
import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
lowercase  : List[Any]						   =							logging.get_logger(__name__)

lowercase  : Union[str, Any]						   =							{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
lowercase  : Dict						   =							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   str:
       for attribute in key.split("""."""    ):
              lowercase							:					Tuple	     =				getattr(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       if weight_type is not None:
              lowercase							:					Any	     =				getattr(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ).shape
       else:
              lowercase							:					List[Any]	     =				hf_pointer.shape

       assert hf_shape == value.shape, (
           f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
           f" {value.shape} for {full_name}"
       )

       if weight_type == "weight":
              lowercase							:					Any	     =				value
       elif weight_type == "weight_g":
              lowercase							:					Any	     =				value
       elif weight_type == "weight_v":
              lowercase							:					int	     =				value
       elif weight_type == "bias":
              lowercase							:					List[Any]	     =				value
       else:
              lowercase							:					int	     =				value

       logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       lowercase							:					Tuple	     =				[]
       lowercase							:					List[Any]	     =				fairseq_model.state_dict()

       lowercase							:					Dict	     =				hf_model.feature_extractor
       lowercase							:					Any	     =				hf_model.adapter

       for name, value in fairseq_dict.items():
              lowercase							:					int	     =				False
              if "conv_layers" in name:
                     load_conv_layer(
                         SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     hf_model.config.feat_extract_norm == """group""" ,     )
                     lowercase							:					Any	     =				True
              elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]    ):
                     load_adapter(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
                     lowercase							:					Optional[int]	     =				True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split("""w2v_model."""    )[-1] == name.split("""."""    )[0]:
                                   lowercase							:					List[Any]	     =				True
                                   if "*" in mapped_key:
                                          lowercase							:					List[str]	     =				name.split(SCREAMING_SNAKE_CASE__    )[0].split("""."""    )[-2]
                                          lowercase							:					int	     =				mapped_key.replace("""*""" ,     SCREAMING_SNAKE_CASE__    )
                                   if "weight_g" in name:
                                          lowercase							:					int	     =				"""weight_g"""
                                   elif "weight_v" in name:
                                          lowercase							:					Optional[Any]	     =				"""weight_v"""
                                   elif "bias" in name:
                                          lowercase							:					str	     =				"""bias"""
                                   elif "weight" in name:
                                          lowercase							:					Optional[Any]	     =				"""weight"""
                                   else:
                                          lowercase							:					List[Any]	     =				None
                                   set_recursively(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
                            continue
              if not is_used:
                     unused_weights.append(SCREAMING_SNAKE_CASE__    )

       logger.warning(f"Unused weights: {unused_weights}"    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       lowercase							:					int	     =				full_name.split("""conv_layers."""    )[-1]
       lowercase							:					Optional[Any]	     =				name.split("""."""    )
       lowercase							:					Dict	     =				int(items[0]    )
       lowercase							:					Tuple	     =				int(items[1]    )

       if type_id == 0:
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                         f"{full_name} has size {value.shape}, but"
                         f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
                     )
                     lowercase							:					List[Any]	     =				value
                     logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."    )
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                         f"{full_name} has size {value.shape}, but"
                         f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
                     )
                     lowercase							:					str	     =				value
                     logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."    )
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                         f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                         " found."
                     )
                     lowercase							:					List[str]	     =				value
                     logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                         f"{full_name} has size {value.shape}, but"
                         f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
                     )
                     lowercase							:					Tuple	     =				value
                     logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
       else:
              unused_weights.append(SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       lowercase							:					List[Any]	     =				full_name.split("""adaptor."""    )[-1]
       lowercase							:					Tuple	     =				name.split("""."""    )

       if items[1].isdigit():
              lowercase							:					Optional[int]	     =				int(items[1]    )
       else:
              lowercase							:					Union[str, Any]	     =				None

       if "adaptor" not in full_name:
              if "proj_ln" in full_name:
                     # has to be layer norm
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.bias.data.shape
                            ), f"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."
                            lowercase							:					Any	     =				value
                            logger.info(f"Adapter proj layer norm bias was initialized from {full_name}."    )
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.weight.data.shape
                            ), f"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."
                            lowercase							:					str	     =				value
              else:
                     # has to be projection layer
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj.bias.data.shape
                            ), f"{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."
                            lowercase							:					Optional[Any]	     =				value
                            logger.info(f"Adapter proj layer bias was initialized from {full_name}."    )
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj.weight.data.shape
                            ), f"{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."
                            lowercase							:					int	     =				value
                            logger.info(f"Adapter proj layer weight was initialized from {full_name}."    )
       elif isinstance(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
              if "bias" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.bias.data.shape
                     ), f"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."
                     lowercase							:					int	     =				value
                     logger.info(f"Adapter layer {layer_id} bias was initialized from {full_name}."    )
              elif "weight" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.weight.data.shape
                     ), f"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."
                     lowercase							:					str	     =				value
                     logger.info(f"Adapter layer {layer_id} bias was initialized from {full_name}."    )
       else:
              unused_weights.append(SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   str:
       lowercase     ,      lowercase							:					Optional[int]	     =				emb.weight.shape
       lowercase							:					int	     =				nn.Linear(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     bias=SCREAMING_SNAKE_CASE__    )
       lowercase							:					int	     =				emb.weight.data
       return lin_layer







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     )			->   List[str]:
       lowercase							:					str	     =				WavaVecaConfig.from_pretrained(
           SCREAMING_SNAKE_CASE__ ,     add_adapter=SCREAMING_SNAKE_CASE__ ,     adapter_stride=SCREAMING_SNAKE_CASE__ ,     adapter_kernel_size=SCREAMING_SNAKE_CASE__ ,     use_auth_token=SCREAMING_SNAKE_CASE__ ,     output_hidden_size=SCREAMING_SNAKE_CASE__ ,     )
       lowercase							:					Tuple	     =				MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__    )

       # load model
       lowercase     ,      lowercase     ,      lowercase							:					str	     =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path] ,     arg_overrides={
               """config_yaml""": config_yaml_path,
               """data""": """/""".join(dict_path.split("""/"""    )[:-1]    ),
               """w2v_path""": checkpoint_path,
               """load_pretrained_decoder_from""": None,
           } ,     )
       lowercase							:					Optional[Any]	     =				model[0].eval()

       # load feature extractor
       lowercase							:					str	     =				WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__ ,     use_auth_token=SCREAMING_SNAKE_CASE__    )

       # set weights for wav2vec2 encoder
       lowercase							:					Dict	     =				WavaVecaModel(SCREAMING_SNAKE_CASE__    )

       recursively_load_weights_wavaveca(model.encoder ,     SCREAMING_SNAKE_CASE__    )

       # load decoder weights
       lowercase							:					str	     =				MBartForCausalLM(SCREAMING_SNAKE_CASE__    )
       lowercase     ,      lowercase							:					Tuple	     =				hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,     strict=SCREAMING_SNAKE_CASE__    )
       logger.warning(f"The following keys are missing when loading the decoder weights: {missing_keys}"    )
       logger.warning(f"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"    )

       lowercase							:					int	     =				SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__ ,     decoder=SCREAMING_SNAKE_CASE__    )
       lowercase							:					Any	     =				False

       lowercase							:					Union[str, Any]	     =				MBartaaTokenizer(SCREAMING_SNAKE_CASE__    )
       tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__    )

       lowercase							:					List[Any]	     =				hf_wavavec.config.to_dict()
       lowercase							:					Tuple	     =				tokenizer.pad_token_id
       lowercase							:					int	     =				tokenizer.bos_token_id
       lowercase							:					List[Any]	     =				tokenizer.eos_token_id
       lowercase							:					Union[str, Any]	     =				"""mbart50"""
       lowercase							:					List[Any]	     =				"""wav2vec2"""

       lowercase							:					List[Any]	     =				tokenizer.eos_token_id
       lowercase							:					str	     =				250_004
       lowercase							:					List[Any]	     =				tokenizer.eos_token_id

       lowercase							:					str	     =				SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE__    )

       hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__    )
       feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
  lowercase  : Union[str, Any]						   =							argparse.ArgumentParser()
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
  parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
  parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
  parser.add_argument(
      """--encoder_config_path""",
      default="""facebook/wav2vec2-xls-r-1b""",
      type=str,
      help="""Path to hf encoder wav2vec2 checkpoint config""",
  )
  parser.add_argument(
      """--decoder_config_path""",
      default="""facebook/mbart-large-50-one-to-many-mmt""",
      type=str,
      help="""Path to hf decoder checkpoint config""",
  )
  parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
  parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
  parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
  parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""")
  parser.add_argument("""--start_token_id""", default=250004, type=int, help="""`decoder_start_token_id` of model config""")

  lowercase  : Any						   =							parser.parse_args()
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.dict_path,
      args.config_yaml_path,
      encoder_config_path=args.encoder_config_path,
      decoder_config_path=args.decoder_config_path,
      add_adapter=args.add_adapter,
      adapter_kernel_size=args.adapter_kernel_size,
      adapter_stride=args.adapter_stride,
      decoder_start_token_id=args.start_token_id,
      encoder_output_dim=args.encoder_output_dim,
  )


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

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


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

lowercase  : Any						   =							"""=======
>>>>>>>
"""

lowercase  : List[str]						   =							[
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

lowercase  : Any						   =							[
    # (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 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   List[Any]:
       return ConvertCommand(args.tfds_path ,     args.datasets_directory    )




class __snake_case		(				lowerCAmelCase					):
    @staticmethod
    def 				_SCREAMING_SNAKE_CASE	(  snake_case				):



           '''simple docstring'''







           lowercase							:					str	     =				parser.add_parser(
               """convert"""		,help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."""		,)
           train_parser.add_argument(
               """--tfds_path"""		,type=snake_case		,required=snake_case		,help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."""		,)
           train_parser.add_argument(
               """--datasets_directory"""		,type=snake_case		,required=snake_case		,help="""Path to the HuggingFace Datasets folder."""				)
           train_parser.set_defaults(func=snake_case				)
    def __init__(  self		,snake_case		,snake_case		,*snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				get_logger("""datasets-cli/converting"""				)

           lowercase							:					Optional[int]	     =				tfds_path
           lowercase							:					Dict	     =				datasets_directory
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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

           lowercase							:					Optional[int]	     =				os.path.abspath(self._datasets_directory				)

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

           lowercase							:					List[Any]	     =				[]
           lowercase							:					Optional[int]	     =				[]
           lowercase							:					Dict	     =				{}

           if os.path.isdir(self._tfds_path				):
                  lowercase							:					int	     =				os.listdir(snake_case				)
           else:
                  lowercase							:					List[Any]	     =				[os.path.basename(self._tfds_path				)]

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

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

                  with open(snake_case		,encoding="""utf-8"""				) as f:
                         lowercase							:					str	     =				f.readlines()

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

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

                  if is_builder or "wmt" in f_name:
                         # We create a new directory for each dataset
                         lowercase							:					Union[str, Any]	     =				f_name.replace(""".py"""		,""""""				)
                         lowercase							:					Optional[Any]	     =				os.path.join(snake_case		,snake_case				)
                         lowercase							:					List[str]	     =				os.path.join(snake_case		,snake_case				)
                         os.makedirs(snake_case		,exist_ok=snake_case				)
                         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(snake_case				)

                  if needs_manual_update:
                         with_manual_update.append(snake_case				)

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

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