






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


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

__a      :       Tuple				  =   ["names", "prefix"]
__a      :       int				  =   ["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
__a      :       Any				  =   ["encoding_errors", "on_bad_lines"]
__a      :       Dict				  =   ["date_format"]


@dataclass
class       __lowercase      (	datasets.BuilderConfig  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				","
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				"infer"
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				True
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				False
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				True
     SCREAMING_SNAKE_CASE   						=				True
     SCREAMING_SNAKE_CASE   						=				False
     SCREAMING_SNAKE_CASE   						=				True
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				"."
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				'"'
     SCREAMING_SNAKE_CASE   						=				0
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				True
     SCREAMING_SNAKE_CASE   						=				True
     SCREAMING_SNAKE_CASE   						=				0
     SCREAMING_SNAKE_CASE   						=				True
     SCREAMING_SNAKE_CASE   						=				False
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				1_00_00
     SCREAMING_SNAKE_CASE   						=				None
     SCREAMING_SNAKE_CASE   						=				"strict"
     SCREAMING_SNAKE_CASE   						=				"error"
     SCREAMING_SNAKE_CASE   						=				None




     def 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



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





     @property
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         __A         =	{
             """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() ,	UpperCamelCase_				):
                 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       __lowercase      (	datasets.ArrowBasedBuilder  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				CsvConfig




     def 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	UpperCamelCase_       :			Union[str, Any]				):


         """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}"				)
         __A         =	dl_manager.download_and_extract(self.config.data_files				)
         if isinstance(UpperCamelCase_ ,	(str, list, tuple)				):
             __A         =	data_files
             if isinstance(UpperCamelCase_ ,	UpperCamelCase_				):
                 __A         =	[files]
             __A         =	[dl_manager.iter_files(UpperCamelCase_				) for file in files]
             return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,	gen_kwargs={"""files""": files}				)]
         __A         =	[]
         for split_name, files in data_files.items():
             if isinstance(UpperCamelCase_ ,	UpperCamelCase_				):
                 __A         =	[files]
             __A         =	[dl_manager.iter_files(UpperCamelCase_				) for file in files]
             splits.append(datasets.SplitGenerator(name=UpperCamelCase_ ,	gen_kwargs={"""files""": files}				)				)
         return splits




     def 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			pa.Table				):


         """simple docstring"""



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





     def 		lowerCAmelCase_     (				self       :			List[Any] ,	UpperCamelCase_       :			Optional[int]				):


         """simple docstring"""



         __A         =	self.config.features.arrow_schema if self.config.features else None
         # dtype allows reading an int column as str
         __A         =	(
             {
                 name: dtype.to_pandas_dtype() if not require_storage_cast(UpperCamelCase_				) 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(UpperCamelCase_				)				):
             __A         =	pd.read_csv(UpperCamelCase_ ,	iterator=UpperCamelCase_ ,	dtype=UpperCamelCase_ ,	**self.config.pd_read_csv_kwargs				)
             try:
                 for batch_idx, df in enumerate(UpperCamelCase_				):
                     __A         =	pa.Table.from_pandas(UpperCamelCase_				)
                     # 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(UpperCamelCase_				)
             except ValueError as e:
                 logger.error(F"Failed to read file '{file}' with error {type(UpperCamelCase_				)}: {e}"				)
                 raise












import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

__a      :       List[Any]				  =   "Usage of script: script_name <size_of_canvas:int>"

__a      :       Dict				  =   [0] * 100 + [1] * 10
random.shuffle(choice)



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int    )						->      list[list[bool]]:





    """simple docstring"""

    __A         =	[[False for i in range(__lowercase    )] for j in range(__lowercase    )]
    return canvas



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[list[bool]]    )						->      None:





    """simple docstring"""

    for i, row in enumerate(__lowercase    ):
        for j, _ in enumerate(__lowercase    ):
            __A         =	bool(random.getrandbits(1    )    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[list[bool]]    )						->      list[list[bool]]:





    """simple docstring"""

    __A         =	np.array(__lowercase    )
    __A         =	np.array(create_canvas(current_canvas.shape[0]    )    )
    for r, row in enumerate(__lowercase    ):
        for c, pt in enumerate(__lowercase    ):
            __A         =	__judge_point(
                __lowercase			,				current_canvas[r - 1 : r + 2, c - 1 : c + 2]    )

    __A         =	next_gen_canvas
    del next_gen_canvas  # cleaning memory as we move on.
    __A         =	current_canvas.tolist()
    return return_canvas



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			bool			,				__lowercase   :			list[list[bool]]    )						->      bool:





    """simple docstring"""

    __A         =	0
    __A         =	0
    # finding dead or alive neighbours count.
    for i in neighbours:
        for status in i:
            if status:
                alive += 1
            else:
                dead += 1

    # handling duplicate entry for focus pt.
    if pt:
        alive -= 1
    else:
        dead -= 1

    # running the rules of game here.
    __A         =	pt
    if pt:
        if alive < 2:
            __A         =	False
        elif alive == 2 or alive == 3:
            __A         =	True
        elif alive > 3:
            __A         =	False
    else:
        if alive == 3:
            __A         =	True

    return state


if __name__ == "__main__":
  if len(sys.argv) != 2:
    raise Exception(usage_doc)

  __a      :       str				  =   int(sys.argv[1])
  # main working structure of this module.
  __a      :       List[Any]				  =   create_canvas(canvas_size)
  seed(c)
  __a  ,__a      :       int				  =   plt.subplots()
  fig.show()
  __a      :       Union[str, Any]				  =   ListedColormap(["w", "k"])
  try:
    while True:
      __a      :       Dict				  =   run(c)
      ax.matshow(c, cmap=cmap)
      fig.canvas.draw()
      ax.cla()
  except KeyboardInterrupt:
    # do nothing.
    pass




