
'''simple docstring'''







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: list[list]							):



						"""simple docstring"""





						lowercase_	:     Union[str, Any]													=       current_set.copy()
						for row_index, row in enumerate(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     List[Any]													=       row[0]
												for column_index, column in enumerate(__SCREAMING_SNAKE_CASE							):
																		if magnitude == 0:
																								lowercase_	:     Dict													=       column
																								continue
																		lowercase_	:     Any													=       column / magnitude
    # Subtract to cancel term
						lowercase_	:     Union[str, Any]													=       current_set[0]
						lowercase_	:     Dict													=       [first_row]
						lowercase_	:     Optional[Any]													=       current_set[1::]
						for row in current_set:
												lowercase_	:     Optional[Any]													=       []
												# If first term is 0, it is already in form we want, so we preserve it
												if row[0] == 0:
																		final_set.append(__SCREAMING_SNAKE_CASE							)
																		continue
												for column_index in range(len(__SCREAMING_SNAKE_CASE							)							):
																		temp_row.append(first_row[column_index] - row[column_index]							)
												final_set.append(__SCREAMING_SNAKE_CASE							)
						# Create next recursion iteration set
						if len(final_set[0]							) != 3:
												lowercase_	:     List[Any]													=       final_set[0]
												lowercase_	:     Any													=       []
												lowercase_	:     List[str]													=       []
												for row in final_set[1::]:
																		current_first_column.append(row[0]							)
																		next_iteration.append(row[1::]							)
												lowercase_	:     Optional[Any]													=       simplify(__SCREAMING_SNAKE_CASE							)
												for i in range(len(__SCREAMING_SNAKE_CASE							)							):
																		resultant[i].insert(0      ,      current_first_column[i]							)
												resultant.insert(0      ,      __SCREAMING_SNAKE_CASE							)
												lowercase_	:     int													=       resultant
						return final_set
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: list[list]							):



						"""simple docstring"""





						if len(__SCREAMING_SNAKE_CASE							) == 0:
												raise IndexError('''solve_simultaneous() requires n lists of length n+1'''							)
						lowercase_	:     Dict													=       len(__SCREAMING_SNAKE_CASE							) + 1
						if any(len(__SCREAMING_SNAKE_CASE							) != _length for item in equations							):
												raise IndexError('''solve_simultaneous() requires n lists of length n+1'''							)
						for row in equations:
												if any(not isinstance(__SCREAMING_SNAKE_CASE      ,      (int, float)							) for column in row							):
																		raise ValueError('''solve_simultaneous() requires lists of integers'''							)
						if len(__SCREAMING_SNAKE_CASE							) == 1:
												return [equations[0][-1] / equations[0][0]]
						lowercase_	:     Optional[int]													=       equations.copy()
						if any(0 in row for row in data_set							):
												lowercase_	:     Tuple													=       data_set.copy()
												lowercase_	:     Any													=       []
												for row_index, row in enumerate(__SCREAMING_SNAKE_CASE							):
																		if 0 not in row:
																								lowercase_	:     Optional[Any]													=       data_set.pop(__SCREAMING_SNAKE_CASE							)
																								break
												if not full_row:
																		raise ValueError('''solve_simultaneous() requires at least 1 full equation'''							)
												data_set.insert(0      ,      __SCREAMING_SNAKE_CASE							)
						lowercase_	:     int													=       data_set.copy()
						lowercase_	:     List[str]													=       simplify(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     Optional[int]													=       simplified[::-1]
						lowercase_	:     list													=       []
						for row in simplified:
												lowercase_	:     Dict													=       row[-1]
												if not solutions:
																		if row[-2] == 0:
																								solutions.append(0							)
																								continue
																		solutions.append(current_solution / row[-2]							)
																		continue
												lowercase_	:     Optional[int]													=       row.copy()[: len(__SCREAMING_SNAKE_CASE							) - 1 :]
												while temp_row[0] == 0:
																		temp_row.pop(0							)
												if len(__SCREAMING_SNAKE_CASE							) == 0:
																		solutions.append(0							)
																		continue
												lowercase_	:     List[str]													=       temp_row[1::]
												lowercase_	:     Tuple													=       temp_row[::-1]
												for column_index, column in enumerate(__SCREAMING_SNAKE_CASE							):
																		current_solution -= column * solutions[column_index]
												solutions.append(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     str													=       []
						for item in solutions:
												final.append(float(round(__SCREAMING_SNAKE_CASE      ,      5							)							)							)
						return final[::-1]


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							_lowercase :  Optional[Any]    							=       [
							    [2, 1, 1, 1, 1, 4],
							    [1, 2, 1, 1, 1, 5],
							    [1, 1, 2, 1, 1, 6],
							    [1, 1, 1, 2, 1, 7],
							    [1, 1, 1, 1, 2, 8],
							]
							print(solve_simultaneous(eq))
							print(solve_simultaneous([[4, 2]]))







"""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 						_A  (  datasets.BuilderConfig  ):







    """simple docstring"""




    UpperCAmelCase       :							int	=       1_0_0_0_0
    UpperCAmelCase       :							Optional[List[str]]	=       None
    UpperCAmelCase       :							Optional[datasets.Features]	=       None






class 						_A  (  datasets.ArrowBasedBuilder  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       ParquetConfig






    def 	__snake_case							(					self				:    Tuple):
      return datasets.DatasetInfo(features=self.config.features)






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






    def 	__snake_case							(					self				:    List[str]				, __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
        a						:		Optional[int]      							=			table_cast(__UpperCAmelCase				, self.info.features.arrow_schema)
      return pa_table






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






