





'''simple docstring'''
import math as m
def     lowercase__       (       __lowercase						:  str    ,  __lowercase						:  Union[str, Any]    ,  __lowercase						:  str=0.0_0_1					)			->				str:



							"""simple docstring"""






							return (f(a + h					) - f(a - h					)) / (2 * h)
def     lowercase__       (       __lowercase						:  List[Any]    ,  __lowercase						:  Optional[Any]=0    ,  __lowercase						:  List[str]=100    ,  __lowercase						:  int=0.0_0_0_1    ,  __lowercase						:  Optional[int]=1e-6    ,  __lowercase						:  Union[str, Any]=False					)			->				Optional[int]:



							"""simple docstring"""






							__UpperCamelCase	=  xa  # set the initial guess
							__UpperCamelCase	=  [a]
							__UpperCamelCase	=  abs(f(_lowerCAmelCase					)					)
							__UpperCamelCase	=  lambda __lowercase					: calc_derivative(_lowerCAmelCase    ,  _lowerCAmelCase    ,  h=_lowerCAmelCase					)  # noqa: E731  Derivative of f(x)
							for _ in range(_lowerCAmelCase					):
														if fa(_lowerCAmelCase					) == 0:
																					raise ValueError('No converging solution found'					)
														__UpperCamelCase	=  a - f(_lowerCAmelCase					) / fa(_lowerCAmelCase					)  # Calculate the next estimate
														if logsteps:
																					steps.append(_lowerCAmelCase					)
														if error < maxerror:
																					break
							else:
														raise ValueError('Iteration limit reached, no converging solution found'					)
							if logsteps:
														# If logstep is true, then log intermediate steps
														return a, error, steps
							return a, error


if __name__ == "__main__":
				from matplotlib import pyplot as plt

				a__							:							Tuple			=lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
				a__							:							List[Any]			=newton_raphson(
				    f, xa=10, maxiter=1_000, step=1E-6, logsteps=True
				)
				plt.plot([abs(f(x)) for x in steps])
				plt.xlabel('''step''')
				plt.ylabel('''error''')
				plt.show()
				print(f'solution = {{{solution:f}}}, error = {{{error:f}}}')







'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


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

@dataclass
class 					snake_case					(   datasets.BuilderConfig	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Optional[datasets.Features]      		=None






class 					snake_case					(   datasets.ArrowBasedBuilder	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : int      		=ArrowConfig






 def 	_lowerCamelCase (	self	:							List[str]     ):
        return datasets.DatasetInfo(features=self.config.features     )






 def 	_lowerCamelCase (	self	:							Union[str, Any]							,     __A	:							Dict     ):
        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}'''     )
        __UpperCamelCase	=  dl_manager.download_and_extract(self.config.data_files     )
        if isinstance(__A							,     (str, list, tuple)     ):
               __UpperCamelCase	=  data_files
               if isinstance(__A							,     __A     ):
                      __UpperCamelCase	=  [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __UpperCamelCase	=  [dl_manager.iter_files(__A     ) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,     gen_kwargs={'files': files}     )]
        __UpperCamelCase	=  []
        for split_name, files in data_files.items():
               if isinstance(__A							,     __A     ):
                      __UpperCamelCase	=  [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __UpperCamelCase	=  [dl_manager.iter_files(__A     ) for file in files]
               # Infer features is they are stoed in the arrow schema
               if self.info.features is None:
                      for file in itertools.chain.from_iterable(__A     ):
                             with open(__A							,     'rb'     ) as f:
                                    __UpperCamelCase	=  datasets.Features.from_arrow_schema(pa.ipc.open_stream(__A     ).schema     )
                             break
               splits.append(datasets.SplitGenerator(name=__A							,     gen_kwargs={'files': files}     )     )
        return splits






 def 	_lowerCamelCase (	self	:							Any							,     __A	:							pa.Table     ):
        if self.info.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
               __UpperCamelCase	=  table_cast(__A							,     self.info.features.arrow_schema     )
        return pa_table






 def 	_lowerCamelCase (	self	:							int							,     __A	:							Optional[Any]     ):
        for file_idx, file in enumerate(itertools.chain.from_iterable(__A     )     ):
               with open(__A							,     'rb'     ) as f:
                      try:
                             for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__A     )     ):
                                    __UpperCamelCase	=  pa.Table.from_batches([record_batch]     )
                                    # 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 f'''{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
