



import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


a_   :Optional[Any]        =      logging.get_logger(__name__)  # pylint: disable=invalid-name




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Optional[int], _snake_case  :  Any, _snake_case  :  List[str]=7_6_8					)   ->str:
            super().__init__(_snake_case					)
            snake_case__							:	Any   	=	proj_size

            snake_case__							:	Dict   	=	CLIPVisionModel(_snake_case					)
            snake_case__							:	List[str]   	=	PaintByExampleMapper(_snake_case					)
            snake_case__							:	Optional[Any]   	=	nn.LayerNorm(config.hidden_size					)
            snake_case__							:	int   	=	nn.Linear(config.hidden_size, self.proj_size					)

            # uncondition for scaling
            snake_case__							:	str   	=	nn.Parameter(torch.randn((1, 1, self.proj_size)					)					)






     def    lowercase_   (       self  :  List[Any], _snake_case  :  Optional[int], _snake_case  :  int=False					)   ->Dict:
            snake_case__							:	Union[str, Any]   	=	self.model(pixel_values=_snake_case					)
            snake_case__							:	int   	=	clip_output.pooler_output
            snake_case__							:	str   	=	self.mapper(latent_states[:, None]					)
            snake_case__							:	List[Any]   	=	self.final_layer_norm(_snake_case					)
            snake_case__							:	Dict   	=	self.proj_out(_snake_case					)
            if return_uncond_vector:
                   return latent_states, self.uncond_vector

            return latent_states




class 					snake_case__						(       nn.Module     ):






     """simple docstring"""


     def __init__(       self  :  str, _snake_case  :  Optional[int]					)   ->Tuple:
            super().__init__()
            snake_case__							:	Tuple   	=	(config.num_hidden_layers + 1) // 5
            snake_case__							:	int   	=	config.hidden_size
            snake_case__							:	Optional[Any]   	=	1
            snake_case__							:	str   	=	nn.ModuleList(
                [
                    BasicTransformerBlock(_snake_case, _snake_case, _snake_case, activation_fn='gelu', attention_bias=_snake_case					)
                    for _ in range(_snake_case					)
                ]					)






     def    lowercase_   (       self  :  List[str], _snake_case  :  Any					)   ->Union[str, Any]:
            for block in self.blocks:
                   snake_case__							:	Tuple   	=	block(_snake_case					)

            return hidden_states












"""simple docstring"""






def      _snake_case			(				lowercase__		):
				stooge(lowercase__							,    0							,    len(lowercase__		) - 1		)
				return arr







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				if i >= h:
								return

				# If first element is smaller than the last then swap them
				if arr[i] > arr[h]:
								_lowerCamelCase, _lowerCamelCase     :			Optional[Any]    =    arr[h], arr[i]

				# If there are more than 2 elements in the array
				if h - i + 1 > 2:
								_lowerCamelCase     :			Union[str, Any]    =    (int)((h - i + 1) / 3		)

								# Recursively sort first 2/3 elements
								stooge(lowercase__							,    lowercase__							,    (h - t)		)

								# Recursively sort last 2/3 elements
								stooge(lowercase__							,    i + t							,    (lowercase__)		)

								# Recursively sort first 2/3 elements
								stooge(lowercase__							,    lowercase__							,    (h - t)		)


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