# Algorithm for the pigeonhole sorting









def      _a					(   lowercase__	:      int			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		min(lowercase__			)  # min() finds the minimum value
     SCREAMING_SNAKE_CASE__					:					List[str]      =		max(lowercase__			)  # max() finds the maximum value

     SCREAMING_SNAKE_CASE__					:					List[str]      =		max_val - min_val + 1  # size is difference of max and min values plus one

     # list of pigeonholes of size equal to the variable size
     SCREAMING_SNAKE_CASE__					:					Any      =		[0] * size

     # Populate the pigeonholes.
     for x in a:
          assert isinstance(lowercase__  ,	lowercase__			), "integers only please"
          holes[x - min_val] += 1

     # Putting the elements back into the array in an order.
     SCREAMING_SNAKE_CASE__					:					List[str]      =		0
     for count in range(lowercase__			):
          while holes[count] > 0:
               holes[count] -= 1
               SCREAMING_SNAKE_CASE__					:					List[Any]      =		count + min_val
               i += 1







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					str      =		[8, 3, 2, 7, 4, 6, 8]
     pigeonhole_sort(lowercase__			)
     print('Sorted order is:'  ,	' '.join(lowercase__			)			)


if __name__ == "__main__":
      main()






'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:  Optional[int]							       =				logging.get_logger(__name__)

_UpperCamelCase						:  Tuple							       =				{
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     '''canine'''

  def __init__(				self      , _SCREAMING_SNAKE_CASE=7_68      , _SCREAMING_SNAKE_CASE=12      , _SCREAMING_SNAKE_CASE=12      , _SCREAMING_SNAKE_CASE=30_72      , _SCREAMING_SNAKE_CASE="gelu"      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=1_63_84      , _SCREAMING_SNAKE_CASE=16      , _SCREAMING_SNAKE_CASE=0.02      , _SCREAMING_SNAKE_CASE=1e-12      , _SCREAMING_SNAKE_CASE=0      , _SCREAMING_SNAKE_CASE=0Xe0_00      , _SCREAMING_SNAKE_CASE=0Xe0_01      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=8      , _SCREAMING_SNAKE_CASE=1_63_84      , _SCREAMING_SNAKE_CASE=1_28      , **_SCREAMING_SNAKE_CASE      , ):





      '''simple docstring'''

      super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE      , bos_token_id=_SCREAMING_SNAKE_CASE      , eos_token_id=_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							max_position_embeddings
      lowerCAmelCase 						=							hidden_size
      lowerCAmelCase 						=							num_hidden_layers
      lowerCAmelCase 						=							num_attention_heads
      lowerCAmelCase 						=							intermediate_size
      lowerCAmelCase 						=							hidden_act
      lowerCAmelCase 						=							hidden_dropout_prob
      lowerCAmelCase 						=							attention_probs_dropout_prob
      lowerCAmelCase 						=							initializer_range
      lowerCAmelCase 						=							type_vocab_size
      lowerCAmelCase 						=							layer_norm_eps

      # Character config:
      lowerCAmelCase 						=							downsampling_rate
      lowerCAmelCase 						=							upsampling_kernel_size
      lowerCAmelCase 						=							num_hash_functions
      lowerCAmelCase 						=							num_hash_buckets
      lowerCAmelCase 						=							local_transformer_stride





