


'''simple docstring'''


import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard






@pytest.mark.parametrize(
    "compression_format, is_archive"					,       [
        ("7z", True),
        ("bz2", False),
        ("gzip", False),
        ("lz4", False),
        ("tar", True),
        ("xz", False),
        ("zip", True),
        ("zstd", False),
    ]					,       )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       ):
		_UpperCAmelCase						:				List[str]	    =					{
		    "7z": (seven_zip_file, SevenZipExtractor),
		    "bz2": (bza_file, BzipaExtractor),
		    "gzip": (gz_file, GzipExtractor),
		    "lz4": (lza_file, LzaExtractor),
		    "tar": (tar_file, TarExtractor),
		    "xz": (xz_file, XzExtractor),
		    "zip": (zip_file, ZipExtractor),
		    "zstd": (zstd_file, ZstdExtractor),
		}
		_UpperCAmelCase    ,			_UpperCAmelCase						:				Tuple	    =					input_paths_and_base_extractors[compression_format]
		if input_path is None:
				_UpperCAmelCase						:				Any	    =					F"""for '{compression_format}' compression_format, """
				if compression_format == "7z":
						reason += require_pyazr.kwargs["reason"]
				elif compression_format == "lz4":
						reason += require_lza.kwargs["reason"]
				elif compression_format == "zstd":
						reason += require_zstandard.kwargs["reason"]
				pytest.skip(__lowerCAmelCase      )
		assert base_extractor.is_extractable(__lowerCAmelCase      )
		_UpperCAmelCase						:				Union[str, Any]	    =					tmp_path / ("extracted" if is_archive else "extracted.txt")
		base_extractor.extract(__lowerCAmelCase					,       __lowerCAmelCase      )
		if is_archive:
				assert output_path.is_dir()
				for file_path in output_path.iterdir():
						assert file_path.name == text_file.name
						_UpperCAmelCase						:				List[Any]	    =					file_path.read_text(encoding="utf-8"      )
		else:
				_UpperCAmelCase						:				Optional[Any]	    =					output_path.read_text(encoding="utf-8"      )
		_UpperCAmelCase						:				str	    =					text_file.read_text(encoding="utf-8"      )
		assert extracted_file_content == expected_file_content






@pytest.mark.parametrize(
    "compression_format, is_archive"					,       [
        ("7z", True),
        ("bz2", False),
        ("gzip", False),
        ("lz4", False),
        ("tar", True),
        ("xz", False),
        ("zip", True),
        ("zstd", False),
    ]					,       )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       ):
		_UpperCAmelCase						:				Tuple	    =					{
		    "7z": seven_zip_file,
		    "bz2": bza_file,
		    "gzip": gz_file,
		    "lz4": lza_file,
		    "tar": tar_file,
		    "xz": xz_file,
		    "zip": zip_file,
		    "zstd": zstd_file,
		}
		_UpperCAmelCase						:				List[Any]	    =					input_paths[compression_format]
		if input_path is None:
				_UpperCAmelCase						:				Optional[int]	    =					F"""for '{compression_format}' compression_format, """
				if compression_format == "7z":
						reason += require_pyazr.kwargs["reason"]
				elif compression_format == "lz4":
						reason += require_lza.kwargs["reason"]
				elif compression_format == "zstd":
						reason += require_zstandard.kwargs["reason"]
				pytest.skip(__lowerCAmelCase      )
		_UpperCAmelCase						:				Optional[int]	    =					Extractor.infer_extractor_format(__lowerCAmelCase      )
		assert extractor_format is not None
		_UpperCAmelCase						:				Dict	    =					tmp_path / ("extracted" if is_archive else "extracted.txt")
		Extractor.extract(__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      )
		if is_archive:
				assert output_path.is_dir()
				for file_path in output_path.iterdir():
						assert file_path.name == text_file.name
						_UpperCAmelCase						:				Any	    =					file_path.read_text(encoding="utf-8"      )
		else:
				_UpperCAmelCase						:				Dict	    =					output_path.read_text(encoding="utf-8"      )
		_UpperCAmelCase						:				Dict	    =					text_file.read_text(encoding="utf-8"      )
		assert extracted_file_content == expected_file_content






@pytest.fixture
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		import tarfile

		_UpperCAmelCase						:				Dict	    =					tmp_path / "data_dot_dot"
		directory.mkdir()
		_UpperCAmelCase						:				Any	    =					directory / "tar_file_with_dot_dot.tar"
		with tarfile.TarFile(__lowerCAmelCase					,       "w"      ) as f:
				f.add(__lowerCAmelCase					,       arcname=os.path.join(".."					,       text_file.name      )      )
		return path






@pytest.fixture
def        __lowerCAmelCase       (__lowerCAmelCase      ):
		import tarfile

		_UpperCAmelCase						:				Optional[Any]	    =					tmp_path / "data_sym_link"
		directory.mkdir()
		_UpperCAmelCase						:				List[Any]	    =					directory / "tar_file_with_sym_link.tar"
		os.symlink(".."					,       directory / "subdir"					,       target_is_directory=__lowerCAmelCase      )
		with tarfile.TarFile(__lowerCAmelCase					,       "w"      ) as f:
				f.add(str(directory / "subdir"      )					,       arcname="subdir"      )  # str required by os.readlink on Windows and Python < 3.8
		return path






@pytest.mark.parametrize(
    "insecure_tar_file, error_log"					,       [("tar_file_with_dot_dot", "illegal path"), ("tar_file_with_sym_link", "Symlink")]					,       )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					{
		    "tar_file_with_dot_dot": tar_file_with_dot_dot,
		    "tar_file_with_sym_link": tar_file_with_sym_link,
		}
		_UpperCAmelCase						:				Any	    =					insecure_tar_files[insecure_tar_file]
		_UpperCAmelCase						:				Dict	    =					tmp_path / "extracted"
		TarExtractor.extract(__lowerCAmelCase					,       __lowerCAmelCase      )
		assert caplog.text
		for record in caplog.records:
				assert record.levelname == "ERROR"
				assert error_log in record.msg






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		# We should have less false positives than zipfile.is_zipfile
		# We do that by checking only the magic number
		_UpperCAmelCase						:				Union[str, Any]	    =					tmpdir / "not_a_zip_file"
		# From: https://github.com/python/cpython/pull/5053
		_UpperCAmelCase						:				Optional[Any]	    =					(
		    B"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"
		    B"\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6'\x00\x00\x00\x15I"
		    B"DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"
		    B"\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"
		)
		with not_a_zip_file.open("wb"      ) as f:
				f.write(__lowerCAmelCase      )
		assert zipfile.is_zipfile(str(__lowerCAmelCase      )      )  # is a false positive for `zipfile`
		assert not ZipExtractor.is_extractable(__lowerCAmelCase      )  # but we're right







'''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        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Dict	    =					filter(lambda __lowerCAmelCase      : 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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		if metric == "rouge2":
				_UpperCAmelCase						:				List[str]	    =					"{val_avg_rouge2:.4f}-{step_count}"
		elif metric == "bleu":
				_UpperCAmelCase						:				int	    =					"{val_avg_bleu:.4f}-{step_count}"
		elif metric == "em":
				_UpperCAmelCase						:				str	    =					"{val_avg_em:.4f}-{step_count}"
		elif metric == "loss":
				_UpperCAmelCase						:				List[Any]	    =					"{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						:				Any	    =					ModelCheckpoint(
		    dirpath=__lowerCAmelCase					,       filename=__lowerCAmelCase					,       monitor=F"""val_{metric}"""					,       mode="max"					,       save_top_k=1					,       every_n_epochs=1					,       )

		return checkpoint_callback






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






class 		lowerCAmelCase__	(	pl.Callback		):




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   Optional[int]		,     lowerCamelCase__     :   Optional[int]       )      ->int:



					'''simple docstring'''







					_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 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   pl.Trainer		,     lowerCamelCase__     :   pl.LightningModule		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Union[str, Any]=True       )      ->None:



					'''simple docstring'''







					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						:				Any	    =					Path(pl_module.hparams.output_dir       )
					if type_path == "test":
							_UpperCAmelCase						:				List[Any]	    =					od / "test_results.txt"
							_UpperCAmelCase						:				Union[str, 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						:				Optional[Any]	    =					od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
							_UpperCAmelCase						:				Dict	    =					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						:				Dict	    =					metrics[key]
									if isinstance(lowerCamelCase__		,     torch.Tensor       ):
											_UpperCAmelCase						:				Tuple	    =					val.item()
									_UpperCAmelCase						:				str	    =					F"""{key}: {val:.6f}\n"""
									writer.write(lowerCamelCase__       )

					if not save_generations:
							return

					if "preds" in metrics:
							_UpperCAmelCase						:				Optional[int]	    =					"\n".join(metrics["preds"]       )
							generations_file.open("w+"       ).write(lowerCamelCase__       )




			@rank_zero_only
			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Optional[int]		,     lowerCamelCase__     :   Optional[int]       )      ->Any:



					'''simple docstring'''







					try:
							_UpperCAmelCase						:				Tuple	    =					pl_module.model.model.num_parameters()
					except AttributeError:
							_UpperCAmelCase						:				int	    =					pl_module.model.num_parameters()

					_UpperCAmelCase						:				int	    =					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     :   Dict		,     lowerCamelCase__     :   pl.Trainer		,     lowerCamelCase__     :   pl.LightningModule       )      ->int:



					'''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     :   Tuple		,     lowerCamelCase__     :   pl.Trainer		,     lowerCamelCase__     :   Union[str, Any]       )      ->Union[str, 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")



