


'''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       :       List[Any]						       =				"<<<<<<< This should probably be modified because it mentions: "

__UpperCamelCase       :       Dict						       =				"=======\n>>>>>>>\n"

__UpperCamelCase       :       Tuple						       =				[
    "TextEncoderConfig",
    "ByteTextEncoder",
    "SubwordTextEncoder",
    "encoder_config",
    "maybe_build_from_corpus",
    "manual_dir",
]

__UpperCamelCase       :       Optional[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 			_a  (				SCREAMING_SNAKE_CASE	:				Tuple     ):







 """simple docstring"""



 return ConvertCommand(args.tfds_path    , args.datasets_directory     )






class       __magic_name__	(     __snake_case):


     @staticmethod
     def      UpperCAmelCase__	(      lowerCamelCase__							:     Union[str, Any]			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   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							:     Dict							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     str							,	*lowerCamelCase__							:     Any			)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   get_logger('''datasets-cli/converting'''			)

      UpperCamelCase__					:    Tuple          =   tfds_path
      UpperCamelCase__					:    Optional[Any]          =   datasets_directory


     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Optional[Any]:





      '''simple docstring'''






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

      UpperCamelCase__					:    Union[str, Any]          =   os.path.abspath(self._datasets_directory			)

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

      UpperCamelCase__					:    Dict          =   []
      UpperCamelCase__					:    Optional[int]          =   []
      UpperCamelCase__					:    Dict          =   {}

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

      for f_name in file_names:
       self._logger.info(F"Looking at file {f_name}"			)
       UpperCamelCase__					:    Union[str, Any]          =   os.path.join(__UpperCamelCase							,	__UpperCamelCase			)
       UpperCamelCase__					:    str          =   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:
        UpperCamelCase__					:    Tuple          =   f.readlines()

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

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

       if is_builder or "wmt" in f_name:
        # We create a new directory for each dataset
        UpperCamelCase__					:    Optional[Any]          =   f_name.replace('''.py'''							,	''''''			)
        UpperCamelCase__					:    Tuple          =   os.path.join(__UpperCamelCase							,	__UpperCamelCase			)
        UpperCamelCase__					:    int          =   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:
        UpperCamelCase__					:    Dict          =   os.path.basename(__UpperCamelCase			)
        UpperCamelCase__					:    Optional[int]          =   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'."			)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase       :       Dict						       =				{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       str						       =				["MobileViTFeatureExtractor"]
       __UpperCamelCase       :       Optional[Any]						       =				["MobileViTImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Union[str, Any]						       =				[
           "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MobileViTForImageClassification",
           "MobileViTForSemanticSegmentation",
           "MobileViTModel",
           "MobileViTPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				[
           "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFMobileViTForImageClassification",
           "TFMobileViTForSemanticSegmentation",
           "TFMobileViTModel",
           "TFMobileViTPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_mobilevit import MobileViTFeatureExtractor
              from .image_processing_mobilevit import MobileViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mobilevit import (
                  MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MobileViTForImageClassification,
                  MobileViTForSemanticSegmentation,
                  MobileViTModel,
                  MobileViTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mobilevit import (
                  TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFMobileViTForImageClassification,
                  TFMobileViTForSemanticSegmentation,
                  TFMobileViTModel,
                  TFMobileViTPreTrainedModel,
              )


else:
       import sys

       __UpperCamelCase       :       str						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


