

from typing import List

import numpy as np
def   a   (    A__			:     Optional[Any]  )					->   Dict:





      """simple docstring"""



      _lowercase			 ={key: len(A_  ) for key, value in gen_kwargs.items() if isinstance(A_   ,     A_  )}
      if len(set(lists_lengths.values()  )  ) > 1:
            raise RuntimeError(
                (
                    'Sharding is ambiguous for this dataset: '
                    + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
                    + '\n'.join(F'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()  )
                    + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
                    + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
                )  )
      _lowercase			 =max(lists_lengths.values()   ,     default=0  )
      return max(1   ,     A_  )
def   a   (    A__			:     int   ,     A__			:     str  )					->   Any:





      """simple docstring"""



      _lowercase			 =[]
      for group_idx in range(A_  ):
            _lowercase			 =num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
            if num_shards_to_add == 0:
                  break
            _lowercase			 =shards_indices_per_group[-1].stop if shards_indices_per_group else 0
            _lowercase			 =range(A_   ,     start + num_shards_to_add  )
            shards_indices_per_group.append(A_  )
      return shards_indices_per_group
def   a   (    A__			:     List[Any]   ,     A__			:     List[str]  )					->   Tuple:





      """simple docstring"""



      _lowercase			 =_number_of_shards_in_gen_kwargs(A_  )
      if num_shards == 1:
            return [dict(A_  )]
      else:
            _lowercase			 =_distribute_shards(num_shards=A_   ,     max_num_jobs=A_  )
            return [
                {
                    key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                    if isinstance(A_   ,     A_  )
                    else value
                    for key, value in gen_kwargs.items()
                }
                for group_idx in range(len(A_  )  )
            ]
def   a   (    A__			:     Tuple  )					->   int:





      """simple docstring"""



      return {
          key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
          if isinstance(gen_kwargs_list[0][key]   ,     A_  )
          else gen_kwargs_list[0][key]
          for key in gen_kwargs_list[0]
      }







def   a   (    A__			:     List[str]   ,     A__			:     str  )					->   List[str]:





      """simple docstring"""



      _lowercase			 ={len(A_  ) for value in gen_kwargs.values() if isinstance(A_   ,     A_  )}
      _lowercase			 ={}
      for size in list_sizes:
            _lowercase			 =list(range(A_  )  )
            rng.shuffle(indices_per_size[size]  )
      # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
      _lowercase			 =dict(A_  )
      for key, value in shuffled_kwargs.items():
            if isinstance(A_   ,     A_  ):
                  _lowercase			 =[value[i] for i in indices_per_size[len(A_  )]]
      return shuffled_kwargs





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

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


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

lowercase_             =     '=======\n>>>>>>>\n'

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

lowercase_             =     [
    # (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   (    A__			:     Namespace  )					->   Any:





      """simple docstring"""



      return ConvertCommand(args.tfds_path   ,     args.datasets_directory  )







class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):




  @staticmethod
  def 						A__  (       lowerCAmelCase      )						->				Tuple:
        '''simple docstring'''






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




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase							,     *lowerCAmelCase      )						->				List[Any]:
        '''simple docstring'''






        _lowercase			 =get_logger('datasets-cli/converting'      )

        _lowercase			 =tfds_path
        _lowercase			 =datasets_directory




  def 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






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

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

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

        _lowercase			 =[]
        _lowercase			 =[]
        _lowercase			 ={}

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

        for f_name in file_names:
              self._logger.info(F'''Looking at file {f_name}'''      )
              _lowercase			 =os.path.join(lowerCAmelCase							,     lowerCAmelCase      )
              _lowercase			 =os.path.join(lowerCAmelCase							,     lowerCAmelCase      )

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

              with open(lowerCAmelCase							,     encoding='utf-8'      ) as f:
                    _lowercase			 =f.readlines()

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

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

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

              if needs_manual_update:
                    with_manual_update.append(lowerCAmelCase      )

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

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


