




"""simple docstring"""






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


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






@dataclass
class lowerCAmelCase__			(  datasets.BuilderConfig		):






			'''simple docstring'''



			lowerCamelCase__   =    1_00_00
			lowerCamelCase__   =    None
			lowerCamelCase__   =    None






class lowerCAmelCase__			(  datasets.ArrowBasedBuilder		):






			'''simple docstring'''



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






			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Dict					=					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(lowercase						)						):
											with open(lowercase						,   'rb'						) as f:
															_lowerCamelCase  :    Tuple					=					pq.ParquetFile(lowercase						)
															try:
																			for batch_idx, record_batch in enumerate(
																			    parquet_file.iter_batches(batch_size=self.config.batch_size						,   columns=self.config.columns						)						):
																							_lowerCamelCase  :    Union[str, Any]					=					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(lowercase						)
															except ValueError as e:
																			logger.error(F'''Failed to read file \'{file}\' with error {type(lowercase						)}: {e}'''						)
																			raise





"""simple docstring"""






from math import pow, sqrt







def   _snake_case    (       *lowercase__	):
				_lowerCamelCase  :    Dict					=					len(lowercase__	) > 0 and all(value > 0.0 for value in values	)
				return result







def   _snake_case    (       lowercase__							,					lowercase__	):
				return (
				    round(sqrt(molar_mass_a / molar_mass_a	)							,					6	)
				    if validate(lowercase__							,					lowercase__	)
				    else ValueError('Input Error: Molar mass values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a	)							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a	)							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(molar_mass / pow(effusion_rate_a / effusion_rate_a							,					2	)							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				return (
				    round(pow(effusion_rate_a / effusion_rate_a							,					2	) / molar_mass							,					6	)
				    if validate(lowercase__							,					lowercase__							,					lowercase__	)
				    else ValueError(
				        'Input Error: Molar mass and effusion rate values must greater than 0.'	)
				)