



'''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


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






@dataclass
class 	lowerCAmelCase_(    datasets.BuilderConfig					):
		'''simple docstring'''




		__lowercase							:				Optional[int]           =				1_0_0_0_0
		__lowercase							:				Optional[Any]           =				None
		__lowercase							:				str           =				None







class 	lowerCAmelCase_(    datasets.ArrowBasedBuilder					):
		'''simple docstring'''




		__lowercase							:				Any           =				ParquetConfig




		def 							UpperCAmelCase_     (   self				)   ->		Dict:
						return datasets.DatasetInfo(features=self.config.features				)




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




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		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__			:  Dict											= table_cast(__lowerCamelCase  ,self.info.features.arrow_schema				)
						return pa_table






		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		List[Any]:
						lowerCAmelCase__			:  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(__lowerCamelCase				)				):
										with open(__lowerCamelCase  ,"""rb"""				) as f:
														lowerCAmelCase__			:  str											= pq.ParquetFile(__lowerCamelCase				)
														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(__lowerCamelCase				)
														except ValueError as e:
																		logger.error(F"""Failed to read file '{file}' with error {type(__lowerCamelCase				)}: {e}"""				)
																		raise







'''simple docstring'''





from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCAmelCase										=     10
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    for i in range(UpperCamelCase							,   UpperCamelCase      ):
        if array[i] == target:
            return i
    return -1
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Optional[Any]											= 0
    lowerCAmelCase__			:  Optional[int]											= len(UpperCamelCase      )
    while left <= right:
        if right - left < precision:
            return lin_search(UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )

        lowerCAmelCase__			:  Tuple											= (left + right) // 3 + 1
        lowerCAmelCase__			:  List[str]											= 2 * (left + right) // 3 + 1

        if array[one_third] == target:
            return one_third
        elif array[two_third] == target:
            return two_third

        elif target < array[one_third]:
            lowerCAmelCase__			:  int											= one_third - 1
        elif array[two_third] < target:
            lowerCAmelCase__			:  Union[str, Any]											= two_third + 1

        else:
            lowerCAmelCase__			:  List[Any]											= one_third + 1
            lowerCAmelCase__			:  List[str]											= two_third - 1
    else:
        return -1
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    if left < right:
        if right - left < precision:
            return lin_search(UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  Dict											= (left + right) // 3 + 1
        lowerCAmelCase__			:  Tuple											= 2 * (left + right) // 3 + 1

        if array[one_third] == target:
            return one_third
        elif array[two_third] == target:
            return two_third

        elif target < array[one_third]:
            return rec_ternary_search(UpperCamelCase							,   one_third - 1							,   UpperCamelCase							,   UpperCamelCase      )
        elif array[two_third] < target:
            return rec_ternary_search(two_third + 1							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )
        else:
            return rec_ternary_search(one_third + 1							,   two_third - 1							,   UpperCamelCase							,   UpperCamelCase      )
    else:
        return -1


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _lowerCAmelCase										=     input('''Enter numbers separated by comma:\n''').strip()
 _lowerCAmelCase										=     [int(item.strip()) for item in user_input.split(''',''')]
 assert collection == sorted(collection), F"List must be ordered.\n{collection}."
 _lowerCAmelCase										=     int(input('''Enter the number to be found in the list:\n''').strip())
 _lowerCAmelCase										=     ite_ternary_search(collection, target)
 _lowerCAmelCase										=     rec_ternary_search(0, len(collection) - 1, collection, target)
 if resulta != -1:
  print(F"""Iterative search: {target} found at positions: {resulta}""")
  print(F"""Recursive search: {target} found at positions: {resulta}""")
 else:
  print('''Not found''')


