

'''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		       =    '''=======
>>>>>>>
'''

__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       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Any:
       return ConvertCommand(args.tfds_path,       args.datasets_directory				)







class 					A__      (      _snake_case			):



    @staticmethod
    def 					snake_case_			(					UpperCamelCase__       )						->      Any:





           '''simple docstring'''
           A_						=      parser.add_parser(
               """convert"""		,				help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."""		,				)
           train_parser.add_argument(
               """--tfds_path"""		,				type=UpperCamelCase__		,				required=UpperCamelCase__		,				help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."""		,				)
           train_parser.add_argument(
               """--datasets_directory"""		,				type=UpperCamelCase__		,				required=UpperCamelCase__		,				help="""Path to the HuggingFace Datasets folder."""       )
           train_parser.set_defaults(func=UpperCamelCase__       )



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				*UpperCamelCase__       )						->      str:





           '''simple docstring'''
           A_						=      get_logger("""datasets-cli/converting"""       )

           A_						=      tfds_path
           A_						=      datasets_directory







    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           if os.path.isdir(self._tfds_path       ):
                  A_						=      os.path.abspath(self._tfds_path       )
           elif os.path.isfile(self._tfds_path       ):
                  A_						=      os.path.dirname(self._tfds_path       )
           else:
                  raise ValueError("""--tfds_path is neither a directory nor a file. Please check path."""       )

           A_						=      os.path.abspath(self._datasets_directory       )

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

           A_						=      []
           A_						=      []
           A_						=      {}

           if os.path.isdir(self._tfds_path       ):
                  A_						=      os.listdir(UpperCamelCase__       )
           else:
                  A_						=      [os.path.basename(self._tfds_path       )]

           for f_name in file_names:
                  self._logger.info(f'''Looking at file {f_name}'''       )
                  A_						=      os.path.join(UpperCamelCase__		,				UpperCamelCase__       )
                  A_						=      os.path.join(UpperCamelCase__		,				UpperCamelCase__       )

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

                  with open(UpperCamelCase__		,				encoding="""utf-8"""       ) as f:
                         A_						=      f.readlines()

                  A_						=      []
                  A_						=      False
                  A_						=      False
                  A_						=      []
                  for line in lines:
                         A_						=      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:
                                A_						=      """import datasets\n"""
                         elif "import tensorflow" in out_line:
                                # order is important here
                                A_						=      """"""
                                continue
                         elif "from absl import logging" in out_line:
                                A_						=      """from datasets import logging\n"""
                         elif "getLogger" in out_line:
                                A_						=      out_line.replace("""getLogger"""		,				"""get_logger"""       )
                         elif any(expression in out_line for expression in TO_HIGHLIGHT       ):
                                A_						=      True
                                A_						=      list(filter(lambda UpperCamelCase__       : e in out_line		,				UpperCamelCase__       )       )
                                out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(UpperCamelCase__       ) + """\n"""       )
                                out_lines.append(UpperCamelCase__       )
                                out_lines.append(UpperCamelCase__       )
                                continue
                         else:
                                for pattern, replacement in TO_CONVERT:
                                       A_						=      re.sub(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )

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

                  if is_builder or "wmt" in f_name:
                         # We create a new directory for each dataset
                         A_						=      f_name.replace(""".py"""		,				""""""       )
                         A_						=      os.path.join(UpperCamelCase__		,				UpperCamelCase__       )
                         A_						=      os.path.join(UpperCamelCase__		,				UpperCamelCase__       )
                         os.makedirs(UpperCamelCase__		,				exist_ok=UpperCamelCase__       )
                         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(UpperCamelCase__       )

                  if needs_manual_update:
                         with_manual_update.append(UpperCamelCase__       )

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

           for utils_file in utils_files:
                  try:
                         A_						=      os.path.basename(UpperCamelCase__       )
                         A_						=      imports_to_builder_map[f_name.replace(""".py"""		,				""""""       )]
                         self._logger.info(f'''Moving {dest_folder} to {utils_file}'''       )
                         shutil.copy(UpperCamelCase__		,				UpperCamelCase__       )
                  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\'.'''       )



'''simple docstring'''







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 					A__      (      _snake_case			):
    lowercase						=		"ClapFeatureExtractor"
    lowercase						=		("RobertaTokenizer", "RobertaTokenizerFast")



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           super().__init__(UpperCamelCase__		,				UpperCamelCase__       )



    def __call__(					self		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				**UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           A_						=      kwargs.pop("""sampling_rate"""		,				UpperCamelCase__       )

           if text is None and audios is None:
                  raise ValueError("""You have to specify either text or audios. Both cannot be none."""       )

           if text is not None:
                  A_						=      self.tokenizer(UpperCamelCase__		,				return_tensors=UpperCamelCase__		,				**UpperCamelCase__       )

           if audios is not None:
                  A_						=      self.feature_extractor(
                      UpperCamelCase__		,				sampling_rate=UpperCamelCase__		,				return_tensors=UpperCamelCase__		,				**UpperCamelCase__       )

           if text is not None and audios is not None:
                  A_						=      audio_features.input_features
                  return encoding
           elif text is not None:
                  return encoding
           else:
                  return BatchEncoding(data=dict(**UpperCamelCase__       )		,				tensor_type=UpperCamelCase__       )



    def 					snake_case_			(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      Optional[int]:





           '''simple docstring'''
           return self.tokenizer.batch_decode(*UpperCamelCase__		,				**UpperCamelCase__       )



    def 					snake_case_			(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      str:





           '''simple docstring'''
           return self.tokenizer.decode(*UpperCamelCase__		,				**UpperCamelCase__       )







    @property
    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           A_						=      self.tokenizer.model_input_names
           A_						=      self.feature_extractor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names       )       )
