

import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
  import numpy as np
  import torch

  from . import (
      AlbertForPreTraining,
      BartForConditionalGeneration,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      CamembertForMaskedLM,
      CTRLLMHeadModel,
      DistilBertForMaskedLM,
      DistilBertForQuestionAnswering,
      DPRContextEncoder,
      DPRQuestionEncoder,
      DPRReader,
      ElectraForPreTraining,
      FlaubertWithLMHeadModel,
      GPTaLMHeadModel,
      LayoutLMForMaskedLM,
      LxmertForPreTraining,
      LxmertVisualFeatureEncoder,
      OpenAIGPTLMHeadModel,
      RobertaForMaskedLM,
      RobertaForSequenceClassification,
      TaForConditionalGeneration,
      TransfoXLLMHeadModel,
      XLMRobertaForMaskedLM,
      XLMWithLMHeadModel,
      XLNetLMHeadModel,
  )


logging.set_verbosity_info()

lowercase_             =     {
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}
def   a   (    A__			:     Tuple   ,     A__			:     List[Any]   ,     A__			:     Optional[int]   ,     A__			:     Dict   ,     A__			:     Any=False   ,     A__			:     str=True  )					->   str:





      """simple docstring"""



      if model_type not in MODEL_CLASSES:
            raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()  )}.'''  )

      _lowercase , _lowercase , _lowercase , _lowercase			 =MODEL_CLASSES[model_type]

      # Initialise TF model
      if config_file in aws_config_map:
            _lowercase			 =cached_file(A__   ,     A__   ,     force_download=not use_cached_models  )
      _lowercase			 =config_class.from_json_file(A__  )
      _lowercase			 =True
      _lowercase			 =True
      print(F'''Building TensorFlow model from configuration: {config}'''  )
      _lowercase			 =model_class(A__  )

      # Load weights from tf checkpoint
      if pytorch_checkpoint_path in aws_config_map.keys():
            _lowercase			 =cached_file(
                A__   ,     A__   ,     force_download=not use_cached_models  )
      # Load PyTorch checkpoint in tf2 model:
      _lowercase			 =load_pytorch_checkpoint_in_tfa_model(A__   ,     A__  )

      if compare_with_pt_model:
            _lowercase			 =tf_model(tf_model.dummy_inputs   ,     training=A__  )  # build the network

            _lowercase			 =torch.load(A__   ,     map_location='cpu'  )
            _lowercase			 =pt_model_class.from_pretrained(
                pretrained_model_name_or_path=A__   ,     config=A__   ,     state_dict=A__  )

            with torch.no_grad():
                  _lowercase			 =pt_model(**pt_model.dummy_inputs  )

            _lowercase			 =pto[0].numpy()
            _lowercase			 =tfo[0].numpy()
            _lowercase			 =np.amax(np.abs(np_pt - np_tf  )  )
            print(F'''Max absolute difference between models outputs {diff}'''  )
            assert diff <= 2e-2, F'''Error, model absolute difference is >2e-2: {diff}'''

      # Save pytorch-model
      print(F'''Save TensorFlow model to {tf_dump_path}'''  )
      tf_model.save_weights(A__   ,     save_format='h5'  )
def   a   (    A__			:     str   ,     A__			:     str   ,     A__			:     Optional[Any]=None   ,     A__			:     Any=None   ,     A__			:     Optional[int]=False   ,     A__			:     Optional[int]=False   ,     A__			:     int=False   ,     A__			:     str=False   ,     )					->   List[Any]:





      """simple docstring"""



      if args_model_type is None:
            _lowercase			 =list(MODEL_CLASSES.keys()  )
      else:
            _lowercase			 =[args_model_type]

      for j, model_type in enumerate(A__   ,     start=1  ):
            print('=' * 100  )
            print(F''' Converting model type {j}/{len(A__  )}: {model_type}'''  )
            print('=' * 100  )
            if model_type not in MODEL_CLASSES:
                  raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()  )}.'''  )

            _lowercase , _lowercase , _lowercase , _lowercase , _lowercase			 =MODEL_CLASSES[model_type]

            if model_shortcut_names_or_path is None:
                  _lowercase			 =list(aws_model_maps.keys()  )
            if config_shortcut_names_or_path is None:
                  _lowercase			 =model_shortcut_names_or_path

            for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                zip(A__   ,     A__  )   ,     start=1  ):
                  print('-' * 100  )
                  if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                        if not only_convert_finetuned_models:
                              print(F'''    Skipping finetuned checkpoint {model_shortcut_name}'''  )
                              continue
                        _lowercase			 =model_shortcut_name
                  elif only_convert_finetuned_models:
                        print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}'''  )
                        continue
                  print(
                      F'''    Converting checkpoint {i}/{len(A__  )}: {model_shortcut_name} - model_type {model_type}'''  )
                  print('-' * 100  )

                  if config_shortcut_name in aws_config_map:
                        _lowercase			 =cached_file(A__   ,     A__   ,     force_download=not use_cached_models  )
                  else:
                        _lowercase			 =config_shortcut_name

                  if model_shortcut_name in aws_model_maps:
                        _lowercase			 =cached_file(A__   ,     A__   ,     force_download=not use_cached_models  )
                  else:
                        _lowercase			 =model_shortcut_name

                  if os.path.isfile(A__  ):
                        _lowercase			 ='converted_model'

                  convert_pt_checkpoint_to_tf(
                      model_type=A__   ,     pytorch_checkpoint_path=A__   ,     config_file=A__   ,     tf_dump_path=os.path.join(A__   ,     model_shortcut_name + '-tf_model.h5'  )   ,     compare_with_pt_model=A__   ,     )
                  if remove_cached_files:
                        os.remove(A__  )
                        os.remove(A__  )


if __name__ == "__main__":
  lowercase_             =     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
  )
  parser.add_argument(
      '--model_type',
      default=None,
      type=str,
      help=(
          f"Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and "
          'convert all the models from AWS.'
      ),
  )
  parser.add_argument(
      '--pytorch_checkpoint_path',
      default=None,
      type=str,
      help=(
          'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
          'If not given, will download and convert all the checkpoints from AWS.'
      ),
  )
  parser.add_argument(
      '--config_file',
      default=None,
      type=str,
      help=(
          'The config json file corresponding to the pre-trained model. \n'
          'This specifies the model architecture. If not given and '
          '--pytorch_checkpoint_path is not given or is a shortcut name '
          'use the configuration associated to the shortcut name on the AWS'
      ),
  )
  parser.add_argument(
      '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
  )
  parser.add_argument(
      '--use_cached_models',
      action='store_true',
      help='Use cached models if possible instead of updating to latest checkpoint versions.',
  )
  parser.add_argument(
      '--remove_cached_files',
      action='store_true',
      help='Remove pytorch models after conversion (save memory when converting in batches).',
  )
  parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
  lowercase_             =     parser.parse_args()

  # if args.pytorch_checkpoint_path is not None:
  #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
  #                                 args.pytorch_checkpoint_path,
  #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
  #                                 args.tf_dump_path,
  #                                 compare_with_pt_model=args.compare_with_pt_model,
  #                                 use_cached_models=args.use_cached_models)
  # else:
  convert_all_pt_checkpoints_to_tf(
      args.model_type.lower() if args.model_type is not None else None,
      args.tf_dump_path,
      model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
      if args.pytorch_checkpoint_path is not None
      else None,
      config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
      compare_with_pt_model=args.compare_with_pt_model,
      use_cached_models=args.use_cached_models,
      remove_cached_files=args.remove_cached_files,
      only_convert_finetuned_models=args.only_convert_finetuned_models,
  )





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\'.'''      )


