






"""simple docstring"""

def 		__lowerCamelCase	(  __UpperCamelCase       )						->    int:

		"""simple docstring"""







		assert (
		    isinstance(__UpperCamelCase      ,       __UpperCamelCase       ) and number_of_steps > 0
		), f'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
		if number_of_steps == 1:
				return 1
		lowerCAmelCase_				,	lowerCAmelCase_   :							Union[str, Any]									=			1, 1
		for _ in range(number_of_steps - 1       ):
				lowerCAmelCase_				,	lowerCAmelCase_   :							Union[str, Any]									=			current + previous, current
		return current


if __name__ == "__main__":
					import doctest

					doctest.testmod()








"""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'''






		a_	:				int            =							1_0000
		a_	:				Optional[List[str]]            =							None
		a_	:				Optional[datasets.Features]            =							None







class 		__lowerCamelCase				( datasets.ArrowBasedBuilder			):





		'''simple docstring'''






		a_	:				Dict            =							ParquetConfig





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				return datasets.DatasetInfo(features=self.config.features	)





		def    lowerCamelCase       (   self							:	Any						,			a_							:	Optional[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}'''	)
				lowerCAmelCase_   :							List[str]									=			dl_manager.download_and_extract(self.config.data_files	)
				if isinstance(a_						,			(str, list, tuple)	):
						lowerCAmelCase_   :							str									=			data_files
						if isinstance(a_						,			a_	):
								lowerCAmelCase_   :							Optional[Any]									=			[files]
						# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
						lowerCAmelCase_   :							str									=			[dl_manager.iter_files(a_	) for file in files]
						return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,			gen_kwargs={"files": files}	)]
				lowerCAmelCase_   :							Optional[Any]									=			[]
				for split_name, files in data_files.items():
						if isinstance(a_						,			a_	):
								lowerCAmelCase_   :							Union[str, Any]									=			[files]
						# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
						lowerCAmelCase_   :							List[str]									=			[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:
												lowerCAmelCase_   :							Dict									=			datasets.Features.from_arrow_schema(pq.read_schema(a_	)	)
										break
						splits.append(datasets.SplitGenerator(name=a_						,			gen_kwargs={"files": files}	)	)
				return splits





		def    lowerCamelCase       (   self							:	int						,			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
						lowerCAmelCase_   :							Tuple									=			table_cast(a_						,			self.info.features.arrow_schema	)
				return pa_table





		def    lowerCamelCase       (   self							:	Dict						,			a_							:	Dict	):
				lowerCAmelCase_   :							Optional[int]									=			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(a_	)	):
						with open(a_						,			"rb"	) as f:
								lowerCAmelCase_   :							Any									=			pq.ParquetFile(a_	)
								try:
										for batch_idx, record_batch in enumerate(
										    parquet_file.iter_batches(batch_size=self.config.batch_size						,			columns=self.config.columns	)	):
												lowerCAmelCase_   :							List[str]									=			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
