



'''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


_UpperCamelCase											=						'<<<<<<< This should probably be modified because it mentions: '

_UpperCamelCase											=						'=======\n>>>>>>>\n'

_UpperCamelCase											=						[
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

_UpperCamelCase											=						[
    # (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_     (							_lowerCAmelCase       )      ->      Union[str, Any]:
  return ConvertCommand(args.tfds_path       ,args.datasets_directory       )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  @staticmethod
  def 						_lowercase     ( _a      :	ArgumentParser   )					->					Any:
    __lowerCamelCase       :       Dict           = 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      :	Union[str, Any]			,	_a      :	str			,	_a      :	str			,	*_a      :	int   )					->					Any:
    __lowerCamelCase       :       List[Any]           = get_logger('datasets-cli/converting'   )

    __lowerCamelCase       :       List[Any]           = tfds_path
    __lowerCamelCase       :       str           = datasets_directory




  def 						_lowercase     ( self      :	int   )					->					Optional[Any]:
    if os.path.isdir(self._tfds_path   ):
      __lowerCamelCase       :       List[str]           = os.path.abspath(self._tfds_path   )
    elif os.path.isfile(self._tfds_path   ):
      __lowerCamelCase       :       str           = os.path.dirname(self._tfds_path   )
    else:
      raise ValueError('--tfds_path is neither a directory nor a file. Please check path.'   )

    __lowerCamelCase       :       List[Any]           = os.path.abspath(self._datasets_directory   )

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

    __lowerCamelCase       :       str           = []
    __lowerCamelCase       :       Optional[Any]           = []
    __lowerCamelCase       :       Optional[int]           = {}

    if os.path.isdir(self._tfds_path   ):
      __lowerCamelCase       :       List[str]           = os.listdir(_a   )
    else:
      __lowerCamelCase       :       Any           = [os.path.basename(self._tfds_path   )]

    for f_name in file_names:
      self._logger.info(f'Looking at file {f_name}'   )
      __lowerCamelCase       :       Union[str, Any]           = os.path.join(_a			,	_a   )
      __lowerCamelCase       :       List[str]           = 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       :       str           = f.readlines()

      __lowerCamelCase       :       Tuple           = []
      __lowerCamelCase       :       Union[str, Any]           = False
      __lowerCamelCase       :       str           = False
      __lowerCamelCase       :       List[str]           = []
      for line in lines:
        __lowerCamelCase       :       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       :       str           = 'import datasets\n'
        elif "import tensorflow" in out_line:
          # order is important here
          __lowerCamelCase       :       List[Any]           = ''
          continue
        elif "from absl import logging" in out_line:
          __lowerCamelCase       :       int           = 'from datasets import logging\n'
        elif "getLogger" in out_line:
          __lowerCamelCase       :       Optional[int]           = out_line.replace('getLogger'			,	'get_logger'   )
        elif any(expression in out_line for expression in TO_HIGHLIGHT   ):
          __lowerCamelCase       :       int           = True
          __lowerCamelCase       :       Any           = list(filter(lambda _a   : 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       :       Union[str, Any]           = 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       :       Tuple           = 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       :       List[str]           = True
        out_lines.append(_a   )

      if is_builder or "wmt" in f_name:
        # We create a new directory for each dataset
        __lowerCamelCase       :       List[str]           = f_name.replace('.py'			,	''   )
        __lowerCamelCase       :       Dict           = os.path.join(_a			,	_a   )
        __lowerCamelCase       :       Optional[Any]           = 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       :       Tuple           = 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\'.'   )











'''simple docstring'''





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,)      ->      Optional[int]:
  if config_name_or_path is None:
    __lowerCamelCase       :       List[Any]           = 'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

  if generator_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Optional[int]           = generator_name_or_path

  if question_encoder_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Tuple           = question_encoder_name_or_path

  __lowerCamelCase       :       Union[str, Any]           = RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

  # Save model.
  __lowerCamelCase       :       Tuple           = RagConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       List[Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       Union[str, Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )

  __lowerCamelCase       :       Tuple           = gen_config
  __lowerCamelCase       :       List[Any]           = question_encoder_config

  __lowerCamelCase       :       str           = model_class.from_pretrained_question_encoder_generator(
      _lowerCAmelCase       ,_lowerCAmelCase       ,config=_lowerCAmelCase       )
  rag_model.save_pretrained(_lowerCAmelCase       )

  # Sanity check.
  model_class.from_pretrained(_lowerCAmelCase       )

  # Save tokenizers.
  __lowerCamelCase       :       List[str]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'       )
  __lowerCamelCase       :       Optional[int]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   parser.add_argument(
       '--model_type',
       choices=['rag_sequence', 'rag_token'],
       required=True,
       type=str,
       help='RAG model type: rag_sequence, rag_token',
   )
   parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
   parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
   parser.add_argument(
       '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
   )

   parser.add_argument(
       '--generator_tokenizer_name_or_path',
       type=str,
       help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
   )
   parser.add_argument(
       '--question_encoder_tokenizer_name_or_path',
       type=str,
       help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
   )
   parser.add_argument(
       '--config_name_or_path',
       type=str,
       help=(
           'Identifier of the model config to use, if not provided, resolves to a base config for a given'
           ' ``model_type``'
       ),
   )

   _UpperCamelCase											=						parser.parse_args()

   _UpperCamelCase											=						Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )






