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

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


snake_case__      :		Tuple				=       logging.get_logger(__name__)  # pylint: disable=invalid-name





class 						_a   (						_UpperCamelCase       ):

       """simple docstring"""





       def __init__(					self       ,					_snake_case       ,					_snake_case=768	):
        super().__init__(_snake_case	)
        _UpperCAmelCase 				=proj_size

        _UpperCAmelCase 				=CLIPVisionModel(_snake_case	)
        _UpperCAmelCase 				=PaintByExampleMapper(_snake_case	)
        _UpperCAmelCase 				=nn.LayerNorm(config.hidden_size	)
        _UpperCAmelCase 				=nn.Linear(config.hidden_size       ,					self.proj_size	)

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

       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case=False	):
        _UpperCAmelCase 				=self.model(pixel_values=_snake_case	)
        _UpperCAmelCase 				=clip_output.pooler_output
        _UpperCAmelCase 				=self.mapper(latent_states[:, None]	)
        _UpperCAmelCase 				=self.final_layer_norm(_snake_case	)
        _UpperCAmelCase 				=self.proj_out(_snake_case	)
        if return_uncond_vector:
         return latent_states, self.uncond_vector

        return latent_states







class 						_a   (						nn.Module       ):

       """simple docstring"""





       def __init__(					self       ,					_snake_case	):
        super().__init__()
        _UpperCAmelCase 				=(config.num_hidden_layers + 1) // 5
        _UpperCAmelCase 				=config.hidden_size
        _UpperCAmelCase 				=1
        _UpperCAmelCase 				=nn.ModuleList(
            [
                BasicTransformerBlock(_snake_case       ,					_snake_case       ,					_snake_case       ,					activation_fn="gelu"       ,					attention_bias=_snake_case	)
                for _ in range(_snake_case	)
            ]	)

       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case	):
        for block in self.blocks:
         _UpperCAmelCase 				=block(_snake_case	)

        return hidden_states










"""simple docstring"""




from __future__ import annotations

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






@total_ordering
class       UpperCAmelCase_    (							_UpperCamelCase		):



      def __lt__(	self	:      int	,      A	:      Dict		):
           return self[-1] < other[-1]



      def __eq__(	self	:      Union[str, Any]	,      A	:      Union[str, Any]		):
           return self[-1] == other[-1]







def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		list							)    ->  list:





     '''simple docstring'''

     _UpperCAmelCase     :      list[Stack]														=       []
     # sort into stacks
     for element in collection:
          _UpperCAmelCase     :      Any														=       Stack([element]							)
          _UpperCAmelCase     :      str														=       bisect_left(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          if i != len(SCREAMING_SNAKE_CASE__							):
               stacks[i].append(SCREAMING_SNAKE_CASE__							)
          else:
               stacks.append(SCREAMING_SNAKE_CASE__							)

    # use a heap-based merge to merge stack efficiently
     _UpperCAmelCase     :      Optional[Any]														=       merge(*(reversed(SCREAMING_SNAKE_CASE__							) for stack in stacks)							)
     return collection


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