code: 
  
  
  
  
  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."   )
  
  
  
  
  
  
style_context: 
  
  
  
  
  """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_    ,				)
  
  
  
  
  
  