

'''simple docstring'''
from typing import List

import numpy as np


def        _lowerCAmelCase					(						_UpperCamelCase						:     dict				)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    ={key: len(_UpperCamelCase				) for key, value in gen_kwargs.items() if isinstance(_UpperCamelCase							, _UpperCamelCase				)}
     if len(set(lists_lengths.values()				)				) > 1:
          raise RuntimeError(
              (
                  'Sharding is ambiguous for this dataset: '
                  + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
                  + '\n'.join(f"\t- key {key} has length {length}" for key, length in lists_lengths.items()				)
                  + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
                  + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
              )				)
     _SCREAMING_SNAKE_CASE							    =max(lists_lengths.values()							, default=0				)
     return max(1							, _UpperCamelCase				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     int							, _UpperCamelCase						:     int				)      ->			List[range]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     for group_idx in range(_UpperCamelCase				):
          _SCREAMING_SNAKE_CASE							    =num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
          if num_shards_to_add == 0:
               break
          _SCREAMING_SNAKE_CASE							    =shards_indices_per_group[-1].stop if shards_indices_per_group else 0
          _SCREAMING_SNAKE_CASE							    =range(_UpperCamelCase							, start + num_shards_to_add				)
          shards_indices_per_group.append(_UpperCamelCase				)
     return shards_indices_per_group


def        _lowerCAmelCase					(						_UpperCamelCase						:     dict							, _UpperCamelCase						:     int				)      ->			List[dict]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =_number_of_shards_in_gen_kwargs(_UpperCamelCase				)
     if num_shards == 1:
          return [dict(_UpperCamelCase				)]
     else:
          _SCREAMING_SNAKE_CASE							    =_distribute_shards(num_shards=_UpperCamelCase							, max_num_jobs=_UpperCamelCase				)
          return [
              {
                  key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                  if isinstance(_UpperCamelCase							, _UpperCamelCase				)
                  else value
                  for key, value in gen_kwargs.items()
              }
              for group_idx in range(len(_UpperCamelCase				)				)
          ]


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[dict]				)      ->			dict:
     """simple docstring"""




     return {
         key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
         if isinstance(gen_kwargs_list[0][key]							, _UpperCamelCase				)
         else gen_kwargs_list[0][key]
         for key in gen_kwargs_list[0]
     }







def        _lowerCAmelCase					(						_UpperCamelCase						:     np.random.Generator							, _UpperCamelCase						:     dict				)      ->			dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    ={len(_UpperCamelCase				) for value in gen_kwargs.values() if isinstance(_UpperCamelCase							, _UpperCamelCase				)}
     _SCREAMING_SNAKE_CASE							    ={}
     for size in list_sizes:
          _SCREAMING_SNAKE_CASE							    =list(range(_UpperCamelCase				)				)
          rng.shuffle(indices_per_size[size]				)
     # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
     _SCREAMING_SNAKE_CASE							    =dict(_UpperCamelCase				)
     for key, value in shuffled_kwargs.items():
          if isinstance(_UpperCamelCase							, _UpperCamelCase				):
               _SCREAMING_SNAKE_CASE							    =[value[i] for i in indices_per_size[len(_UpperCamelCase				)]]
     return shuffled_kwargs





'''simple docstring'''




class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[Any]	=     [
				    [],
				    [],
				    [],
				]


	def     _a	(self       ,      lowercase       ,      lowercase							):
				try:
							if len(self.queues[priority]							) >= 100:
										raise OverflowError("""Maximum queue size is 100"""							)
							self.queues[priority].append(lowercase							)
				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 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[str]	=     []


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


	def     _a	(self							):
				if not self.queue:
							raise UnderFlowError("""The queue is empty"""							)
				else:
							A_	:						List[Any]	=     min(self.queue							)
							self.queue.remove(lowercase							)
							return data


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






def 	a			( ):
			'''simple docstring'''





			A_	:						Optional[int]	=     FixedPriorityQueue()
			fpq.enqueue(0						,      10    )
			fpq.enqueue(1						,      70    )
			fpq.enqueue(0						,      1_00    )
			fpq.enqueue(2						,      1    )
			fpq.enqueue(2						,      5    )
			fpq.enqueue(1						,      7    )
			fpq.enqueue(2						,      4    )
			fpq.enqueue(1						,      64    )
			fpq.enqueue(0						,      1_28    )
			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 	a			( ):
			'''simple docstring'''





			A_	:						int	=     ElementPriorityQueue()
			epq.enqueue(10    )
			epq.enqueue(70    )
			epq.enqueue(1_00    )
			epq.enqueue(1    )
			epq.enqueue(5    )
			epq.enqueue(7    )
			epq.enqueue(4    )
			epq.enqueue(64    )
			epq.enqueue(1_28    )
			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()