

'''simple docstring'''



import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


_UpperCamelCase						:  Optional[Any]							       =				datasets.utils.logging.get_logger(__name__)

_UpperCamelCase						:  int							       =				["names", "prefix"]
_UpperCamelCase						:  Optional[Any]							       =				["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
_UpperCamelCase						:  Optional[Any]							       =				["encoding_errors", "on_bad_lines"]
_UpperCamelCase						:  str							       =				["date_format"]



@dataclass
class 				_snake_case					(   datasets.BuilderConfig			):
  SCREAMING_SNAKE_CASE			:       str				=     ","
  SCREAMING_SNAKE_CASE			:       Optional[str]				=     None
  SCREAMING_SNAKE_CASE			:       Optional[Union[int, List[int], str]]				=     "infer"
  SCREAMING_SNAKE_CASE			:       Optional[List[str]]				=     None
  SCREAMING_SNAKE_CASE			:       Optional[List[str]]				=     None
  SCREAMING_SNAKE_CASE			:       Optional[Union[int, str, List[int], List[str]]]				=     None
  SCREAMING_SNAKE_CASE			:       Optional[Union[List[int], List[str]]]				=     None
  SCREAMING_SNAKE_CASE			:       Optional[str]				=     None
  SCREAMING_SNAKE_CASE			:       bool				=     True
  SCREAMING_SNAKE_CASE			:       Optional[Literal["c", "python", "pyarrow"]]				=     None
  SCREAMING_SNAKE_CASE			:       Dict[Union[int, str], Callable[[Any], Any]]				=     None
  SCREAMING_SNAKE_CASE			:       Optional[list]				=     None
  SCREAMING_SNAKE_CASE			:       Optional[list]				=     None
  SCREAMING_SNAKE_CASE			:       bool				=     False
  SCREAMING_SNAKE_CASE			:       Optional[Union[int, List[int]]]				=     None
  SCREAMING_SNAKE_CASE			:       Optional[int]				=     None
  SCREAMING_SNAKE_CASE			:       Optional[Union[str, List[str]]]				=     None
  SCREAMING_SNAKE_CASE			:       bool				=     True
  SCREAMING_SNAKE_CASE			:       bool				=     True
  SCREAMING_SNAKE_CASE			:       bool				=     False
  SCREAMING_SNAKE_CASE			:       bool				=     True
  SCREAMING_SNAKE_CASE			:       Optional[str]				=     None
  SCREAMING_SNAKE_CASE			:       str				=     "."
  SCREAMING_SNAKE_CASE			:       Optional[str]				=     None
  SCREAMING_SNAKE_CASE			:       str				=     '"'
  SCREAMING_SNAKE_CASE			:       int				=     0
  SCREAMING_SNAKE_CASE			:       Optional[str]				=     None
  SCREAMING_SNAKE_CASE			:       Optional[str]				=     None
  SCREAMING_SNAKE_CASE			:       Optional[str]				=     None
  SCREAMING_SNAKE_CASE			:       Optional[str]				=     None
  SCREAMING_SNAKE_CASE			:       bool				=     True
  SCREAMING_SNAKE_CASE			:       bool				=     True
  SCREAMING_SNAKE_CASE			:       int				=     0
  SCREAMING_SNAKE_CASE			:       bool				=     True
  SCREAMING_SNAKE_CASE			:       bool				=     False
  SCREAMING_SNAKE_CASE			:       Optional[str]				=     None
  SCREAMING_SNAKE_CASE			:       int				=     10000
  SCREAMING_SNAKE_CASE			:       Optional[datasets.Features]				=     None
  SCREAMING_SNAKE_CASE			:       Optional[str]				=     "strict"
  SCREAMING_SNAKE_CASE			:       Literal["error", "warn", "skip"]				=     "error"
  SCREAMING_SNAKE_CASE			:       Optional[str]				=     None

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      if self.delimiter is not None:
          lowerCAmelCase 						=							self.delimiter
      if self.column_names is not None:
          lowerCAmelCase 						=							self.column_names


  @property
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							{
          'sep': self.sep,
          'header': self.header,
          'names': self.names,
          'index_col': self.index_col,
          'usecols': self.usecols,
          'prefix': self.prefix,
          'mangle_dupe_cols': self.mangle_dupe_cols,
          'engine': self.engine,
          'converters': self.converters,
          'true_values': self.true_values,
          'false_values': self.false_values,
          'skipinitialspace': self.skipinitialspace,
          'skiprows': self.skiprows,
          'nrows': self.nrows,
          'na_values': self.na_values,
          'keep_default_na': self.keep_default_na,
          'na_filter': self.na_filter,
          'verbose': self.verbose,
          'skip_blank_lines': self.skip_blank_lines,
          'thousands': self.thousands,
          'decimal': self.decimal,
          'lineterminator': self.lineterminator,
          'quotechar': self.quotechar,
          'quoting': self.quoting,
          'escapechar': self.escapechar,
          'comment': self.comment,
          'encoding': self.encoding,
          'dialect': self.dialect,
          'error_bad_lines': self.error_bad_lines,
          'warn_bad_lines': self.warn_bad_lines,
          'skipfooter': self.skipfooter,
          'doublequote': self.doublequote,
          'memory_map': self.memory_map,
          'float_precision': self.float_precision,
          'chunksize': self.chunksize,
          'encoding_errors': self.encoding_errors,
          'on_bad_lines': self.on_bad_lines,
          'date_format': self.date_format,
      }

      # some kwargs must not be passed if they don't have a default value
      # some others are deprecated and we can also not pass them if they are the default value
      for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
          if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()      , _SCREAMING_SNAKE_CASE	):
              del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
      if not (datasets.config.PANDAS_VERSION.major >= 2):
          for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
              del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
      if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
          for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
              del pd_read_csv_kwargs[pd_read_csv_parameter]

      return pd_read_csv_kwargs



class 				_snake_case					(   datasets.ArrowBasedBuilder			):
  SCREAMING_SNAKE_CASE			:       Optional[Any]				=     CsvConfig

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return datasets.DatasetInfo(features=self.config.features	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      if not self.config.data_files:
          raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}'	)
      lowerCAmelCase 						=							dl_manager.download_and_extract(self.config.data_files	)
      if isinstance(_SCREAMING_SNAKE_CASE      , (str, list, tuple)	):
          lowerCAmelCase 						=							data_files
          if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
              lowerCAmelCase 						=							[files]
          lowerCAmelCase 						=							[dl_manager.iter_files(_SCREAMING_SNAKE_CASE	) for file in files]
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN      , gen_kwargs={'files': files}	)]
      lowerCAmelCase 						=							[]
      for split_name, files in data_files.items():
          if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
              lowerCAmelCase 						=							[files]
          lowerCAmelCase 						=							[dl_manager.iter_files(_SCREAMING_SNAKE_CASE	) for file in files]
          splits.append(datasets.SplitGenerator(name=_SCREAMING_SNAKE_CASE      , gen_kwargs={'files': files}	)	)
      return splits

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      if self.config.features is not None:
          lowerCAmelCase 						=							self.config.features.arrow_schema
          if all(not require_storage_cast(_SCREAMING_SNAKE_CASE	) for feature in self.config.features.values()	):
              # cheaper cast
              lowerCAmelCase 						=							pa.Table.from_arrays([pa_table[field.name] for field in schema]      , schema=_SCREAMING_SNAKE_CASE	)
          else:
              # more expensive cast; allows str <-> int/float or str to Audio for example
              lowerCAmelCase 						=							table_cast(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)
      return pa_table


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.config.features.arrow_schema if self.config.features else None
      # dtype allows reading an int column as str
      lowerCAmelCase 						=							(
          {
              name: dtype.to_pandas_dtype() if not require_storage_cast(_SCREAMING_SNAKE_CASE	) else object
              for name, dtype, feature in zip(schema.names      , schema.types      , self.config.features.values()	)
          }
          if schema is not None
          else None
      )
      for file_idx, file in enumerate(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE	)	):
          lowerCAmelCase 						=							pd.read_csv(_SCREAMING_SNAKE_CASE      , iterator=_SCREAMING_SNAKE_CASE      , dtype=_SCREAMING_SNAKE_CASE      , **self.config.pd_read_csv_kwargs	)
          try:
              for batch_idx, df in enumerate(_SCREAMING_SNAKE_CASE	):
                  lowerCAmelCase 						=							pa.Table.from_pandas(_SCREAMING_SNAKE_CASE	)
                  # Uncomment for debugging (will print the Arrow table size and elements)
                  # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                  # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                  yield (file_idx, batch_idx), self._cast_table(_SCREAMING_SNAKE_CASE	)
          except ValueError as e:
              logger.error(F'Failed to read file \'{file}\' with error {type(_SCREAMING_SNAKE_CASE	)}: {e}'	)
              raise








'''simple docstring'''



import colorsys

from PIL import Image  # type: ignore


def       snake_case   (	snake_case      : float					,		snake_case      : float					,		snake_case      : int			) ->  float:


    """simple docstring"""


    lowerCAmelCase 						=							x
    lowerCAmelCase 						=							y
    for step in range(snake_case			):  # noqa: B007
        lowerCAmelCase 						=							a * a - b * b + x
        lowerCAmelCase 						=							2 * a * b + y
        lowerCAmelCase 						=							a_new

        # divergence happens for all complex number with an absolute value
        # greater than 4
        if a * a + b * b > 4:
            break
    return step / (max_step - 1)


def       snake_case   (	snake_case      : float			) ->  tuple:


    """simple docstring"""


    if distance == 1:
        return (0, 0, 0)
    else:
        return (255, 255, 255)


def       snake_case   (	snake_case      : float			) ->  tuple:


    """simple docstring"""


    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 255			) for i in colorsys.hsv_to_rgb(snake_case					,		1					,		1			)			)


def       snake_case   (	snake_case      : int = 800					,		snake_case      : int = 600					,		snake_case      : float = -0.6					,		snake_case      : float = 0					,		snake_case      : float = 3.2					,		snake_case      : int = 50					,		snake_case      : bool = True					,		) ->  Image.Image:


    """simple docstring"""


    lowerCAmelCase 						=							Image.new('RGB'					,		(image_width, image_height)			)
    lowerCAmelCase 						=							img.load()

    # loop through the image-coordinates
    for image_x in range(snake_case			):
        for image_y in range(snake_case			):
            # determine the figure-coordinates based on the image-coordinates
            lowerCAmelCase 						=							figure_width / image_width * image_height
            lowerCAmelCase 						=							figure_center_x + (image_x / image_width - 0.5) * figure_width
            lowerCAmelCase 						=							figure_center_y + (image_y / image_height - 0.5) * figure_height

            lowerCAmelCase 						=							get_distance(snake_case					,		snake_case					,		snake_case			)

            # color the corresponding pixel based on the selected coloring-function
            if use_distance_color_coding:
                lowerCAmelCase 						=							get_color_coded_rgb(snake_case			)
            else:
                lowerCAmelCase 						=							get_black_and_white_rgb(snake_case			)

    return img


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # colored version, full figure
     _UpperCamelCase						:  Dict							       =				get_image()

     # uncomment for colored version, different section, zoomed in
     # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
     # figure_width = 0.8)

     # uncomment for black and white version, full figure
     # img = get_image(use_distance_color_coding = False)

     # uncomment to save the image
     # img.save("mandelbrot.png")

     img.show()





