



'''simple docstring'''



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


a__						:			Optional[Any]										=			get_logger(__name__)







def _lowercase   (  __A       ):



 '''simple docstring'''



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



 @staticmethod
 def    __lowerCamelCase				(  lowercase			)  ->							Tuple:
  __UpperCamelCase				      =  parser.add_parser("""test"""		,	help="""Test dataset implementation."""			)
  test_parser.add_argument("""--name"""		,	type=lowercase		,	default=lowercase		,	help="""Dataset processing name"""			)
  test_parser.add_argument(
      """--cache_dir"""		,	type=lowercase		,	default=lowercase		,	help="""Cache directory where the datasets are stored."""		,	)
  test_parser.add_argument(
      """--data_dir"""		,	type=lowercase		,	default=lowercase		,	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=lowercase		,	help="""Name of the dataset to download"""			)
  test_parser.set_defaults(func=_test_command_factory			)



 def __init__(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)  ->							Tuple:
  __UpperCamelCase				      =  dataset
  __UpperCamelCase				      =  name
  __UpperCamelCase				      =  cache_dir
  __UpperCamelCase				      =  data_dir
  __UpperCamelCase				      =  all_configs
  __UpperCamelCase				      =  save_infos
  __UpperCamelCase				      =  ignore_verifications
  __UpperCamelCase				      =  force_redownload
  __UpperCamelCase				      =  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:
   __UpperCamelCase				      =  True




 def    __lowerCamelCase				(  self			)  ->							Dict:
  fl_logger().setLevel(lowercase			)
  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			)
  __UpperCamelCase      ,		__UpperCamelCase				      =  self._dataset, self._name
  __UpperCamelCase				      =  dataset_module_factory(lowercase			)
  __UpperCamelCase				      =  import_main_class(module.module_path			)
  __UpperCamelCase				      =  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=lowercase		,	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})"			)
   __UpperCamelCase				      =  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=lowercase		,	)
   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:
    __UpperCamelCase				      =  os.path.join(builder_cls.get_imported_module_dir()		,	"""README.md"""			)
    __UpperCamelCase				      =  Path(lowercase			).name + """.py"""
    __UpperCamelCase				      =  os.path.join(lowercase		,	lowercase			)
    if os.path.isfile(lowercase			):
     __UpperCamelCase				      =  os.path.dirname(lowercase			)
    elif os.path.isfile(lowercase			):
     __UpperCamelCase				      =  path
    elif os.path.isdir(lowercase			):  # for local directories containing only data files
     __UpperCamelCase				      =  path
    else:  # in case of a remote dataset
     __UpperCamelCase				      =  None
     print(f"Dataset card saved at {dataset_readme_path}"			)

    # Move dataset_info back to the user
    if dataset_dir is not None:
     __UpperCamelCase				      =  os.path.join(lowercase		,	"""README.md"""			)
     copyfile(lowercase		,	lowercase			)
     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			)
    __UpperCamelCase				      =  os.path.join(self._cache_dir		,	datasets.config.DOWNLOADED_DATASETS_DIR			)
    if os.path.isdir(lowercase			):
     logger.warning(f"Clearing cache at {download_dir}"			)
     rmtree(lowercase			)

  print("""Test successful."""			)










'''simple docstring'''



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def _lowercase   (  __A							,__A							,__A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
 print(f"Building PyTorch model from configuration: {config}"       )
 __UpperCamelCase				      =  TaForConditionalGeneration(__A       )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(__A							,__A							,__A       )

 # Save pytorch-model
 print(f"Save PyTorch model to {pytorch_dump_path}"       )
 model.save_pretrained(__A       )


if __name__ == "__main__":
 a__						:			int										=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
 )
 parser.add_argument(
     '--config_file',
     default=None,
     type=str,
     required=True,
     help=(
         'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
     ),
 )
 parser.add_argument(
     '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 a__						:			Optional[Any]										=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





