


import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__lowerCAmelCase      :					Optional[Any]    							=datasets.utils.logging.get_logger(__name__)
@dataclass
class 			_lowercase (     datasets.BuilderConfig			):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__							:     int         =						10_000
				SCREAMING_SNAKE_CASE__							:     Union[str, Any]         =						None
				SCREAMING_SNAKE_CASE__							:     List[Any]         =						None




class 			_lowercase (     datasets.ArrowBasedBuilder			):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__							:     int         =						ParquetConfig


				def 						__magic_name__(			self     :Union[str, Any]      )						->     Tuple:
										return datasets.DatasetInfo(features=self.config.features      )


				def 						__magic_name__(			self     :List[Any]     ,     lowerCAmelCase__     :Optional[Any]      )						->     Union[str, Any]:

										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}'''      )
										__SCREAMING_SNAKE_CASE		:	Any						     =						dl_manager.download_and_extract(self.config.data_files      )
										if isinstance(__snake_case     ,     (str, list, tuple)      ):
																__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						data_files
																if isinstance(__snake_case     ,     __snake_case      ):
																						__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						[files]
																# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																__SCREAMING_SNAKE_CASE		:	List[str]						     =						[dl_manager.iter_files(__snake_case      ) for file in files]
																return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,     gen_kwargs={'''files''': files}      )]
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						[]
										for split_name, files in data_files.items():
																if isinstance(__snake_case     ,     __snake_case      ):
																						__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						[files]
																# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						[dl_manager.iter_files(__snake_case      ) 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(__snake_case      ):
																												with open(__snake_case     ,     '''rb'''      ) as f:
																																		__SCREAMING_SNAKE_CASE		:	List[Any]						     =						datasets.Features.from_arrow_schema(pq.read_schema(__snake_case      )      )
																												break
																splits.append(datasets.SplitGenerator(name=__snake_case     ,     gen_kwargs={'''files''': files}      )      )
										return splits


				def 						__magic_name__(			self     :List[Any]     ,     lowerCAmelCase__     :pa.Table      )						->     List[str]:

										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
																__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						table_cast(__snake_case     ,     self.info.features.arrow_schema      )
										return pa_table



				def 						__magic_name__(			self     :Tuple     ,     lowerCAmelCase__     :str      )						->     Optional[Any]:
										__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						self.info.features.arrow_schema if self.info.features is not None else None
										if self.info.features is not None and self.config.columns is not None:
																if sorted(field.name for field in schema      ) != sorted(self.config.columns      ):
																						raise ValueError(
																						    f'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''      )
										for file_idx, file in enumerate(itertools.chain.from_iterable(__snake_case      )      ):
																with open(__snake_case     ,     '''rb'''      ) as f:
																						__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						pq.ParquetFile(__snake_case      )
																						try:
																												for batch_idx, record_batch in enumerate(
																												    parquet_file.iter_batches(batch_size=self.config.batch_size     ,     columns=self.config.columns      )      ):
																																		__SCREAMING_SNAKE_CASE		:	Dict						     =						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(__snake_case      )
																						except ValueError as e:
																												logger.error(f'''Failed to read file \'{file}\' with error {type(__snake_case      )}: {e}'''      )
																												raise






from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean




def 		lowerCAmelCase_ (						lowerCamelCase ):

				# A local function to see if a dot lands in the circle.
				def is_in_circle(lowerCamelCase						,  lowerCamelCase ) -> bool:
								__magic_name__						:						Dict										=sqrt((x**2) + (y**2) )
								# Our circle has a radius of 1, so a distance
								# greater than 1 would land outside the circle.
								return distance_from_centre <= 1

				# The proportion of guesses that landed in the circle
				__magic_name__						:						Union[str, Any]										=mean(
				    int(is_in_circle(uniform(-1.0						,  1.0 )						,  uniform(-1.0						,  1.0 ) ) )
				    for _ in range(lowerCamelCase ) )
				# The ratio of the area for circle to square is pi/4.
				__magic_name__						:						List[Any]										=proportion * 4
				print(F"The estimated value of pi is {pi_estimate}" )
				print(F"The numpy value of pi is {pi}" )
				print(F"The total error is {abs(pi - pi_estimate )}" )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase = 0.0						,  lowerCamelCase = 1.0						,  ):
				return mean(
				    function_to_integrate(uniform(lowerCamelCase						,  lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value)




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase = 0.0						,  lowerCamelCase = 1.0 ):

				def identity_function(lowerCamelCase ) -> float:
								return x

				__magic_name__						:						Optional[int]										=area_under_curve_estimator(
				    lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
				__magic_name__						:						str										=(max_value * max_value - min_value * min_value) / 2

				print("""******************""" )
				print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" )
				print(F"Estimated value is {estimated_value}" )
				print(F"Expected value is {expected_value}" )
				print(F"Total error is {abs(estimated_value - expected_value )}" )
				print("""******************""" )




def 		lowerCAmelCase_ (						lowerCamelCase ):

				def function_to_integrate(lowerCamelCase ) -> float:
								return sqrt(4.0 - x * x )

				__magic_name__						:						Dict										=area_under_curve_estimator(
				    lowerCamelCase						,  lowerCamelCase						,  0.0						,  2.0 )

				print("""******************""" )
				print("""Estimating pi using area_under_curve_estimator""" )
				print(F"Estimated value is {estimated_value}" )
				print(F"Expected value is {pi}" )
				print(F"Total error is {abs(estimated_value - pi )}" )
				print("""******************""" )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




