



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

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


a__      :				Dict   	=			"<<<<<<< This should probably be modified because it mentions: "

a__      :				Optional[Any]   	=			"=======\n>>>>>>>\n"

a__      :				int   	=			[
    "TextEncoderConfig",
    "ByteTextEncoder",
    "SubwordTextEncoder",
    "encoder_config",
    "maybe_build_from_corpus",
    "manual_dir",
]

a__      :				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   _lowerCAmelCase   (						A__	):
      return ConvertCommand(args.tfds_path						,  args.datasets_directory	)



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   @staticmethod
   def 			UpperCAmelCase		(						lowerCAmelCase  :			ArgumentParser)	->				Any:




         """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  :			List[str]			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			str			,				*lowerCAmelCase  :			Any)	->				Dict:




         """simple docstring"""

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

         lowercase__     =  tfds_path
         lowercase__     =  datasets_directory



   def 			UpperCAmelCase		(						self  :			int)	->				Union[str, Any]:




         """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\'.''')





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a__      :				Any   	=			{"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Optional[int]   	=			["ReformerTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Union[str, Any]   	=			["ReformerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Any   	=			[
           "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ReformerAttention",
           "ReformerForMaskedLM",
           "ReformerForQuestionAnswering",
           "ReformerForSequenceClassification",
           "ReformerLayer",
           "ReformerModel",
           "ReformerModelWithLMHead",
           "ReformerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_reformer import ReformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_reformer_fast import ReformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_reformer import (
                  REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ReformerAttention,
                  ReformerForMaskedLM,
                  ReformerForQuestionAnswering,
                  ReformerForSequenceClassification,
                  ReformerLayer,
                  ReformerModel,
                  ReformerModelWithLMHead,
                  ReformerPreTrainedModel,
              )

else:
       import sys

       a__      :				Tuple   	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
