


'''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


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

A				:					Union[str, Any]    						=			'''=======
>>>>>>>
'''

A				:					int    						=			[
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

A				:					Tuple    						=			[
    # (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__	(    lowerCamelCase					:      Namespace       ):
       return ConvertCommand(args.tfds_path  ,args.datasets_directory       )


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    @staticmethod
    def        A   (  SCREAMING_SNAKE_CASE      :							ArgumentParser):
           _A					:     Any     = 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      :							Dict				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							str				,							*SCREAMING_SNAKE_CASE      :							List[str]):
           _A					:     str     = get_logger('datasets-cli/converting')

           _A					:     List[Any]     = tfds_path
           _A					:     Tuple     = datasets_directory


    def        A   (  self      :							List[str]):
           if os.path.isdir(self._tfds_path):
                  _A					:     Optional[int]     = os.path.abspath(self._tfds_path)
           elif os.path.isfile(self._tfds_path):
                  _A					:     Optional[int]     = os.path.dirname(self._tfds_path)
           else:
                  raise ValueError('--tfds_path is neither a directory nor a file. Please check path.')

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

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

           _A					:     Tuple     = []
           _A					:     Union[str, Any]     = []
           _A					:     Optional[int]     = {}

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

           for f_name in file_names:
                  self._logger.info(F'Looking at file {f_name}')
                  _A					:     Tuple     = os.path.join(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                  _A					:     Optional[int]     = 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:
                         _A					:     List[str]     = f.readlines()

                  _A					:     List[str]     = []
                  _A					:     List[str]     = False
                  _A					:     str     = False
                  _A					:     List[str]     = []
                  for line in lines:
                         _A					:     List[str]     = 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:
                                _A					:     Any     = 'import datasets\n'
                         elif "import tensorflow" in out_line:
                                # order is important here
                                _A					:     Optional[Any]     = ''
                                continue
                         elif "from absl import logging" in out_line:
                                _A					:     Tuple     = 'from datasets import logging\n'
                         elif "getLogger" in out_line:
                                _A					:     str     = out_line.replace('getLogger'				,							'get_logger')
                         elif any(expression in out_line for expression in TO_HIGHLIGHT):
                                _A					:     Tuple     = True
                                _A					:     Any     = 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:
                                       _A					:     Union[str, Any]     = 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:
                                _A					:     List[str]     = 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(','))
                                _A					:     List[Any]     = '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:
                                _A					:     str     = True
                         out_lines.append(SCREAMING_SNAKE_CASE)

                  if is_builder or "wmt" in f_name:
                         # We create a new directory for each dataset
                         _A					:     Union[str, Any]     = f_name.replace('.py'				,							'')
                         _A					:     int     = os.path.join(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                         _A					:     Tuple     = 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:
                         _A					:     int     = os.path.basename(SCREAMING_SNAKE_CASE)
                         _A					:     List[str]     = 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 __future__ import annotations


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							list[list[int]]):
           _A					:     Dict     = TypeError(
               'Matrices must be formed from a list of zero or more lists containing at '
               'least one and the same number of values, each of which must be of type '
               'int or float.')
           if len(SCREAMING_SNAKE_CASE) != 0:
                  _A					:     str     = len(rows[0])
                  if cols == 0:
                         raise error
                  for row in rows:
                         if len(SCREAMING_SNAKE_CASE) != cols:
                                raise error
                         for value in row:
                                if not isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                                       raise error
                  _A					:     str     = rows
           else:
                  _A					:     Tuple     = []


    def        A   (  self      :							Optional[Any]):
           return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))]


    @property
    def        A   (  self      :							List[str]):
           return len(self.rows)


    @property
    def        A   (  self      :							Optional[int]):
           return len(self.rows[0])


    @property
    def        A   (  self      :							Optional[int]):
           return (self.num_rows, self.num_columns)


    @property
    def        A   (  self      :							Any):
           return self.order[0] == self.order[1]


    def        A   (  self      :							int):
           _A					:     Union[str, Any]     = [
               [0 if column_num != row_num else 1 for column_num in range(self.num_rows)]
               for row_num in range(self.num_rows)
           ]
           return Matrix(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[Any]):
           if not self.is_square:
                  return 0
           if self.order == (0, 0):
                  return 1
           if self.order == (1, 1):
                  return int(self.rows[0][0])
           if self.order == (2, 2):
                  return int(
                      (self.rows[0][0] * self.rows[1][1])
                      - (self.rows[0][1] * self.rows[1][0]))
           else:
                  return sum(
                      self.rows[0][column] * self.cofactors().rows[0][column]
                      for column in range(self.num_columns))


    def        A   (  self      :							Tuple):
           return bool(self.determinant())


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int):
           _A					:     str     = [
               [
                   self.rows[other_row][other_column]
                   for other_column in range(self.num_columns)
                   if other_column != column
               ]
               for other_row in range(self.num_rows)
               if other_row != row
           ]
           return Matrix(SCREAMING_SNAKE_CASE).determinant()


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int):
           if (row + column) % 2 == 0:
                  return self.get_minor(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           return -1 * self.get_minor(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]):
           return Matrix(
               [
                   [self.get_minor(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) for column in range(self.num_columns)]
                   for row in range(self.num_rows)
               ])


    def        A   (  self      :							Optional[int]):
           return Matrix(
               [
                   [
                       self.minors().rows[row][column]
                       if (row + column) % 2 == 0
                       else self.minors().rows[row][column] * -1
                       for column in range(self.minors().num_columns)
                   ]
                   for row in range(self.minors().num_rows)
               ])


    def        A   (  self      :							Tuple):
           _A					:     str     = [
               [self.cofactors().rows[column][row] for column in range(self.num_columns)]
               for row in range(self.num_rows)
           ]
           return Matrix(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Tuple):
           _A					:     str     = self.determinant()
           if not determinant:
                  raise TypeError('Only matrices with a non-zero determinant have an inverse')
           return self.adjugate() * (1 / determinant)


    def __repr__(  self      :							str):
           return str(self.rows)


    def __str__(  self      :							Optional[int]):
           if self.num_rows == 0:
                  return "[]"
           if self.num_rows == 1:
                  return "[[" + ". ".join(str(self.rows[0])) + "]]"
           return (
               "["
               + "\n ".join(
                   [
                       '[' + '. '.join([str(SCREAMING_SNAKE_CASE) for value in row]) + '.]'
                       for row in self.rows
                   ])
               + "]"
           )


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							list[int]				,							SCREAMING_SNAKE_CASE      :							int | None = None):
           _A					:     Tuple     = TypeError('Row must be a list containing all ints and/or floats')
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  raise type_error
           for value in row:
                  if not isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                         raise type_error
           if len(SCREAMING_SNAKE_CASE) != self.num_columns:
                  raise ValueError(
                      'Row must be equal in length to the other rows in the matrix')
           if position is None:
                  self.rows.append(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     List[str]     = self.rows[0:position] + [row] + self.rows[position:]


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							list[int]				,							SCREAMING_SNAKE_CASE      :							int | None = None):
           _A					:     int     = TypeError(
               'Column must be a list containing all ints and/or floats')
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  raise type_error
           for value in column:
                  if not isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                         raise type_error
           if len(SCREAMING_SNAKE_CASE) != self.num_rows:
                  raise ValueError(
                      'Column must be equal in length to the other columns in the matrix')
           if position is None:
                  _A					:     str     = [self.rows[i] + [column[i]] for i in range(self.num_rows)]
           else:
                  _A					:     Dict     = [
                      self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                      for i in range(self.num_rows)
                  ]


    def __eq__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							object):
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  return NotImplemented
           return self.rows == other.rows


    def __ne__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							object):
           return not self == other


    def __neg__(  self      :							Optional[Any]):
           return self * -1


    def __add__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Matrix):
           if self.order != other.order:
                  raise ValueError('Addition requires matrices of the same order')
           return Matrix(
               [
                   [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)]
                   for i in range(self.num_rows)
               ])


    def __sub__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Matrix):
           if self.order != other.order:
                  raise ValueError('Subtraction requires matrices of the same order')
           return Matrix(
               [
                   [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)]
                   for i in range(self.num_rows)
               ])


    def __mul__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Matrix | int | float):
           if isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                  return Matrix(
                      [[int(element * other) for element in row] for row in self.rows])
           elif isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  if self.num_columns != other.num_rows:
                         raise ValueError(
                             'The number of columns in the first matrix must '
                             'be equal to the number of rows in the second')
                  return Matrix(
                      [
                          [Matrix.dot_product(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) for column in other.columns()]
                          for row in self.rows
                      ])
           else:
                  raise TypeError(
                      'A Matrix can only be multiplied by an int, float, or another matrix')


    def __pow__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							int):
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  raise TypeError('A Matrix can only be raised to the power of an int')
           if not self.is_square:
                  raise ValueError('Only square matrices can be raised to a power')
           if other == 0:
                  return self.identity()
           if other < 0:
                  if self.is_invertable():
                         return self.inverse() ** (-other)
                  raise ValueError(
                      'Only invertable matrices can be raised to a negative power')
           _A					:     Union[str, Any]     = self
           for _ in range(other - 1):
                  result *= self
           return result


    @classmethod
    def        A   (  cls      :							List[str]				,							SCREAMING_SNAKE_CASE      :							list[int]				,							SCREAMING_SNAKE_CASE      :							list[int]):
           return sum(row[i] * column[i] for i in range(len(SCREAMING_SNAKE_CASE)))


if __name__ == "__main__":
   import doctest

   doctest.testmod()






