

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					:			int  				=			datasets.utils.logging.get_logger(__name__)






@dataclass
class 	__magic_name__    (			datasets.BuilderConfig      ):
			SCREAMING_SNAKE_CASE							   =						1_0_0_0_0
			SCREAMING_SNAKE_CASE							   =						None
			SCREAMING_SNAKE_CASE							   =						None






class 	__magic_name__    (			datasets.ArrowBasedBuilder      ):
			SCREAMING_SNAKE_CASE							   =						ParquetConfig





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						return datasets.DatasetInfo(features=self.config.features					)





			def 		__magic_name__    (		self					,  __snake_case					)   -> Optional[int]:




						'''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(__snake_case					,  (str, list, tuple)					):
									__a        =data_files
									if isinstance(__snake_case					,  __snake_case					):
												__a        =[files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									__a        =[dl_manager.iter_files(__snake_case					) 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(__snake_case					,  __snake_case					):
												__a        =[files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									__a        =[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:
																		__a        =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					,  __snake_case					)   -> pa.Table:




						'''simple docstring'''
						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
									__a        =table_cast(__snake_case					,  self.info.features.arrow_schema					)
						return pa_table





			def 		__magic_name__    (		self					,  __snake_case					)   -> Dict:




						'''simple docstring'''
						__a        =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:
												__a        =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					)					):
																		__a        =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











'''simple docstring'''






from math import isclose, sqrt


def  snake_case_     (					_lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float  )      ->   tuple[float, float, float]:
							UpperCAmelCase :       Optional[int] 							=		point_y / 4 / point_x
							UpperCAmelCase :       str 							=		2 * normal_gradient / (1 + normal_gradient * normal_gradient)
							UpperCAmelCase :       Any 							=		(1 - normal_gradient * normal_gradient) / (
							    1 + normal_gradient * normal_gradient
							)
							UpperCAmelCase :       Union[str, Any] 							=		(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

							# to find the next point, solve the simultaeneous equations:
							# y^2 + 4x^2 = 100
							# y - b = m * (x - a)
							# ==> A x^2 + B x + C = 0
							UpperCAmelCase :       Union[str, Any] 							=		outgoing_gradient**2 + 4
							UpperCAmelCase :       Dict 							=		2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
							UpperCAmelCase :       List[Any] 							=		(point_y - outgoing_gradient * point_x) ** 2 - 100

							UpperCAmelCase :       List[str] 							=		(
							    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
							) / (2 * quadratic_term)
							UpperCAmelCase :       Optional[int] 							=		(
							    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
							) / (2 * quadratic_term)

							# two solutions, one of which is our input point
							UpperCAmelCase :       Optional[Any] 							=		x_minus if isclose(_lowerCAmelCase   ,      _lowerCAmelCase  ) else x_plus
							UpperCAmelCase :       Union[str, Any] 							=		point_y + outgoing_gradient * (next_x - point_x)

							return next_x, next_y, outgoing_gradient


def  snake_case_     (					_lowerCAmelCase  :						float = 1.4   ,      _lowerCAmelCase  :						float = -9.6  )      ->   int:
							UpperCAmelCase :       int 							=		0
							UpperCAmelCase :       float 							=		first_x_coord
							UpperCAmelCase :       float 							=		first_y_coord
							UpperCAmelCase :       float 							=		(1_0.1 - point_y) / (0.0 - point_x)

							while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       str 							=		next_point(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														num_reflections += 1

							return num_reflections


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





