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


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





@dataclass
class      A							(				datasets.BuilderConfig     ):
   __UpperCAmelCase : int      =			1_00_00
   __UpperCAmelCase : Optional[List[str]]      =			None
   __UpperCAmelCase : Optional[datasets.Features]      =			None





class      A							(				datasets.ArrowBasedBuilder     ):
   __UpperCAmelCase : List[Any]      =			ParquetConfig


   def 			lowercase_      (self     :						Optional[Any]		)						->				Optional[Any]:
         """simple docstring"""



         return datasets.DatasetInfo(features=self.config.features		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						int		)						->				Union[str, Any]:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						pa.Table		)						->				pa.Table:
         """simple docstring"""



         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
               UpperCAmelCase__								=  table_cast(__UpperCAmelCase       ,  self.info.features.arrow_schema		)
         return pa_table





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Optional[Any]		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  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:
                     UpperCAmelCase__								=  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		)		):
                                 UpperCAmelCase__								=  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




def    lowerCAmelCase_ (     __A,  __A							)     ->				None:







      '''simple docstring'''


      UpperCAmelCase__								=  len(__A							)
      print("The following activities are selected:"							)

      # The first activity is always selected
      UpperCAmelCase__								=  0
      print(__A,  end=","							)

      # Consider rest of the activities
      for j in range(__A							):
            # If this activity has start time greater than
            # or equal to the finish time of previously
            # selected activity, then select it
            if start[j] >= finish[i]:
                  print(__A,  end=","							)
                  UpperCAmelCase__								=  j


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase__											=					[1, 3, 0, 5, 8, 5]
      UpperCamelCase__											=					[2, 4, 6, 7, 9, 9]
      print_max_activities(start, finish)



