





"""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


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

lowerCamelCase__						= "=======\n>>>>>>>\n"

lowerCamelCase__						= [
    "TextEncoderConfig",
    "ByteTextEncoder",
    "SubwordTextEncoder",
    "encoder_config",
    "maybe_build_from_corpus",
    "manual_dir",
]

lowerCamelCase__						= [
    # (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        lowercase__     (       lowercase_      )       ->		Any:




      """simple docstring"""

      return ConvertCommand(args.tfds_path		,args.datasets_directory      )



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  @staticmethod
  def  __SCREAMING_SNAKE_CASE					(		__a :							ArgumentParser						)		->					str:
        _UpperCamelCase   :						str    =							parser.add_parser(
            "convert"       , help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."       , )
        train_parser.add_argument(
            "--tfds_path"       , type=__a       , required=__a       , help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."       , )
        train_parser.add_argument(
            "--datasets_directory"       , type=__a       , required=__a       , help="Path to the HuggingFace Datasets folder."						)
        train_parser.set_defaults(func=__a						)

  def __init__(		self :							Optional[int]       , __a :							str       , __a :							str       , *__a :							List[Any]						)		->					Optional[int]:
        _UpperCamelCase   :						str    =							get_logger("datasets-cli/converting"						)

        _UpperCamelCase   :						str    =							tfds_path
        _UpperCamelCase   :						Optional[Any]    =							datasets_directory


  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					Dict:
        if os.path.isdir(self._tfds_path						):
              _UpperCamelCase   :						List[Any]    =							os.path.abspath(self._tfds_path						)
        elif os.path.isfile(self._tfds_path						):
              _UpperCamelCase   :						Optional[Any]    =							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   :						int    =							[]
        _UpperCamelCase   :						str    =							[]
        _UpperCamelCase   :						Optional[int]    =							{}

        if os.path.isdir(self._tfds_path						):
              _UpperCamelCase   :						Optional[int]    =							os.listdir(__a						)
        else:
              _UpperCamelCase   :						List[Any]    =							[os.path.basename(self._tfds_path						)]

        for f_name in file_names:
              self._logger.info(F'''Looking at file {f_name}'''						)
              _UpperCamelCase   :						List[Any]    =							os.path.join(__a       , __a						)
              _UpperCamelCase   :						Dict    =							os.path.join(__a       , __a						)

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

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

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

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

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

              if needs_manual_update:
                    with_manual_update.append(__a						)

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

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


import numpy as np




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_ = 1e-12		,lowercase_ = 100		,)       ->		tuple[float, np.ndarray]:




      """simple docstring"""

      assert np.shape(lowercase_      )[0] == np.shape(lowercase_      )[1]
      # Ensure proper dimensionality.
      assert np.shape(lowercase_      )[0] == np.shape(lowercase_      )[0]
      # Ensure inputs are either both complex or both real
      assert np.iscomplexobj(lowercase_      ) == np.iscomplexobj(lowercase_      )
      _UpperCamelCase   :						Dict    =							np.iscomplexobj(lowercase_      )
      if is_complex:
            # Ensure complex input_matrix is Hermitian
            assert np.array_equal(lowercase_		,input_matrix.conj().T      )

      # Set convergence to False. Will define convergence when we exceed max_iterations
      # or when we have small changes from one iteration to next.

      _UpperCamelCase   :						List[Any]    =							False
      _UpperCamelCase   :						Optional[int]    =							0
      _UpperCamelCase   :						int    =							0
      _UpperCamelCase   :						Optional[int]    =							1e12

      while not convergence:
            # Multiple matrix by the vector.
            _UpperCamelCase   :						Optional[int]    =							np.dot(lowercase_		,lowercase_      )
            # Normalize the resulting output vector.
            _UpperCamelCase   :						List[Any]    =							w / np.linalg.norm(lowercase_      )
            # Find rayleigh quotient
            # (faster than usual b/c we know vector is normalized already)
            _UpperCamelCase   :						Optional[Any]    =							vector.conj().T if is_complex else vector.T
            _UpperCamelCase   :						Dict    =							np.dot(lowercase_		,np.dot(lowercase_		,lowercase_      )      )

            # Check convergence.
            _UpperCamelCase   :						Optional[int]    =							np.abs(lambda_ - lambda_previous      ) / lambda_
            iterations += 1

            if error <= error_tol or iterations >= max_iterations:
                  _UpperCamelCase   :						List[Any]    =							True

            _UpperCamelCase   :						Tuple    =							lambda_

      if is_complex:
            _UpperCamelCase   :						Tuple    =							np.real(lambda_      )

      return lambda_, vector




def        lowercase__     (       )       ->		None:




      """simple docstring"""

      _UpperCamelCase   :						Dict    =							np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]      )
      _UpperCamelCase   :						Optional[int]    =							np.array([41, 4, 20]      )
      _UpperCamelCase   :						Union[str, Any]    =							real_input_matrix.astype(np.complexaaa      )
      _UpperCamelCase   :						int    =							np.triu(1J * complex_input_matrix		,1      )
      complex_input_matrix += imag_matrix
      complex_input_matrix += -1 * imag_matrix.T
      _UpperCamelCase   :						Union[str, Any]    =							np.array([41, 4, 20]      ).astype(np.complexaaa      )

      for problem_type in ["real", "complex"]:
            if problem_type == "real":
                  _UpperCamelCase   :						Dict    =							real_input_matrix
                  _UpperCamelCase   :						Any    =							real_vector
            elif problem_type == "complex":
                  _UpperCamelCase   :						int    =							complex_input_matrix
                  _UpperCamelCase   :						Union[str, Any]    =							complex_vector

            # Our implementation.
            _UpperCamelCase,	_UpperCamelCase   :						int    =							power_iteration(lowercase_		,lowercase_      )

            # Numpy implementation.

            # Get eigenvalues and eigenvectors using built-in numpy
            # eigh (eigh used for symmetric or hermetian matrices).
            _UpperCamelCase,	_UpperCamelCase   :						str    =							np.linalg.eigh(lowercase_      )
            # Last eigenvalue is the maximum one.
            _UpperCamelCase   :						Any    =							eigen_values[-1]
            # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
            _UpperCamelCase   :						Tuple    =							eigen_vectors[:, -1]

            # Check our implementation and numpy gives close answers.
            assert np.abs(eigen_value - eigen_value_max      ) <= 1e-6
            # Take absolute values element wise of each eigenvector.
            # as they are only unique to a minus sign.
            assert np.linalg.norm(np.abs(lowercase_      ) - np.abs(lowercase_      )      ) <= 1e-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()
