"""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


_a   :	Union[str, Any] 					=     datasets.utils.logging.get_logger(__name__)
@dataclass
class 		__A							(				datasets.BuilderConfig	):
     _UpperCamelCase							:    int	      =	10_000
     _UpperCamelCase							:    Optional[List[str]]	      =	None
     _UpperCamelCase							:    Optional[datasets.Features]	      =	None







class 		__A							(				datasets.ArrowBasedBuilder	):
     _UpperCamelCase							:    List[str]	      =	ParquetConfig






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






     def 					__A						(	self       ,  a__						):
       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		: int						    =  dl_manager.download_and_extract(self.config.data_files						)
       if isinstance(a__       ,  (str, list, tuple)						):
         _lowerCAmelCase		: Union[str, Any]						    =  data_files
         if isinstance(a__       ,  a__						):
           _lowerCAmelCase		: Optional[Any]						    =  [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _lowerCAmelCase		: int						    =  [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		: List[Any]						    =  [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _lowerCAmelCase		: int						    =  [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		: Tuple						    =  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__						):
       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		: List[Any]						    =  table_cast(a__       ,  self.info.features.arrow_schema						)
       return pa_table






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: Union[str, Any]						    =  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		: Tuple						    =  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		: Tuple						    =  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





"""simple docstring"""




class 		__A							(				SCREAMING_SNAKE_CASE_	):
     pass







class 		__A							(				SCREAMING_SNAKE_CASE_	):
     pass







class 		__A							:






     def __init__(	self						):
       _lowerCAmelCase		: Union[str, Any]						    =  [
           [],
           [],
           [],
       ]






     def 					__A						(	self       ,  a__       ,  a__						):
       try:
         if len(self.queues[priority]						) >= 100:
           raise OverflowError("""Maximum queue size is 100"""						)
         self.queues[priority].append(a__						)
       except IndexError:
         raise ValueError("""Valid priorities are 0, 1, and 2"""						)






     def 					__A						(	self						):
       for queue in self.queues:
         if queue:
           return queue.pop(0						)
       raise UnderFlowError("""All queues are empty"""						)






     def __str__(	self						):
       return "\n".join(F"Priority {i}: {q}" for i, q in enumerate(self.queues						)						)







class 		__A							:






     def __init__(	self						):
       _lowerCAmelCase		: int						    =  []






     def 					__A						(	self       ,  a__						):
       if len(self.queue						) == 100:
         raise OverFlowError("""Maximum queue size is 100"""						)
       self.queue.append(a__						)






     def 					__A						(	self						):
       if not self.queue:
         raise UnderFlowError("""The queue is empty"""						)
       else:
         _lowerCAmelCase		: int						    =  min(self.queue						)
         self.queue.remove(a__						)
         return data






     def __str__(	self						):
       return str(self.queue						)







def 							SCREAMING_SNAKE_CASE  (    ) ->   str:
  _lowerCAmelCase		: Union[str, Any]						    =  FixedPriorityQueue()
  fpq.enqueue(0    ,10			)
  fpq.enqueue(1    ,70			)
  fpq.enqueue(0    ,100			)
  fpq.enqueue(2    ,1			)
  fpq.enqueue(2    ,5			)
  fpq.enqueue(1    ,7			)
  fpq.enqueue(2    ,4			)
  fpq.enqueue(1    ,64			)
  fpq.enqueue(0    ,128			)
  print(_lowerCamelCase			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)
  print(_lowerCamelCase			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)





def 							SCREAMING_SNAKE_CASE  (    ) ->   Union[str, Any]:
  _lowerCAmelCase		: Tuple						    =  ElementPriorityQueue()
  epq.enqueue(10			)
  epq.enqueue(70			)
  epq.enqueue(100			)
  epq.enqueue(1			)
  epq.enqueue(5			)
  epq.enqueue(7			)
  epq.enqueue(4			)
  epq.enqueue(64			)
  epq.enqueue(128			)
  print(_lowerCamelCase			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)
  print(_lowerCamelCase			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)


if __name__ == "__main__":
     fixed_priority_queue()
     element_priority_queue()




