

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

UpperCAmelCase					:			List[str]       =	'=======\n>>>>>>>\n'

UpperCAmelCase					:			Union[str, Any]       =	[
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

UpperCAmelCase					:			List[str]       =	[
    # (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__		):




     """simple docstring"""


     return ConvertCommand(args.tfds_path ,       args.datasets_directory		)







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 @staticmethod
 def  UpperCAmelCase__						(						__SCREAMING_SNAKE_CASE   :			ArgumentParser     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = parser.add_parser(
          """convert"""   ,				help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."""   ,				)
      train_parser.add_argument(
          """--tfds_path"""   ,				type=__SCREAMING_SNAKE_CASE   ,				required=__SCREAMING_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=__SCREAMING_SNAKE_CASE   ,				required=__SCREAMING_SNAKE_CASE   ,				help="""Path to the HuggingFace Datasets folder."""     )
      train_parser.set_defaults(func=__SCREAMING_SNAKE_CASE     )
 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			str   ,				*__SCREAMING_SNAKE_CASE   :			int     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = get_logger("""datasets-cli/converting"""     )

      __SCREAMING_SNAKE_CASE      = tfds_path
      __SCREAMING_SNAKE_CASE      = datasets_directory


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


      """simple docstring"""




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

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

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

      __SCREAMING_SNAKE_CASE      = []
      __SCREAMING_SNAKE_CASE      = []
      __SCREAMING_SNAKE_CASE      = {}

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

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

           if not os.path.isfile(__SCREAMING_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(__SCREAMING_SNAKE_CASE   ,				encoding="""utf-8"""     ) as f:
                __SCREAMING_SNAKE_CASE      = f.readlines()

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

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

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

           if needs_manual_update:
                with_manual_update.append(__SCREAMING_SNAKE_CASE     )

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

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









'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase					:			Union[str, Any]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Any       =	{
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "mra"
 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict=50_265   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=768   ,				__SCREAMING_SNAKE_CASE   :			int=12   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=12   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=3_072   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=512   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Any=0.02   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1E-5   ,				__SCREAMING_SNAKE_CASE   :			str="absolute"   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=4   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="full"   ,				__SCREAMING_SNAKE_CASE   :			str=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=2   ,				**__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				)    ->					Tuple:


      """simple docstring"""




      super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE   ,				bos_token_id=__SCREAMING_SNAKE_CASE   ,				eos_token_id=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = hidden_size
      __SCREAMING_SNAKE_CASE      = num_hidden_layers
      __SCREAMING_SNAKE_CASE      = num_attention_heads
      __SCREAMING_SNAKE_CASE      = intermediate_size
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = type_vocab_size
      __SCREAMING_SNAKE_CASE      = layer_norm_eps
      __SCREAMING_SNAKE_CASE      = position_embedding_type
      __SCREAMING_SNAKE_CASE      = block_per_row
      __SCREAMING_SNAKE_CASE      = approx_mode
      __SCREAMING_SNAKE_CASE      = initial_prior_first_n_blocks
      __SCREAMING_SNAKE_CASE      = initial_prior_diagonal_n_blocks






