text: """
  Module contains tools for processing files into DataFrames or other objects
  """
  from __future__ import annotations
  
  from collections import abc
  import csv
  import sys
  from textwrap import fill
  from typing import (
      IO,
      Any,
      Callable,
      NamedTuple,
  )
  import warnings
  
  import numpy as np
  
  import pandas._libs.lib as lib
  from pandas._libs.parsers import STR_NA_VALUES
  from pandas._typing import (
      ArrayLike,
      CompressionOptions,
      CSVEngine,
      DtypeArg,
      FilePath,
      ReadCsvBuffer,
      StorageOptions,
  )
  from pandas.errors import (
      AbstractMethodError,
      ParserWarning,
  )
  from pandas.util._decorators import (
      Appender,
      deprecate_nonkeyword_arguments,
  )
  from pandas.util._exceptions import find_stack_level
  from pandas.util._validators import validate_bool_kwarg
  
  from pandas.core.dtypes.common import (
      is_file_like,
      is_float,
      is_integer,
      is_list_like,
  )
  
  from pandas.core.frame import DataFrame
  from pandas.core.indexes.api import RangeIndex
  from pandas.core.shared_docs import _shared_docs
  
  from pandas.io.common import (
      IOHandles,
      get_handle,
      validate_header_arg,
  )
  from pandas.io.parsers.arrow_parser_wrapper import ArrowParserWrapper
  from pandas.io.parsers.base_parser import (
      ParserBase,
      is_index_col,
      parser_defaults,
  )
  from pandas.io.parsers.c_parser_wrapper import CParserWrapper
  from pandas.io.parsers.python_parser import (
      FixedWidthFieldParser,
      PythonParser,
  )
  
  _doc_read_csv_and_table = (
      r"""
  {summary}
  
  Also supports optionally iterating or breaking of the file
  into chunks.
  
  Additional help can be found in the online docs for
  `IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.
  
  Parameters
  ----------
  filepath_or_buffer : str, path object or file-like object
      Any valid string path is acceptable. The string could be a URL. Valid
      URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is
      expected. A local file could be: file://localhost/path/to/table.csv.
  
      If you want to pass in a path object, pandas accepts any ``os.PathLike``.
  
      By file-like object, we refer to objects with a ``read()`` method, such as
      a file handle (e.g. via builtin ``open`` function) or ``StringIO``.
  sep : str, default {_default_sep}
      Delimiter to use. If sep is None, the C engine cannot automatically detect
      the separator, but the Python parsing engine can, meaning the latter will
      be used and automatically detect the separator by Python's builtin sniffer
      tool, ``csv.Sniffer``. In addition, separators longer than 1 character and
      different from ``'\s+'`` will be interpreted as regular expressions and
      will also force the use of the Python parsing engine. Note that regex
      delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``.
  delimiter : str, default ``None``
      Alias for sep.
  header : int, list of int, None, default 'infer'
      Row number(s) to use as the column names, and the start of the
      data.  Default behavior is to infer the column names: if no names
      are passed the behavior is identical to ``header=0`` and column
      names are inferred from the first line of the file, if column
      names are passed explicitly then the behavior is identical to
      ``header=None``. Explicitly pass ``header=0`` to be able to
      replace existing names. The header can be a list of integers that
      specify row locations for a multi-index on the columns
      e.g. [0,1,3]. Intervening rows that are not specified will be
      skipped (e.g. 2 in this example is skipped). Note that this
      parameter ignores commented lines and empty lines if
      ``skip_blank_lines=True``, so ``header=0`` denotes the first line of
      data rather than the first line of the file.
  names : array-like, optional
      List of column names to use. If the file contains a header row,
      then you should explicitly pass ``header=0`` to override the column names.
      Duplicates in this list are not allowed.
  index_col : int, str, sequence of int / str, or False, optional, default ``None``
    Column(s) to use as the row labels of the ``DataFrame``, either given as
    string name or column index. If a sequence of int / str is given, a
    MultiIndex is used.
  
    Note: ``index_col=False`` can be used to force pandas to *not* use the first
    column as the index, e.g. when you have a malformed file with delimiters at
    the end of each line.
  usecols : list-like or callable, optional
      Return a subset of the columns. If list-like, all elements must either
      be positional (i.e. integer indices into the document columns) or strings
      that correspond to column names provided either by the user in `names` or
      inferred from the document header row(s). If ``names`` are given, the document
      header row(s) are not taken into account. For example, a valid list-like
      `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.
      Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
      To instantiate a DataFrame from ``data`` with element order preserved use
      ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns
      in ``['foo', 'bar']`` order or
      ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``
      for ``['bar', 'foo']`` order.
  
      If callable, the callable function will be evaluated against the column
      names, returning names where the callable function evaluates to True. An
      example of a valid callable argument would be ``lambda x: x.upper() in
      ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
      parsing time and lower memory usage.
  squeeze : bool, default False
      If the parsed data only contains one column then return a Series.
  
      .. deprecated:: 1.4.0
          Append ``.squeeze("columns")`` to the call to ``{func_name}`` to squeeze
          the data.
  prefix : str, optional
      Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
  
      .. deprecated:: 1.4.0
         Use a list comprehension on the DataFrame's columns after calling ``read_csv``.
  mangle_dupe_cols : bool, default True
      Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than
      'X'...'X'. Passing in False will cause data to be overwritten if there
      are duplicate names in the columns.
  dtype : Type name or dict of column -> type, optional
      Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32,
      'c': 'Int64'}}
      Use `str` or `object` together with suitable `na_values` settings
      to preserve and not interpret dtype.
      If converters are specified, they will be applied INSTEAD
      of dtype conversion.
  engine : {{'c', 'python', 'pyarrow'}}, optional
      Parser engine to use. The C and pyarrow engines are faster, while the python engine
      is currently more feature-complete. Multithreading is currently only supported by
      the pyarrow engine.
  
      .. versionadded:: 1.4.0
  
          The "pyarrow" engine was added as an *experimental* engine, and some features
          are unsupported, or may not work correctly, with this engine.
  converters : dict, optional
      Dict of functions for converting values in certain columns. Keys can either
      be integers or column labels.
  true_values : list, optional
      Values to consider as True.
  false_values : list, optional
      Values to consider as False.
  skipinitialspace : bool, default False
      Skip spaces after delimiter.
  skiprows : list-like, int or callable, optional
      Line numbers to skip (0-indexed) or number of lines to skip (int)
      at the start of the file.
  
      If callable, the callable function will be evaluated against the row
      indices, returning True if the row should be skipped and False otherwise.
      An example of a valid callable argument would be ``lambda x: x in [0, 2]``.
  skipfooter : int, default 0
      Number of lines at bottom of file to skip (Unsupported with engine='c').
  nrows : int, optional
      Number of rows of file to read. Useful for reading pieces of large files.
  na_values : scalar, str, list-like, or dict, optional
      Additional strings to recognize as NA/NaN. If dict passed, specific
      per-column NA values.  By default the following values are interpreted as
      NaN: '"""
      + fill("', '".join(sorted(STR_NA_VALUES)), 70, subsequent_indent="    ")
      + """'.
  keep_default_na : bool, default True
      Whether or not to include the default NaN values when parsing the data.
      Depending on whether `na_values` is passed in, the behavior is as follows:
  
      * If `keep_default_na` is True, and `na_values` are specified, `na_values`
        is appended to the default NaN values used for parsing.
      * If `keep_default_na` is True, and `na_values` are not specified, only
        the default NaN values are used for parsing.
      * If `keep_default_na` is False, and `na_values` are specified, only
        the NaN values specified `na_values` are used for parsing.
      * If `keep_default_na` is False, and `na_values` are not specified, no
        strings will be parsed as NaN.
  
      Note that if `na_filter` is passed in as False, the `keep_default_na` and
      `na_values` parameters will be ignored.
  na_filter : bool, default True
      Detect missing value markers (empty strings and the value of na_values). In
      data without any NAs, passing na_filter=False can improve the performance
      of reading a large file.
  verbose : bool, default False
      Indicate number of NA values placed in non-numeric columns.
  skip_blank_lines : bool, default True
      If True, skip over blank lines rather than interpreting as NaN values.
  parse_dates : bool or list of int or names or list of lists or dict, \
  default False
      The behavior is as follows:
  
      * boolean. If True -> try parsing the index.
      * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
        each as a separate date column.
      * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as
        a single date column.
      * dict, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call
        result 'foo'
  
      If a column or index cannot be represented as an array of datetimes,
      say because of an unparsable value or a mixture of timezones, the column
      or index will be returned unaltered as an object data type. For
      non-standard datetime parsing, use ``pd.to_datetime`` after
      ``pd.read_csv``. To parse an index or column with a mixture of timezones,
      specify ``date_parser`` to be a partially-applied
      :func:`pandas.to_datetime` with ``utc=True``. See
      :ref:`io.csv.mixed_timezones` for more.
  
      Note: A fast-path exists for iso8601-formatted dates.
  infer_datetime_format : bool, default False
      If True and `parse_dates` is enabled, pandas will attempt to infer the
      format of the datetime strings in the columns, and if it can be inferred,
      switch to a faster method of parsing them. In some cases this can increase
      the parsing speed by 5-10x.
  keep_date_col : bool, default False
      If True and `parse_dates` specifies combining multiple columns then
      keep the original columns.
  date_parser : function, optional
      Function to use for converting a sequence of string columns to an array of
      datetime instances. The default uses ``dateutil.parser.parser`` to do the
      conversion. Pandas will try to call `date_parser` in three different ways,
      advancing to the next if an exception occurs: 1) Pass one or more arrays
      (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
      string values from the columns defined by `parse_dates` into a single array
      and pass that; and 3) call `date_parser` once for each row using one or
      more strings (corresponding to the columns defined by `parse_dates`) as
      arguments.
  dayfirst : bool, default False
      DD/MM format dates, international and European format.
  cache_dates : bool, default True
      If True, use a cache of unique, converted dates to apply the datetime
      conversion. May produce significant speed-up when parsing duplicate
      date strings, especially ones with timezone offsets.
  
      .. versionadded:: 0.25.0
  iterator : bool, default False
      Return TextFileReader object for iteration or getting chunks with
      ``get_chunk()``.
  
      .. versionchanged:: 1.2
  
         ``TextFileReader`` is a context manager.
  chunksize : int, optional
      Return TextFileReader object for iteration.
      See the `IO Tools docs
      <https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_
      for more information on ``iterator`` and ``chunksize``.
  
      .. versionchanged:: 1.2
  
         ``TextFileReader`` is a context manager.
  {decompression_options}
  
      .. versionchanged:: 1.4.0 Zstandard support.
  
  thousands : str, optional
      Thousands separator.
  decimal : str, default '.'
      Character to recognize as decimal point (e.g. use ',' for European data).
  lineterminator : str (length 1), optional
      Character to break file into lines. Only valid with C parser.
  quotechar : str (length 1), optional
      The character used to denote the start and end of a quoted item. Quoted
      items can include the delimiter and it will be ignored.
  quoting : int or csv.QUOTE_* instance, default 0
      Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of
      QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
  doublequote : bool, default ``True``
     When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate
     whether or not to interpret two consecutive quotechar elements INSIDE a
     field as a single ``quotechar`` element.
  escapechar : str (length 1), optional
      One-character string used to escape other characters.
  comment : str, optional
      Indicates remainder of line should not be parsed. If found at the beginning
      of a line, the line will be ignored altogether. This parameter must be a
      single character. Like empty lines (as long as ``skip_blank_lines=True``),
      fully commented lines are ignored by the parameter `header` but not by
      `skiprows`. For example, if ``comment='#'``, parsing
      ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being
      treated as the header.
  encoding : str, optional
      Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python
      standard encodings
      <https://docs.python.org/3/library/codecs.html#standard-encodings>`_ .
  
      .. versionchanged:: 1.2
  
         When ``encoding`` is ``None``, ``errors="replace"`` is passed to
         ``open()``. Otherwise, ``errors="strict"`` is passed to ``open()``.
         This behavior was previously only the case for ``engine="python"``.
  
      .. versionchanged:: 1.3.0
  
         ``encoding_errors`` is a new argument. ``encoding`` has no longer an
         influence on how encoding errors are handled.
  
  encoding_errors : str, optional, default "strict"
      How encoding errors are treated. `List of possible values
      <https://docs.python.org/3/library/codecs.html#error-handlers>`_ .
  
      .. versionadded:: 1.3.0
  
  dialect : str or csv.Dialect, optional
      If provided, this parameter will override values (default or not) for the
      following parameters: `delimiter`, `doublequote`, `escapechar`,
      `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
      override values, a ParserWarning will be issued. See csv.Dialect
      documentation for more details.
  error_bad_lines : bool, optional, default ``None``
      Lines with too many fields (e.g. a csv line with too many commas) will by
      default cause an exception to be raised, and no DataFrame will be returned.
      If False, then these "bad lines" will be dropped from the DataFrame that is
      returned.
  
      .. deprecated:: 1.3.0
         The ``on_bad_lines`` parameter should be used instead to specify behavior upon
         encountering a bad line instead.
  warn_bad_lines : bool, optional, default ``None``
      If error_bad_lines is False, and warn_bad_lines is True, a warning for each
      "bad line" will be output.
  
      .. deprecated:: 1.3.0
         The ``on_bad_lines`` parameter should be used instead to specify behavior upon
         encountering a bad line instead.
  on_bad_lines : {{'error', 'warn', 'skip'}} or callable, default 'error'
      Specifies what to do upon encountering a bad line (a line with too many fields).
      Allowed values are :
  
          - 'error', raise an Exception when a bad line is encountered.
          - 'warn', raise a warning when a bad line is encountered and skip that line.
          - 'skip', skip bad lines without raising or warning when they are encountered.
  
      .. versionadded:: 1.3.0
  
          - callable, function with signature
            ``(bad_line: list[str]) -> list[str] | None`` that will process a single
            bad line. ``bad_line`` is a list of strings split by the ``sep``.
            If the function returns ``None``, the bad line will be ignored.
            If the function returns a new list of strings with more elements than
            expected, a ``ParserWarning`` will be emitted while dropping extra elements.
            Only supported when ``engine="python"``
  
      .. versionadded:: 1.4.0
  
  delim_whitespace : bool, default False
      Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be
      used as the sep. Equivalent to setting ``sep='\\s+'``. If this option
      is set to True, nothing should be passed in for the ``delimiter``
      parameter.
  low_memory : bool, default True
      Internally process the file in chunks, resulting in lower memory use
      while parsing, but possibly mixed type inference.  To ensure no mixed
      types either set False, or specify the type with the `dtype` parameter.
      Note that the entire file is read into a single DataFrame regardless,
      use the `chunksize` or `iterator` parameter to return the data in chunks.
      (Only valid with C parser).
  memory_map : bool, default False
      If a filepath is provided for `filepath_or_buffer`, map the file object
      directly onto memory and access the data directly from there. Using this
      option can improve performance because there is no longer any I/O overhead.
  float_precision : str, optional
      Specifies which converter the C engine should use for floating-point
      values. The options are ``None`` or 'high' for the ordinary converter,
      'legacy' for the original lower precision pandas converter, and
      'round_trip' for the round-trip converter.
  
      .. versionchanged:: 1.2
  
  {storage_options}
  
      .. versionadded:: 1.2
  
  Returns
  -------
  DataFrame or TextParser
      A comma-separated values (csv) file is returned as two-dimensional
      data structure with labeled axes.
  
  See Also
  --------
  DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
  read_csv : Read a comma-separated values (csv) file into DataFrame.
  read_fwf : Read a table of fixed-width formatted lines into DataFrame.
  
  Examples
  --------
  >>> pd.{func_name}('data.csv')  # doctest: +SKIP
  """
  )
  
  
  _c_parser_defaults = {
      "delim_whitespace": False,
      "na_filter": True,
      "low_memory": True,
      "memory_map": False,
      "float_precision": None,
  }
  
  _fwf_defaults = {"colspecs": "infer", "infer_nrows": 100, "widths": None}
  
  _c_unsupported = {"skipfooter"}
  _python_unsupported = {"low_memory", "float_precision"}
  _pyarrow_unsupported = {
      "skipfooter",
      "float_precision",
      "chunksize",
      "comment",
      "nrows",
      "thousands",
      "memory_map",
      "dialect",
      "warn_bad_lines",
      "error_bad_lines",
      "on_bad_lines",
      "delim_whitespace",
      "quoting",
      "lineterminator",
      "converters",
      "decimal",
      "iterator",
      "dayfirst",
      "infer_datetime_format",
      "verbose",
      "skipinitialspace",
      "low_memory",
  }
  
  
  class _DeprecationConfig(NamedTuple):
      default_value: Any
      msg: str | None
  
  
  _deprecated_defaults: dict[str, _DeprecationConfig] = {
      "error_bad_lines": _DeprecationConfig(None, "Use on_bad_lines in the future."),
      "warn_bad_lines": _DeprecationConfig(None, "Use on_bad_lines in the future."),
      "squeeze": _DeprecationConfig(
          None, 'Append .squeeze("columns") to the call to squeeze.'
      ),
      "prefix": _DeprecationConfig(
          None, "Use a list comprehension on the column names in the future."
      ),
  }
  
  
  def validate_integer(name, val, min_val=0):
      """
      Checks whether the 'name' parameter for parsing is either
      an integer OR float that can SAFELY be cast to an integer
      without losing accuracy. Raises a ValueError if that is
      not the case.
  
      Parameters
      ----------
      name : str
          Parameter name (used for error reporting)
      val : int or float
          The value to check
      min_val : int
          Minimum allowed value (val < min_val will result in a ValueError)
      """
      msg = f"'{name:s}' must be an integer >={min_val:d}"
  
      if val is not None:
          if is_float(val):
              if int(val) != val:
                  raise ValueError(msg)
              val = int(val)
          elif not (is_integer(val) and val >= min_val):
              raise ValueError(msg)
  
      return val
  
  
  def _validate_names(names):
      """
      Raise ValueError if the `names` parameter contains duplicates or has an
      invalid data type.
  
      Parameters
      ----------
      names : array-like or None
          An array containing a list of the names used for the output DataFrame.
  
      Raises
      ------
      ValueError
          If names are not unique or are not ordered (e.g. set).
      """
      if names is not None:
          if len(names) != len(set(names)):
              raise ValueError("Duplicate names are not allowed.")
          if not (
              is_list_like(names, allow_sets=False) or isinstance(names, abc.KeysView)
          ):
              raise ValueError("Names should be an ordered collection.")
  
  
  def _read(
      filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str], kwds
  ):
      """Generic reader of line files."""
      # if we pass a date_parser and parse_dates=False, we should not parse the
      # dates GH#44366
      if (
          kwds.get("date_parser", None) is not None
          and kwds.get("parse_dates", None) is None
      ):
          kwds["parse_dates"] = True
      elif kwds.get("parse_dates", None) is None:
          kwds["parse_dates"] = False
  
      # Extract some of the arguments (pass chunksize on).
      iterator = kwds.get("iterator", False)
      chunksize = kwds.get("chunksize", None)
      if kwds.get("engine") == "pyarrow":
          if iterator:
              raise ValueError(
                  "The 'iterator' option is not supported with the 'pyarrow' engine"
              )
  
          if chunksize is not None:
              raise ValueError(
                  "The 'chunksize' option is not supported with the 'pyarrow' engine"
              )
      else:
          chunksize = validate_integer("chunksize", kwds.get("chunksize", None), 1)
  
      nrows = kwds.get("nrows", None)
  
      # Check for duplicates in names.
      _validate_names(kwds.get("names", None))
  
      # Create the parser.
      parser = TextFileReader(filepath_or_buffer, **kwds)
  
      if chunksize or iterator:
          return parser
  
      with parser:
          return parser.read(nrows)
  
  
  @deprecate_nonkeyword_arguments(
      version=None, allowed_args=["filepath_or_buffer"], stacklevel=3
  )
  @Appender(
      _doc_read_csv_and_table.format(
          func_name="read_csv",
          summary="Read a comma-separated values (csv) file into DataFrame.",
          _default_sep="','",
          storage_options=_shared_docs["storage_options"],
          decompression_options=_shared_docs["decompression_options"],
      )
  )
  def read_csv(
      filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
      sep=lib.no_default,
      delimiter=None,
      # Column and Index Locations and Names
      header="infer",
      names=lib.no_default,
      index_col=None,
      usecols=None,
      squeeze=None,
      prefix=lib.no_default,
      mangle_dupe_cols=True,
      # General Parsing Configuration
      dtype: DtypeArg | None = None,
      engine: CSVEngine | None = None,
      converters=None,
      true_values=None,
      false_values=None,
      skipinitialspace=False,
      skiprows=None,
      skipfooter=0,
      nrows=None,
      # NA and Missing Data Handling
      na_values=None,
      keep_default_na=True,
      na_filter=True,
      verbose=False,
      skip_blank_lines=True,
      # Datetime Handling
      parse_dates=None,
      infer_datetime_format=False,
      keep_date_col=False,
      date_parser=None,
      dayfirst=False,
      cache_dates=True,
      # Iteration
      iterator=False,
      chunksize=None,
      # Quoting, Compression, and File Format
      compression: CompressionOptions = "infer",
      thousands=None,
      decimal: str = ".",
      lineterminator=None,
      quotechar='"',
      quoting=csv.QUOTE_MINIMAL,
      doublequote=True,
      escapechar=None,
      comment=None,
      encoding=None,
      encoding_errors: str | None = "strict",
      dialect=None,
      # Error Handling
      error_bad_lines=None,
      warn_bad_lines=None,
      # TODO(2.0): set on_bad_lines to "error".
      # See _refine_defaults_read comment for why we do this.
      on_bad_lines=None,
      # Internal
      delim_whitespace=False,
      low_memory=_c_parser_defaults["low_memory"],
      memory_map=False,
      float_precision=None,
      storage_options: StorageOptions = None,
  ):
      # locals() should never be modified
      kwds = locals().copy()
      del kwds["filepath_or_buffer"]
      del kwds["sep"]
  
      kwds_defaults = _refine_defaults_read(
          dialect,
          delimiter,
          delim_whitespace,
          engine,
          sep,
          error_bad_lines,
          warn_bad_lines,
          on_bad_lines,
          names,
          prefix,
          defaults={"delimiter": ","},
      )
      kwds.update(kwds_defaults)
  
      return _read(filepath_or_buffer, kwds)
  
  
  @deprecate_nonkeyword_arguments(
      version=None, allowed_args=["filepath_or_buffer"], stacklevel=3
  )
  @Appender(
      _doc_read_csv_and_table.format(
          func_name="read_table",
          summary="Read general delimited file into DataFrame.",
          _default_sep=r"'\\t' (tab-stop)",
          storage_options=_shared_docs["storage_options"],
          decompression_options=_shared_docs["decompression_options"],
      )
  )
  def read_table(
      filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
      sep=lib.no_default,
      delimiter=None,
      # Column and Index Locations and Names
      header="infer",
      names=lib.no_default,
      index_col=None,
      usecols=None,
      squeeze=None,
      prefix=lib.no_default,
      mangle_dupe_cols=True,
      # General Parsing Configuration
      dtype: DtypeArg | None = None,
      engine: CSVEngine | None = None,
      converters=None,
      true_values=None,
      false_values=None,
      skipinitialspace=False,
      skiprows=None,
      skipfooter=0,
      nrows=None,
      # NA and Missing Data Handling
      na_values=None,
      keep_default_na=True,
      na_filter=True,
      verbose=False,
      skip_blank_lines=True,
      # Datetime Handling
      parse_dates=False,
      infer_datetime_format=False,
      keep_date_col=False,
      date_parser=None,
      dayfirst=False,
      cache_dates=True,
      # Iteration
      iterator=False,
      chunksize=None,
      # Quoting, Compression, and File Format
      compression: CompressionOptions = "infer",
      thousands=None,
      decimal: str = ".",
      lineterminator=None,
      quotechar='"',
      quoting=csv.QUOTE_MINIMAL,
      doublequote=True,
      escapechar=None,
      comment=None,
      encoding=None,
      encoding_errors: str | None = "strict",
      dialect=None,
      # Error Handling
      error_bad_lines=None,
      warn_bad_lines=None,
      # TODO(2.0): set on_bad_lines to "error".
      # See _refine_defaults_read comment for why we do this.
      on_bad_lines=None,
      # Internal
      delim_whitespace=False,
      low_memory=_c_parser_defaults["low_memory"],
      memory_map=False,
      float_precision=None,
      storage_options: StorageOptions = None,
  ):
      # locals() should never be modified
      kwds = locals().copy()
      del kwds["filepath_or_buffer"]
      del kwds["sep"]
  
      kwds_defaults = _refine_defaults_read(
          dialect,
          delimiter,
          delim_whitespace,
          engine,
          sep,
          error_bad_lines,
          warn_bad_lines,
          on_bad_lines,
          names,
          prefix,
          defaults={"delimiter": "\t"},
      )
      kwds.update(kwds_defaults)
  
      return _read(filepath_or_buffer, kwds)
  
  
  @deprecate_nonkeyword_arguments(
      version=None, allowed_args=["filepath_or_buffer"], stacklevel=2
  )
  def read_fwf(
      filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str],
      colspecs: list[tuple[int, int]] | str | None = "infer",
      widths: list[int] | None = None,
      infer_nrows: int = 100,
      **kwds,
  ) -> DataFrame | TextFileReader:
      r"""
      Read a table of fixed-width formatted lines into DataFrame.
  
      Also supports optionally iterating or breaking of the file
      into chunks.
  
      Additional help can be found in the `online docs for IO Tools
      <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.
  
      Parameters
      ----------
      filepath_or_buffer : str, path object, or file-like object
          String, path object (implementing ``os.PathLike[str]``), or file-like
          object implementing a text ``read()`` function.The string could be a URL.
          Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is
          expected. A local file could be:
          ``file://localhost/path/to/table.csv``.
      colspecs : list of tuple (int, int) or 'infer'. optional
          A list of tuples giving the extents of the fixed-width
          fields of each line as half-open intervals (i.e.,  [from, to[ ).
          String value 'infer' can be used to instruct the parser to try
          detecting the column specifications from the first 100 rows of
          the data which are not being skipped via skiprows (default='infer').
      widths : list of int, optional
          A list of field widths which can be used instead of 'colspecs' if
          the intervals are contiguous.
      infer_nrows : int, default 100
          The number of rows to consider when letting the parser determine the
          `colspecs`.
      **kwds : optional
          Optional keyword arguments can be passed to ``TextFileReader``.
  
      Returns
      -------
      DataFrame or TextFileReader
          A comma-separated values (csv) file is returned as two-dimensional
          data structure with labeled axes.
  
      See Also
      --------
      DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
      read_csv : Read a comma-separated values (csv) file into DataFrame.
  
      Examples
      --------
      >>> pd.read_fwf('data.csv')  # doctest: +SKIP
      """
      # Check input arguments.
      if colspecs is None and widths is None:
          raise ValueError("Must specify either colspecs or widths")
      elif colspecs not in (None, "infer") and widths is not None:
          raise ValueError("You must specify only one of 'widths' and 'colspecs'")
  
      # Compute 'colspecs' from 'widths', if specified.
      if widths is not None:
          colspecs, col = [], 0
          for w in widths:
              colspecs.append((col, col + w))
              col += w
  
      # for mypy
      assert colspecs is not None
  
      # GH#40830
      # Ensure length of `colspecs` matches length of `names`
      names = kwds.get("names")
      if names is not None:
          if len(names) != len(colspecs) and colspecs != "infer":
              # need to check len(index_col) as it might contain
              # unnamed indices, in which case it's name is not required
              len_index = 0
              if kwds.get("index_col") is not None:
                  index_col: Any = kwds.get("index_col")
                  if index_col is not False:
                      if not is_list_like(index_col):
                          len_index = 1
                      else:
                          len_index = len(index_col)
              if len(names) + len_index != len(colspecs):
                  raise ValueError("Length of colspecs must match length of names")
  
      kwds["colspecs"] = colspecs
      kwds["infer_nrows"] = infer_nrows
      kwds["engine"] = "python-fwf"
      return _read(filepath_or_buffer, kwds)
  
  
  class TextFileReader(abc.Iterator):
      """
  
      Passed dialect overrides any of the related parser options
  
      """
  
      def __init__(
          self,
          f: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str] | list,
          engine: CSVEngine | None = None,
          **kwds,
      ):
          if engine is not None:
              engine_specified = True
          else:
              engine = "python"
              engine_specified = False
          self.engine = engine
          self._engine_specified = kwds.get("engine_specified", engine_specified)
  
          _validate_skipfooter(kwds)
  
          dialect = _extract_dialect(kwds)
          if dialect is not None:
              if engine == "pyarrow":
                  raise ValueError(
                      "The 'dialect' option is not supported with the 'pyarrow' engine"
                  )
              kwds = _merge_with_dialect_properties(dialect, kwds)
  
          if kwds.get("header", "infer") == "infer":
              kwds["header"] = 0 if kwds.get("names") is None else None
  
          self.orig_options = kwds
  
          # miscellanea
          self._currow = 0
  
          options = self._get_options_with_defaults(engine)
          options["storage_options"] = kwds.get("storage_options", None)
  
          self.chunksize = options.pop("chunksize", None)
          self.nrows = options.pop("nrows", None)
  
          self._check_file_or_buffer(f, engine)
          self.options, self.engine = self._clean_options(options, engine)
  
          self.squeeze = self.options.pop("squeeze", False)
  
          if "has_index_names" in kwds:
              self.options["has_index_names"] = kwds["has_index_names"]
  
          self.handles: IOHandles | None = None
          self._engine = self._make_engine(f, self.engine)
  
      def close(self):
          if self.handles is not None:
              self.handles.close()
          self._engine.close()
  
      def _get_options_with_defaults(self, engine):
          kwds = self.orig_options
  
          options = {}
          default: object | None
  
          for argname, default in parser_defaults.items():
              value = kwds.get(argname, default)
  
              # see gh-12935
              if (
                  engine == "pyarrow"
                  and argname in _pyarrow_unsupported
                  and value != default
                  and value != getattr(value, "value", default)
              ):
                  if (
                      argname == "on_bad_lines"
                      and kwds.get("error_bad_lines") is not None
                  ):
                      argname = "error_bad_lines"
                  elif (
                      argname == "on_bad_lines" and kwds.get("warn_bad_lines") is not None
                  ):
                      argname = "warn_bad_lines"
  
                  raise ValueError(
                      f"The {repr(argname)} option is not supported with the "
                      f"'pyarrow' engine"
                  )
              elif argname == "mangle_dupe_cols" and value is False:
                  # GH12935
                  raise ValueError("Setting mangle_dupe_cols=False is not supported yet")
              else:
                  options[argname] = value
  
          for argname, default in _c_parser_defaults.items():
              if argname in kwds:
                  value = kwds[argname]
  
                  if engine != "c" and value != default:
                      if "python" in engine and argname not in _python_unsupported:
                          pass
                      elif (
                          value
                          == _deprecated_defaults.get(
                              argname, _DeprecationConfig(default, None)
                          ).default_value
                      ):
                          pass
                      else:
                          raise ValueError(
                              f"The {repr(argname)} option is not supported with the "
                              f"{repr(engine)} engine"
                          )
              else:
                  value = _deprecated_defaults.get(
                      argname, _DeprecationConfig(default, None)
                  ).default_value
              options[argname] = value
  
          if engine == "python-fwf":
              for argname, default in _fwf_defaults.items():
                  options[argname] = kwds.get(argname, default)
  
          return options
  
      def _check_file_or_buffer(self, f, engine):
          # see gh-16530
          if is_file_like(f) and engine != "c" and not hasattr(f, "__iter__"):
              # The C engine doesn't need the file-like to have the "__iter__"
              # attribute. However, the Python engine needs "__iter__(...)"
              # when iterating through such an object, meaning it
              # needs to have that attribute
              raise ValueError(
                  "The 'python' engine cannot iterate through this file buffer."
              )
  
      def _clean_options(self, options, engine):
          result = options.copy()
  
          fallback_reason = None
  
          # C engine not supported yet
          if engine == "c":
              if options["skipfooter"] > 0:
                  fallback_reason = "the 'c' engine does not support skipfooter"
                  engine = "python"
  
          sep = options["delimiter"]
          delim_whitespace = options["delim_whitespace"]
  
          if sep is None and not delim_whitespace:
              if engine in ("c", "pyarrow"):
                  fallback_reason = (
                      f"the '{engine}' engine does not support "
                      "sep=None with delim_whitespace=False"
                  )
                  engine = "python"
          elif sep is not None and len(sep) > 1:
              if engine == "c" and sep == r"\s+":
                  result["delim_whitespace"] = True
                  del result["delimiter"]
              elif engine not in ("python", "python-fwf"):
                  # wait until regex engine integrated
                  fallback_reason = (
                      f"the '{engine}' engine does not support "
                      "regex separators (separators > 1 char and "
                      r"different from '\s+' are interpreted as regex)"
                  )
                  engine = "python"
          elif delim_whitespace:
              if "python" in engine:
                  result["delimiter"] = r"\s+"
          elif sep is not None:
              encodeable = True
              encoding = sys.getfilesystemencoding() or "utf-8"
              try:
                  if len(sep.encode(encoding)) > 1:
                      encodeable = False
              except UnicodeDecodeError:
                  encodeable = False
              if not encodeable and engine not in ("python", "python-fwf"):
                  fallback_reason = (
                      f"the separator encoded in {encoding} "
                      f"is > 1 char long, and the '{engine}' engine "
                      "does not support such separators"
                  )
                  engine = "python"
  
          quotechar = options["quotechar"]
          if quotechar is not None and isinstance(quotechar, (str, bytes)):
              if (
                  len(quotechar) == 1
                  and ord(quotechar) > 127
                  and engine not in ("python", "python-fwf")
              ):
                  fallback_reason = (
                      "ord(quotechar) > 127, meaning the "
                      "quotechar is larger than one byte, "
                      f"and the '{engine}' engine does not support such quotechars"
                  )
                  engine = "python"
  
          if fallback_reason and self._engine_specified:
              raise ValueError(fallback_reason)
  
          if engine == "c":
              for arg in _c_unsupported:
                  del result[arg]
  
          if "python" in engine:
              for arg in _python_unsupported:
                  if fallback_reason and result[arg] != _c_parser_defaults[arg]:
                      raise ValueError(
                          "Falling back to the 'python' engine because "
                          f"{fallback_reason}, but this causes {repr(arg)} to be "
                          "ignored as it is not supported by the 'python' engine."
                      )
                  del result[arg]
  
          if fallback_reason:
              warnings.warn(
                  (
                      "Falling back to the 'python' engine because "
                      f"{fallback_reason}; you can avoid this warning by specifying "
                      "engine='python'."
                  ),
                  ParserWarning,
                  stacklevel=find_stack_level(),
              )
  
          index_col = options["index_col"]
          names = options["names"]
          converters = options["converters"]
          na_values = options["na_values"]
          skiprows = options["skiprows"]
  
          validate_header_arg(options["header"])
  
          for arg in _deprecated_defaults.keys():
              parser_default = _c_parser_defaults.get(arg, parser_defaults[arg])
              depr_default = _deprecated_defaults[arg]
              if result.get(arg, depr_default) != depr_default.default_value:
                  msg = (
                      f"The {arg} argument has been deprecated and will be "
                      f"removed in a future version. {depr_default.msg}\n\n"
                  )
                  warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())
              else:
                  result[arg] = parser_default
  
          if index_col is True:
              raise ValueError("The value of index_col couldn't be 'True'")
          if is_index_col(index_col):
              if not isinstance(index_col, (list, tuple, np.ndarray)):
                  index_col = [index_col]
          result["index_col"] = index_col
  
          names = list(names) if names is not None else names
  
          # type conversion-related
          if converters is not None:
              if not isinstance(converters, dict):
                  raise TypeError(
                      "Type converters must be a dict or subclass, "
                      f"input was a {type(converters).__name__}"
                  )
          else:
              converters = {}
  
          # Converting values to NA
          keep_default_na = options["keep_default_na"]
          na_values, na_fvalues = _clean_na_values(na_values, keep_default_na)
  
          # handle skiprows; this is internally handled by the
          # c-engine, so only need for python and pyarrow parsers
          if engine == "pyarrow":
              if not is_integer(skiprows) and skiprows is not None:
                  # pyarrow expects skiprows to be passed as an integer
                  raise ValueError(
                      "skiprows argument must be an integer when using "
                      "engine='pyarrow'"
                  )
          else:
              if is_integer(skiprows):
                  skiprows = list(range(skiprows))
              if skiprows is None:
                  skiprows = set()
              elif not callable(skiprows):
                  skiprows = set(skiprows)
  
          # put stuff back
          result["names"] = names
          result["converters"] = converters
          result["na_values"] = na_values
          result["na_fvalues"] = na_fvalues
          result["skiprows"] = skiprows
          # Default for squeeze is none since we need to check
          # if user sets it. We then set to False to preserve
          # previous behavior.
          result["squeeze"] = False if options["squeeze"] is None else options["squeeze"]
  
          return result, engine
  
      def __next__(self):
          try:
              return self.get_chunk()
          except StopIteration:
              self.close()
              raise
  
      def _make_engine(
          self,
          f: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str] | list | IO,
          engine: CSVEngine = "c",
      ):
          mapping: dict[str, type[ParserBase]] = {
              "c": CParserWrapper,
              "python": PythonParser,
              "pyarrow": ArrowParserWrapper,
              "python-fwf": FixedWidthFieldParser,
          }
          if engine not in mapping:
              raise ValueError(
                  f"Unknown engine: {engine} (valid options are {mapping.keys()})"
              )
          if not isinstance(f, list):
              # open file here
              is_text = True
              mode = "r"
              if engine == "pyarrow":
                  is_text = False
                  mode = "rb"
              # error: No overload variant of "get_handle" matches argument types
              # "Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]"
              # , "str", "bool", "Any", "Any", "Any", "Any", "Any"
              self.handles = get_handle(  # type: ignore[call-overload]
                  f,
                  mode,
                  encoding=self.options.get("encoding", None),
                  compression=self.options.get("compression", None),
                  memory_map=self.options.get("memory_map", False),
                  is_text=is_text,
                  errors=self.options.get("encoding_errors", "strict"),
                  storage_options=self.options.get("storage_options", None),
              )
              assert self.handles is not None
              f = self.handles.handle
  
          try:
              return mapping[engine](f, **self.options)
          except Exception:
              if self.handles is not None:
                  self.handles.close()
              raise
  
      def _failover_to_python(self):
          raise AbstractMethodError(self)
  
      def read(self, nrows=None):
          if self.engine == "pyarrow":
              try:
                  df = self._engine.read()
              except Exception:
                  self.close()
                  raise
          else:
              nrows = validate_integer("nrows", nrows)
              try:
                  index, columns, col_dict = self._engine.read(nrows)
              except Exception:
                  self.close()
                  raise
  
              if index is None:
                  if col_dict:
                      # Any column is actually fine:
                      new_rows = len(next(iter(col_dict.values())))
                      index = RangeIndex(self._currow, self._currow + new_rows)
                  else:
                      new_rows = 0
              else:
                  new_rows = len(index)
  
              df = DataFrame(col_dict, columns=columns, index=index)
  
              self._currow += new_rows
  
          if self.squeeze and len(df.columns) == 1:
              return df.squeeze("columns").copy()
          return df
  
      def get_chunk(self, size=None):
          if size is None:
              size = self.chunksize
          if self.nrows is not None:
              if self._currow >= self.nrows:
                  raise StopIteration
              size = min(size, self.nrows - self._currow)
          return self.read(nrows=size)
  
      def __enter__(self):
          return self
  
      def __exit__(self, exc_type, exc_value, traceback):
          self.close()
  
  
  def TextParser(*args, **kwds):
      """
      Converts lists of lists/tuples into DataFrames with proper type inference
      and optional (e.g. string to datetime) conversion. Also enables iterating
      lazily over chunks of large files
  
      Parameters
      ----------
      data : file-like object or list
      delimiter : separator character to use
      dialect : str or csv.Dialect instance, optional
          Ignored if delimiter is longer than 1 character
      names : sequence, default
      header : int, default 0
          Row to use to parse column labels. Defaults to the first row. Prior
          rows will be discarded
      index_col : int or list, optional
          Column or columns to use as the (possibly hierarchical) index
      has_index_names: bool, default False
          True if the cols defined in index_col have an index name and are
          not in the header.
      na_values : scalar, str, list-like, or dict, optional
          Additional strings to recognize as NA/NaN.
      keep_default_na : bool, default True
      thousands : str, optional
          Thousands separator
      comment : str, optional
          Comment out remainder of line
      parse_dates : bool, default False
      keep_date_col : bool, default False
      date_parser : function, optional
      skiprows : list of integers
          Row numbers to skip
      skipfooter : int
          Number of line at bottom of file to skip
      converters : dict, optional
          Dict of functions for converting values in certain columns. Keys can
          either be integers or column labels, values are functions that take one
          input argument, the cell (not column) content, and return the
          transformed content.
      encoding : str, optional
          Encoding to use for UTF when reading/writing (ex. 'utf-8')
      squeeze : bool, default False
          returns Series if only one column.
      infer_datetime_format: bool, default False
          If True and `parse_dates` is True for a column, try to infer the
          datetime format based on the first datetime string. If the format
          can be inferred, there often will be a large parsing speed-up.
      float_precision : str, optional
          Specifies which converter the C engine should use for floating-point
          values. The options are `None` or `high` for the ordinary converter,
          `legacy` for the original lower precision pandas converter, and
          `round_trip` for the round-trip converter.
  
          .. versionchanged:: 1.2
      """
      kwds["engine"] = "python"
      return TextFileReader(*args, **kwds)
  
  
  def _clean_na_values(na_values, keep_default_na=True):
      na_fvalues: set | dict
      if na_values is None:
          if keep_default_na:
              na_values = STR_NA_VALUES
          else:
              na_values = set()
          na_fvalues = set()
      elif isinstance(na_values, dict):
          old_na_values = na_values.copy()
          na_values = {}  # Prevent aliasing.
  
          # Convert the values in the na_values dictionary
          # into array-likes for further use. This is also
          # where we append the default NaN values, provided
          # that `keep_default_na=True`.
          for k, v in old_na_values.items():
              if not is_list_like(v):
                  v = [v]
  
              if keep_default_na:
                  v = set(v) | STR_NA_VALUES
  
              na_values[k] = v
          na_fvalues = {k: _floatify_na_values(v) for k, v in na_values.items()}
      else:
          if not is_list_like(na_values):
              na_values = [na_values]
          na_values = _stringify_na_values(na_values)
          if keep_default_na:
              na_values = na_values | STR_NA_VALUES
  
          na_fvalues = _floatify_na_values(na_values)
  
      return na_values, na_fvalues
  
  
  def _floatify_na_values(na_values):
      # create float versions of the na_values
      result = set()
      for v in na_values:
          try:
              v = float(v)
              if not np.isnan(v):
                  result.add(v)
          except (TypeError, ValueError, OverflowError):
              pass
      return result
  
  
  def _stringify_na_values(na_values):
      """return a stringified and numeric for these values"""
      result: list[int | str | float] = []
      for x in na_values:
          result.append(str(x))
          result.append(x)
          try:
              v = float(x)
  
              # we are like 999 here
              if v == int(v):
                  v = int(v)
                  result.append(f"{v}.0")
                  result.append(str(v))
  
              result.append(v)
          except (TypeError, ValueError, OverflowError):
              pass
          try:
              result.append(int(x))
          except (TypeError, ValueError, OverflowError):
              pass
      return set(result)
  
  
  def _refine_defaults_read(
      dialect: str | csv.Dialect,
      delimiter: str | object,
      delim_whitespace: bool,
      engine: CSVEngine | None,
      sep: str | object,
      error_bad_lines: bool | None,
      warn_bad_lines: bool | None,
      on_bad_lines: str | Callable | None,
      names: ArrayLike | None | object,
      prefix: str | None | object,
      defaults: dict[str, Any],
  ):
      """Validate/refine default values of input parameters of read_csv, read_table.
  
      Parameters
      ----------
      dialect : str or csv.Dialect
          If provided, this parameter will override values (default or not) for the
          following parameters: `delimiter`, `doublequote`, `escapechar`,
          `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
          override values, a ParserWarning will be issued. See csv.Dialect
          documentation for more details.
      delimiter : str or object
          Alias for sep.
      delim_whitespace : bool
          Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be
          used as the sep. Equivalent to setting ``sep='\\s+'``. If this option
          is set to True, nothing should be passed in for the ``delimiter``
          parameter.
      engine : {{'c', 'python'}}
          Parser engine to use. The C engine is faster while the python engine is
          currently more feature-complete.
      sep : str or object
          A delimiter provided by the user (str) or a sentinel value, i.e.
          pandas._libs.lib.no_default.
      error_bad_lines : str or None
          Whether to error on a bad line or not.
      warn_bad_lines : str or None
          Whether to warn on a bad line or not.
      on_bad_lines : str, callable or None
          An option for handling bad lines or a sentinel value(None).
      names : array-like, optional
          List of column names to use. If the file contains a header row,
          then you should explicitly pass ``header=0`` to override the column names.
          Duplicates in this list are not allowed.
      prefix : str, optional
          Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
      defaults: dict
          Default values of input parameters.
  
      Returns
      -------
      kwds : dict
          Input parameters with correct values.
  
      Raises
      ------
      ValueError :
          If a delimiter was specified with ``sep`` (or ``delimiter``) and
          ``delim_whitespace=True``.
          If on_bad_lines is specified(not ``None``) and ``error_bad_lines``/
          ``warn_bad_lines`` is True.
      """
      # fix types for sep, delimiter to Union(str, Any)
      delim_default = defaults["delimiter"]
      kwds: dict[str, Any] = {}
      # gh-23761
      #
      # When a dialect is passed, it overrides any of the overlapping
      # parameters passed in directly. We don't want to warn if the
      # default parameters were passed in (since it probably means
      # that the user didn't pass them in explicitly in the first place).
      #
      # "delimiter" is the annoying corner case because we alias it to
      # "sep" before doing comparison to the dialect values later on.
      # Thus, we need a flag to indicate that we need to "override"
      # the comparison to dialect values by checking if default values
      # for BOTH "delimiter" and "sep" were provided.
      if dialect is not None:
          kwds["sep_override"] = delimiter is None and (
              sep is lib.no_default or sep == delim_default
          )
  
      if delimiter and (sep is not lib.no_default):
          raise ValueError("Specified a sep and a delimiter; you can only specify one.")
  
      if (
          names is not None
          and names is not lib.no_default
          and prefix is not None
          and prefix is not lib.no_default
      ):
          raise ValueError("Specified named and prefix; you can only specify one.")
  
      kwds["names"] = None if names is lib.no_default else names
      kwds["prefix"] = None if prefix is lib.no_default else prefix
  
      # Alias sep -> delimiter.
      if delimiter is None:
          delimiter = sep
  
      if delim_whitespace and (delimiter is not lib.no_default):
          raise ValueError(
              "Specified a delimiter with both sep and "
              "delim_whitespace=True; you can only specify one."
          )
  
      if delimiter == "\n":
          raise ValueError(
              r"Specified \n as separator or delimiter. This forces the python engine "
              "which does not accept a line terminator. Hence it is not allowed to use "
              "the line terminator as separator.",
          )
  
      if delimiter is lib.no_default:
          # assign default separator value
          kwds["delimiter"] = delim_default
      else:
          kwds["delimiter"] = delimiter
  
      if engine is not None:
          kwds["engine_specified"] = True
      else:
          kwds["engine"] = "c"
          kwds["engine_specified"] = False
  
      # Ensure that on_bad_lines and error_bad_lines/warn_bad_lines
      # aren't specified at the same time. If so, raise. Otherwise,
      # alias on_bad_lines to "error" if error/warn_bad_lines not set
      # and on_bad_lines is not set. on_bad_lines is defaulted to None
      # so we can tell if it is set (this is why this hack exists).
      if on_bad_lines is not None:
          if error_bad_lines is not None or warn_bad_lines is not None:
              raise ValueError(
                  "Both on_bad_lines and error_bad_lines/warn_bad_lines are set. "
                  "Please only set on_bad_lines."
              )
          if on_bad_lines == "error":
              kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.ERROR
          elif on_bad_lines == "warn":
              kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.WARN
          elif on_bad_lines == "skip":
              kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.SKIP
          elif callable(on_bad_lines):
              if engine != "python":
                  raise ValueError(
                      "on_bad_line can only be a callable function if engine='python'"
                  )
              kwds["on_bad_lines"] = on_bad_lines
          else:
              raise ValueError(f"Argument {on_bad_lines} is invalid for on_bad_lines")
      else:
          if error_bad_lines is not None:
              # Must check is_bool, because other stuff(e.g. non-empty lists) eval to true
              validate_bool_kwarg(error_bad_lines, "error_bad_lines")
              if error_bad_lines:
                  kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.ERROR
              else:
                  if warn_bad_lines is not None:
                      # This is the case where error_bad_lines is False
                      # We can only warn/skip if error_bad_lines is False
                      # None doesn't work because backwards-compatibility reasons
                      validate_bool_kwarg(warn_bad_lines, "warn_bad_lines")
                      if warn_bad_lines:
                          kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.WARN
                      else:
                          kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.SKIP
                  else:
                      # Backwards compat, when only error_bad_lines = false, we warn
                      kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.WARN
          else:
              # Everything None -> Error
              kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.ERROR
  
      return kwds
  
  
  def _extract_dialect(kwds: dict[str, Any]) -> csv.Dialect | None:
      """
      Extract concrete csv dialect instance.
  
      Returns
      -------
      csv.Dialect or None
      """
      if kwds.get("dialect") is None:
          return None
  
      dialect = kwds["dialect"]
      if dialect in csv.list_dialects():
          dialect = csv.get_dialect(dialect)
  
      _validate_dialect(dialect)
  
      return dialect
  
  
  MANDATORY_DIALECT_ATTRS = (
      "delimiter",
      "doublequote",
      "escapechar",
      "skipinitialspace",
      "quotechar",
      "quoting",
  )
  
  
  def _validate_dialect(dialect: csv.Dialect) -> None:
      """
      Validate csv dialect instance.
  
      Raises
      ------
      ValueError
          If incorrect dialect is provided.
      """
      for param in MANDATORY_DIALECT_ATTRS:
          if not hasattr(dialect, param):
              raise ValueError(f"Invalid dialect {dialect} provided")
  
  
  def _merge_with_dialect_properties(
      dialect: csv.Dialect,
      defaults: dict[str, Any],
  ) -> dict[str, Any]:
      """
      Merge default kwargs in TextFileReader with dialect parameters.
  
      Parameters
      ----------
      dialect : csv.Dialect
          Concrete csv dialect. See csv.Dialect documentation for more details.
      defaults : dict
          Keyword arguments passed to TextFileReader.
  
      Returns
      -------
      kwds : dict
          Updated keyword arguments, merged with dialect parameters.
      """
      kwds = defaults.copy()
  
      for param in MANDATORY_DIALECT_ATTRS:
          dialect_val = getattr(dialect, param)
  
          parser_default = parser_defaults[param]
          provided = kwds.get(param, parser_default)
  
          # Messages for conflicting values between the dialect
          # instance and the actual parameters provided.
          conflict_msgs = []
  
          # Don't warn if the default parameter was passed in,
          # even if it conflicts with the dialect (gh-23761).
          if provided != parser_default and provided != dialect_val:
              msg = (
                  f"Conflicting values for '{param}': '{provided}' was "
                  f"provided, but the dialect specifies '{dialect_val}'. "
                  "Using the dialect-specified value."
              )
  
              # Annoying corner case for not warning about
              # conflicts between dialect and delimiter parameter.
              # Refer to the outer "_read_" function for more info.
              if not (param == "delimiter" and kwds.pop("sep_override", False)):
                  conflict_msgs.append(msg)
  
          if conflict_msgs:
              warnings.warn(
                  "\n\n".join(conflict_msgs), ParserWarning, stacklevel=find_stack_level()
              )
          kwds[param] = dialect_val
      return kwds
  
  
  def _validate_skipfooter(kwds: dict[str, Any]) -> None:
      """
      Check whether skipfooter is compatible with other kwargs in TextFileReader.
  
      Parameters
      ----------
      kwds : dict
          Keyword arguments passed to TextFileReader.
  
      Raises
      ------
      ValueError
          If skipfooter is not compatible with other parameters.
      """
      if kwds.get("skipfooter"):
          if kwds.get("iterator") or kwds.get("chunksize"):
              raise ValueError("'skipfooter' not supported for iteration")
          if kwds.get("nrows"):
              raise ValueError("'skipfooter' not supported with 'nrows'")
  