       every_n_epochs=1				,       )

	return checkpoint_callback





def 	_lowerCAmelCase					( UpperCamelCase__:  Dict				,       UpperCamelCase__:  Dict    )			-> Tuple:





	"""simple docstring"""







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






class    _UpperCamelCase     (       pl.Callback					):

				"""simple docstring"""







				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					Tuple:
					A             =					{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   _UpperCAmelCase       (    self					, a__					, a__					, a__					, a__=True )						->					None:
					logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****' )
					A             =					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
					A             =					Path(pl_module.hparams.output_dir )
					if type_path == "test":
						A             =					od / """test_results.txt"""
						A             =					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.
						A             =					od / f'{type_path}_results/{trainer.global_step:05d}.txt'
						A             =					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
							A             =					metrics[key]
							if isinstance(a__					, torch.Tensor ):
								A             =					val.item()
							A             =					f'{key}: {val:.6f}\n'
							writer.write(a__ )

					if not save_generations:
						return

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

				@rank_zero_only
				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					Union[str, Any]:
					try:
						A             =					pl_module.model.model.num_parameters()
					except AttributeError:
						A             =					pl_module.model.num_parameters()

					A             =					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   _UpperCAmelCase       (    self					, a__					, a__ )						->					Optional[int]:
					save_json(pl_module.metrics					, pl_module.metrics_save_path )
					return self._write_logs(a__					, a__					, """test""" )

				@rank_zero_only
				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					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")







import numpy as np

from transformers import Pipeline





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]    )			-> Optional[int]:





	"""simple docstring"""







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






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def   _UpperCAmelCase       (    self					, **a__ )						->					Union[str, Any]:
					A             =					{}
					if "second_text" in kwargs:
						A             =					kwargs["""second_text"""]
					return preprocess_kwargs, {}, {}

				def   _UpperCAmelCase       (    self					, a__					, a__=None )						->					str:
					return self.tokenizer(a__					, text_pair=a__					, return_tensors=self.framework )

				def   _UpperCAmelCase       (    self					, a__ )						->					Tuple:
					return self.model(**a__ )

				def   _UpperCAmelCase       (    self					, a__ )						->					Optional[int]:
					A             =					model_outputs.logits[0].numpy()
					A             =					softmax(a__ )

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



