




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


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







@dataclass
class 		lowercase							(     datasets.BuilderConfig	):
     _a											=		1_0_0_0_0
     _a											=		None
     _a											=		None







class 		lowercase							(     datasets.ArrowBasedBuilder	):
     _a											=		ParquetConfig

     def       a__			(						self       )		->							Optional[int]:
           return datasets.DatasetInfo(features=self.config.features       )

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

     def       a__			(						self	,  _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
                 _A							:  List[Any]    =		table_cast(_a	,  self.info.features.arrow_schema       )
           return pa_table



     def       a__			(						self	,  _a       )		->							Tuple:
           _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(_a       )       ):
                 with open(_a	,  """rb"""       ) as f:
                       _A							:  Union[str, 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       )       ):
                                   _A							:  Optional[int]    =		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







from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  List[str]    =		list(snake_case_  )
      _A							:  List[Any]    =		list(snake_case_  )
      _A							:  Tuple    =		0
      for i in range(len(snake_case_  )  ):
            if lista[i] != lista[i]:
                  count += 1
                  _A							:  Optional[Any]    =		"""_"""
      if count > 1:
            return False
      else:
            return "".join(snake_case_  )







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  Optional[Any]    =		[]
      while True:
            _A							:  int    =		["""$"""] * len(snake_case_  )
            _A							:  Any    =		[]
            for i in range(len(snake_case_  )  ):
                  for j in range(i + 1,len(snake_case_  )  ):
                        _A							:  Tuple    =		compare_string(binary[i],binary[j]  )
                        if k is False:
                              _A							:  str    =		"""*"""
                              _A							:  str    =		"""*"""
                              temp.append("""X"""  )
            for i in range(len(snake_case_  )  ):
                  if checka[i] == "$":
                        pi.append(binary[i]  )
            if len(snake_case_  ) == 0:
                  return pi
            _A							:  Dict    =		list(set(snake_case_  )  )







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  List[str]    =		[]
      for minterm in minterms:
            _A							:  Tuple    =		""""""
            for _ in range(snake_case_  ):
                  _A							:  Optional[Any]    =		str(minterm % 2  ) + string
                  minterm //= 2
            temp.append(snake_case_  )
      return temp







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      _A							:  Dict    =		list(snake_case_  )
      _A							:  Tuple    =		list(snake_case_  )
      _A							:  Dict    =		0
      for i in range(len(snake_case_  )  ):
            if lista[i] != lista[i]:
                  count_n += 1
      return count_n == count







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Optional[int]    =		[]
      _A							:  str    =		[0] * len(snake_case_  )
      for i in range(len(chart[0]  )  ):
            _A							:  Union[str, Any]    =		0
            _A							:  Optional[Any]    =		-1
            for j in range(len(snake_case_  )  ):
                  if chart[j][i] == 1:
                        count += 1
                        _A							:  Dict    =		j
            if count == 1:
                  _A							:  int    =		1
      for i in range(len(snake_case_  )  ):
            if select[i] == 1:
                  for j in range(len(chart[0]  )  ):
                        if chart[i][j] == 1:
                              for k in range(len(snake_case_  )  ):
                                    _A							:  int    =		0
                  temp.append(prime_implicants[i]  )
      while True:
            _A							:  Optional[Any]    =		0
            _A							:  Tuple    =		-1
            _A							:  List[Any]    =		0
            for i in range(len(snake_case_  )  ):
                  _A							:  List[str]    =		chart[i].count(1  )
                  if count_n > max_n:
                        _A							:  Optional[int]    =		count_n
                        _A							:  Tuple    =		i

            if max_n == 0:
                  return temp

            temp.append(prime_implicants[rem]  )

            for i in range(len(chart[0]  )  ):
                  if chart[rem][i] == 1:
                        for j in range(len(snake_case_  )  ):
                              _A							:  Optional[int]    =		0







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Optional[int]    =		[[0 for x in range(len(snake_case_  )  )] for x in range(len(snake_case_  )  )]
      for i in range(len(snake_case_  )  ):
            _A							:  List[Any]    =		prime_implicants[i].count("""_"""  )
            for j in range(len(snake_case_  )  ):
                  if is_for_table(prime_implicants[i],binary[j],snake_case_  ):
                        _A							:  Union[str, Any]    =		1

      return chart







def        lowerCAmelCase_			(  ):
      _A							:  Dict    =		int(input("""Enter the no. of variables\n"""  )  )
      _A							:  Dict    =		[
          float(snake_case_  )
          for x in input(
              """Enter the decimal representation of Minterms 'Spaces Separated'\n"""  ).split()
      ]
      _A							:  int    =		decimal_to_binary(snake_case_,snake_case_  )

      _A							:  Optional[Any]    =		check(snake_case_  )
      print("""Prime Implicants are:"""  )
      print(snake_case_  )
      _A							:  int    =		prime_implicant_chart(snake_case_,snake_case_  )

      _A							:  int    =		selection(snake_case_,snake_case_  )
      print("""Essential Prime Implicants are:"""  )
      print(snake_case_  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

