





import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast




@dataclass
class        SCREAMING_SNAKE_CASE__					(		datasets.BuilderConfig ):
 __SCREAMING_SNAKE_CASE							=      None




class        SCREAMING_SNAKE_CASE__					(		datasets.ArrowBasedBuilder ):
 __SCREAMING_SNAKE_CASE							=      PandasConfig



 def 							UpperCamelCase     (				self					):
        return datasets.DatasetInfo(features=self.config.features					)



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        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(__lowerCamelCase,(str, list, tuple)					):
               A__		   =		data_files
               if isinstance(__lowerCamelCase,__lowerCamelCase					):
                      A__		   =		[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               A__		   =		[dl_manager.iter_files(__lowerCamelCase					) 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(__lowerCamelCase,__lowerCamelCase					):
                      A__		   =		[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               A__		   =		[dl_manager.iter_files(__lowerCamelCase					) for file in files]
               splits.append(datasets.SplitGenerator(name=__lowerCamelCase,gen_kwargs={'''files''': files}					)					)
        return splits



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        if self.config.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               A__		   =		table_cast(__lowerCamelCase,self.config.features.arrow_schema					)
        return pa_table




 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        for i, file in enumerate(itertools.chain.from_iterable(__lowerCamelCase					)					):
               with open(__lowerCamelCase,'''rb'''					) as f:
                      A__		   =		pa.Table.from_pandas(pd.read_pickle(__lowerCamelCase					)					)
                      yield i, self._cast_table(__lowerCamelCase					)












from __future__ import annotations

import math
from collections.abc import Callable


def       UpperCamelCase__(			UpperCamelCase__     :			Callable[[int | float], int | float]					,	UpperCamelCase__     :			int | float					,	UpperCamelCase__     :			int | float					,	UpperCamelCase__     :			int = 1_00					,	)->float:
       A__		   =		x_start
       A__		   =		fnc(UpperCamelCase__    )
       A__		   =		0.0

       for _ in range(UpperCamelCase__    ):
              # Approximates curve as a sequence of linear lines and sums their length
              A__		   =		(x_end - x_start) / steps + xa
              A__		   =		fnc(UpperCamelCase__    )
              length += math.hypot(xa - xa					,	fxa - fxa    )

              # Increment step
              A__		   =		xa
              A__		   =		fxa

       return length


if __name__ == "__main__":


       def       UpperCamelCase__(			UpperCamelCase__     :			Union[str, Any]    )->Dict:
              return math.sin(10 * x    )

       print('f(x) = sin(10 * x)')
       print('The length of the curve from x = -10 to x = 10 is:')
       a__: str							   =						10
       while i <= 100_000:
              print(F"With {i} steps: {line_length(f, -10, 10, i)}")
              i *= 10





