






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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any					)		->			int:






	"""simple docstring"""



	UpperCamelCase     :int          =	filter(lambda __magic_name__					: p.requires_grad	,			model.parameters()					)
	UpperCamelCase     :List[str]          =	sum([np.prod(p.size()					) for p in model_parameters]					)
	return params


UpperCAmelCase_ :						str      =      logging.getLogger(__name__)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Dict					)		->			Dict:






	"""simple docstring"""



	if metric == "rouge2":
		UpperCamelCase     :Tuple          =	"""{val_avg_rouge2:.4f}-{step_count}"""
	elif metric == "bleu":
		UpperCamelCase     :List[Any]          =	"""{val_avg_bleu:.4f}-{step_count}"""
	elif metric == "em":
		UpperCamelCase     :Tuple          =	"""{val_avg_em:.4f}-{step_count}"""
	elif metric == "loss":
		UpperCamelCase     :Dict          =	"""{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."""					)

	UpperCamelCase     :List[str]          =	ModelCheckpoint(
	    dirpath=__magic_name__	,			filename=__magic_name__	,			monitor=f"""val_{metric}"""	,			mode="""max"""	,			save_top_k=1	,			every_n_epochs=1	,			)

	return checkpoint_callback

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any	,			__magic_name__					:  str					)		->			Optional[int]:






	"""simple docstring"""



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

class     _SCREAMING_SNAKE_CASE       (			pl.Callback      ):





	def 						_A (		self				:					str ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					Any	):
		UpperCamelCase     :Optional[int]          =	{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 						_A (		self				:					List[str] ,  __lowerCamelCase				:					pl.Trainer ,  __lowerCamelCase				:					pl.LightningModule ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					List[str]=True	):
		logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""	)
		UpperCamelCase     :str          =	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
		UpperCamelCase     :Union[str, Any]          =	Path(pl_module.hparams.output_dir	)
		if type_path == "test":
			UpperCamelCase     :Dict          =	od / """test_results.txt"""
			UpperCamelCase     :str          =	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.
			UpperCamelCase     :Any          =	od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
			UpperCamelCase     :List[str]          =	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
				UpperCamelCase     :int          =	metrics[key]
				if isinstance(__lowerCamelCase ,  torch.Tensor	):
					UpperCamelCase     :Any          =	val.item()
				UpperCamelCase     :Union[str, Any]          =	F"""{key}: {val:.6f}\n"""
				writer.write(__lowerCamelCase	)

		if not save_generations:
			return

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





	@rank_zero_only
	def 						_A (		self				:					str ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					str	):
		try:
			UpperCamelCase     :Union[str, Any]          =	pl_module.model.model.num_parameters()
		except AttributeError:
			UpperCamelCase     :int          =	pl_module.model.num_parameters()

		UpperCamelCase     :Union[str, Any]          =	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 						_A (		self				:					List[str] ,  __lowerCamelCase				:					pl.Trainer ,  __lowerCamelCase				:					pl.LightningModule	):
		save_json(pl_module.metrics ,  pl_module.metrics_save_path	)
		return self._write_logs(__lowerCamelCase ,  __lowerCamelCase ,  """test"""	)





	@rank_zero_only
	def 						_A (		self				:					Optional[int] ,  __lowerCamelCase				:					pl.Trainer ,  __lowerCamelCase				:					Union[str, Any]	):
		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 typing import Dict

from .base import GenericTensor, Pipeline







class 			__A( a			):




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								if tokenize_kwargs is None:
												__a										=    {}

								if truncation is not None:
												if "truncation" in tokenize_kwargs:
																raise ValueError(
																    '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''				)
												__a										=    truncation

								__a										=    tokenize_kwargs

								__a										=    {}
								if return_tensors is not None:
												__a										=    return_tensors

								return preprocess_params, {}, postprocess_params




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     **_snake_case				)    ->      Dict[str, GenericTensor]:


								'''simple docstring'''




								__a										=    self.framework
								__a										=    self.tokenizer(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)
								return model_inputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.model(**_snake_case				)
								return model_outputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=False				)    ->      Optional[int]:


								'''simple docstring'''




								if return_tensors:
												return model_outputs[0]
								if self.framework == "pt":
												return model_outputs[0].tolist()
								elif self.framework == "tf":
												return model_outputs[0].numpy().tolist()




				def __call__(     self		,     *_snake_case		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




								return super().__call__(*_snake_case		,     **_snake_case				)