




import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


SCREAMING_SNAKE_CASE       = get_logger(__name__)







def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	str:
 return TestCommand(
     args.dataset    ,				args.name    ,				args.cache_dir    ,				args.data_dir    ,				args.all_configs    ,				args.save_info or args.save_infos    ,				args.ignore_verifications    ,				args.force_redownload    ,				args.clear_cache    ,				)






class 			UpperCAmelCase_						(		a_							):
    lowercase__		      =   False  # to tell pytest it's not a test class




    @staticmethod
    def 							__magic_name__							(					snake_case_		: Optional[int]   )   ->							Optional[int]:




     '''simple docstring'''

     A__ =       parser.add_parser("test"			,				help="Test dataset implementation."   )
     test_parser.add_argument("--name"			,				type=_snake_case			,				default=_snake_case			,				help="Dataset processing name"   )
     test_parser.add_argument(
         "--cache_dir"			,				type=_snake_case			,				default=_snake_case			,				help="Cache directory where the datasets are stored."			,				)
     test_parser.add_argument(
         "--data_dir"			,				type=_snake_case			,				default=_snake_case			,				help="Can be used to specify a manual directory to get the files from."			,				)
     test_parser.add_argument("--all_configs"			,				action="store_true"			,				help="Test all dataset configurations"   )
     test_parser.add_argument(
         "--save_info"			,				action="store_true"			,				help="Save the dataset infos in the dataset card (README.md)"   )
     test_parser.add_argument(
         "--ignore_verifications"			,				action="store_true"			,				help="Run the test without checksums and splits checks."			,				)
     test_parser.add_argument("--force_redownload"			,				action="store_true"			,				help="Force dataset redownload"   )
     test_parser.add_argument(
         "--clear_cache"			,				action="store_true"			,				help="Remove downloaded files and cached datasets after each config test"			,				)
     # aliases
     test_parser.add_argument("--save_infos"			,				action="store_true"			,				help="alias to save_info"   )
     test_parser.add_argument("dataset"			,				type=_snake_case			,				help="Name of the dataset to download"   )
     test_parser.set_defaults(func=_test_command_factory   )




    def __init__(					self		: Optional[Any]			,				snake_case_		: Union[str, Any]			,				snake_case_		: Optional[Any]			,				snake_case_		: Union[str, Any]			,				snake_case_		: Optional[Any]			,				snake_case_		: Union[str, Any]			,				snake_case_		: Dict			,				snake_case_		: Union[str, Any]			,				snake_case_		: List[str]			,				snake_case_		: Optional[int]			,				)   ->							Tuple:




     '''simple docstring'''

     A__ =       dataset
     A__ =       name
     A__ =       cache_dir
     A__ =       data_dir
     A__ =       all_configs
     A__ =       save_infos
     A__ =       ignore_verifications
     A__ =       force_redownload
     A__ =       clear_cache
     if clear_cache and not cache_dir:
      print(
          "When --clear_cache is used, specifying a cache directory is mandatory.\n"
          "The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"
          "Please provide a --cache_dir that will be used to test the dataset script."   )
      exit(1   )
     if save_infos:
      A__ =       True




    def 							__magic_name__							(					self		: str   )   ->							Optional[Any]:




     '''simple docstring'''

     fl_logger().setLevel(_snake_case   )
     if self._name is not None and self._all_configs:
      print("Both parameters `config` and `all_configs` can\'t be used at once."   )
      exit(1   )
     A__ =       self._dataset, self._name
     A__ =       dataset_module_factory(_snake_case   )
     A__ =       import_main_class(module.module_path   )
     A__ =       len(builder_cls.BUILDER_CONFIGS   ) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

     def get_builders() -> Generator[DatasetBuilder, None, None]:
      if self._all_configs and builder_cls.BUILDER_CONFIGS:
       for i, config in enumerate(builder_cls.BUILDER_CONFIGS   ):
        if "config_name" in module.builder_kwargs:
         yield builder_cls(
             cache_dir=self._cache_dir			,				data_dir=self._data_dir			,				**module.builder_kwargs			,				)
        else:
         yield builder_cls(
             config_name=config.name			,				cache_dir=self._cache_dir			,				data_dir=self._data_dir			,				**module.builder_kwargs			,				)
      else:
       if "config_name" in module.builder_kwargs:
        yield builder_cls(cache_dir=self._cache_dir			,				data_dir=self._data_dir			,				**module.builder_kwargs   )
       else:
        yield builder_cls(
            config_name=_snake_case			,				cache_dir=self._cache_dir			,				data_dir=self._data_dir			,				**module.builder_kwargs			,				)

     for j, builder in enumerate(get_builders()   ):
      print(F"""Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})"""   )
      A__ =       os.path.exists(
          os.path.join(builder.get_imported_module_dir()			,				datasets.config.DATASETDICT_INFOS_FILENAME   )   )  # record checksums only if we need to update a (deprecated) dataset_infos.json
      builder.download_and_prepare(
          download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
          if not self._force_redownload
          else DownloadMode.FORCE_REDOWNLOAD			,				verification_mode=VerificationMode.NO_CHECKS
          if self._ignore_verifications
          else VerificationMode.ALL_CHECKS			,				try_from_hf_gcs=_snake_case			,				)
      builder.as_dataset()
      if self._save_infos:
       builder._save_infos()

      # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
      # The dataset_infos are saved in the YAML part of the README.md

      # Let's move it to the original directory of the dataset script, to allow the user to
      # upload them on S3 at the same time afterwards.
      if self._save_infos:
       A__ =       os.path.join(builder_cls.get_imported_module_dir()			,				"README.md"   )
       A__ =       Path(_snake_case   ).name + '''.py'''
       A__ =       os.path.join(_snake_case			,				_snake_case   )
       if os.path.isfile(_snake_case   ):
        A__ =       os.path.dirname(_snake_case   )
       elif os.path.isfile(_snake_case   ):
        A__ =       path
       elif os.path.isdir(_snake_case   ):  # for local directories containing only data files
        A__ =       path
       else:  # in case of a remote dataset
        A__ =       None
        print(F"""Dataset card saved at {dataset_readme_path}"""   )

       # Move dataset_info back to the user
       if dataset_dir is not None:
        A__ =       os.path.join(_snake_case			,				"README.md"   )
        copyfile(_snake_case			,				_snake_case   )
        print(F"""Dataset card saved at {user_dataset_readme_path}"""   )

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
      if self._clear_cache:
       if os.path.isdir(builder._cache_dir   ):
        logger.warning(F"""Clearing cache at {builder._cache_dir}"""   )
        rmtree(builder._cache_dir   )
       A__ =       os.path.join(self._cache_dir			,				datasets.config.DOWNLOADED_DATASETS_DIR   )
       if os.path.isdir(_snake_case   ):
        logger.warning(F"""Clearing cache at {download_dir}"""   )
        rmtree(_snake_case   )

     print("Test successful."   )











"""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 import save_json







def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	List[Any]:
 A__ =       filter(lambda lowercase_		: p.requires_grad    ,				model.parameters()		)
 A__ =       sum([np.prod(p.size()		) for p in model_parameters]		)
 return params


SCREAMING_SNAKE_CASE       = logging.getLogger(__name__)




class 			UpperCAmelCase_						(		pl.Callback							):




    def 							__magic_name__							(					self		: List[str]			,				snake_case_		: Optional[Any]			,				snake_case_		: Tuple   )   ->							Optional[int]:




     '''simple docstring'''

     A__ =       {F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
     pl_module.logger.log_metrics(snake_case_   )




    @rank_zero_only
    def 							__magic_name__							(					self		: Dict			,				snake_case_		: pl.Trainer			,				snake_case_		: pl.LightningModule			,				snake_case_		: str			,				snake_case_		: List[str]=True   )   ->							None:




     '''simple docstring'''

     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=snake_case_   )
      generations_file.parent.mkdir(exist_ok=snake_case_   )
     with open(snake_case_			,				"a+"   ) as writer:
      for key in sorted(snake_case_   ):
       if key in ["log", "progress_bar", "preds"]:
        continue
       A__ =       metrics[key]
       if isinstance(snake_case_			,				torch.Tensor   ):
        A__ =       val.item()
       A__ =       F"""{key}: {val:.6f}\n"""
       writer.write(snake_case_   )

     if not save_generations:
      return

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




    @rank_zero_only
    def 							__magic_name__							(					self		: str			,				snake_case_		: Tuple			,				snake_case_		: Dict   )   ->							Any:




     '''simple docstring'''

     try:
      A__ =       pl_module.model.model.num_parameters()
     except AttributeError:
      A__ =       pl_module.model.num_parameters()

     A__ =       count_trainable_parameters(snake_case_   )
     # 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 							__magic_name__							(					self		: List[Any]			,				snake_case_		: pl.Trainer			,				snake_case_		: pl.LightningModule   )   ->							Union[str, Any]:




     '''simple docstring'''

     save_json(pl_module.metrics			,				pl_module.metrics_save_path   )
     return self._write_logs(snake_case_			,				snake_case_			,				"test"   )




    @rank_zero_only
    def 							__magic_name__							(					self		: Optional[int]			,				snake_case_		: pl.Trainer			,				snake_case_		: int   )   ->							Optional[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")







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_=1    ,				lowercase_=False		)		->	Dict:
 if metric == "rouge2":
  A__ =       "{val_avg_rouge2:.4f}-{step_count}"
 elif metric == "bleu":
  A__ =       "{val_avg_bleu:.4f}-{step_count}"
 elif metric == "loss":
  A__ =       "{val_avg_loss:.4f}-{step_count}"
 else:
  raise NotImplementedError(
      f"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
      " this function."		)

 A__ =       ModelCheckpoint(
     dirpath=lowercase_    ,				filename=lowercase_    ,				monitor=f"""val_{metric}"""    ,				mode="min" if "loss" in metric else "max"    ,				save_top_k=lowercase_    ,				)
 return checkpoint_callback







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	Union[str, Any]:
 return EarlyStopping(
     monitor=f"""val_{metric}"""    ,				mode="min" if "loss" in metric else "max"    ,				patience=lowercase_    ,				verbose=lowercase_    ,				)





