



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__    =						datasets.utils.logging.get_logger(__name__)

UpperCamelCase__    =						["names", "prefix"]
UpperCamelCase__    =						["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
UpperCamelCase__    =						["encoding_errors", "on_bad_lines"]
UpperCamelCase__    =						["date_format"]
@dataclass
class 			_lowerCAmelCase   ( datasets.BuilderConfig			):






			"""simple docstring"""





			lowercase__      :  str      =			","
			lowercase__      :  Optional[str]      =			None
			lowercase__      :  Optional[Union[int, List[int], str]]      =			"infer"
			lowercase__      :  Optional[List[str]]      =			None
			lowercase__      :  Optional[List[str]]      =			None
			lowercase__      :  Optional[Union[int, str, List[int], List[str]]]      =			None
			lowercase__      :  Optional[Union[List[int], List[str]]]      =			None
			lowercase__      :  Optional[str]      =			None
			lowercase__      :  bool      =			True
			lowercase__      :  Optional[Literal["c", "python", "pyarrow"]]      =			None
			lowercase__      :  Dict[Union[int, str], Callable[[Any], Any]]      =			None
			lowercase__      :  Optional[list]      =			None
			lowercase__      :  Optional[list]      =			None
			lowercase__      :  bool      =			False
			lowercase__      :  Optional[Union[int, List[int]]]      =			None
			lowercase__      :  Optional[int]      =			None
			lowercase__      :  Optional[Union[str, List[str]]]      =			None
			lowercase__      :  bool      =			True
			lowercase__      :  bool      =			True
			lowercase__      :  bool      =			False
			lowercase__      :  bool      =			True
			lowercase__      :  Optional[str]      =			None
			lowercase__      :  str      =			"."
			lowercase__      :  Optional[str]      =			None
			lowercase__      :  str      =			'"'
			lowercase__      :  int      =			0
			lowercase__      :  Optional[str]      =			None
			lowercase__      :  Optional[str]      =			None
			lowercase__      :  Optional[str]      =			None
			lowercase__      :  Optional[str]      =			None
			lowercase__      :  bool      =			True
			lowercase__      :  bool      =			True
			lowercase__      :  int      =			0
			lowercase__      :  bool      =			True
			lowercase__      :  bool      =			False
			lowercase__      :  Optional[str]      =			None
			lowercase__      :  int      =			10_000
			lowercase__      :  Optional[datasets.Features]      =			None
			lowercase__      :  Optional[str]      =			"strict"
			lowercase__      :  Literal["error", "warn", "skip"]      =			"error"
			lowercase__      :  Optional[str]      =			None





			def 		snake_case__			(				self	:       Optional[Any]   )						->					List[str]:
									"""simple docstring"""







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







			@property
			def 		snake_case__			(				self	:       Tuple   )						->					Any:
									"""simple docstring"""







									__lowercase					=				{
									    "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()			,     __a   ):
																					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 			_lowerCAmelCase   ( datasets.ArrowBasedBuilder			):






			"""simple docstring"""





			lowercase__      :  Optional[Any]      =			CsvConfig





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					Any:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Optional[int]			,     lowercase	:       Optional[Any]   )						->					List[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}"   )
									__lowercase					=				dl_manager.download_and_extract(self.config.data_files   )
									if isinstance(__a			,     (str, list, tuple)   ):
															__lowercase					=				data_files
															if isinstance(__a			,     __a   ):
																					__lowercase					=				[files]
															__lowercase					=				[dl_manager.iter_files(__a   ) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,     gen_kwargs={"""files""": files}   )]
									__lowercase					=				[]
									for split_name, files in data_files.items():
															if isinstance(__a			,     __a   ):
																					__lowercase					=				[files]
															__lowercase					=				[dl_manager.iter_files(__a   ) for file in files]
															splits.append(datasets.SplitGenerator(name=__a			,     gen_kwargs={"""files""": files}   )   )
									return splits





			def 		snake_case__			(				self	:       List[str]			,     lowercase	:       pa.Table   )						->					pa.Table:
									"""simple docstring"""







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







			def 		snake_case__			(				self	:       Union[str, Any]			,     lowercase	:       Dict   )						->					str:
									"""simple docstring"""







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










from __future__ import annotations

from collections.abc import Callable

UpperCamelCase__ =						list[list[float | int]]






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      Matrix:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				[[0 for _ in range(size + 1   )] for _ in range(lowercase__   )]
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42

						for row in range(lowercase__   ):
												for col in range(lowercase__   ):
																		__lowercase					=				matrix[row][col]

												__lowercase					=				vector[row][0]

						__lowercase					=				0
						__lowercase					=				0
						while row < size and col < size:
												# pivoting
												__lowercase					=				max((abs(augmented[rowa][col]   ), rowa) for rowa in range(lowercase__						,       lowercase__   )   )[
												    1
												]
												if augmented[pivot_row][col] == 0:
																		col += 1
																		continue
												else:
																		__lowercase	,			__lowercase					=				augmented[pivot_row], augmented[row]

												for rowa in range(row + 1						,       lowercase__   ):
																		__lowercase					=				augmented[rowa][col] / augmented[row][col]
																		__lowercase					=				0
																		for cola in range(col + 1						,       size + 1   ):
																								augmented[rowa][cola] -= augmented[row][cola] * ratio

												row += 1
												col += 1

						# back substitution
						for col in range(1						,       lowercase__   ):
												for row in range(lowercase__   ):
																		__lowercase					=				augmented[row][col] / augmented[col][col]
																		for cola in range(lowercase__						,       size + 1   ):
																								augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
						return [
						    [round(augmented[row][size] / augmented[row][row]						,       10   )] for row in range(lowercase__   )
						]






def  UpperCAmelCase__    (    lowercase__   ) ->      Callable[[int], int]:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				[[0 for _ in range(lowercase__   )] for _ in range(lowercase__   )]
						__lowercase					=				[[0] for _ in range(lowercase__   )]
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42

						for x_val, y_val in enumerate(lowercase__   ):
												for col in range(lowercase__   ):
																		__lowercase					=				(x_val + 1) ** (size - col - 1)
												__lowercase					=				y_val

						__lowercase					=				solve(lowercase__						,       lowercase__   )

						def interpolated_func(lowercase__   ) -> int:
												return sum(
												    round(coeffs[x_val][0]   ) * (var ** (size - x_val - 1))
												    for x_val in range(lowercase__   )   )

						return interpolated_func






def  UpperCAmelCase__    (    lowercase__   ) ->      int:
						return (
						    1
						    - variable
						    + variable**2
						    - variable**3
						    + variable**4
						    - variable**5
						    + variable**6
						    - variable**7
						    + variable**8
						    - variable**9
						    + variable**10
						)






def  UpperCAmelCase__    (    lowercase__ = question_function						,       lowercase__ = 10   ) ->      int:
						__lowercase					=				[func(lowercase__   ) for x_val in range(1						,       order + 1   )]

						__lowercase					=				[
						    interpolate(data_points[:max_coeff]   ) for max_coeff in range(1						,       order + 1   )
						]

						__lowercase					=				0
						__lowercase					=				42
						__lowercase					=				42

						for poly in polynomials:
												__lowercase					=				1
												while func(lowercase__   ) == poly(lowercase__   ):
																		x_val += 1

												ret += poly(lowercase__   )

						return ret


if __name__ == "__main__":
			print(F"""{solution() = }""")





