




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A        =    logging.get_logger(__name__)

__A        =    {
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}







class     __lowerCAmelCase (       __magic_name__			,				__magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       '''nat'''

			snake_case_   		=       {
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}


			def __init__(      self			,						lowerCamelCase__=4			,						lowerCamelCase__=3			,						lowerCamelCase__=64			,						lowerCamelCase__=[3, 4, 6, 5]			,						lowerCamelCase__=[2, 4, 8, 16]			,						lowerCamelCase__=7			,						lowerCamelCase__=3.0			,						lowerCamelCase__=True			,						lowerCamelCase__=0.0			,						lowerCamelCase__=0.0			,						lowerCamelCase__=0.1			,						lowerCamelCase__="gelu"			,						lowerCamelCase__=0.02			,						lowerCamelCase__=1e-5			,						lowerCamelCase__=0.0			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						**lowerCamelCase__			,						)    ->      List[Any]:



							'''simple docstring'''
							super().__init__(**lowerCamelCase__							)

							__lowerCamelCase		       =    patch_size
							__lowerCamelCase		       =    num_channels
							__lowerCamelCase		       =    embed_dim
							__lowerCamelCase		       =    depths
							__lowerCamelCase		       =    len(lowerCamelCase__							)
							__lowerCamelCase		       =    num_heads
							__lowerCamelCase		       =    kernel_size
							__lowerCamelCase		       =    mlp_ratio
							__lowerCamelCase		       =    qkv_bias
							__lowerCamelCase		       =    hidden_dropout_prob
							__lowerCamelCase		       =    attention_probs_dropout_prob
							__lowerCamelCase		       =    drop_path_rate
							__lowerCamelCase		       =    hidden_act
							__lowerCamelCase		       =    layer_norm_eps
							__lowerCamelCase		       =    initializer_range
							# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							__lowerCamelCase		       =    int(embed_dim * 2 ** (len(lowerCamelCase__							) - 1)							)
							__lowerCamelCase		       =    layer_scale_init_value
							__lowerCamelCase		       =    ['stem'] + [f"""stage{idx}""" for idx in range(1			,						len(lowerCamelCase__							) + 1							)]
							__lowerCamelCase       ,						__lowerCamelCase		       =    get_aligned_output_features_output_indices(
							    out_features=lowerCamelCase__			,						out_indices=lowerCamelCase__			,						stage_names=self.stage_names							)






'''simple docstring'''




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class 		UpperCAmelCase_      (  __lowercase		):





     def __lt__( self       :      Optional[int]    ,      UpperCAmelCase__       :      List[str]    )				->  List[Any]:
        return self[-1] < other[-1]





     def __eq__( self       :      str    ,      UpperCAmelCase__       :      List[str]    )				->  Tuple:
        return self[-1] == other[-1]





def 	a_					( lowerCamelCase	:     list				):
   lowerCAmelCase     =			[]
   # sort into stacks
   for element in collection:
      lowerCAmelCase     =			Stack([element]				)
      lowerCAmelCase     =			bisect_left(lowerCamelCase	, lowerCamelCase				)
      if i != len(lowerCamelCase				):
         stacks[i].append(lowerCamelCase				)
      else:
         stacks.append(lowerCamelCase				)

    # use a heap-based merge to merge stack efficiently
   lowerCAmelCase     =			merge(*(reversed(lowerCamelCase				) for stack in stacks)				)
   return collection


if __name__ == "__main__":
       __snake_case    						=input("""Enter numbers separated by a comma:\n""").strip()
       __snake_case    						=[int(item) for item in user_input.split(""",""")]
       print(patience_sort(unsorted))





