


"""simple docstring"""







import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


lowerCAmelCase__ 							=  "<<<<<<< This should probably be modified because it mentions: "

lowerCAmelCase__ 							=  "=======\n>>>>>>>\n"

lowerCAmelCase__ 							=  [
    "TextEncoderConfig",
    "ByteTextEncoder",
    "SubwordTextEncoder",
    "encoder_config",
    "maybe_build_from_corpus",
    "manual_dir",
]

lowerCAmelCase__ 							=  [
    # (pattern, replacement)
    # Order is important here for some replacements
    (R"tfds\.core", R"datasets"),
    (R"tf\.io\.gfile\.GFile", R"open"),
    (R"tf\.([\w\d]+)", R"datasets.Value('\1')"),
    (R"tfds\.features\.Text\(\)", R"datasets.Value('string')"),
    (R"tfds\.features\.Text\(", R"datasets.Value('string'),"),
    (R"features\s*=\s*tfds.features.FeaturesDict\(", R"features=datasets.Features("),
    (R"tfds\.features\.FeaturesDict\(", R"dict("),
    (R"The TensorFlow Datasets Authors", R"The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"),
    (R"tfds\.", R"datasets."),
    (R"dl_manager\.manual_dir", R"self.config.data_dir"),
    (R"self\.builder_config", R"self.config"),
]
def    a__     (							SCREAMING_SNAKE_CASE       :       Namespace							):


  '''simple docstring'''







  return ConvertCommand(args.tfds_path							, args.datasets_directory							)





class  SCREAMING_SNAKE_CASE__							(						__snake_case							):
       """simple docstring"""


       @staticmethod
       def 			lowercase__				(		snake_case__		):






         """simple docstring"""





         lowerCAmelCase	:	List[str]								=				parser.add_parser(
             "convert"						,	help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."						,	)
         train_parser.add_argument(
             "--tfds_path"						,	type=a__						,	required=a__						,	help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."						,	)
         train_parser.add_argument(
             "--datasets_directory"						,	type=a__						,	required=a__						,	help="Path to the HuggingFace Datasets folder."		)
         train_parser.set_defaults(func=a__		)

       def __init__(		self						,	snake_case__						,	snake_case__						,	*snake_case__		):






         """simple docstring"""





         lowerCAmelCase	:	Any								=				get_logger("datasets-cli/converting"		)

         lowerCAmelCase	:	List[Any]								=				tfds_path
         lowerCAmelCase	:	Optional[int]								=				datasets_directory







       def 			lowercase__				(		self		):






         """simple docstring"""





         if os.path.isdir(self._tfds_path		):
           lowerCAmelCase	:	Tuple								=				os.path.abspath(self._tfds_path		)
         elif os.path.isfile(self._tfds_path		):
           lowerCAmelCase	:	List[str]								=				os.path.dirname(self._tfds_path		)
         else:
           raise ValueError("--tfds_path is neither a directory nor a file. Please check path."		)

         lowerCAmelCase	:	str								=				os.path.abspath(self._datasets_directory		)

         self._logger.info(f"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}"""		)

         lowerCAmelCase	:	Tuple								=				[]
         lowerCAmelCase	:	Tuple								=				[]
         lowerCAmelCase	:	Union[str, Any]								=				{}

         if os.path.isdir(self._tfds_path		):
           lowerCAmelCase	:	List[str]								=				os.listdir(a__		)
         else:
           lowerCAmelCase	:	List[str]								=				[os.path.basename(self._tfds_path		)]

         for f_name in file_names:
           self._logger.info(f"""Looking at file {f_name}"""		)
           lowerCAmelCase	:	List[Any]								=				os.path.join(a__						,	a__		)
           lowerCAmelCase	:	Optional[Any]								=				os.path.join(a__						,	a__		)

           if not os.path.isfile(a__		) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
             self._logger.info("Skipping file"		)
             continue

           with open(a__						,	encoding="utf-8"		) as f:
             lowerCAmelCase	:	int								=				f.readlines()

           lowerCAmelCase	:	Any								=				[]
           lowerCAmelCase	:	Dict								=				False
           lowerCAmelCase	:	List[Any]								=				False
           lowerCAmelCase	:	Tuple								=				[]
           for line in lines:
             lowerCAmelCase	:	Optional[Any]								=				line

             # Convert imports
             if "import tensorflow.compat.v2 as tf" in out_line:
               continue
             elif "@tfds.core" in out_line:
               continue
             elif "builder=self" in out_line:
               continue
             elif "import tensorflow_datasets.public_api as tfds" in out_line:
               lowerCAmelCase	:	Union[str, Any]								=				"import datasets\n"
             elif "import tensorflow" in out_line:
               # order is important here
               lowerCAmelCase	:	Any								=				""
               continue
             elif "from absl import logging" in out_line:
               lowerCAmelCase	:	Union[str, Any]								=				"from datasets import logging\n"
             elif "getLogger" in out_line:
               lowerCAmelCase	:	str								=				out_line.replace("getLogger"						,	"get_logger"		)
             elif any(expression in out_line for expression in TO_HIGHLIGHT		):
               lowerCAmelCase	:	Union[str, Any]								=				True
               lowerCAmelCase	:	Union[str, Any]								=				list(filter(lambda snake_case__		: e in out_line						,	a__		)		)
               out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(a__		) + "\n"		)
               out_lines.append(a__		)
               out_lines.append(a__		)
               continue
             else:
               for pattern, replacement in TO_CONVERT:
                 lowerCAmelCase	:	Tuple								=				re.sub(a__						,	a__						,	a__		)

                # Take care of saving utilities (to later move them together with main script)
             if "tensorflow_datasets" in out_line:
               lowerCAmelCase	:	Dict								=				re.match(r"from\stensorflow_datasets.*import\s([^\.\r\n]+)"						,	a__		)
               tfds_imports.extend(imp.strip() for imp in match.group(1		).split(","		)		)
               lowerCAmelCase	:	Optional[Any]								=				"from . import " + match.group(1		)

             # Check we have not forget anything
             if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
               raise ValueError(f"""Error converting {out_line.strip()}"""		)

             if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
               lowerCAmelCase	:	Any								=				True
             out_lines.append(a__		)

           if is_builder or "wmt" in f_name:
             # We create a new directory for each dataset
             lowerCAmelCase	:	Optional[int]								=				f_name.replace(".py"						,	""		)
             lowerCAmelCase	:	List[str]								=				os.path.join(a__						,	a__		)
             lowerCAmelCase	:	Optional[int]								=				os.path.join(a__						,	a__		)
             os.makedirs(a__						,	exist_ok=a__		)
             self._logger.info(f"""Adding directory {output_dir}"""		)
             imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}		)
           else:
             # Utilities will be moved at the end
             utils_files.append(a__		)

           if needs_manual_update:
             with_manual_update.append(a__		)

           with open(a__						,	"w"						,	encoding="utf-8"		) as f:
             f.writelines(a__		)
           self._logger.info(f"""Converted in {output_file}"""		)

         for utils_file in utils_files:
           try:
             lowerCAmelCase	:	List[Any]								=				os.path.basename(a__		)
             lowerCAmelCase	:	str								=				imports_to_builder_map[f_name.replace(".py"						,	""		)]
             self._logger.info(f"""Moving {dest_folder} to {utils_file}"""		)
             shutil.copy(a__						,	a__		)
           except KeyError:
             self._logger.error(f"""Cannot find destination folder for {utils_file}. Please copy manually."""		)

         if with_manual_update:
           for file_path in with_manual_update:
             self._logger.warning(
                 f"""You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'."""		)









import numpy as np

from transformers import Pipeline





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]    )			-> Optional[int]:





	"""simple docstring"""







	A             =					np.max(UpperCamelCase__				,       axis=-1				,       keepdims=UpperCamelCase__    )
	A             =					np.exp(outputs - maxes    )
	return shifted_exp / shifted_exp.sum(axis=-1				,       keepdims=UpperCamelCase__    )






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def   _UpperCAmelCase       (    self					, **a__ )						->					Union[str, Any]:
					A             =					{}
					if "second_text" in kwargs:
						A             =					kwargs["""second_text"""]
					return preprocess_kwargs, {}, {}

				def   _UpperCAmelCase       (    self					, a__					, a__=None )						->					str:
					return self.tokenizer(a__					, text_pair=a__					, return_tensors=self.framework )

				def   _UpperCAmelCase       (    self					, a__ )						->					Tuple:
					return self.model(**a__ )

				def   _UpperCAmelCase       (    self					, a__ )						->					Optional[int]:
					A             =					model_outputs.logits[0].numpy()
					A             =					softmax(a__ )

					A             =					np.argmax(a__ )
					A             =					self.model.config.idalabel[best_class]
					A             =					probabilities[best_class].item()
					A             =					logits.tolist()
					return {"label": label, "score": score, "logits": logits}



