






"""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 							snake_case (  A__		):
			UpperCAmelCase_	:					Optional[int]     	=   filter(lambda A__		: p.requires_grad      ,model.parameters()		)
			UpperCAmelCase_	:					Optional[Any]     	=   sum([np.prod(p.size()		) for p in model_parameters]		)
			return params


lowerCamelCase_							      = logging.getLogger(__name__)

def 							snake_case (  A__      ,A__		):
			if metric == "rouge2":
						UpperCAmelCase_	:					int     	=   "{val_avg_rouge2:.4f}-{step_count}"
			elif metric == "bleu":
						UpperCAmelCase_	:					Optional[Any]     	=   "{val_avg_bleu:.4f}-{step_count}"
			elif metric == "em":
						UpperCAmelCase_	:					Tuple     	=   "{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."		)

			UpperCAmelCase_	:					Dict     	=   ModelCheckpoint(
			    dirpath=A__      ,filename=A__      ,monitor=F"""val_{metric}"""      ,mode="max"      ,save_top_k=3      ,every_n_epochs=1      ,)
			return checkpoint_callback

def 							snake_case (  A__      ,A__		):
			return EarlyStopping(
			    monitor=F"""val_{metric}"""      ,mode="min" if "loss" in metric else "max"      ,patience=A__      ,verbose=A__      ,)







class      UpperCamelCase_			(pl.Callback	):





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

				if not save_generations:
							return

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





	@rank_zero_only
	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					List[str]		)		->     Dict:
				try:
							UpperCAmelCase_	:					Optional[Any]     	=   pl_module.model.model.num_parameters()
				except AttributeError:
							UpperCAmelCase_	:					Optional[Any]     	=   pl_module.model.num_parameters()

				UpperCAmelCase_	:					Tuple     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					pl.Trainer      ,      lowerCAmelCase_   :					pl.LightningModule		)		->     Optional[int]:
				save_json(pl_module.metrics      ,      pl_module.metrics_save_path		)
				return self._write_logs(lowerCAmelCase_      ,      lowerCAmelCase_      ,      "test"		)





	@rank_zero_only
	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					pl.Trainer      ,      lowerCAmelCase_   :					List[Any]		)		->     List[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")







# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

a			:			Dict												=					'''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
	raise ImportWarning(
	    '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
	)

if version.parse(pyarrow.__version__).major < 8:
	raise ImportWarning(
	    '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
	    '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
	)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

a			:			Optional[int]												=					concatenate_datasets
a			:			List[Any]												=					DownloadConfig
a			:			List[Any]												=					DownloadManager
a			:			str												=					DownloadMode
a			:			int												=					DownloadConfig
a			:			List[str]												=					DownloadMode
a			:			Optional[int]												=					DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


