text: """
  This module is imported from the pandas package __init__.py file
  in order to ensure that the core.config options registered here will
  be available as soon as the user loads the package. if register_option
  is invoked inside specific modules, they will not be registered until that
  module is imported, which may or may not be a problem.
  
  If you need to make sure options are available even before a certain
  module is imported, register them here rather than in the module.
  
  """
  import os
  import warnings
  
  import pandas._config.config as cf
  from pandas._config.config import (
      is_bool,
      is_callable,
      is_instance_factory,
      is_int,
      is_nonnegative_int,
      is_one_of_factory,
      is_text,
  )
  
  # compute
  
  use_bottleneck_doc = """
  : bool
      Use the bottleneck library to accelerate if it is installed,
      the default is True
      Valid values: False,True
  """
  
  
  def use_bottleneck_cb(key):
      from pandas.core import nanops
  
      nanops.set_use_bottleneck(cf.get_option(key))
  
  
  use_numexpr_doc = """
  : bool
      Use the numexpr library to accelerate computation if it is installed,
      the default is True
      Valid values: False,True
  """
  
  
  def use_numexpr_cb(key):
      from pandas.core.computation import expressions
  
      expressions.set_use_numexpr(cf.get_option(key))
  
  
  use_numba_doc = """
  : bool
      Use the numba engine option for select operations if it is installed,
      the default is False
      Valid values: False,True
  """
  
  
  def use_numba_cb(key):
      from pandas.core.util import numba_
  
      numba_.set_use_numba(cf.get_option(key))
  
  
  with cf.config_prefix("compute"):
      cf.register_option(
          "use_bottleneck",
          True,
          use_bottleneck_doc,
          validator=is_bool,
          cb=use_bottleneck_cb,
      )
      cf.register_option(
          "use_numexpr", True, use_numexpr_doc, validator=is_bool, cb=use_numexpr_cb
      )
      cf.register_option(
          "use_numba", False, use_numba_doc, validator=is_bool, cb=use_numba_cb
      )
  #
  # options from the "display" namespace
  
  pc_precision_doc = """
  : int
      Floating point output precision in terms of number of places after the
      decimal, for regular formatting as well as scientific notation. Similar
      to ``precision`` in :meth:`numpy.set_printoptions`.
  """
  
  pc_colspace_doc = """
  : int
      Default space for DataFrame columns.
  """
  
  pc_max_rows_doc = """
  : int
      If max_rows is exceeded, switch to truncate view. Depending on
      `large_repr`, objects are either centrally truncated or printed as
      a summary view. 'None' value means unlimited.
  
      In case python/IPython is running in a terminal and `large_repr`
      equals 'truncate' this can be set to 0 and pandas will auto-detect
      the height of the terminal and print a truncated object which fits
      the screen height. The IPython notebook, IPython qtconsole, or
      IDLE do not run in a terminal and hence it is not possible to do
      correct auto-detection.
  """
  
  pc_max_groups_doc = """
  : int
      If max_groups is exceeded, switch to truncate groupby view. 'None' value
      means unlimited.
  """
  
  pc_min_rows_doc = """
  : int
      The numbers of rows to show in a truncated view (when `max_rows` is
      exceeded). Ignored when `max_rows` is set to None or 0. When set to
      None, follows the value of `max_rows`.
  """
  
  pc_max_cols_doc = """
  : int
      If max_cols is exceeded, switch to truncate view. Depending on
      `large_repr`, objects are either centrally truncated or printed as
      a summary view. 'None' value means unlimited.
  
      In case python/IPython is running in a terminal and `large_repr`
      equals 'truncate' this can be set to 0 and pandas will auto-detect
      the width of the terminal and print a truncated object which fits
      the screen width. The IPython notebook, IPython qtconsole, or IDLE
      do not run in a terminal and hence it is not possible to do
      correct auto-detection.
  """
  
  pc_max_categories_doc = """
  : int
      This sets the maximum number of categories pandas should output when
      printing out a `Categorical` or a Series of dtype "category".
  """
  
  pc_max_info_cols_doc = """
  : int
      max_info_columns is used in DataFrame.info method to decide if
      per column information will be printed.
  """
  
  pc_nb_repr_h_doc = """
  : boolean
      When True, IPython notebook will use html representation for
      pandas objects (if it is available).
  """
  
  pc_pprint_nest_depth = """
  : int
      Controls the number of nested levels to process when pretty-printing
  """
  
  pc_multi_sparse_doc = """
  : boolean
      "sparsify" MultiIndex display (don't display repeated
      elements in outer levels within groups)
  """
  
  float_format_doc = """
  : callable
      The callable should accept a floating point number and return
      a string with the desired format of the number. This is used
      in some places like SeriesFormatter.
      See formats.format.EngFormatter for an example.
  """
  
  max_colwidth_doc = """
  : int or None
      The maximum width in characters of a column in the repr of
      a pandas data structure. When the column overflows, a "..."
      placeholder is embedded in the output. A 'None' value means unlimited.
  """
  
  colheader_justify_doc = """
  : 'left'/'right'
      Controls the justification of column headers. used by DataFrameFormatter.
  """
  
  pc_expand_repr_doc = """
  : boolean
      Whether to print out the full DataFrame repr for wide DataFrames across
      multiple lines, `max_columns` is still respected, but the output will
      wrap-around across multiple "pages" if its width exceeds `display.width`.
  """
  
  pc_show_dimensions_doc = """
  : boolean or 'truncate'
      Whether to print out dimensions at the end of DataFrame repr.
      If 'truncate' is specified, only print out the dimensions if the
      frame is truncated (e.g. not display all rows and/or columns)
  """
  
  pc_east_asian_width_doc = """
  : boolean
      Whether to use the Unicode East Asian Width to calculate the display text
      width.
      Enabling this may affect to the performance (default: False)
  """
  
  pc_ambiguous_as_wide_doc = """
  : boolean
      Whether to handle Unicode characters belong to Ambiguous as Wide (width=2)
      (default: False)
  """
  
  pc_latex_repr_doc = """
  : boolean
      Whether to produce a latex DataFrame representation for jupyter
      environments that support it.
      (default: False)
  """
  
  pc_table_schema_doc = """
  : boolean
      Whether to publish a Table Schema representation for frontends
      that support it.
      (default: False)
  """
  
  pc_html_border_doc = """
  : int
      A ``border=value`` attribute is inserted in the ``<table>`` tag
      for the DataFrame HTML repr.
  """
  
  pc_html_use_mathjax_doc = """\
  : boolean
      When True, Jupyter notebook will process table contents using MathJax,
      rendering mathematical expressions enclosed by the dollar symbol.
      (default: True)
  """
  
  pc_width_doc = """
  : int
      Width of the display in characters. In case python/IPython is running in
      a terminal this can be set to None and pandas will correctly auto-detect
      the width.
      Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a
      terminal and hence it is not possible to correctly detect the width.
  """
  
  pc_chop_threshold_doc = """
  : float or None
      if set to a float value, all float values smaller then the given threshold
      will be displayed as exactly 0 by repr and friends.
  """
  
  pc_max_seq_items = """
  : int or None
      When pretty-printing a long sequence, no more then `max_seq_items`
      will be printed. If items are omitted, they will be denoted by the
      addition of "..." to the resulting string.
  
      If set to None, the number of items to be printed is unlimited.
  """
  
  pc_max_info_rows_doc = """
  : int or None
      df.info() will usually show null-counts for each column.
      For large frames this can be quite slow. max_info_rows and max_info_cols
      limit this null check only to frames with smaller dimensions than
      specified.
  """
  
  pc_large_repr_doc = """
  : 'truncate'/'info'
      For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can
      show a truncated table (the default from 0.13), or switch to the view from
      df.info() (the behaviour in earlier versions of pandas).
  """
  
  pc_memory_usage_doc = """
  : bool, string or None
      This specifies if the memory usage of a DataFrame should be displayed when
      df.info() is called. Valid values True,False,'deep'
  """
  
  pc_latex_escape = """
  : bool
      This specifies if the to_latex method of a Dataframe uses escapes special
      characters.
      Valid values: False,True
  """
  
  pc_latex_longtable = """
  :bool
      This specifies if the to_latex method of a Dataframe uses the longtable
      format.
      Valid values: False,True
  """
  
  pc_latex_multicolumn = """
  : bool
      This specifies if the to_latex method of a Dataframe uses multicolumns
      to pretty-print MultiIndex columns.
      Valid values: False,True
  """
  
  pc_latex_multicolumn_format = """
  : string
      This specifies the format for multicolumn headers.
      Can be surrounded with '|'.
      Valid values: 'l', 'c', 'r', 'p{<width>}'
  """
  
  pc_latex_multirow = """
  : bool
      This specifies if the to_latex method of a Dataframe uses multirows
      to pretty-print MultiIndex rows.
      Valid values: False,True
  """
  
  
  def table_schema_cb(key):
      from pandas.io.formats.printing import enable_data_resource_formatter
  
      enable_data_resource_formatter(cf.get_option(key))
  
  
  def is_terminal() -> bool:
      """
      Detect if Python is running in a terminal.
  
      Returns True if Python is running in a terminal or False if not.
      """
      try:
          # error: Name 'get_ipython' is not defined
          ip = get_ipython()  # type: ignore[name-defined]
      except NameError:  # assume standard Python interpreter in a terminal
          return True
      else:
          if hasattr(ip, "kernel"):  # IPython as a Jupyter kernel
              return False
          else:  # IPython in a terminal
              return True
  
  
  with cf.config_prefix("display"):
      cf.register_option("precision", 6, pc_precision_doc, validator=is_nonnegative_int)
      cf.register_option(
          "float_format",
          None,
          float_format_doc,
          validator=is_one_of_factory([None, is_callable]),
      )
      cf.register_option("column_space", 12, validator=is_int)
      cf.register_option(
          "max_info_rows",
          1690785,
          pc_max_info_rows_doc,
          validator=is_instance_factory((int, type(None))),
      )
      cf.register_option("max_rows", 60, pc_max_rows_doc, validator=is_nonnegative_int)
      cf.register_option(
          "max_groups", 10, pc_max_groups_doc, validator=is_nonnegative_int
      )
      cf.register_option(
          "min_rows",
          10,
          pc_min_rows_doc,
          validator=is_instance_factory([type(None), int]),
      )
      cf.register_option("max_categories", 8, pc_max_categories_doc, validator=is_int)
  
      def _deprecate_negative_int_max_colwidth(key):
          value = cf.get_option(key)
          if value is not None and value < 0:
              warnings.warn(
                  "Passing a negative integer is deprecated in version 1.0 and "
                  "will not be supported in future version. Instead, use None "
                  "to not limit the column width.",
                  FutureWarning,
                  stacklevel=4,
              )
  
      cf.register_option(
          # TODO(2.0): change `validator=is_nonnegative_int` see GH#31569
          "max_colwidth",
          50,
          max_colwidth_doc,
          validator=is_instance_factory([type(None), int]),
          cb=_deprecate_negative_int_max_colwidth,
      )
      if is_terminal():
          max_cols = 0  # automatically determine optimal number of columns
      else:
          max_cols = 20  # cannot determine optimal number of columns
      cf.register_option(
          "max_columns", max_cols, pc_max_cols_doc, validator=is_nonnegative_int
      )
      cf.register_option(
          "large_repr",
          "truncate",
          pc_large_repr_doc,
          validator=is_one_of_factory(["truncate", "info"]),
      )
      cf.register_option("max_info_columns", 100, pc_max_info_cols_doc, validator=is_int)
      cf.register_option(
          "colheader_justify", "right", colheader_justify_doc, validator=is_text
      )
      cf.register_option("notebook_repr_html", True, pc_nb_repr_h_doc, validator=is_bool)
      cf.register_option("pprint_nest_depth", 3, pc_pprint_nest_depth, validator=is_int)
      cf.register_option("multi_sparse", True, pc_multi_sparse_doc, validator=is_bool)
      cf.register_option("expand_frame_repr", True, pc_expand_repr_doc)
      cf.register_option(
          "show_dimensions",
          "truncate",
          pc_show_dimensions_doc,
          validator=is_one_of_factory([True, False, "truncate"]),
      )
      cf.register_option("chop_threshold", None, pc_chop_threshold_doc)
      cf.register_option("max_seq_items", 100, pc_max_seq_items)
      cf.register_option(
          "width", 80, pc_width_doc, validator=is_instance_factory([type(None), int])
      )
      cf.register_option(
          "memory_usage",
          True,
          pc_memory_usage_doc,
          validator=is_one_of_factory([None, True, False, "deep"]),
      )
      cf.register_option(
          "unicode.east_asian_width", False, pc_east_asian_width_doc, validator=is_bool
      )
      cf.register_option(
          "unicode.ambiguous_as_wide", False, pc_east_asian_width_doc, validator=is_bool
      )
      cf.register_option("latex.repr", False, pc_latex_repr_doc, validator=is_bool)
      cf.register_option("latex.escape", True, pc_latex_escape, validator=is_bool)
      cf.register_option("latex.longtable", False, pc_latex_longtable, validator=is_bool)
      cf.register_option(
          "latex.multicolumn", True, pc_latex_multicolumn, validator=is_bool
      )
      cf.register_option(
          "latex.multicolumn_format", "l", pc_latex_multicolumn, validator=is_text
      )
      cf.register_option("latex.multirow", False, pc_latex_multirow, validator=is_bool)
      cf.register_option(
          "html.table_schema",
          False,
          pc_table_schema_doc,
          validator=is_bool,
          cb=table_schema_cb,
      )
      cf.register_option("html.border", 1, pc_html_border_doc, validator=is_int)
      cf.register_option(
          "html.use_mathjax", True, pc_html_use_mathjax_doc, validator=is_bool
      )
  
  tc_sim_interactive_doc = """
  : boolean
      Whether to simulate interactive mode for purposes of testing
  """
  
  with cf.config_prefix("mode"):
      cf.register_option("sim_interactive", False, tc_sim_interactive_doc)
  
  use_inf_as_null_doc = """
  : boolean
      use_inf_as_null had been deprecated and will be removed in a future
      version. Use `use_inf_as_na` instead.
  """
  
  use_inf_as_na_doc = """
  : boolean
      True means treat None, NaN, INF, -INF as NA (old way),
      False means None and NaN are null, but INF, -INF are not NA
      (new way).
  """
  
  # We don't want to start importing everything at the global context level
  # or we'll hit circular deps.
  
  
  def use_inf_as_na_cb(key):
      from pandas.core.dtypes.missing import _use_inf_as_na
  
      _use_inf_as_na(key)
  
  
  with cf.config_prefix("mode"):
      cf.register_option("use_inf_as_na", False, use_inf_as_na_doc, cb=use_inf_as_na_cb)
      cf.register_option(
          "use_inf_as_null", False, use_inf_as_null_doc, cb=use_inf_as_na_cb
      )
  
  
  cf.deprecate_option(
      "mode.use_inf_as_null", msg=use_inf_as_null_doc, rkey="mode.use_inf_as_na"
  )
  
  
  data_manager_doc = """
  : string
      Internal data manager type; can be "block" or "array". Defaults to "block",
      unless overridden by the 'PANDAS_DATA_MANAGER' environment variable (needs
      to be set before pandas is imported).
  """
  
  
  with cf.config_prefix("mode"):
      cf.register_option(
          "data_manager",
          # Get the default from an environment variable, if set, otherwise defaults
          # to "block". This environment variable can be set for testing.
          os.environ.get("PANDAS_DATA_MANAGER", "block"),
          data_manager_doc,
          validator=is_one_of_factory(["block", "array"]),
      )
  
  
  # user warnings
  chained_assignment = """
  : string
      Raise an exception, warn, or no action if trying to use chained assignment,
      The default is warn
  """
  
  with cf.config_prefix("mode"):
      cf.register_option(
          "chained_assignment",
          "warn",
          chained_assignment,
          validator=is_one_of_factory([None, "warn", "raise"]),
      )
  
  
  string_storage_doc = """
  : string
      The default storage for StringDtype.
  """
  
  with cf.config_prefix("mode"):
      cf.register_option(
          "string_storage",
          "python",
          string_storage_doc,
          validator=is_one_of_factory(["python", "pyarrow"]),
      )
  
  # Set up the io.excel specific reader configuration.
  reader_engine_doc = """
  : string
      The default Excel reader engine for '{ext}' files. Available options:
      auto, {others}.
  """
  
  _xls_options = ["xlrd"]
  _xlsm_options = ["xlrd", "openpyxl"]
  _xlsx_options = ["xlrd", "openpyxl"]
  _ods_options = ["odf"]
  _xlsb_options = ["pyxlsb"]
  
  
  with cf.config_prefix("io.excel.xls"):
      cf.register_option(
          "reader",
          "auto",
          reader_engine_doc.format(ext="xls", others=", ".join(_xls_options)),
          validator=is_one_of_factory(_xls_options + ["auto"]),
      )
  
  with cf.config_prefix("io.excel.xlsm"):
      cf.register_option(
          "reader",
          "auto",
          reader_engine_doc.format(ext="xlsm", others=", ".join(_xlsm_options)),
          validator=is_one_of_factory(_xlsm_options + ["auto"]),
      )
  
  
  with cf.config_prefix("io.excel.xlsx"):
      cf.register_option(
          "reader",
          "auto",
          reader_engine_doc.format(ext="xlsx", others=", ".join(_xlsx_options)),
          validator=is_one_of_factory(_xlsx_options + ["auto"]),
      )
  
  
  with cf.config_prefix("io.excel.ods"):
      cf.register_option(
          "reader",
          "auto",
          reader_engine_doc.format(ext="ods", others=", ".join(_ods_options)),
          validator=is_one_of_factory(_ods_options + ["auto"]),
      )
  
  with cf.config_prefix("io.excel.xlsb"):
      cf.register_option(
          "reader",
          "auto",
          reader_engine_doc.format(ext="xlsb", others=", ".join(_xlsb_options)),
          validator=is_one_of_factory(_xlsb_options + ["auto"]),
      )
  
  # Set up the io.excel specific writer configuration.
  writer_engine_doc = """
  : string
      The default Excel writer engine for '{ext}' files. Available options:
      auto, {others}.
  """
  
  _xls_options = ["xlwt"]
  _xlsm_options = ["openpyxl"]
  _xlsx_options = ["openpyxl", "xlsxwriter"]
  _ods_options = ["odf"]
  
  
  with cf.config_prefix("io.excel.xls"):
      cf.register_option(
          "writer",
          "auto",
          writer_engine_doc.format(ext="xls", others=", ".join(_xls_options)),
          validator=str,
      )
  cf.deprecate_option(
      "io.excel.xls.writer",
      msg="As the xlwt package is no longer maintained, the xlwt engine will be "
      "removed in a future version of pandas. This is the only engine in pandas that "
      "supports writing in the xls format. Install openpyxl and write to an "
      "xlsx file instead.",
  )
  
  with cf.config_prefix("io.excel.xlsm"):
      cf.register_option(
          "writer",
          "auto",
          writer_engine_doc.format(ext="xlsm", others=", ".join(_xlsm_options)),
          validator=str,
      )
  
  
  with cf.config_prefix("io.excel.xlsx"):
      cf.register_option(
          "writer",
          "auto",
          writer_engine_doc.format(ext="xlsx", others=", ".join(_xlsx_options)),
          validator=str,
      )
  
  
  with cf.config_prefix("io.excel.ods"):
      cf.register_option(
          "writer",
          "auto",
          writer_engine_doc.format(ext="ods", others=", ".join(_ods_options)),
          validator=str,
      )
  
  
  # Set up the io.parquet specific configuration.
  parquet_engine_doc = """
  : string
      The default parquet reader/writer engine. Available options:
      'auto', 'pyarrow', 'fastparquet', the default is 'auto'
  """
  
  with cf.config_prefix("io.parquet"):
      cf.register_option(
          "engine",
          "auto",
          parquet_engine_doc,
          validator=is_one_of_factory(["auto", "pyarrow", "fastparquet"]),
      )
  
  
  # Set up the io.sql specific configuration.
  sql_engine_doc = """
  : string
      The default sql reader/writer engine. Available options:
      'auto', 'sqlalchemy', the default is 'auto'
  """
  
  with cf.config_prefix("io.sql"):
      cf.register_option(
          "engine",
          "auto",
          sql_engine_doc,
          validator=is_one_of_factory(["auto", "sqlalchemy"]),
      )
  
  # --------
  # Plotting
  # ---------
  
  plotting_backend_doc = """
  : str
      The plotting backend to use. The default value is "matplotlib", the
      backend provided with pandas. Other backends can be specified by
      providing the name of the module that implements the backend.
  """
  
  
  def register_plotting_backend_cb(key):
      if key == "matplotlib":
          # We defer matplotlib validation, since it's the default
          return
      from pandas.plotting._core import _get_plot_backend
  
      _get_plot_backend(key)
  
  
  with cf.config_prefix("plotting"):
      cf.register_option(
          "backend",
          defval="matplotlib",
          doc=plotting_backend_doc,
          validator=register_plotting_backend_cb,
      )
  
  
  register_converter_doc = """
  : bool or 'auto'.
      Whether to register converters with matplotlib's units registry for
      dates, times, datetimes, and Periods. Toggling to False will remove
      the converters, restoring any converters that pandas overwrote.
  """
  
  
  def register_converter_cb(key):
      from pandas.plotting import (
          deregister_matplotlib_converters,
          register_matplotlib_converters,
      )
  
      if cf.get_option(key):
          register_matplotlib_converters()
      else:
          deregister_matplotlib_converters()
  
  
  with cf.config_prefix("plotting.matplotlib"):
      cf.register_option(
          "register_converters",
          "auto",
          register_converter_doc,
          validator=is_one_of_factory(["auto", True, False]),
          cb=register_converter_cb,
      )
  
  # ------
  # Styler
  # ------
  
  styler_sparse_index_doc = """
  : bool
      Whether to sparsify the display of a hierarchical index. Setting to False will
      display each explicit level element in a hierarchical key for each row.
  """
  
  styler_sparse_columns_doc = """
  : bool
      Whether to sparsify the display of hierarchical columns. Setting to False will
      display each explicit level element in a hierarchical key for each column.
  """
  
  styler_max_elements = """
  : int
      The maximum number of data-cell (<td>) elements that will be rendered before
      trimming will occur over columns, rows or both if needed.
  """
  
  with cf.config_prefix("styler"):
      cf.register_option("sparse.index", True, styler_sparse_index_doc, validator=bool)
  
      cf.register_option(
          "sparse.columns", True, styler_sparse_columns_doc, validator=bool
      )
  
      cf.register_option(
          "render.max_elements",
          2 ** 18,
          styler_max_elements,
          validator=is_nonnegative_int,
      )
  