text: """
  Module contains tools for processing Stata files into DataFrames
  
  The StataReader below was originally written by Joe Presbrey as part of PyDTA.
  It has been extended and improved by Skipper Seabold from the Statsmodels
  project who also developed the StataWriter and was finally added to pandas in
  a once again improved version.
  
  You can find more information on http://presbrey.mit.edu/PyDTA and
  https://www.statsmodels.org/devel/
  """
  from __future__ import annotations
  
  from collections import abc
  import datetime
  from io import BytesIO
  import os
  import struct
  import sys
  from typing import (
      IO,
      TYPE_CHECKING,
      Any,
      AnyStr,
      Hashable,
      Sequence,
      cast,
  )
  import warnings
  
  from dateutil.relativedelta import relativedelta
  import numpy as np
  
  from pandas._libs.lib import infer_dtype
  from pandas._libs.writers import max_len_string_array
  from pandas._typing import (
      CompressionOptions,
      FilePath,
      ReadBuffer,
      StorageOptions,
      WriteBuffer,
  )
  from pandas.util._decorators import (
      Appender,
      doc,
  )
  
  from pandas.core.dtypes.common import (
      ensure_object,
      is_categorical_dtype,
      is_datetime64_dtype,
      is_numeric_dtype,
  )
  
  from pandas import (
      Categorical,
      DatetimeIndex,
      NaT,
      Timestamp,
      isna,
      to_datetime,
      to_timedelta,
  )
  from pandas.core.arrays.boolean import BooleanDtype
  from pandas.core.arrays.integer import _IntegerDtype
  from pandas.core.frame import DataFrame
  from pandas.core.indexes.base import Index
  from pandas.core.series import Series
  from pandas.core.shared_docs import _shared_docs
  
  from pandas.io.common import get_handle
  
  if TYPE_CHECKING:
      from typing import Literal
  
  _version_error = (
      "Version of given Stata file is {version}. pandas supports importing "
      "versions 105, 108, 111 (Stata 7SE), 113 (Stata 8/9), "
      "114 (Stata 10/11), 115 (Stata 12), 117 (Stata 13), 118 (Stata 14/15/16),"
      "and 119 (Stata 15/16, over 32,767 variables)."
  )
  
  _statafile_processing_params1 = """\
  convert_dates : bool, default True
      Convert date variables to DataFrame time values.
  convert_categoricals : bool, default True
      Read value labels and convert columns to Categorical/Factor variables."""
  
  _statafile_processing_params2 = """\
  index_col : str, optional
      Column to set as index.
  convert_missing : bool, default False
      Flag indicating whether to convert missing values to their Stata
      representations.  If False, missing values are replaced with nan.
      If True, columns containing missing values are returned with
      object data types and missing values are represented by
      StataMissingValue objects.
  preserve_dtypes : bool, default True
      Preserve Stata datatypes. If False, numeric data are upcast to pandas
      default types for foreign data (float64 or int64).
  columns : list or None
      Columns to retain.  Columns will be returned in the given order.  None
      returns all columns.
  order_categoricals : bool, default True
      Flag indicating whether converted categorical data are ordered."""
  
  _chunksize_params = """\
  chunksize : int, default None
      Return StataReader object for iterations, returns chunks with
      given number of lines."""
  
  _iterator_params = """\
  iterator : bool, default False
      Return StataReader object."""
  
  _reader_notes = """\
  Notes
  -----
  Categorical variables read through an iterator may not have the same
  categories and dtype. This occurs when  a variable stored in a DTA
  file is associated to an incomplete set of value labels that only
  label a strict subset of the values."""
  
  _read_stata_doc = f"""
  Read Stata file into DataFrame.
  
  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, and file. For file URLs, a host is
      expected. A local file could be: ``file://localhost/path/to/table.dta``.
  
      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``.
  {_statafile_processing_params1}
  {_statafile_processing_params2}
  {_chunksize_params}
  {_iterator_params}
  {_shared_docs["decompression_options"]}
  {_shared_docs["storage_options"]}
  
  Returns
  -------
  DataFrame or StataReader
  
  See Also
  --------
  io.stata.StataReader : Low-level reader for Stata data files.
  DataFrame.to_stata: Export Stata data files.
  
  {_reader_notes}
  
  Examples
  --------
  
  Creating a dummy stata for this example
  >>> df = pd.DataFrame({{'animal': ['falcon', 'parrot', 'falcon',
  ...                              'parrot'],
  ...                   'speed': [350, 18, 361, 15]}})  # doctest: +SKIP
  >>> df.to_stata('animals.dta')  # doctest: +SKIP
  
  Read a Stata dta file:
  
  >>> df = pd.read_stata('animals.dta')  # doctest: +SKIP
  
  Read a Stata dta file in 10,000 line chunks:
  >>> values = np.random.randint(0, 10, size=(20_000, 1), dtype="uint8")  # doctest: +SKIP
  >>> df = pd.DataFrame(values, columns=["i"])  # doctest: +SKIP
  >>> df.to_stata('filename.dta')  # doctest: +SKIP
  
  >>> itr = pd.read_stata('filename.dta', chunksize=10000)  # doctest: +SKIP
  >>> for chunk in itr:
  ...    # Operate on a single chunk, e.g., chunk.mean()
  ...    pass  # doctest: +SKIP
  """
  
  _read_method_doc = f"""\
  Reads observations from Stata file, converting them into a dataframe
  
  Parameters
  ----------
  nrows : int
      Number of lines to read from data file, if None read whole file.
  {_statafile_processing_params1}
  {_statafile_processing_params2}
  
  Returns
  -------
  DataFrame
  """
  
  _stata_reader_doc = f"""\
  Class for reading Stata dta files.
  
  Parameters
  ----------
  path_or_buf : path (string), buffer or path object
      string, path object (pathlib.Path or py._path.local.LocalPath) or object
      implementing a binary read() functions.
  {_statafile_processing_params1}
  {_statafile_processing_params2}
  {_chunksize_params}
  {_shared_docs["decompression_options"]}
  {_shared_docs["storage_options"]}
  
  {_reader_notes}
  """
  
  
  _date_formats = ["%tc", "%tC", "%td", "%d", "%tw", "%tm", "%tq", "%th", "%ty"]
  
  
  stata_epoch = datetime.datetime(1960, 1, 1)
  
  
  # TODO: Add typing. As of January 2020 it is not possible to type this function since
  #  mypy doesn't understand that a Series and an int can be combined using mathematical
  #  operations. (+, -).
  def _stata_elapsed_date_to_datetime_vec(dates, fmt) -> Series:
      """
      Convert from SIF to datetime. https://www.stata.com/help.cgi?datetime
  
      Parameters
      ----------
      dates : Series
          The Stata Internal Format date to convert to datetime according to fmt
      fmt : str
          The format to convert to. Can be, tc, td, tw, tm, tq, th, ty
          Returns
  
      Returns
      -------
      converted : Series
          The converted dates
  
      Examples
      --------
      >>> dates = pd.Series([52])
      >>> _stata_elapsed_date_to_datetime_vec(dates , "%tw")
      0   1961-01-01
      dtype: datetime64[ns]
  
      Notes
      -----
      datetime/c - tc
          milliseconds since 01jan1960 00:00:00.000, assuming 86,400 s/day
      datetime/C - tC - NOT IMPLEMENTED
          milliseconds since 01jan1960 00:00:00.000, adjusted for leap seconds
      date - td
          days since 01jan1960 (01jan1960 = 0)
      weekly date - tw
          weeks since 1960w1
          This assumes 52 weeks in a year, then adds 7 * remainder of the weeks.
          The datetime value is the start of the week in terms of days in the
          year, not ISO calendar weeks.
      monthly date - tm
          months since 1960m1
      quarterly date - tq
          quarters since 1960q1
      half-yearly date - th
          half-years since 1960h1 yearly
      date - ty
          years since 0000
      """
      MIN_YEAR, MAX_YEAR = Timestamp.min.year, Timestamp.max.year
      MAX_DAY_DELTA = (Timestamp.max - datetime.datetime(1960, 1, 1)).days
      MIN_DAY_DELTA = (Timestamp.min - datetime.datetime(1960, 1, 1)).days
      MIN_MS_DELTA = MIN_DAY_DELTA * 24 * 3600 * 1000
      MAX_MS_DELTA = MAX_DAY_DELTA * 24 * 3600 * 1000
  
      def convert_year_month_safe(year, month) -> Series:
          """
          Convert year and month to datetimes, using pandas vectorized versions
          when the date range falls within the range supported by pandas.
          Otherwise it falls back to a slower but more robust method
          using datetime.
          """
          if year.max() < MAX_YEAR and year.min() > MIN_YEAR:
              return to_datetime(100 * year + month, format="%Y%m")
          else:
              index = getattr(year, "index", None)
              return Series(
                  [datetime.datetime(y, m, 1) for y, m in zip(year, month)], index=index
              )
  
      def convert_year_days_safe(year, days) -> Series:
          """
          Converts year (e.g. 1999) and days since the start of the year to a
          datetime or datetime64 Series
          """
          if year.max() < (MAX_YEAR - 1) and year.min() > MIN_YEAR:
              return to_datetime(year, format="%Y") + to_timedelta(days, unit="d")
          else:
              index = getattr(year, "index", None)
              value = [
                  datetime.datetime(y, 1, 1) + relativedelta(days=int(d))
                  for y, d in zip(year, days)
              ]
              return Series(value, index=index)
  
      def convert_delta_safe(base, deltas, unit) -> Series:
          """
          Convert base dates and deltas to datetimes, using pandas vectorized
          versions if the deltas satisfy restrictions required to be expressed
          as dates in pandas.
          """
          index = getattr(deltas, "index", None)
          if unit == "d":
              if deltas.max() > MAX_DAY_DELTA or deltas.min() < MIN_DAY_DELTA:
                  values = [base + relativedelta(days=int(d)) for d in deltas]
                  return Series(values, index=index)
          elif unit == "ms":
              if deltas.max() > MAX_MS_DELTA or deltas.min() < MIN_MS_DELTA:
                  values = [
                      base + relativedelta(microseconds=(int(d) * 1000)) for d in deltas
                  ]
                  return Series(values, index=index)
          else:
              raise ValueError("format not understood")
          base = to_datetime(base)
          deltas = to_timedelta(deltas, unit=unit)
          return base + deltas
  
      # TODO(non-nano): If/when pandas supports more than datetime64[ns], this
      #  should be improved to use correct range, e.g. datetime[Y] for yearly
      bad_locs = np.isnan(dates)
      has_bad_values = False
      if bad_locs.any():
          has_bad_values = True
          data_col = Series(dates)
          data_col[bad_locs] = 1.0  # Replace with NaT
      dates = dates.astype(np.int64)
  
      if fmt.startswith(("%tc", "tc")):  # Delta ms relative to base
          base = stata_epoch
          ms = dates
          conv_dates = convert_delta_safe(base, ms, "ms")
      elif fmt.startswith(("%tC", "tC")):
  
          warnings.warn("Encountered %tC format. Leaving in Stata Internal Format.")
          conv_dates = Series(dates, dtype=object)
          if has_bad_values:
              conv_dates[bad_locs] = NaT
          return conv_dates
      # Delta days relative to base
      elif fmt.startswith(("%td", "td", "%d", "d")):
          base = stata_epoch
          days = dates
          conv_dates = convert_delta_safe(base, days, "d")
      # does not count leap days - 7 days is a week.
      # 52nd week may have more than 7 days
      elif fmt.startswith(("%tw", "tw")):
          year = stata_epoch.year + dates // 52
          days = (dates % 52) * 7
          conv_dates = convert_year_days_safe(year, days)
      elif fmt.startswith(("%tm", "tm")):  # Delta months relative to base
          year = stata_epoch.year + dates // 12
          month = (dates % 12) + 1
          conv_dates = convert_year_month_safe(year, month)
      elif fmt.startswith(("%tq", "tq")):  # Delta quarters relative to base
          year = stata_epoch.year + dates // 4
          quarter_month = (dates % 4) * 3 + 1
          conv_dates = convert_year_month_safe(year, quarter_month)
      elif fmt.startswith(("%th", "th")):  # Delta half-years relative to base
          year = stata_epoch.year + dates // 2
          month = (dates % 2) * 6 + 1
          conv_dates = convert_year_month_safe(year, month)
      elif fmt.startswith(("%ty", "ty")):  # Years -- not delta
          year = dates
          first_month = np.ones_like(dates)
          conv_dates = convert_year_month_safe(year, first_month)
      else:
          raise ValueError(f"Date fmt {fmt} not understood")
  
      if has_bad_values:  # Restore NaT for bad values
          conv_dates[bad_locs] = NaT
  
      return conv_dates
  
  
  def _datetime_to_stata_elapsed_vec(dates: Series, fmt: str) -> Series:
      """
      Convert from datetime to SIF. https://www.stata.com/help.cgi?datetime
  
      Parameters
      ----------
      dates : Series
          Series or array containing datetime.datetime or datetime64[ns] to
          convert to the Stata Internal Format given by fmt
      fmt : str
          The format to convert to. Can be, tc, td, tw, tm, tq, th, ty
      """
      index = dates.index
      NS_PER_DAY = 24 * 3600 * 1000 * 1000 * 1000
      US_PER_DAY = NS_PER_DAY / 1000
  
      def parse_dates_safe(dates, delta=False, year=False, days=False):
          d = {}
          if is_datetime64_dtype(dates.dtype):
              if delta:
                  time_delta = dates - stata_epoch
                  d["delta"] = time_delta._values.view(np.int64) // 1000  # microseconds
              if days or year:
                  date_index = DatetimeIndex(dates)
                  d["year"] = date_index._data.year
                  d["month"] = date_index._data.month
              if days:
                  days_in_ns = dates.view(np.int64) - to_datetime(
                      d["year"], format="%Y"
                  ).view(np.int64)
                  d["days"] = days_in_ns // NS_PER_DAY
  
          elif infer_dtype(dates, skipna=False) == "datetime":
              if delta:
                  delta = dates._values - stata_epoch
  
                  def f(x: datetime.timedelta) -> float:
                      return US_PER_DAY * x.days + 1000000 * x.seconds + x.microseconds
  
                  v = np.vectorize(f)
                  d["delta"] = v(delta)
              if year:
                  year_month = dates.apply(lambda x: 100 * x.year + x.month)
                  d["year"] = year_month._values // 100
                  d["month"] = year_month._values - d["year"] * 100
              if days:
  
                  def g(x: datetime.datetime) -> int:
                      return (x - datetime.datetime(x.year, 1, 1)).days
  
                  v = np.vectorize(g)
                  d["days"] = v(dates)
          else:
              raise ValueError(
                  "Columns containing dates must contain either "
                  "datetime64, datetime.datetime or null values."
              )
  
          return DataFrame(d, index=index)
  
      bad_loc = isna(dates)
      index = dates.index
      if bad_loc.any():
          dates = Series(dates)
          if is_datetime64_dtype(dates):
              dates[bad_loc] = to_datetime(stata_epoch)
          else:
              dates[bad_loc] = stata_epoch
  
      if fmt in ["%tc", "tc"]:
          d = parse_dates_safe(dates, delta=True)
          conv_dates = d.delta / 1000
      elif fmt in ["%tC", "tC"]:
          warnings.warn("Stata Internal Format tC not supported.")
          conv_dates = dates
      elif fmt in ["%td", "td"]:
          d = parse_dates_safe(dates, delta=True)
          conv_dates = d.delta // US_PER_DAY
      elif fmt in ["%tw", "tw"]:
          d = parse_dates_safe(dates, year=True, days=True)
          conv_dates = 52 * (d.year - stata_epoch.year) + d.days // 7
      elif fmt in ["%tm", "tm"]:
          d = parse_dates_safe(dates, year=True)
          conv_dates = 12 * (d.year - stata_epoch.year) + d.month - 1
      elif fmt in ["%tq", "tq"]:
          d = parse_dates_safe(dates, year=True)
          conv_dates = 4 * (d.year - stata_epoch.year) + (d.month - 1) // 3
      elif fmt in ["%th", "th"]:
          d = parse_dates_safe(dates, year=True)
          conv_dates = 2 * (d.year - stata_epoch.year) + (d.month > 6).astype(int)
      elif fmt in ["%ty", "ty"]:
          d = parse_dates_safe(dates, year=True)
          conv_dates = d.year
      else:
          raise ValueError(f"Format {fmt} is not a known Stata date format")
  
      conv_dates = Series(conv_dates, dtype=np.float64)
      missing_value = struct.unpack("<d", b"\x00\x00\x00\x00\x00\x00\xe0\x7f")[0]
      conv_dates[bad_loc] = missing_value
  
      return Series(conv_dates, index=index)
  
  
  excessive_string_length_error = """
  Fixed width strings in Stata .dta files are limited to 244 (or fewer)
  characters.  Column '{0}' does not satisfy this restriction. Use the
  'version=117' parameter to write the newer (Stata 13 and later) format.
  """
  
  
  class PossiblePrecisionLoss(Warning):
      pass
  
  
  precision_loss_doc = """
  Column converted from {0} to {1}, and some data are outside of the lossless
  conversion range. This may result in a loss of precision in the saved data.
  """
  
  
  class ValueLabelTypeMismatch(Warning):
      pass
  
  
  value_label_mismatch_doc = """
  Stata value labels (pandas categories) must be strings. Column {0} contains
  non-string labels which will be converted to strings.  Please check that the
  Stata data file created has not lost information due to duplicate labels.
  """
  
  
  class InvalidColumnName(Warning):
      pass
  
  
  invalid_name_doc = """
  Not all pandas column names were valid Stata variable names.
  The following replacements have been made:
  
      {0}
  
  If this is not what you expect, please make sure you have Stata-compliant
  column names in your DataFrame (strings only, max 32 characters, only
  alphanumerics and underscores, no Stata reserved words)
  """
  
  
  class CategoricalConversionWarning(Warning):
      pass
  
  
  categorical_conversion_warning = """
  One or more series with value labels are not fully labeled. Reading this
  dataset with an iterator results in categorical variable with different
  categories. This occurs since it is not possible to know all possible values
  until the entire dataset has been read. To avoid this warning, you can either
  read dataset without an iterator, or manually convert categorical data by
  ``convert_categoricals`` to False and then accessing the variable labels
  through the value_labels method of the reader.
  """
  
  
  def _cast_to_stata_types(data: DataFrame) -> DataFrame:
      """
      Checks the dtypes of the columns of a pandas DataFrame for
      compatibility with the data types and ranges supported by Stata, and
      converts if necessary.
  
      Parameters
      ----------
      data : DataFrame
          The DataFrame to check and convert
  
      Notes
      -----
      Numeric columns in Stata must be one of int8, int16, int32, float32 or
      float64, with some additional value restrictions.  int8 and int16 columns
      are checked for violations of the value restrictions and upcast if needed.
      int64 data is not usable in Stata, and so it is downcast to int32 whenever
      the value are in the int32 range, and sidecast to float64 when larger than
      this range.  If the int64 values are outside of the range of those
      perfectly representable as float64 values, a warning is raised.
  
      bool columns are cast to int8.  uint columns are converted to int of the
      same size if there is no loss in precision, otherwise are upcast to a
      larger type.  uint64 is currently not supported since it is concerted to
      object in a DataFrame.
      """
      ws = ""
      # original, if small, if large
      conversion_data = (
          (np.bool_, np.int8, np.int8),
          (np.uint8, np.int8, np.int16),
          (np.uint16, np.int16, np.int32),
          (np.uint32, np.int32, np.int64),
          (np.uint64, np.int64, np.float64),
      )
  
      float32_max = struct.unpack("<f", b"\xff\xff\xff\x7e")[0]
      float64_max = struct.unpack("<d", b"\xff\xff\xff\xff\xff\xff\xdf\x7f")[0]
  
      for col in data:
          # Cast from unsupported types to supported types
          is_nullable_int = isinstance(data[col].dtype, (_IntegerDtype, BooleanDtype))
          orig = data[col]
          # We need to find orig_missing before altering data below
          orig_missing = orig.isna()
          if is_nullable_int:
              missing_loc = data[col].isna()
              if missing_loc.any():
                  # Replace with always safe value
                  fv = 0 if isinstance(data[col].dtype, _IntegerDtype) else False
                  data.loc[missing_loc, col] = fv
              # Replace with NumPy-compatible column
              data[col] = data[col].astype(data[col].dtype.numpy_dtype)
          dtype = data[col].dtype
          for c_data in conversion_data:
              if dtype == c_data[0]:
                  # Value of type variable "_IntType" of "iinfo" cannot be "object"
                  if data[col].max() <= np.iinfo(c_data[1]).max:  # type: ignore[type-var]
                      dtype = c_data[1]
                  else:
                      dtype = c_data[2]
                  if c_data[2] == np.int64:  # Warn if necessary
                      if data[col].max() >= 2 ** 53:
                          ws = precision_loss_doc.format("uint64", "float64")
  
                  data[col] = data[col].astype(dtype)
  
          # Check values and upcast if necessary
          if dtype == np.int8:
              if data[col].max() > 100 or data[col].min() < -127:
                  data[col] = data[col].astype(np.int16)
          elif dtype == np.int16:
              if data[col].max() > 32740 or data[col].min() < -32767:
                  data[col] = data[col].astype(np.int32)
          elif dtype == np.int64:
              if data[col].max() <= 2147483620 and data[col].min() >= -2147483647:
                  data[col] = data[col].astype(np.int32)
              else:
                  data[col] = data[col].astype(np.float64)
                  if data[col].max() >= 2 ** 53 or data[col].min() <= -(2 ** 53):
                      ws = precision_loss_doc.format("int64", "float64")
          elif dtype in (np.float32, np.float64):
              if np.isinf(data[col]).any():
                  raise ValueError(
                      f"Column {col} contains infinity or -infinity"
                      "which is outside the range supported by Stata."
                  )
              value = data[col].max()
              if dtype == np.float32 and value > float32_max:
                  data[col] = data[col].astype(np.float64)
              elif dtype == np.float64:
                  if value > float64_max:
                      raise ValueError(
                          f"Column {col} has a maximum value ({value}) outside the range "
                          f"supported by Stata ({float64_max})"
                      )
          if is_nullable_int:
              if orig_missing.any():
                  # Replace missing by Stata sentinel value
                  sentinel = StataMissingValue.BASE_MISSING_VALUES[data[col].dtype.name]
                  data.loc[orig_missing, col] = sentinel
      if ws:
          warnings.warn(ws, PossiblePrecisionLoss)
  
      return data
  
  
  class StataValueLabel:
      """
      Parse a categorical column and prepare formatted output
  
      Parameters
      ----------
      catarray : Series
          Categorical Series to encode
      encoding : {"latin-1", "utf-8"}
          Encoding to use for value labels.
      """
  
      def __init__(self, catarray: Series, encoding: str = "latin-1"):
  
          if encoding not in ("latin-1", "utf-8"):
              raise ValueError("Only latin-1 and utf-8 are supported.")
          self.labname = catarray.name
          self._encoding = encoding
          categories = catarray.cat.categories
          self.value_labels: list[tuple[int | float, str]] = list(
              zip(np.arange(len(categories)), categories)
          )
          self.value_labels.sort(key=lambda x: x[0])
  
          self._prepare_value_labels()
  
      def _prepare_value_labels(self):
          """Encode value labels."""
  
          self.text_len = 0
          self.txt: list[bytes] = []
          self.n = 0
          # Offsets (length of categories), converted to int32
          self.off = np.array([], dtype=np.int32)
          # Values, converted to int32
          self.val = np.array([], dtype=np.int32)
          self.len = 0
  
          # Compute lengths and setup lists of offsets and labels
          offsets: list[int] = []
          values: list[int | float] = []
          for vl in self.value_labels:
              category: str | bytes = vl[1]
              if not isinstance(category, str):
                  category = str(category)
                  warnings.warn(
                      value_label_mismatch_doc.format(self.labname),
                      ValueLabelTypeMismatch,
                  )
              category = category.encode(self._encoding)
              offsets.append(self.text_len)
              self.text_len += len(category) + 1  # +1 for the padding
              values.append(vl[0])
              self.txt.append(category)
              self.n += 1
  
          if self.text_len > 32000:
              raise ValueError(
                  "Stata value labels for a single variable must "
                  "have a combined length less than 32,000 characters."
              )
  
          # Ensure int32
          self.off = np.array(offsets, dtype=np.int32)
          self.val = np.array(values, dtype=np.int32)
  
          # Total length
          self.len = 4 + 4 + 4 * self.n + 4 * self.n + self.text_len
  
      def generate_value_label(self, byteorder: str) -> bytes:
          """
          Generate the binary representation of the value labels.
  
          Parameters
          ----------
          byteorder : str
              Byte order of the output
  
          Returns
          -------
          value_label : bytes
              Bytes containing the formatted value label
          """
          encoding = self._encoding
          bio = BytesIO()
          null_byte = b"\x00"
  
          # len
          bio.write(struct.pack(byteorder + "i", self.len))
  
          # labname
          labname = str(self.labname)[:32].encode(encoding)
          lab_len = 32 if encoding not in ("utf-8", "utf8") else 128
          labname = _pad_bytes(labname, lab_len + 1)
          bio.write(labname)
  
          # padding - 3 bytes
          for i in range(3):
              bio.write(struct.pack("c", null_byte))
  
          # value_label_table
          # n - int32
          bio.write(struct.pack(byteorder + "i", self.n))
  
          # textlen  - int32
          bio.write(struct.pack(byteorder + "i", self.text_len))
  
          # off - int32 array (n elements)
          for offset in self.off:
              bio.write(struct.pack(byteorder + "i", offset))
  
          # val - int32 array (n elements)
          for value in self.val:
              bio.write(struct.pack(byteorder + "i", value))
  
          # txt - Text labels, null terminated
          for text in self.txt:
              bio.write(text + null_byte)
  
          return bio.getvalue()
  
  
  class StataNonCatValueLabel(StataValueLabel):
      """
      Prepare formatted version of value labels
  
      Parameters
      ----------
      labname : str
          Value label name
      value_labels: Dictionary
          Mapping of values to labels
      encoding : {"latin-1", "utf-8"}
          Encoding to use for value labels.
      """
  
      def __init__(
          self,
          labname: str,
          value_labels: dict[float | int, str],
          encoding: Literal["latin-1", "utf-8"] = "latin-1",
      ):
  
          if encoding not in ("latin-1", "utf-8"):
              raise ValueError("Only latin-1 and utf-8 are supported.")
  
          self.labname = labname
          self._encoding = encoding
          self.value_labels: list[tuple[int | float, str]] = sorted(
              value_labels.items(), key=lambda x: x[0]
          )
          self._prepare_value_labels()
  
  
  class StataMissingValue:
      """
      An observation's missing value.
  
      Parameters
      ----------
      value : {int, float}
          The Stata missing value code
  
      Notes
      -----
      More information: <https://www.stata.com/help.cgi?missing>
  
      Integer missing values make the code '.', '.a', ..., '.z' to the ranges
      101 ... 127 (for int8), 32741 ... 32767  (for int16) and 2147483621 ...
      2147483647 (for int32).  Missing values for floating point data types are
      more complex but the pattern is simple to discern from the following table.
  
      np.float32 missing values (float in Stata)
      0000007f    .
      0008007f    .a
      0010007f    .b
      ...
      00c0007f    .x
      00c8007f    .y
      00d0007f    .z
  
      np.float64 missing values (double in Stata)
      000000000000e07f    .
      000000000001e07f    .a
      000000000002e07f    .b
      ...
      000000000018e07f    .x
      000000000019e07f    .y
      00000000001ae07f    .z
      """
  
      # Construct a dictionary of missing values
      MISSING_VALUES: dict[float, str] = {}
      bases = (101, 32741, 2147483621)
      for b in bases:
          # Conversion to long to avoid hash issues on 32 bit platforms #8968
          MISSING_VALUES[b] = "."
          for i in range(1, 27):
              MISSING_VALUES[i + b] = "." + chr(96 + i)
  
      float32_base = b"\x00\x00\x00\x7f"
      increment = struct.unpack("<i", b"\x00\x08\x00\x00")[0]
      for i in range(27):
          key = struct.unpack("<f", float32_base)[0]
          MISSING_VALUES[key] = "."
          if i > 0:
              MISSING_VALUES[key] += chr(96 + i)
          int_value = struct.unpack("<i", struct.pack("<f", key))[0] + increment
          float32_base = struct.pack("<i", int_value)
  
      float64_base = b"\x00\x00\x00\x00\x00\x00\xe0\x7f"
      increment = struct.unpack("q", b"\x00\x00\x00\x00\x00\x01\x00\x00")[0]
      for i in range(27):
          key = struct.unpack("<d", float64_base)[0]
          MISSING_VALUES[key] = "."
          if i > 0:
              MISSING_VALUES[key] += chr(96 + i)
          int_value = struct.unpack("q", struct.pack("<d", key))[0] + increment
          float64_base = struct.pack("q", int_value)
  
      BASE_MISSING_VALUES = {
          "int8": 101,
          "int16": 32741,
          "int32": 2147483621,
          "float32": struct.unpack("<f", float32_base)[0],
          "float64": struct.unpack("<d", float64_base)[0],
      }
  
      def __init__(self, value: int | float):
          self._value = value
          # Conversion to int to avoid hash issues on 32 bit platforms #8968
          value = int(value) if value < 2147483648 else float(value)
          self._str = self.MISSING_VALUES[value]
  
      @property
      def string(self) -> str:
          """
          The Stata representation of the missing value: '.', '.a'..'.z'
  
          Returns
          -------
          str
              The representation of the missing value.
          """
          return self._str
  
      @property
      def value(self) -> int | float:
          """
          The binary representation of the missing value.
  
          Returns
          -------
          {int, float}
              The binary representation of the missing value.
          """
          return self._value
  
      def __str__(self) -> str:
          return self.string
  
      def __repr__(self) -> str:
          return f"{type(self)}({self})"
  
      def __eq__(self, other: Any) -> bool:
          return (
              isinstance(other, type(self))
              and self.string == other.string
              and self.value == other.value
          )
  
      @classmethod
      def get_base_missing_value(cls, dtype: np.dtype) -> int | float:
          if dtype.type is np.int8:
              value = cls.BASE_MISSING_VALUES["int8"]
          elif dtype.type is np.int16:
              value = cls.BASE_MISSING_VALUES["int16"]
          elif dtype.type is np.int32:
              value = cls.BASE_MISSING_VALUES["int32"]
          elif dtype.type is np.float32:
              value = cls.BASE_MISSING_VALUES["float32"]
          elif dtype.type is np.float64:
              value = cls.BASE_MISSING_VALUES["float64"]
          else:
              raise ValueError("Unsupported dtype")
          return value
  
  
  class StataParser:
      def __init__(self):
  
          # type          code.
          # --------------------
          # str1        1 = 0x01
          # str2        2 = 0x02
          # ...
          # str244    244 = 0xf4
          # byte      251 = 0xfb  (sic)
          # int       252 = 0xfc
          # long      253 = 0xfd
          # float     254 = 0xfe
          # double    255 = 0xff
          # --------------------
          # NOTE: the byte type seems to be reserved for categorical variables
          # with a label, but the underlying variable is -127 to 100
          # we're going to drop the label and cast to int
          self.DTYPE_MAP = dict(
              list(zip(range(1, 245), [np.dtype("a" + str(i)) for i in range(1, 245)]))
              + [
                  (251, np.dtype(np.int8)),
                  (252, np.dtype(np.int16)),
                  (253, np.dtype(np.int32)),
                  (254, np.dtype(np.float32)),
                  (255, np.dtype(np.float64)),
              ]
          )
          self.DTYPE_MAP_XML = {
              32768: np.dtype(np.uint8),  # Keys to GSO
              65526: np.dtype(np.float64),
              65527: np.dtype(np.float32),
              65528: np.dtype(np.int32),
              65529: np.dtype(np.int16),
              65530: np.dtype(np.int8),
          }
          # error: Argument 1 to "list" has incompatible type "str";
          #  expected "Iterable[int]"  [arg-type]
          self.TYPE_MAP = list(range(251)) + list("bhlfd")  # type: ignore[arg-type]
          self.TYPE_MAP_XML = {
              # Not really a Q, unclear how to handle byteswap
              32768: "Q",
              65526: "d",
              65527: "f",
              65528: "l",
              65529: "h",
              65530: "b",
          }
          # NOTE: technically, some of these are wrong. there are more numbers
          # that can be represented. it's the 27 ABOVE and BELOW the max listed
          # numeric data type in [U] 12.2.2 of the 11.2 manual
          float32_min = b"\xff\xff\xff\xfe"
          float32_max = b"\xff\xff\xff\x7e"
          float64_min = b"\xff\xff\xff\xff\xff\xff\xef\xff"
          float64_max = b"\xff\xff\xff\xff\xff\xff\xdf\x7f"
          self.VALID_RANGE = {
              "b": (-127, 100),
              "h": (-32767, 32740),
              "l": (-2147483647, 2147483620),
              "f": (
                  np.float32(struct.unpack("<f", float32_min)[0]),
                  np.float32(struct.unpack("<f", float32_max)[0]),
              ),
              "d": (
                  np.float64(struct.unpack("<d", float64_min)[0]),
                  np.float64(struct.unpack("<d", float64_max)[0]),
              ),
          }
  
          self.OLD_TYPE_MAPPING = {
              98: 251,  # byte
              105: 252,  # int
              108: 253,  # long
              102: 254,  # float
              100: 255,  # double
          }
  
          # These missing values are the generic '.' in Stata, and are used
          # to replace nans
          self.MISSING_VALUES = {
              "b": 101,
              "h": 32741,
              "l": 2147483621,
              "f": np.float32(struct.unpack("<f", b"\x00\x00\x00\x7f")[0]),
              "d": np.float64(
                  struct.unpack("<d", b"\x00\x00\x00\x00\x00\x00\xe0\x7f")[0]
              ),
          }
          self.NUMPY_TYPE_MAP = {
              "b": "i1",
              "h": "i2",
              "l": "i4",
              "f": "f4",
              "d": "f8",
              "Q": "u8",
          }
  
          # Reserved words cannot be used as variable names
          self.RESERVED_WORDS = (
              "aggregate",
              "array",
              "boolean",
              "break",
              "byte",
              "case",
              "catch",
              "class",
              "colvector",
              "complex",
              "const",
              "continue",
              "default",
              "delegate",
              "delete",
              "do",
              "double",
              "else",
              "eltypedef",
              "end",
              "enum",
              "explicit",
              "export",
              "external",
              "float",
              "for",
              "friend",
              "function",
              "global",
              "goto",
              "if",
              "inline",
              "int",
              "local",
              "long",
              "NULL",
              "pragma",
              "protected",
              "quad",
              "rowvector",
              "short",
              "typedef",
              "typename",
              "virtual",
              "_all",
              "_N",
              "_skip",
              "_b",
              "_pi",
              "str#",
              "in",
              "_pred",
              "strL",
              "_coef",
              "_rc",
              "using",
              "_cons",
              "_se",
              "with",
              "_n",
          )
  
  
  class StataReader(StataParser, abc.Iterator):
      __doc__ = _stata_reader_doc
  
      def __init__(
          self,
          path_or_buf: FilePath | ReadBuffer[bytes],
          convert_dates: bool = True,
          convert_categoricals: bool = True,
          index_col: str | None = None,
          convert_missing: bool = False,
          preserve_dtypes: bool = True,
          columns: Sequence[str] | None = None,
          order_categoricals: bool = True,
          chunksize: int | None = None,
          compression: CompressionOptions = "infer",
          storage_options: StorageOptions = None,
      ):
          super().__init__()
          self.col_sizes: list[int] = []
  
          # Arguments to the reader (can be temporarily overridden in
          # calls to read).
          self._convert_dates = convert_dates
          self._convert_categoricals = convert_categoricals
          self._index_col = index_col
          self._convert_missing = convert_missing
          self._preserve_dtypes = preserve_dtypes
          self._columns = columns
          self._order_categoricals = order_categoricals
          self._encoding = ""
          self._chunksize = chunksize
          self._using_iterator = False
          if self._chunksize is None:
              self._chunksize = 1
          elif not isinstance(chunksize, int) or chunksize <= 0:
              raise ValueError("chunksize must be a positive integer when set.")
  
          # State variables for the file
          self._has_string_data = False
          self._missing_values = False
          self._can_read_value_labels = False
          self._column_selector_set = False
          self._value_labels_read = False
          self._data_read = False
          self._dtype: np.dtype | None = None
          self._lines_read = 0
  
          self._native_byteorder = _set_endianness(sys.byteorder)
          with get_handle(
              path_or_buf,
              "rb",
              storage_options=storage_options,
              is_text=False,
              compression=compression,
          ) as handles:
              # Copy to BytesIO, and ensure no encoding
              self.path_or_buf = BytesIO(handles.handle.read())
  
          self._read_header()
          self._setup_dtype()
  
      def __enter__(self) -> StataReader:
          """enter context manager"""
          return self
  
      def __exit__(self, exc_type, exc_value, traceback) -> None:
          """exit context manager"""
          self.close()
  
      def close(self) -> None:
          """close the handle if its open"""
          self.path_or_buf.close()
  
      def _set_encoding(self) -> None:
          """
          Set string encoding which depends on file version
          """
          if self.format_version < 118:
              self._encoding = "latin-1"
          else:
              self._encoding = "utf-8"
  
      def _read_header(self) -> None:
          first_char = self.path_or_buf.read(1)
          if struct.unpack("c", first_char)[0] == b"<":
              self._read_new_header()
          else:
              self._read_old_header(first_char)
  
          self.has_string_data = len([x for x in self.typlist if type(x) is int]) > 0
  
          # calculate size of a data record
          self.col_sizes = [self._calcsize(typ) for typ in self.typlist]
  
      def _read_new_header(self) -> None:
          # The first part of the header is common to 117 - 119.
          self.path_or_buf.read(27)  # stata_dta><header><release>
          self.format_version = int(self.path_or_buf.read(3))
          if self.format_version not in [117, 118, 119]:
              raise ValueError(_version_error.format(version=self.format_version))
          self._set_encoding()
          self.path_or_buf.read(21)  # </release><byteorder>
          self.byteorder = self.path_or_buf.read(3) == b"MSF" and ">" or "<"
          self.path_or_buf.read(15)  # </byteorder><K>
          nvar_type = "H" if self.format_version <= 118 else "I"
          nvar_size = 2 if self.format_version <= 118 else 4
          self.nvar = struct.unpack(
              self.byteorder + nvar_type, self.path_or_buf.read(nvar_size)
          )[0]
          self.path_or_buf.read(7)  # </K><N>
  
          self.nobs = self._get_nobs()
          self.path_or_buf.read(11)  # </N><label>
          self._data_label = self._get_data_label()
          self.path_or_buf.read(19)  # </label><timestamp>
          self.time_stamp = self._get_time_stamp()
          self.path_or_buf.read(26)  # </timestamp></header><map>
          self.path_or_buf.read(8)  # 0x0000000000000000
          self.path_or_buf.read(8)  # position of <map>
  
          self._seek_vartypes = (
              struct.unpack(self.byteorder + "q", self.path_or_buf.read(8))[0] + 16
          )
          self._seek_varnames = (
              struct.unpack(self.byteorder + "q", self.path_or_buf.read(8))[0] + 10
          )
          self._seek_sortlist = (
              struct.unpack(self.byteorder + "q", self.path_or_buf.read(8))[0] + 10
          )
          self._seek_formats = (
              struct.unpack(self.byteorder + "q", self.path_or_buf.read(8))[0] + 9
          )
          self._seek_value_label_names = (
              struct.unpack(self.byteorder + "q", self.path_or_buf.read(8))[0] + 19
          )
  
          # Requires version-specific treatment
          self._seek_variable_labels = self._get_seek_variable_labels()
  
          self.path_or_buf.read(8)  # <characteristics>
          self.data_location = (
              struct.unpack(self.byteorder + "q", self.path_or_buf.read(8))[0] + 6
          )
          self.seek_strls = (
              struct.unpack(self.byteorder + "q", self.path_or_buf.read(8))[0] + 7
          )
          self.seek_value_labels = (
              struct.unpack(self.byteorder + "q", self.path_or_buf.read(8))[0] + 14
          )
  
          self.typlist, self.dtyplist = self._get_dtypes(self._seek_vartypes)
  
          self.path_or_buf.seek(self._seek_varnames)
          self.varlist = self._get_varlist()
  
          self.path_or_buf.seek(self._seek_sortlist)
          self.srtlist = struct.unpack(
              self.byteorder + ("h" * (self.nvar + 1)),
              self.path_or_buf.read(2 * (self.nvar + 1)),
          )[:-1]
  
          self.path_or_buf.seek(self._seek_formats)
          self.fmtlist = self._get_fmtlist()
  
          self.path_or_buf.seek(self._seek_value_label_names)
          self.lbllist = self._get_lbllist()
  
          self.path_or_buf.seek(self._seek_variable_labels)
          self._variable_labels = self._get_variable_labels()
  
      # Get data type information, works for versions 117-119.
      def _get_dtypes(
          self, seek_vartypes: int
      ) -> tuple[list[int | str], list[str | np.dtype]]:
  
          self.path_or_buf.seek(seek_vartypes)
          raw_typlist = [
              struct.unpack(self.byteorder + "H", self.path_or_buf.read(2))[0]
              for _ in range(self.nvar)
          ]
  
          def f(typ: int) -> int | str:
              if typ <= 2045:
                  return typ
              try:
                  return self.TYPE_MAP_XML[typ]
              except KeyError as err:
                  raise ValueError(f"cannot convert stata types [{typ}]") from err
  
          typlist = [f(x) for x in raw_typlist]
  
          def g(typ: int) -> str | np.dtype:
              if typ <= 2045:
                  return str(typ)
              try:
                  # error: Incompatible return value type (got "Type[number]", expected
                  # "Union[str, dtype]")
                  return self.DTYPE_MAP_XML[typ]  # type: ignore[return-value]
              except KeyError as err:
                  raise ValueError(f"cannot convert stata dtype [{typ}]") from err
  
          dtyplist = [g(x) for x in raw_typlist]
  
          return typlist, dtyplist
  
      def _get_varlist(self) -> list[str]:
          # 33 in order formats, 129 in formats 118 and 119
          b = 33 if self.format_version < 118 else 129
          return [self._decode(self.path_or_buf.read(b)) for _ in range(self.nvar)]
  
      # Returns the format list
      def _get_fmtlist(self) -> list[str]:
          if self.format_version >= 118:
              b = 57
          elif self.format_version > 113:
              b = 49
          elif self.format_version > 104:
              b = 12
          else:
              b = 7
  
          return [self._decode(self.path_or_buf.read(b)) for _ in range(self.nvar)]
  
      # Returns the label list
      def _get_lbllist(self) -> list[str]:
          if self.format_version >= 118:
              b = 129
          elif self.format_version > 108:
              b = 33
          else:
              b = 9
          return [self._decode(self.path_or_buf.read(b)) for _ in range(self.nvar)]
  
      def _get_variable_labels(self) -> list[str]:
          if self.format_version >= 118:
              vlblist = [
                  self._decode(self.path_or_buf.read(321)) for _ in range(self.nvar)
              ]
          elif self.format_version > 105:
              vlblist = [
                  self._decode(self.path_or_buf.read(81)) for _ in range(self.nvar)
              ]
          else:
              vlblist = [
                  self._decode(self.path_or_buf.read(32)) for _ in range(self.nvar)
              ]
          return vlblist
  
      def _get_nobs(self) -> int:
          if self.format_version >= 118:
              return struct.unpack(self.byteorder + "Q", self.path_or_buf.read(8))[0]
          else:
              return struct.unpack(self.byteorder + "I", self.path_or_buf.read(4))[0]
  
      def _get_data_label(self) -> str:
          if self.format_version >= 118:
              strlen = struct.unpack(self.byteorder + "H", self.path_or_buf.read(2))[0]
              return self._decode(self.path_or_buf.read(strlen))
          elif self.format_version == 117:
              strlen = struct.unpack("b", self.path_or_buf.read(1))[0]
              return self._decode(self.path_or_buf.read(strlen))
          elif self.format_version > 105:
              return self._decode(self.path_or_buf.read(81))
          else:
              return self._decode(self.path_or_buf.read(32))
  
      def _get_time_stamp(self) -> str:
          if self.format_version >= 118:
              strlen = struct.unpack("b", self.path_or_buf.read(1))[0]
              return self.path_or_buf.read(strlen).decode("utf-8")
          elif self.format_version == 117:
              strlen = struct.unpack("b", self.path_or_buf.read(1))[0]
              return self._decode(self.path_or_buf.read(strlen))
          elif self.format_version > 104:
              return self._decode(self.path_or_buf.read(18))
          else:
              raise ValueError()
  
      def _get_seek_variable_labels(self) -> int:
          if self.format_version == 117:
              self.path_or_buf.read(8)  # <variable_labels>, throw away
              # Stata 117 data files do not follow the described format.  This is
              # a work around that uses the previous label, 33 bytes for each
              # variable, 20 for the closing tag and 17 for the opening tag
              return self._seek_value_label_names + (33 * self.nvar) + 20 + 17
          elif self.format_version >= 118:
              return struct.unpack(self.byteorder + "q", self.path_or_buf.read(8))[0] + 17
          else:
              raise ValueError()
  
      def _read_old_header(self, first_char: bytes) -> None:
          self.format_version = struct.unpack("b", first_char)[0]
          if self.format_version not in [104, 105, 108, 111, 113, 114, 115]:
              raise ValueError(_version_error.format(version=self.format_version))
          self._set_encoding()
          self.byteorder = (
              struct.unpack("b", self.path_or_buf.read(1))[0] == 0x1 and ">" or "<"
          )
          self.filetype = struct.unpack("b", self.path_or_buf.read(1))[0]
          self.path_or_buf.read(1)  # unused
  
          self.nvar = struct.unpack(self.byteorder + "H", self.path_or_buf.read(2))[0]
          self.nobs = self._get_nobs()
  
          self._data_label = self._get_data_label()
  
          self.time_stamp = self._get_time_stamp()
  
          # descriptors
          if self.format_version > 108:
              typlist = [ord(self.path_or_buf.read(1)) for _ in range(self.nvar)]
          else:
              buf = self.path_or_buf.read(self.nvar)
              typlistb = np.frombuffer(buf, dtype=np.uint8)
              typlist = []
              for tp in typlistb:
                  if tp in self.OLD_TYPE_MAPPING:
                      typlist.append(self.OLD_TYPE_MAPPING[tp])
                  else:
                      typlist.append(tp - 127)  # bytes
  
          try:
              self.typlist = [self.TYPE_MAP[typ] for typ in typlist]
          except ValueError as err:
              invalid_types = ",".join([str(x) for x in typlist])
              raise ValueError(f"cannot convert stata types [{invalid_types}]") from err
          try:
              self.dtyplist = [self.DTYPE_MAP[typ] for typ in typlist]
          except ValueError as err:
              invalid_dtypes = ",".join([str(x) for x in typlist])
              raise ValueError(f"cannot convert stata dtypes [{invalid_dtypes}]") from err
  
          if self.format_version > 108:
              self.varlist = [
                  self._decode(self.path_or_buf.read(33)) for _ in range(self.nvar)
              ]
          else:
              self.varlist = [
                  self._decode(self.path_or_buf.read(9)) for _ in range(self.nvar)
              ]
          self.srtlist = struct.unpack(
              self.byteorder + ("h" * (self.nvar + 1)),
              self.path_or_buf.read(2 * (self.nvar + 1)),
          )[:-1]
  
          self.fmtlist = self._get_fmtlist()
  
          self.lbllist = self._get_lbllist()
  
          self._variable_labels = self._get_variable_labels()
  
          # ignore expansion fields (Format 105 and later)
          # When reading, read five bytes; the last four bytes now tell you
          # the size of the next read, which you discard.  You then continue
          # like this until you read 5 bytes of zeros.
  
          if self.format_version > 104:
              while True:
                  data_type = struct.unpack(
                      self.byteorder + "b", self.path_or_buf.read(1)
                  )[0]
                  if self.format_version > 108:
                      data_len = struct.unpack(
                          self.byteorder + "i", self.path_or_buf.read(4)
                      )[0]
                  else:
                      data_len = struct.unpack(
                          self.byteorder + "h", self.path_or_buf.read(2)
                      )[0]
                  if data_type == 0:
                      break
                  self.path_or_buf.read(data_len)
  
          # necessary data to continue parsing
          self.data_location = self.path_or_buf.tell()
  
      def _setup_dtype(self) -> np.dtype:
          """Map between numpy and state dtypes"""
          if self._dtype is not None:
              return self._dtype
  
          dtypes = []  # Convert struct data types to numpy data type
          for i, typ in enumerate(self.typlist):
              if typ in self.NUMPY_TYPE_MAP:
                  typ = cast(str, typ)  # only strs in NUMPY_TYPE_MAP
                  dtypes.append(("s" + str(i), self.byteorder + self.NUMPY_TYPE_MAP[typ]))
              else:
                  dtypes.append(("s" + str(i), "S" + str(typ)))
          self._dtype = np.dtype(dtypes)
  
          return self._dtype
  
      def _calcsize(self, fmt: int | str) -> int:
          if isinstance(fmt, int):
              return fmt
          return struct.calcsize(self.byteorder + fmt)
  
      def _decode(self, s: bytes) -> str:
          # have bytes not strings, so must decode
          s = s.partition(b"\0")[0]
          try:
              return s.decode(self._encoding)
          except UnicodeDecodeError:
              # GH 25960, fallback to handle incorrect format produced when 117
              # files are converted to 118 files in Stata
              encoding = self._encoding
              msg = f"""
  One or more strings in the dta file could not be decoded using {encoding}, and
  so the fallback encoding of latin-1 is being used.  This can happen when a file
  has been incorrectly encoded by Stata or some other software. You should verify
  the string values returned are correct."""
              warnings.warn(msg, UnicodeWarning)
              return s.decode("latin-1")
  
      def _read_value_labels(self) -> None:
          if self._value_labels_read:
              # Don't read twice
              return
          if self.format_version <= 108:
              # Value labels are not supported in version 108 and earlier.
              self._value_labels_read = True
              self.value_label_dict: dict[str, dict[float | int, str]] = {}
              return
  
          if self.format_version >= 117:
              self.path_or_buf.seek(self.seek_value_labels)
          else:
              assert self._dtype is not None
              offset = self.nobs * self._dtype.itemsize
              self.path_or_buf.seek(self.data_location + offset)
  
          self._value_labels_read = True
          self.value_label_dict = {}
  
          while True:
              if self.format_version >= 117:
                  if self.path_or_buf.read(5) == b"</val":  # <lbl>
                      break  # end of value label table
  
              slength = self.path_or_buf.read(4)
              if not slength:
                  break  # end of value label table (format < 117)
              if self.format_version <= 117:
                  labname = self._decode(self.path_or_buf.read(33))
              else:
                  labname = self._decode(self.path_or_buf.read(129))
              self.path_or_buf.read(3)  # padding
  
              n = struct.unpack(self.byteorder + "I", self.path_or_buf.read(4))[0]
              txtlen = struct.unpack(self.byteorder + "I", self.path_or_buf.read(4))[0]
              off = np.frombuffer(
                  self.path_or_buf.read(4 * n), dtype=self.byteorder + "i4", count=n
              )
              val = np.frombuffer(
                  self.path_or_buf.read(4 * n), dtype=self.byteorder + "i4", count=n
              )
              ii = np.argsort(off)
              off = off[ii]
              val = val[ii]
              txt = self.path_or_buf.read(txtlen)
              self.value_label_dict[labname] = {}
              for i in range(n):
                  end = off[i + 1] if i < n - 1 else txtlen
                  self.value_label_dict[labname][val[i]] = self._decode(txt[off[i] : end])
              if self.format_version >= 117:
                  self.path_or_buf.read(6)  # </lbl>
          self._value_labels_read = True
  
      def _read_strls(self) -> None:
          self.path_or_buf.seek(self.seek_strls)
          # Wrap v_o in a string to allow uint64 values as keys on 32bit OS
          self.GSO = {"0": ""}
          while True:
              if self.path_or_buf.read(3) != b"GSO":
                  break
  
              if self.format_version == 117:
                  v_o = struct.unpack(self.byteorder + "Q", self.path_or_buf.read(8))[0]
              else:
                  buf = self.path_or_buf.read(12)
                  # Only tested on little endian file on little endian machine.
                  v_size = 2 if self.format_version == 118 else 3
                  if self.byteorder == "<":
                      buf = buf[0:v_size] + buf[4 : (12 - v_size)]
                  else:
                      # This path may not be correct, impossible to test
                      buf = buf[0:v_size] + buf[(4 + v_size) :]
                  v_o = struct.unpack("Q", buf)[0]
              typ = struct.unpack("B", self.path_or_buf.read(1))[0]
              length = struct.unpack(self.byteorder + "I", self.path_or_buf.read(4))[0]
              va = self.path_or_buf.read(length)
              if typ == 130:
                  decoded_va = va[0:-1].decode(self._encoding)
              else:
                  # Stata says typ 129 can be binary, so use str
                  decoded_va = str(va)
                  # Wrap v_o in a string to allow uint64 values as keys on 32bit OS
              self.GSO[str(v_o)] = decoded_va
  
      def __next__(self) -> DataFrame:
          self._using_iterator = True
          return self.read(nrows=self._chunksize)
  
      def get_chunk(self, size: int | None = None) -> DataFrame:
          """
          Reads lines from Stata file and returns as dataframe
  
          Parameters
          ----------
          size : int, defaults to None
              Number of lines to read.  If None, reads whole file.
  
          Returns
          -------
          DataFrame
          """
          if size is None:
              size = self._chunksize
          return self.read(nrows=size)
  
      @Appender(_read_method_doc)
      def read(
          self,
          nrows: int | None = None,
          convert_dates: bool | None = None,
          convert_categoricals: bool | None = None,
          index_col: str | None = None,
          convert_missing: bool | None = None,
          preserve_dtypes: bool | None = None,
          columns: Sequence[str] | None = None,
          order_categoricals: bool | None = None,
      ) -> DataFrame:
          # Handle empty file or chunk.  If reading incrementally raise
          # StopIteration.  If reading the whole thing return an empty
          # data frame.
          if (self.nobs == 0) and (nrows is None):
              self._can_read_value_labels = True
              self._data_read = True
              self.close()
              return DataFrame(columns=self.varlist)
  
          # Handle options
          if convert_dates is None:
              convert_dates = self._convert_dates
          if convert_categoricals is None:
              convert_categoricals = self._convert_categoricals
          if convert_missing is None:
              convert_missing = self._convert_missing
          if preserve_dtypes is None:
              preserve_dtypes = self._preserve_dtypes
          if columns is None:
              columns = self._columns
          if order_categoricals is None:
              order_categoricals = self._order_categoricals
          if index_col is None:
              index_col = self._index_col
  
          if nrows is None:
              nrows = self.nobs
  
          if (self.format_version >= 117) and (not self._value_labels_read):
              self._can_read_value_labels = True
              self._read_strls()
  
          # Read data
          assert self._dtype is not None
          dtype = self._dtype
          max_read_len = (self.nobs - self._lines_read) * dtype.itemsize
          read_len = nrows * dtype.itemsize
          read_len = min(read_len, max_read_len)
          if read_len <= 0:
              # Iterator has finished, should never be here unless
              # we are reading the file incrementally
              if convert_categoricals:
                  self._read_value_labels()
              self.close()
              raise StopIteration
          offset = self._lines_read * dtype.itemsize
          self.path_or_buf.seek(self.data_location + offset)
          read_lines = min(nrows, self.nobs - self._lines_read)
          raw_data = np.frombuffer(
              self.path_or_buf.read(read_len), dtype=dtype, count=read_lines
          )
  
          self._lines_read += read_lines
          if self._lines_read == self.nobs:
              self._can_read_value_labels = True
              self._data_read = True
          # if necessary, swap the byte order to native here
          if self.byteorder != self._native_byteorder:
              raw_data = raw_data.byteswap().newbyteorder()
  
          if convert_categoricals:
              self._read_value_labels()
  
          if len(raw_data) == 0:
              data = DataFrame(columns=self.varlist)
          else:
              data = DataFrame.from_records(raw_data)
              data.columns = Index(self.varlist)
  
          # If index is not specified, use actual row number rather than
          # restarting at 0 for each chunk.
          if index_col is None:
              rng = np.arange(self._lines_read - read_lines, self._lines_read)
              data.index = Index(rng)  # set attr instead of set_index to avoid copy
  
          if columns is not None:
              try:
                  data = self._do_select_columns(data, columns)
              except ValueError:
                  self.close()
                  raise
  
          # Decode strings
          for col, typ in zip(data, self.typlist):
              if type(typ) is int:
                  data[col] = data[col].apply(self._decode, convert_dtype=True)
  
          data = self._insert_strls(data)
  
          cols_ = np.where([dtyp is not None for dtyp in self.dtyplist])[0]
          # Convert columns (if needed) to match input type
          ix = data.index
          requires_type_conversion = False
          data_formatted = []
          for i in cols_:
              if self.dtyplist[i] is not None:
                  col = data.columns[i]
                  dtype = data[col].dtype
                  if dtype != np.dtype(object) and dtype != self.dtyplist[i]:
                      requires_type_conversion = True
                      data_formatted.append(
                          (col, Series(data[col], ix, self.dtyplist[i]))
                      )
                  else:
                      data_formatted.append((col, data[col]))
          if requires_type_conversion:
              data = DataFrame.from_dict(dict(data_formatted))
          del data_formatted
  
          data = self._do_convert_missing(data, convert_missing)
  
          if convert_dates:
  
              def any_startswith(x: str) -> bool:
                  return any(x.startswith(fmt) for fmt in _date_formats)
  
              cols = np.where([any_startswith(x) for x in self.fmtlist])[0]
              for i in cols:
                  col = data.columns[i]
                  try:
                      data[col] = _stata_elapsed_date_to_datetime_vec(
                          data[col], self.fmtlist[i]
                      )
                  except ValueError:
                      self.close()
                      raise
  
          if convert_categoricals and self.format_version > 108:
              data = self._do_convert_categoricals(
                  data, self.value_label_dict, self.lbllist, order_categoricals
              )
  
          if not preserve_dtypes:
              retyped_data = []
              convert = False
              for col in data:
                  dtype = data[col].dtype
                  if dtype in (np.dtype(np.float16), np.dtype(np.float32)):
                      dtype = np.dtype(np.float64)
                      convert = True
                  elif dtype in (
                      np.dtype(np.int8),
                      np.dtype(np.int16),
                      np.dtype(np.int32),
                  ):
                      dtype = np.dtype(np.int64)
                      convert = True
                  retyped_data.append((col, data[col].astype(dtype)))
              if convert:
                  data = DataFrame.from_dict(dict(retyped_data))
  
          if index_col is not None:
              data = data.set_index(data.pop(index_col))
  
          return data
  
      def _do_convert_missing(self, data: DataFrame, convert_missing: bool) -> DataFrame:
          # Check for missing values, and replace if found
          replacements = {}
          for i, colname in enumerate(data):
              fmt = self.typlist[i]
              if fmt not in self.VALID_RANGE:
                  continue
  
              fmt = cast(str, fmt)  # only strs in VALID_RANGE
              nmin, nmax = self.VALID_RANGE[fmt]
              series = data[colname]
  
              # appreciably faster to do this with ndarray instead of Series
              svals = series._values
              missing = (svals < nmin) | (svals > nmax)
  
              if not missing.any():
                  continue
  
              if convert_missing:  # Replacement follows Stata notation
                  missing_loc = np.nonzero(np.asarray(missing))[0]
                  umissing, umissing_loc = np.unique(series[missing], return_inverse=True)
                  replacement = Series(series, dtype=object)
                  for j, um in enumerate(umissing):
                      missing_value = StataMissingValue(um)
  
                      loc = missing_loc[umissing_loc == j]
                      replacement.iloc[loc] = missing_value
              else:  # All replacements are identical
                  dtype = series.dtype
                  if dtype not in (np.float32, np.float64):
                      dtype = np.float64
                  replacement = Series(series, dtype=dtype)
                  if not replacement._values.flags["WRITEABLE"]:
                      # only relevant for ArrayManager; construction
                      #  path for BlockManager ensures writeability
                      replacement = replacement.copy()
                  # Note: operating on ._values is much faster than directly
                  # TODO: can we fix that?
                  replacement._values[missing] = np.nan
              replacements[colname] = replacement
  
          if replacements:
              for col in replacements:
                  data[col] = replacements[col]
          return data
  
      def _insert_strls(self, data: DataFrame) -> DataFrame:
          if not hasattr(self, "GSO") or len(self.GSO) == 0:
              return data
          for i, typ in enumerate(self.typlist):
              if typ != "Q":
                  continue
              # Wrap v_o in a string to allow uint64 values as keys on 32bit OS
              data.iloc[:, i] = [self.GSO[str(k)] for k in data.iloc[:, i]]
          return data
  
      def _do_select_columns(self, data: DataFrame, columns: Sequence[str]) -> DataFrame:
  
          if not self._column_selector_set:
              column_set = set(columns)
              if len(column_set) != len(columns):
                  raise ValueError("columns contains duplicate entries")
              unmatched = column_set.difference(data.columns)
              if unmatched:
                  joined = ", ".join(list(unmatched))
                  raise ValueError(
                      "The following columns were not "
                      f"found in the Stata data set: {joined}"
                  )
              # Copy information for retained columns for later processing
              dtyplist = []
              typlist = []
              fmtlist = []
              lbllist = []
              for col in columns:
                  i = data.columns.get_loc(col)
                  dtyplist.append(self.dtyplist[i])
                  typlist.append(self.typlist[i])
                  fmtlist.append(self.fmtlist[i])
                  lbllist.append(self.lbllist[i])
  
              self.dtyplist = dtyplist
              self.typlist = typlist
              self.fmtlist = fmtlist
              self.lbllist = lbllist
              self._column_selector_set = True
  
          return data[columns]
  
      def _do_convert_categoricals(
          self,
          data: DataFrame,
          value_label_dict: dict[str, dict[float | int, str]],
          lbllist: Sequence[str],
          order_categoricals: bool,
      ) -> DataFrame:
          """
          Converts categorical columns to Categorical type.
          """
          value_labels = list(value_label_dict.keys())
          cat_converted_data = []
          for col, label in zip(data, lbllist):
              if label in value_labels:
                  # Explicit call with ordered=True
                  vl = value_label_dict[label]
                  keys = np.array(list(vl.keys()))
                  column = data[col]
                  key_matches = column.isin(keys)
                  if self._using_iterator and key_matches.all():
                      initial_categories: np.ndarray | None = keys
                      # If all categories are in the keys and we are iterating,
                      # use the same keys for all chunks. If some are missing
                      # value labels, then we will fall back to the categories
                      # varying across chunks.
                  else:
                      if self._using_iterator:
                          # warn is using an iterator
                          warnings.warn(
                              categorical_conversion_warning, CategoricalConversionWarning
                          )
                      initial_categories = None
                  cat_data = Categorical(
                      column, categories=initial_categories, ordered=order_categoricals
                  )
                  if initial_categories is None:
                      # If None here, then we need to match the cats in the Categorical
                      categories = []
                      for category in cat_data.categories:
                          if category in vl:
                              categories.append(vl[category])
                          else:
                              categories.append(category)
                  else:
                      # If all cats are matched, we can use the values
                      categories = list(vl.values())
                  try:
                      # Try to catch duplicate categories
                      cat_data.categories = categories
                  except ValueError as err:
                      vc = Series(categories).value_counts()
                      repeated_cats = list(vc.index[vc > 1])
                      repeats = "-" * 80 + "\n" + "\n".join(repeated_cats)
                      # GH 25772
                      msg = f"""
  Value labels for column {col} are not unique. These cannot be converted to
  pandas categoricals.
  
  Either read the file with `convert_categoricals` set to False or use the
  low level interface in `StataReader` to separately read the values and the
  value_labels.
  
  The repeated labels are:
  {repeats}
  """
                      raise ValueError(msg) from err
                  # TODO: is the next line needed above in the data(...) method?
                  cat_series = Series(cat_data, index=data.index)
                  cat_converted_data.append((col, cat_series))
              else:
                  cat_converted_data.append((col, data[col]))
          data = DataFrame(dict(cat_converted_data), copy=False)
          return data
  
      @property
      def data_label(self) -> str:
          """
          Return data label of Stata file.
          """
          return self._data_label
  
      def variable_labels(self) -> dict[str, str]:
          """
          Return variable labels as a dict, associating each variable name
          with corresponding label.
  
          Returns
          -------
          dict
          """
          return dict(zip(self.varlist, self._variable_labels))
  
      def value_labels(self) -> dict[str, dict[float | int, str]]:
          """
          Return a dict, associating each variable name a dict, associating
          each value its corresponding label.
  
          Returns
          -------
          dict
          """
          if not self._value_labels_read:
              self._read_value_labels()
  
          return self.value_label_dict
  
  
  @Appender(_read_stata_doc)
  def read_stata(
      filepath_or_buffer: FilePath | ReadBuffer[bytes],
      convert_dates: bool = True,
      convert_categoricals: bool = True,
      index_col: str | None = None,
      convert_missing: bool = False,
      preserve_dtypes: bool = True,
      columns: Sequence[str] | None = None,
      order_categoricals: bool = True,
      chunksize: int | None = None,
      iterator: bool = False,
      compression: CompressionOptions = "infer",
      storage_options: StorageOptions = None,
  ) -> DataFrame | StataReader:
  
      reader = StataReader(
          filepath_or_buffer,
          convert_dates=convert_dates,
          convert_categoricals=convert_categoricals,
          index_col=index_col,
          convert_missing=convert_missing,
          preserve_dtypes=preserve_dtypes,
          columns=columns,
          order_categoricals=order_categoricals,
          chunksize=chunksize,
          storage_options=storage_options,
          compression=compression,
      )
  
      if iterator or chunksize:
          return reader
  
      with reader:
          return reader.read()
  
  
  def _set_endianness(endianness: str) -> str:
      if endianness.lower() in ["<", "little"]:
          return "<"
      elif endianness.lower() in [">", "big"]:
          return ">"
      else:  # pragma : no cover
          raise ValueError(f"Endianness {endianness} not understood")
  
  
  def _pad_bytes(name: AnyStr, length: int) -> AnyStr:
      """
      Take a char string and pads it with null bytes until it's length chars.
      """
      if isinstance(name, bytes):
          return name + b"\x00" * (length - len(name))
      return name + "\x00" * (length - len(name))
  
  
  def _convert_datetime_to_stata_type(fmt: str) -> np.dtype:
      """
      Convert from one of the stata date formats to a type in TYPE_MAP.
      """
      if fmt in [
          "tc",
          "%tc",
          "td",
          "%td",
          "tw",
          "%tw",
          "tm",
          "%tm",
          "tq",
          "%tq",
          "th",
          "%th",
          "ty",
          "%ty",
      ]:
          return np.dtype(np.float64)  # Stata expects doubles for SIFs
      else:
          raise NotImplementedError(f"Format {fmt} not implemented")
  
  
  def _maybe_convert_to_int_keys(convert_dates: dict, varlist: list[Hashable]) -> dict:
      new_dict = {}
      for key in convert_dates:
          if not convert_dates[key].startswith("%"):  # make sure proper fmts
              convert_dates[key] = "%" + convert_dates[key]
          if key in varlist:
              new_dict.update({varlist.index(key): convert_dates[key]})
          else:
              if not isinstance(key, int):
                  raise ValueError("convert_dates key must be a column or an integer")
              new_dict.update({key: convert_dates[key]})
      return new_dict
  
  
  def _dtype_to_stata_type(dtype: np.dtype, column: Series) -> int:
      """
      Convert dtype types to stata types. Returns the byte of the given ordinal.
      See TYPE_MAP and comments for an explanation. This is also explained in
      the dta spec.
      1 - 244 are strings of this length
                           Pandas    Stata
      251 - for int8      byte
      252 - for int16     int
      253 - for int32     long
      254 - for float32   float
      255 - for double    double
  
      If there are dates to convert, then dtype will already have the correct
      type inserted.
      """
      # TODO: expand to handle datetime to integer conversion
      if dtype.type is np.object_:  # try to coerce it to the biggest string
          # not memory efficient, what else could we
          # do?
          itemsize = max_len_string_array(ensure_object(column._values))
          return max(itemsize, 1)
      elif dtype.type is np.float64:
          return 255
      elif dtype.type is np.float32:
          return 254
      elif dtype.type is np.int32:
          return 253
      elif dtype.type is np.int16:
          return 252
      elif dtype.type is np.int8:
          return 251
      else:  # pragma : no cover
          raise NotImplementedError(f"Data type {dtype} not supported.")
  
  
  def _dtype_to_default_stata_fmt(
      dtype, column: Series, dta_version: int = 114, force_strl: bool = False
  ) -> str:
      """
      Map numpy dtype to stata's default format for this type. Not terribly
      important since users can change this in Stata. Semantics are
  
      object  -> "%DDs" where DD is the length of the string.  If not a string,
                  raise ValueError
      float64 -> "%10.0g"
      float32 -> "%9.0g"
      int64   -> "%9.0g"
      int32   -> "%12.0g"
      int16   -> "%8.0g"
      int8    -> "%8.0g"
      strl    -> "%9s"
      """
      # TODO: Refactor to combine type with format
      # TODO: expand this to handle a default datetime format?
      if dta_version < 117:
          max_str_len = 244
      else:
          max_str_len = 2045
          if force_strl:
              return "%9s"
      if dtype.type is np.object_:
          itemsize = max_len_string_array(ensure_object(column._values))
          if itemsize > max_str_len:
              if dta_version >= 117:
                  return "%9s"
              else:
                  raise ValueError(excessive_string_length_error.format(column.name))
          return "%" + str(max(itemsize, 1)) + "s"
      elif dtype == np.float64:
          return "%10.0g"
      elif dtype == np.float32:
          return "%9.0g"
      elif dtype == np.int32:
          return "%12.0g"
      elif dtype == np.int8 or dtype == np.int16:
          return "%8.0g"
      else:  # pragma : no cover
          raise NotImplementedError(f"Data type {dtype} not supported.")
  
  
  @doc(
      storage_options=_shared_docs["storage_options"],
      compression_options=_shared_docs["compression_options"] % "fname",
  )
  class StataWriter(StataParser):
      """
      A class for writing Stata binary dta files
  
      Parameters
      ----------
      fname : path (string), buffer or path object
          string, path object (pathlib.Path or py._path.local.LocalPath) or
          object implementing a binary write() functions. If using a buffer
          then the buffer will not be automatically closed after the file
          is written.
      data : DataFrame
          Input to save
      convert_dates : dict
          Dictionary mapping columns containing datetime types to stata internal
          format to use when writing the dates. Options are 'tc', 'td', 'tm',
          'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name.
          Datetime columns that do not have a conversion type specified will be
          converted to 'tc'. Raises NotImplementedError if a datetime column has
          timezone information
      write_index : bool
          Write the index to Stata dataset.
      byteorder : str
          Can be ">", "<", "little", or "big". default is `sys.byteorder`
      time_stamp : datetime
          A datetime to use as file creation date.  Default is the current time
      data_label : str
          A label for the data set.  Must be 80 characters or smaller.
      variable_labels : dict
          Dictionary containing columns as keys and variable labels as values.
          Each label must be 80 characters or smaller.
      {compression_options}
  
          .. versionadded:: 1.1.0
  
          .. versionchanged:: 1.4.0 Zstandard support.
  
      {storage_options}
  
          .. versionadded:: 1.2.0
  
      value_labels : dict of dicts
          Dictionary containing columns as keys and dictionaries of column value
          to labels as values. The combined length of all labels for a single
          variable must be 32,000 characters or smaller.
  
          .. versionadded:: 1.4.0
  
      Returns
      -------
      writer : StataWriter instance
          The StataWriter instance has a write_file method, which will
          write the file to the given `fname`.
  
      Raises
      ------
      NotImplementedError
          * If datetimes contain timezone information
      ValueError
          * Columns listed in convert_dates are neither datetime64[ns]
            or datetime.datetime
          * Column dtype is not representable in Stata
          * Column listed in convert_dates is not in DataFrame
          * Categorical label contains more than 32,000 characters
  
      Examples
      --------
      >>> data = pd.DataFrame([[1.0, 1]], columns=['a', 'b'])
      >>> writer = StataWriter('./data_file.dta', data)
      >>> writer.write_file()
  
      Directly write a zip file
      >>> compression = {{"method": "zip", "archive_name": "data_file.dta"}}
      >>> writer = StataWriter('./data_file.zip', data, compression=compression)
      >>> writer.write_file()
  
      Save a DataFrame with dates
      >>> from datetime import datetime
      >>> data = pd.DataFrame([[datetime(2000,1,1)]], columns=['date'])
      >>> writer = StataWriter('./date_data_file.dta', data, {{'date' : 'tw'}})
      >>> writer.write_file()
      """
  
      _max_string_length = 244
      _encoding = "latin-1"
  
      def __init__(
          self,
          fname: FilePath | WriteBuffer[bytes],
          data: DataFrame,
          convert_dates: dict[Hashable, str] | None = None,
          write_index: bool = True,
          byteorder: str | None = None,
          time_stamp: datetime.datetime | None = None,
          data_label: str | None = None,
          variable_labels: dict[Hashable, str] | None = None,
          compression: CompressionOptions = "infer",
          storage_options: StorageOptions = None,
          *,
          value_labels: dict[Hashable, dict[float | int, str]] | None = None,
      ):
          super().__init__()
          self.data = data
          self._convert_dates = {} if convert_dates is None else convert_dates
          self._write_index = write_index
          self._time_stamp = time_stamp
          self._data_label = data_label
          self._variable_labels = variable_labels
          self._non_cat_value_labels = value_labels
          self._value_labels: list[StataValueLabel] = []
          self._has_value_labels = np.array([], dtype=bool)
          self._compression = compression
          self._output_file: IO[bytes] | None = None
          self._converted_names: dict[Hashable, str] = {}
          # attach nobs, nvars, data, varlist, typlist
          self._prepare_pandas(data)
          self.storage_options = storage_options
  
          if byteorder is None:
              byteorder = sys.byteorder
          self._byteorder = _set_endianness(byteorder)
          self._fname = fname
          self.type_converters = {253: np.int32, 252: np.int16, 251: np.int8}
  
      def _write(self, to_write: str) -> None:
          """
          Helper to call encode before writing to file for Python 3 compat.
          """
          self.handles.handle.write(to_write.encode(self._encoding))
  
      def _write_bytes(self, value: bytes) -> None:
          """
          Helper to assert file is open before writing.
          """
          self.handles.handle.write(value)
  
      def _prepare_non_cat_value_labels(
          self, data: DataFrame
      ) -> list[StataNonCatValueLabel]:
          """
          Check for value labels provided for non-categorical columns. Value
          labels
          """
          non_cat_value_labels: list[StataNonCatValueLabel] = []
          if self._non_cat_value_labels is None:
              return non_cat_value_labels
  
          for labname, labels in self._non_cat_value_labels.items():
              if labname in self._converted_names:
                  colname = self._converted_names[labname]
              elif labname in data.columns:
                  colname = str(labname)
              else:
                  raise KeyError(
                      f"Can't create value labels for {labname}, it wasn't "
                      "found in the dataset."
                  )
  
              if not is_numeric_dtype(data[colname].dtype):
                  # Labels should not be passed explicitly for categorical
                  # columns that will be converted to int
                  raise ValueError(
                      f"Can't create value labels for {labname}, value labels "
                      "can only be applied to numeric columns."
                  )
              svl = StataNonCatValueLabel(colname, labels)
              non_cat_value_labels.append(svl)
          return non_cat_value_labels
  
      def _prepare_categoricals(self, data: DataFrame) -> DataFrame:
          """
          Check for categorical columns, retain categorical information for
          Stata file and convert categorical data to int
          """
          is_cat = [is_categorical_dtype(data[col].dtype) for col in data]
          if not any(is_cat):
              return data
  
          self._has_value_labels |= np.array(is_cat)
  
          get_base_missing_value = StataMissingValue.get_base_missing_value
          data_formatted = []
          for col, col_is_cat in zip(data, is_cat):
              if col_is_cat:
                  svl = StataValueLabel(data[col], encoding=self._encoding)
                  self._value_labels.append(svl)
                  dtype = data[col].cat.codes.dtype
                  if dtype == np.int64:
                      raise ValueError(
                          "It is not possible to export "
                          "int64-based categorical data to Stata."
                      )
                  values = data[col].cat.codes._values.copy()
  
                  # Upcast if needed so that correct missing values can be set
                  if values.max() >= get_base_missing_value(dtype):
                      if dtype == np.int8:
                          dtype = np.dtype(np.int16)
                      elif dtype == np.int16:
                          dtype = np.dtype(np.int32)
                      else:
                          dtype = np.dtype(np.float64)
                      values = np.array(values, dtype=dtype)
  
                  # Replace missing values with Stata missing value for type
                  values[values == -1] = get_base_missing_value(dtype)
                  data_formatted.append((col, values))
              else:
                  data_formatted.append((col, data[col]))
          return DataFrame.from_dict(dict(data_formatted))
  
      def _replace_nans(self, data: DataFrame) -> DataFrame:
          # return data
          """
          Checks floating point data columns for nans, and replaces these with
          the generic Stata for missing value (.)
          """
          for c in data:
              dtype = data[c].dtype
              if dtype in (np.float32, np.float64):
                  if dtype == np.float32:
                      replacement = self.MISSING_VALUES["f"]
                  else:
                      replacement = self.MISSING_VALUES["d"]
                  data[c] = data[c].fillna(replacement)
  
          return data
  
      def _update_strl_names(self) -> None:
          """No-op, forward compatibility"""
          pass
  
      def _validate_variable_name(self, name: str) -> str:
          """
          Validate variable names for Stata export.
  
          Parameters
          ----------
          name : str
              Variable name
  
          Returns
          -------
          str
              The validated name with invalid characters replaced with
              underscores.
  
          Notes
          -----
          Stata 114 and 117 support ascii characters in a-z, A-Z, 0-9
          and _.
          """
          for c in name:
              if (
                  (c < "A" or c > "Z")
                  and (c < "a" or c > "z")
                  and (c < "0" or c > "9")
                  and c != "_"
              ):
                  name = name.replace(c, "_")
          return name
  
      def _check_column_names(self, data: DataFrame) -> DataFrame:
          """
          Checks column names to ensure that they are valid Stata column names.
          This includes checks for:
              * Non-string names
              * Stata keywords
              * Variables that start with numbers
              * Variables with names that are too long
  
          When an illegal variable name is detected, it is converted, and if
          dates are exported, the variable name is propagated to the date
          conversion dictionary
          """
          converted_names: dict[Hashable, str] = {}
          columns = list(data.columns)
          original_columns = columns[:]
  
          duplicate_var_id = 0
          for j, name in enumerate(columns):
              orig_name = name
              if not isinstance(name, str):
                  name = str(name)
  
              name = self._validate_variable_name(name)
  
              # Variable name must not be a reserved word
              if name in self.RESERVED_WORDS:
                  name = "_" + name
  
              # Variable name may not start with a number
              if "0" <= name[0] <= "9":
                  name = "_" + name
  
              name = name[: min(len(name), 32)]
  
              if not name == orig_name:
                  # check for duplicates
                  while columns.count(name) > 0:
                      # prepend ascending number to avoid duplicates
                      name = "_" + str(duplicate_var_id) + name
                      name = name[: min(len(name), 32)]
                      duplicate_var_id += 1
                  converted_names[orig_name] = name
  
              columns[j] = name
  
          data.columns = Index(columns)
  
          # Check date conversion, and fix key if needed
          if self._convert_dates:
              for c, o in zip(columns, original_columns):
                  if c != o:
                      self._convert_dates[c] = self._convert_dates[o]
                      del self._convert_dates[o]
  
          if converted_names:
              conversion_warning = []
              for orig_name, name in converted_names.items():
                  msg = f"{orig_name}   ->   {name}"
                  conversion_warning.append(msg)
  
              ws = invalid_name_doc.format("\n    ".join(conversion_warning))
              warnings.warn(ws, InvalidColumnName)
  
          self._converted_names = converted_names
          self._update_strl_names()
  
          return data
  
      def _set_formats_and_types(self, dtypes: Series) -> None:
          self.fmtlist: list[str] = []
          self.typlist: list[int] = []
          for col, dtype in dtypes.items():
              self.fmtlist.append(_dtype_to_default_stata_fmt(dtype, self.data[col]))
              self.typlist.append(_dtype_to_stata_type(dtype, self.data[col]))
  
      def _prepare_pandas(self, data: DataFrame) -> None:
          # NOTE: we might need a different API / class for pandas objects so
          # we can set different semantics - handle this with a PR to pandas.io
  
          data = data.copy()
  
          if self._write_index:
              temp = data.reset_index()
              if isinstance(temp, DataFrame):
                  data = temp
  
          # Ensure column names are strings
          data = self._check_column_names(data)
  
          # Check columns for compatibility with stata, upcast if necessary
          # Raise if outside the supported range
          data = _cast_to_stata_types(data)
  
          # Replace NaNs with Stata missing values
          data = self._replace_nans(data)
  
          # Set all columns to initially unlabelled
          self._has_value_labels = np.repeat(False, data.shape[1])
  
          # Create value labels for non-categorical data
          non_cat_value_labels = self._prepare_non_cat_value_labels(data)
  
          non_cat_columns = [svl.labname for svl in non_cat_value_labels]
          has_non_cat_val_labels = data.columns.isin(non_cat_columns)
          self._has_value_labels |= has_non_cat_val_labels
          self._value_labels.extend(non_cat_value_labels)
  
          # Convert categoricals to int data, and strip labels
          data = self._prepare_categoricals(data)
  
          self.nobs, self.nvar = data.shape
          self.data = data
          self.varlist = data.columns.tolist()
  
          dtypes = data.dtypes
  
          # Ensure all date columns are converted
          for col in data:
              if col in self._convert_dates:
                  continue
              if is_datetime64_dtype(data[col]):
                  self._convert_dates[col] = "tc"
  
          self._convert_dates = _maybe_convert_to_int_keys(
              self._convert_dates, self.varlist
          )
          for key in self._convert_dates:
              new_type = _convert_datetime_to_stata_type(self._convert_dates[key])
              dtypes[key] = np.dtype(new_type)
  
          # Verify object arrays are strings and encode to bytes
          self._encode_strings()
  
          self._set_formats_and_types(dtypes)
  
          # set the given format for the datetime cols
          if self._convert_dates is not None:
              for key in self._convert_dates:
                  if isinstance(key, int):
                      self.fmtlist[key] = self._convert_dates[key]
  
      def _encode_strings(self) -> None:
          """
          Encode strings in dta-specific encoding
  
          Do not encode columns marked for date conversion or for strL
          conversion. The strL converter independently handles conversion and
          also accepts empty string arrays.
          """
          convert_dates = self._convert_dates
          # _convert_strl is not available in dta 114
          convert_strl = getattr(self, "_convert_strl", [])
          for i, col in enumerate(self.data):
              # Skip columns marked for date conversion or strl conversion
              if i in convert_dates or col in convert_strl:
                  continue
              column = self.data[col]
              dtype = column.dtype
              if dtype.type is np.object_:
                  inferred_dtype = infer_dtype(column, skipna=True)
                  if not ((inferred_dtype == "string") or len(column) == 0):
                      col = column.name
                      raise ValueError(
                          f"""\
  Column `{col}` cannot be exported.\n\nOnly string-like object arrays
  containing all strings or a mix of strings and None can be exported.
  Object arrays containing only null values are prohibited. Other object
  types cannot be exported and must first be converted to one of the
  supported types."""
                      )
                  encoded = self.data[col].str.encode(self._encoding)
                  # If larger than _max_string_length do nothing
                  if (
                      max_len_string_array(ensure_object(encoded._values))
                      <= self._max_string_length
                  ):
                      self.data[col] = encoded
  
      def write_file(self) -> None:
          """
          Export DataFrame object to Stata dta format.
          """
          with get_handle(
              self._fname,
              "wb",
              compression=self._compression,
              is_text=False,
              storage_options=self.storage_options,
          ) as self.handles:
  
              if self.handles.compression["method"] is not None:
                  # ZipFile creates a file (with the same name) for each write call.
                  # Write it first into a buffer and then write the buffer to the ZipFile.
                  self._output_file, self.handles.handle = self.handles.handle, BytesIO()
                  self.handles.created_handles.append(self.handles.handle)
  
              try:
                  self._write_header(
                      data_label=self._data_label, time_stamp=self._time_stamp
                  )
                  self._write_map()
                  self._write_variable_types()
                  self._write_varnames()
                  self._write_sortlist()
                  self._write_formats()
                  self._write_value_label_names()
                  self._write_variable_labels()
                  self._write_expansion_fields()
                  self._write_characteristics()
                  records = self._prepare_data()
                  self._write_data(records)
                  self._write_strls()
                  self._write_value_labels()
                  self._write_file_close_tag()
                  self._write_map()
                  self._close()
              except Exception as exc:
                  self.handles.close()
                  if isinstance(self._fname, (str, os.PathLike)) and os.path.isfile(
                      self._fname
                  ):
                      try:
                          os.unlink(self._fname)
                      except OSError:
                          warnings.warn(
                              f"This save was not successful but {self._fname} could not "
                              "be deleted. This file is not valid.",
                              ResourceWarning,
                          )
                  raise exc
  
      def _close(self) -> None:
          """
          Close the file if it was created by the writer.
  
          If a buffer or file-like object was passed in, for example a GzipFile,
          then leave this file open for the caller to close.
          """
          # write compression
          if self._output_file is not None:
              assert isinstance(self.handles.handle, BytesIO)
              bio, self.handles.handle = self.handles.handle, self._output_file
              self.handles.handle.write(bio.getvalue())
  
      def _write_map(self) -> None:
          """No-op, future compatibility"""
          pass
  
      def _write_file_close_tag(self) -> None:
          """No-op, future compatibility"""
          pass
  
      def _write_characteristics(self) -> None:
          """No-op, future compatibility"""
          pass
  
      def _write_strls(self) -> None:
          """No-op, future compatibility"""
          pass
  
      def _write_expansion_fields(self) -> None:
          """Write 5 zeros for expansion fields"""
          self._write(_pad_bytes("", 5))
  
      def _write_value_labels(self) -> None:
          for vl in self._value_labels:
              self._write_bytes(vl.generate_value_label(self._byteorder))
  
      def _write_header(
          self,
          data_label: str | None = None,
          time_stamp: datetime.datetime | None = None,
      ) -> None:
          byteorder = self._byteorder
          # ds_format - just use 114
          self._write_bytes(struct.pack("b", 114))
          # byteorder
          self._write(byteorder == ">" and "\x01" or "\x02")
          # filetype
          self._write("\x01")
          # unused
          self._write("\x00")
          # number of vars, 2 bytes
          self._write_bytes(struct.pack(byteorder + "h", self.nvar)[:2])
          # number of obs, 4 bytes
          self._write_bytes(struct.pack(byteorder + "i", self.nobs)[:4])
          # data label 81 bytes, char, null terminated
          if data_label is None:
              self._write_bytes(self._null_terminate_bytes(_pad_bytes("", 80)))
          else:
              self._write_bytes(
                  self._null_terminate_bytes(_pad_bytes(data_label[:80], 80))
              )
          # time stamp, 18 bytes, char, null terminated
          # format dd Mon yyyy hh:mm
          if time_stamp is None:
              time_stamp = datetime.datetime.now()
          elif not isinstance(time_stamp, datetime.datetime):
              raise ValueError("time_stamp should be datetime type")
          # GH #13856
          # Avoid locale-specific month conversion
          months = [
              "Jan",
              "Feb",
              "Mar",
              "Apr",
              "May",
              "Jun",
              "Jul",
              "Aug",
              "Sep",
              "Oct",
              "Nov",
              "Dec",
          ]
          month_lookup = {i + 1: month for i, month in enumerate(months)}
          ts = (
              time_stamp.strftime("%d ")
              + month_lookup[time_stamp.month]
              + time_stamp.strftime(" %Y %H:%M")
          )
          self._write_bytes(self._null_terminate_bytes(ts))
  
      def _write_variable_types(self) -> None:
          for typ in self.typlist:
              self._write_bytes(struct.pack("B", typ))
  
      def _write_varnames(self) -> None:
          # varlist names are checked by _check_column_names
          # varlist, requires null terminated
          for name in self.varlist:
              name = self._null_terminate_str(name)
              name = _pad_bytes(name[:32], 33)
              self._write(name)
  
      def _write_sortlist(self) -> None:
          # srtlist, 2*(nvar+1), int array, encoded by byteorder
          srtlist = _pad_bytes("", 2 * (self.nvar + 1))
          self._write(srtlist)
  
      def _write_formats(self) -> None:
          # fmtlist, 49*nvar, char array
          for fmt in self.fmtlist:
              self._write(_pad_bytes(fmt, 49))
  
      def _write_value_label_names(self) -> None:
          # lbllist, 33*nvar, char array
          for i in range(self.nvar):
              # Use variable name when categorical
              if self._has_value_labels[i]:
                  name = self.varlist[i]
                  name = self._null_terminate_str(name)
                  name = _pad_bytes(name[:32], 33)
                  self._write(name)
              else:  # Default is empty label
                  self._write(_pad_bytes("", 33))
  
      def _write_variable_labels(self) -> None:
          # Missing labels are 80 blank characters plus null termination
          blank = _pad_bytes("", 81)
  
          if self._variable_labels is None:
              for i in range(self.nvar):
                  self._write(blank)
              return
  
          for col in self.data:
              if col in self._variable_labels:
                  label = self._variable_labels[col]
                  if len(label) > 80:
                      raise ValueError("Variable labels must be 80 characters or fewer")
                  is_latin1 = all(ord(c) < 256 for c in label)
                  if not is_latin1:
                      raise ValueError(
                          "Variable labels must contain only characters that "
                          "can be encoded in Latin-1"
                      )
                  self._write(_pad_bytes(label, 81))
              else:
                  self._write(blank)
  
      def _convert_strls(self, data: DataFrame) -> DataFrame:
          """No-op, future compatibility"""
          return data
  
      def _prepare_data(self) -> np.recarray:
          data = self.data
          typlist = self.typlist
          convert_dates = self._convert_dates
          # 1. Convert dates
          if self._convert_dates is not None:
              for i, col in enumerate(data):
                  if i in convert_dates:
                      data[col] = _datetime_to_stata_elapsed_vec(
                          data[col], self.fmtlist[i]
                      )
          # 2. Convert strls
          data = self._convert_strls(data)
  
          # 3. Convert bad string data to '' and pad to correct length
          dtypes = {}
          native_byteorder = self._byteorder == _set_endianness(sys.byteorder)
          for i, col in enumerate(data):
              typ = typlist[i]
              if typ <= self._max_string_length:
                  data[col] = data[col].fillna("").apply(_pad_bytes, args=(typ,))
                  stype = f"S{typ}"
                  dtypes[col] = stype
                  data[col] = data[col].astype(stype)
              else:
                  dtype = data[col].dtype
                  if not native_byteorder:
                      dtype = dtype.newbyteorder(self._byteorder)
                  dtypes[col] = dtype
  
          return data.to_records(index=False, column_dtypes=dtypes)
  
      def _write_data(self, records: np.recarray) -> None:
          self._write_bytes(records.tobytes())
  
      @staticmethod
      def _null_terminate_str(s: str) -> str:
          s += "\x00"
          return s
  
      def _null_terminate_bytes(self, s: str) -> bytes:
          return self._null_terminate_str(s).encode(self._encoding)
  
  
  def _dtype_to_stata_type_117(dtype: np.dtype, column: Series, force_strl: bool) -> int:
      """
      Converts dtype types to stata types. Returns the byte of the given ordinal.
      See TYPE_MAP and comments for an explanation. This is also explained in
      the dta spec.
      1 - 2045 are strings of this length
                  Pandas    Stata
      32768 - for object    strL
      65526 - for int8      byte
      65527 - for int16     int
      65528 - for int32     long
      65529 - for float32   float
      65530 - for double    double
  
      If there are dates to convert, then dtype will already have the correct
      type inserted.
      """
      # TODO: expand to handle datetime to integer conversion
      if force_strl:
          return 32768
      if dtype.type is np.object_:  # try to coerce it to the biggest string
          # not memory efficient, what else could we
          # do?
          itemsize = max_len_string_array(ensure_object(column._values))
          itemsize = max(itemsize, 1)
          if itemsize <= 2045:
              return itemsize
          return 32768
      elif dtype.type is np.float64:
          return 65526
      elif dtype.type is np.float32:
          return 65527
      elif dtype.type is np.int32:
          return 65528
      elif dtype.type is np.int16:
          return 65529
      elif dtype.type is np.int8:
          return 65530
      else:  # pragma : no cover
          raise NotImplementedError(f"Data type {dtype} not supported.")
  
  
  def _pad_bytes_new(name: str | bytes, length: int) -> bytes:
      """
      Takes a bytes instance and pads it with null bytes until it's length chars.
      """
      if isinstance(name, str):
          name = bytes(name, "utf-8")
      return name + b"\x00" * (length - len(name))
  
  
  class StataStrLWriter:
      """
      Converter for Stata StrLs
  
      Stata StrLs map 8 byte values to strings which are stored using a
      dictionary-like format where strings are keyed to two values.
  
      Parameters
      ----------
      df : DataFrame
          DataFrame to convert
      columns : Sequence[str]
          List of columns names to convert to StrL
      version : int, optional
          dta version.  Currently supports 117, 118 and 119
      byteorder : str, optional
          Can be ">", "<", "little", or "big". default is `sys.byteorder`
  
      Notes
      -----
      Supports creation of the StrL block of a dta file for dta versions
      117, 118 and 119.  These differ in how the GSO is stored.  118 and
      119 store the GSO lookup value as a uint32 and a uint64, while 117
      uses two uint32s. 118 and 119 also encode all strings as unicode
      which is required by the format.  117 uses 'latin-1' a fixed width
      encoding that extends the 7-bit ascii table with an additional 128
      characters.
      """
  
      def __init__(
          self,
          df: DataFrame,
          columns: Sequence[str],
          version: int = 117,
          byteorder: str | None = None,
      ):
          if version not in (117, 118, 119):
              raise ValueError("Only dta versions 117, 118 and 119 supported")
          self._dta_ver = version
  
          self.df = df
          self.columns = columns
          self._gso_table = {"": (0, 0)}
          if byteorder is None:
              byteorder = sys.byteorder
          self._byteorder = _set_endianness(byteorder)
  
          gso_v_type = "I"  # uint32
          gso_o_type = "Q"  # uint64
          self._encoding = "utf-8"
          if version == 117:
              o_size = 4
              gso_o_type = "I"  # 117 used uint32
              self._encoding = "latin-1"
          elif version == 118:
              o_size = 6
          else:  # version == 119
              o_size = 5
          self._o_offet = 2 ** (8 * (8 - o_size))
          self._gso_o_type = gso_o_type
          self._gso_v_type = gso_v_type
  
      def _convert_key(self, key: tuple[int, int]) -> int:
          v, o = key
          return v + self._o_offet * o
  
      def generate_table(self) -> tuple[dict[str, tuple[int, int]], DataFrame]:
          """
          Generates the GSO lookup table for the DataFrame
  
          Returns
          -------
          gso_table : dict
              Ordered dictionary using the string found as keys
              and their lookup position (v,o) as values
          gso_df : DataFrame
              DataFrame where strl columns have been converted to
              (v,o) values
  
          Notes
          -----
          Modifies the DataFrame in-place.
  
          The DataFrame returned encodes the (v,o) values as uint64s. The
          encoding depends on the dta version, and can be expressed as
  
          enc = v + o * 2 ** (o_size * 8)
  
          so that v is stored in the lower bits and o is in the upper
          bits. o_size is
  
            * 117: 4
            * 118: 6
            * 119: 5
          """
          gso_table = self._gso_table
          gso_df = self.df
          columns = list(gso_df.columns)
          selected = gso_df[self.columns]
          col_index = [(col, columns.index(col)) for col in self.columns]
          keys = np.empty(selected.shape, dtype=np.uint64)
          for o, (idx, row) in enumerate(selected.iterrows()):
              for j, (col, v) in enumerate(col_index):
                  val = row[col]
                  # Allow columns with mixed str and None (GH 23633)
                  val = "" if val is None else val
                  key = gso_table.get(val, None)
                  if key is None:
                      # Stata prefers human numbers
                      key = (v + 1, o + 1)
                      gso_table[val] = key
                  keys[o, j] = self._convert_key(key)
          for i, col in enumerate(self.columns):
              gso_df[col] = keys[:, i]
  
          return gso_table, gso_df
  
      def generate_blob(self, gso_table: dict[str, tuple[int, int]]) -> bytes:
          """
          Generates the binary blob of GSOs that is written to the dta file.
  
          Parameters
          ----------
          gso_table : dict
              Ordered dictionary (str, vo)
  
          Returns
          -------
          gso : bytes
              Binary content of dta file to be placed between strl tags
  
          Notes
          -----
          Output format depends on dta version.  117 uses two uint32s to
          express v and o while 118+ uses a uint32 for v and a uint64 for o.
          """
          # Format information
          # Length includes null term
          # 117
          # GSOvvvvooootllllxxxxxxxxxxxxxxx...x
          #  3  u4  u4 u1 u4  string + null term
          #
          # 118, 119
          # GSOvvvvooooooootllllxxxxxxxxxxxxxxx...x
          #  3  u4   u8   u1 u4    string + null term
  
          bio = BytesIO()
          gso = bytes("GSO", "ascii")
          gso_type = struct.pack(self._byteorder + "B", 130)
          null = struct.pack(self._byteorder + "B", 0)
          v_type = self._byteorder + self._gso_v_type
          o_type = self._byteorder + self._gso_o_type
          len_type = self._byteorder + "I"
          for strl, vo in gso_table.items():
              if vo == (0, 0):
                  continue
              v, o = vo
  
              # GSO
              bio.write(gso)
  
              # vvvv
              bio.write(struct.pack(v_type, v))
  
              # oooo / oooooooo
              bio.write(struct.pack(o_type, o))
  
              # t
              bio.write(gso_type)
  
              # llll
              utf8_string = bytes(strl, "utf-8")
              bio.write(struct.pack(len_type, len(utf8_string) + 1))
  
              # xxx...xxx
              bio.write(utf8_string)
              bio.write(null)
  
          return bio.getvalue()
  
  
  class StataWriter117(StataWriter):
      """
      A class for writing Stata binary dta files in Stata 13 format (117)
  
      Parameters
      ----------
      fname : path (string), buffer or path object
          string, path object (pathlib.Path or py._path.local.LocalPath) or
          object implementing a binary write() functions. If using a buffer
          then the buffer will not be automatically closed after the file
          is written.
      data : DataFrame
          Input to save
      convert_dates : dict
          Dictionary mapping columns containing datetime types to stata internal
          format to use when writing the dates. Options are 'tc', 'td', 'tm',
          'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name.
          Datetime columns that do not have a conversion type specified will be
          converted to 'tc'. Raises NotImplementedError if a datetime column has
          timezone information
      write_index : bool
          Write the index to Stata dataset.
      byteorder : str
          Can be ">", "<", "little", or "big". default is `sys.byteorder`
      time_stamp : datetime
          A datetime to use as file creation date.  Default is the current time
      data_label : str
          A label for the data set.  Must be 80 characters or smaller.
      variable_labels : dict
          Dictionary containing columns as keys and variable labels as values.
          Each label must be 80 characters or smaller.
      convert_strl : list
          List of columns names to convert to Stata StrL format.  Columns with
          more than 2045 characters are automatically written as StrL.
          Smaller columns can be converted by including the column name.  Using
          StrLs can reduce output file size when strings are longer than 8
          characters, and either frequently repeated or sparse.
      {compression_options}
  
          .. versionadded:: 1.1.0
  
          .. versionchanged:: 1.4.0 Zstandard support.
  
      value_labels : dict of dicts
          Dictionary containing columns as keys and dictionaries of column value
          to labels as values. The combined length of all labels for a single
          variable must be 32,000 characters or smaller.
  
          .. versionadded:: 1.4.0
  
      Returns
      -------
      writer : StataWriter117 instance
          The StataWriter117 instance has a write_file method, which will
          write the file to the given `fname`.
  
      Raises
      ------
      NotImplementedError
          * If datetimes contain timezone information
      ValueError
          * Columns listed in convert_dates are neither datetime64[ns]
            or datetime.datetime
          * Column dtype is not representable in Stata
          * Column listed in convert_dates is not in DataFrame
          * Categorical label contains more than 32,000 characters
  
      Examples
      --------
      >>> from pandas.io.stata import StataWriter117
      >>> data = pd.DataFrame([[1.0, 1, 'a']], columns=['a', 'b', 'c'])
      >>> writer = StataWriter117('./data_file.dta', data)
      >>> writer.write_file()
  
      Directly write a zip file
      >>> compression = {"method": "zip", "archive_name": "data_file.dta"}
      >>> writer = StataWriter117('./data_file.zip', data, compression=compression)
      >>> writer.write_file()
  
      Or with long strings stored in strl format
      >>> data = pd.DataFrame([['A relatively long string'], [''], ['']],
      ...                     columns=['strls'])
      >>> writer = StataWriter117('./data_file_with_long_strings.dta', data,
      ...                         convert_strl=['strls'])
      >>> writer.write_file()
      """
  
      _max_string_length = 2045
      _dta_version = 117
  
      def __init__(
          self,
          fname: FilePath | WriteBuffer[bytes],
          data: DataFrame,
          convert_dates: dict[Hashable, str] | None = None,
          write_index: bool = True,
          byteorder: str | None = None,
          time_stamp: datetime.datetime | None = None,
          data_label: str | None = None,
          variable_labels: dict[Hashable, str] | None = None,
          convert_strl: Sequence[Hashable] | None = None,
          compression: CompressionOptions = "infer",
          storage_options: StorageOptions = None,
          *,
          value_labels: dict[Hashable, dict[float | int, str]] | None = None,
      ):
          # Copy to new list since convert_strl might be modified later
          self._convert_strl: list[Hashable] = []
          if convert_strl is not None:
              self._convert_strl.extend(convert_strl)
  
          super().__init__(
              fname,
              data,
              convert_dates,
              write_index,
              byteorder=byteorder,
              time_stamp=time_stamp,
              data_label=data_label,
              variable_labels=variable_labels,
              value_labels=value_labels,
              compression=compression,
              storage_options=storage_options,
          )
          self._map: dict[str, int] = {}
          self._strl_blob = b""
  
      @staticmethod
      def _tag(val: str | bytes, tag: str) -> bytes:
          """Surround val with <tag></tag>"""
          if isinstance(val, str):
              val = bytes(val, "utf-8")
          return bytes("<" + tag + ">", "utf-8") + val + bytes("</" + tag + ">", "utf-8")
  
      def _update_map(self, tag: str) -> None:
          """Update map location for tag with file position"""
          assert self.handles.handle is not None
          self._map[tag] = self.handles.handle.tell()
  
      def _write_header(
          self,
          data_label: str | None = None,
          time_stamp: datetime.datetime | None = None,
      ) -> None:
          """Write the file header"""
          byteorder = self._byteorder
          self._write_bytes(bytes("<stata_dta>", "utf-8"))
          bio = BytesIO()
          # ds_format - 117
          bio.write(self._tag(bytes(str(self._dta_version), "utf-8"), "release"))
          # byteorder
          bio.write(self._tag(byteorder == ">" and "MSF" or "LSF", "byteorder"))
          # number of vars, 2 bytes in 117 and 118, 4 byte in 119
          nvar_type = "H" if self._dta_version <= 118 else "I"
          bio.write(self._tag(struct.pack(byteorder + nvar_type, self.nvar), "K"))
          # 117 uses 4 bytes, 118 uses 8
          nobs_size = "I" if self._dta_version == 117 else "Q"
          bio.write(self._tag(struct.pack(byteorder + nobs_size, self.nobs), "N"))
          # data label 81 bytes, char, null terminated
          label = data_label[:80] if data_label is not None else ""
          encoded_label = label.encode(self._encoding)
          label_size = "B" if self._dta_version == 117 else "H"
          label_len = struct.pack(byteorder + label_size, len(encoded_label))
          encoded_label = label_len + encoded_label
          bio.write(self._tag(encoded_label, "label"))
          # time stamp, 18 bytes, char, null terminated
          # format dd Mon yyyy hh:mm
          if time_stamp is None:
              time_stamp = datetime.datetime.now()
          elif not isinstance(time_stamp, datetime.datetime):
              raise ValueError("time_stamp should be datetime type")
          # Avoid locale-specific month conversion
          months = [
              "Jan",
              "Feb",
              "Mar",
              "Apr",
              "May",
              "Jun",
              "Jul",
              "Aug",
              "Sep",
              "Oct",
              "Nov",
              "Dec",
          ]
          month_lookup = {i + 1: month for i, month in enumerate(months)}
          ts = (
              time_stamp.strftime("%d ")
              + month_lookup[time_stamp.month]
              + time_stamp.strftime(" %Y %H:%M")
          )
          # '\x11' added due to inspection of Stata file
          stata_ts = b"\x11" + bytes(ts, "utf-8")
          bio.write(self._tag(stata_ts, "timestamp"))
          self._write_bytes(self._tag(bio.getvalue(), "header"))
  
      def _write_map(self) -> None:
          """
          Called twice during file write. The first populates the values in
          the map with 0s.  The second call writes the final map locations when
          all blocks have been written.
          """
          if not self._map:
              self._map = {
                  "stata_data": 0,
                  "map": self.handles.handle.tell(),
                  "variable_types": 0,
                  "varnames": 0,
                  "sortlist": 0,
                  "formats": 0,
                  "value_label_names": 0,
                  "variable_labels": 0,
                  "characteristics": 0,
                  "data": 0,
                  "strls": 0,
                  "value_labels": 0,
                  "stata_data_close": 0,
                  "end-of-file": 0,
              }
          # Move to start of map
          self.handles.handle.seek(self._map["map"])
          bio = BytesIO()
          for val in self._map.values():
              bio.write(struct.pack(self._byteorder + "Q", val))
          self._write_bytes(self._tag(bio.getvalue(), "map"))
  
      def _write_variable_types(self) -> None:
          self._update_map("variable_types")
          bio = BytesIO()
          for typ in self.typlist:
              bio.write(struct.pack(self._byteorder + "H", typ))
          self._write_bytes(self._tag(bio.getvalue(), "variable_types"))
  
      def _write_varnames(self) -> None:
          self._update_map("varnames")
          bio = BytesIO()
          # 118 scales by 4 to accommodate utf-8 data worst case encoding
          vn_len = 32 if self._dta_version == 117 else 128
          for name in self.varlist:
              name = self._null_terminate_str(name)
              name = _pad_bytes_new(name[:32].encode(self._encoding), vn_len + 1)
              bio.write(name)
          self._write_bytes(self._tag(bio.getvalue(), "varnames"))
  
      def _write_sortlist(self) -> None:
          self._update_map("sortlist")
          sort_size = 2 if self._dta_version < 119 else 4
          self._write_bytes(self._tag(b"\x00" * sort_size * (self.nvar + 1), "sortlist"))
  
      def _write_formats(self) -> None:
          self._update_map("formats")
          bio = BytesIO()
          fmt_len = 49 if self._dta_version == 117 else 57
          for fmt in self.fmtlist:
              bio.write(_pad_bytes_new(fmt.encode(self._encoding), fmt_len))
          self._write_bytes(self._tag(bio.getvalue(), "formats"))
  
      def _write_value_label_names(self) -> None:
          self._update_map("value_label_names")
          bio = BytesIO()
          # 118 scales by 4 to accommodate utf-8 data worst case encoding
          vl_len = 32 if self._dta_version == 117 else 128
          for i in range(self.nvar):
              # Use variable name when categorical
              name = ""  # default name
              if self._has_value_labels[i]:
                  name = self.varlist[i]
              name = self._null_terminate_str(name)
              encoded_name = _pad_bytes_new(name[:32].encode(self._encoding), vl_len + 1)
              bio.write(encoded_name)
          self._write_bytes(self._tag(bio.getvalue(), "value_label_names"))
  
      def _write_variable_labels(self) -> None:
          # Missing labels are 80 blank characters plus null termination
          self._update_map("variable_labels")
          bio = BytesIO()
          # 118 scales by 4 to accommodate utf-8 data worst case encoding
          vl_len = 80 if self._dta_version == 117 else 320
          blank = _pad_bytes_new("", vl_len + 1)
  
          if self._variable_labels is None:
              for _ in range(self.nvar):
                  bio.write(blank)
              self._write_bytes(self._tag(bio.getvalue(), "variable_labels"))
              return
  
          for col in self.data:
              if col in self._variable_labels:
                  label = self._variable_labels[col]
                  if len(label) > 80:
                      raise ValueError("Variable labels must be 80 characters or fewer")
                  try:
                      encoded = label.encode(self._encoding)
                  except UnicodeEncodeError as err:
                      raise ValueError(
                          "Variable labels must contain only characters that "
                          f"can be encoded in {self._encoding}"
                      ) from err
  
                  bio.write(_pad_bytes_new(encoded, vl_len + 1))
              else:
                  bio.write(blank)
          self._write_bytes(self._tag(bio.getvalue(), "variable_labels"))
  
      def _write_characteristics(self) -> None:
          self._update_map("characteristics")
          self._write_bytes(self._tag(b"", "characteristics"))
  
      def _write_data(self, records) -> None:
          self._update_map("data")
          self._write_bytes(b"<data>")
          self._write_bytes(records.tobytes())
          self._write_bytes(b"</data>")
  
      def _write_strls(self) -> None:
          self._update_map("strls")
          self._write_bytes(self._tag(self._strl_blob, "strls"))
  
      def _write_expansion_fields(self) -> None:
          """No-op in dta 117+"""
          pass
  
      def _write_value_labels(self) -> None:
          self._update_map("value_labels")
          bio = BytesIO()
          for vl in self._value_labels:
              lab = vl.generate_value_label(self._byteorder)
              lab = self._tag(lab, "lbl")
              bio.write(lab)
          self._write_bytes(self._tag(bio.getvalue(), "value_labels"))
  
      def _write_file_close_tag(self) -> None:
          self._update_map("stata_data_close")
          self._write_bytes(bytes("</stata_dta>", "utf-8"))
          self._update_map("end-of-file")
  
      def _update_strl_names(self) -> None:
          """
          Update column names for conversion to strl if they might have been
          changed to comply with Stata naming rules
          """
          # Update convert_strl if names changed
          for orig, new in self._converted_names.items():
              if orig in self._convert_strl:
                  idx = self._convert_strl.index(orig)
                  self._convert_strl[idx] = new
  
      def _convert_strls(self, data: DataFrame) -> DataFrame:
          """
          Convert columns to StrLs if either very large or in the
          convert_strl variable
          """
          convert_cols = [
              col
              for i, col in enumerate(data)
              if self.typlist[i] == 32768 or col in self._convert_strl
          ]
  
          if convert_cols:
              ssw = StataStrLWriter(data, convert_cols, version=self._dta_version)
              tab, new_data = ssw.generate_table()
              data = new_data
              self._strl_blob = ssw.generate_blob(tab)
          return data
  
      def _set_formats_and_types(self, dtypes: Series) -> None:
          self.typlist = []
          self.fmtlist = []
          for col, dtype in dtypes.items():
              force_strl = col in self._convert_strl
              fmt = _dtype_to_default_stata_fmt(
                  dtype,
                  self.data[col],
                  dta_version=self._dta_version,
                  force_strl=force_strl,
              )
              self.fmtlist.append(fmt)
              self.typlist.append(
                  _dtype_to_stata_type_117(dtype, self.data[col], force_strl)
              )
  
  
  class StataWriterUTF8(StataWriter117):
      """
      Stata binary dta file writing in Stata 15 (118) and 16 (119) formats
  
      DTA 118 and 119 format files support unicode string data (both fixed
      and strL) format. Unicode is also supported in value labels, variable
      labels and the dataset label. Format 119 is automatically used if the
      file contains more than 32,767 variables.
  
      .. versionadded:: 1.0.0
  
      Parameters
      ----------
      fname : path (string), buffer or path object
          string, path object (pathlib.Path or py._path.local.LocalPath) or
          object implementing a binary write() functions. If using a buffer
          then the buffer will not be automatically closed after the file
          is written.
      data : DataFrame
          Input to save
      convert_dates : dict, default None
          Dictionary mapping columns containing datetime types to stata internal
          format to use when writing the dates. Options are 'tc', 'td', 'tm',
          'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name.
          Datetime columns that do not have a conversion type specified will be
          converted to 'tc'. Raises NotImplementedError if a datetime column has
          timezone information
      write_index : bool, default True
          Write the index to Stata dataset.
      byteorder : str, default None
          Can be ">", "<", "little", or "big". default is `sys.byteorder`
      time_stamp : datetime, default None
          A datetime to use as file creation date.  Default is the current time
      data_label : str, default None
          A label for the data set.  Must be 80 characters or smaller.
      variable_labels : dict, default None
          Dictionary containing columns as keys and variable labels as values.
          Each label must be 80 characters or smaller.
      convert_strl : list, default None
          List of columns names to convert to Stata StrL format.  Columns with
          more than 2045 characters are automatically written as StrL.
          Smaller columns can be converted by including the column name.  Using
          StrLs can reduce output file size when strings are longer than 8
          characters, and either frequently repeated or sparse.
      version : int, default None
          The dta version to use. By default, uses the size of data to determine
          the version. 118 is used if data.shape[1] <= 32767, and 119 is used
          for storing larger DataFrames.
      {compression_options}
  
          .. versionadded:: 1.1.0
  
          .. versionchanged:: 1.4.0 Zstandard support.
  
      value_labels : dict of dicts
          Dictionary containing columns as keys and dictionaries of column value
          to labels as values. The combined length of all labels for a single
          variable must be 32,000 characters or smaller.
  
          .. versionadded:: 1.4.0
  
      Returns
      -------
      StataWriterUTF8
          The instance has a write_file method, which will write the file to the
          given `fname`.
  
      Raises
      ------
      NotImplementedError
          * If datetimes contain timezone information
      ValueError
          * Columns listed in convert_dates are neither datetime64[ns]
            or datetime.datetime
          * Column dtype is not representable in Stata
          * Column listed in convert_dates is not in DataFrame
          * Categorical label contains more than 32,000 characters
  
      Examples
      --------
      Using Unicode data and column names
  
      >>> from pandas.io.stata import StataWriterUTF8
      >>> data = pd.DataFrame([[1.0, 1, 'ᴬ']], columns=['a', 'β', 'ĉ'])
      >>> writer = StataWriterUTF8('./data_file.dta', data)
      >>> writer.write_file()
  
      Directly write a zip file
      >>> compression = {"method": "zip", "archive_name": "data_file.dta"}
      >>> writer = StataWriterUTF8('./data_file.zip', data, compression=compression)
      >>> writer.write_file()
  
      Or with long strings stored in strl format
  
      >>> data = pd.DataFrame([['ᴀ relatively long ŝtring'], [''], ['']],
      ...                     columns=['strls'])
      >>> writer = StataWriterUTF8('./data_file_with_long_strings.dta', data,
      ...                          convert_strl=['strls'])
      >>> writer.write_file()
      """
  
      _encoding = "utf-8"
  
      def __init__(
          self,
          fname: FilePath | WriteBuffer[bytes],
          data: DataFrame,
          convert_dates: dict[Hashable, str] | None = None,
          write_index: bool = True,
          byteorder: str | None = None,
          time_stamp: datetime.datetime | None = None,
          data_label: str | None = None,
          variable_labels: dict[Hashable, str] | None = None,
          convert_strl: Sequence[Hashable] | None = None,
          version: int | None = None,
          compression: CompressionOptions = "infer",
          storage_options: StorageOptions = None,
          *,
          value_labels: dict[Hashable, dict[float | int, str]] | None = None,
      ):
          if version is None:
              version = 118 if data.shape[1] <= 32767 else 119
          elif version not in (118, 119):
              raise ValueError("version must be either 118 or 119.")
          elif version == 118 and data.shape[1] > 32767:
              raise ValueError(
                  "You must use version 119 for data sets containing more than"
                  "32,767 variables"
              )
  
          super().__init__(
              fname,
              data,
              convert_dates=convert_dates,
              write_index=write_index,
              byteorder=byteorder,
              time_stamp=time_stamp,
              data_label=data_label,
              variable_labels=variable_labels,
              value_labels=value_labels,
              convert_strl=convert_strl,
              compression=compression,
              storage_options=storage_options,
          )
          # Override version set in StataWriter117 init
          self._dta_version = version
  
      def _validate_variable_name(self, name: str) -> str:
          """
          Validate variable names for Stata export.
  
          Parameters
          ----------
          name : str
              Variable name
  
          Returns
          -------
          str
              The validated name with invalid characters replaced with
              underscores.
  
          Notes
          -----
          Stata 118+ support most unicode characters. The only limitation is in
          the ascii range where the characters supported are a-z, A-Z, 0-9 and _.
          """
          # High code points appear to be acceptable
          for c in name:
              if (
                  ord(c) < 128
                  and (c < "A" or c > "Z")
                  and (c < "a" or c > "z")
                  and (c < "0" or c > "9")
                  and c != "_"
              ) or 128 <= ord(c) < 256:
                  name = name.replace(c, "_")
  
          return name
  