






"""simple docstring"""



from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin




@dataclass
class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    =42




class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__   ):
   @register_to_config
   def __init__(		self      :	Union[str, Any]							,			snake_case__      :	int = 1_6							,			snake_case__      :	int = 8_8							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	int = 1							,			snake_case__      :	float = 0.0							,			snake_case__      :	int = 3_2							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	bool = False							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	str = "geglu"							,			snake_case__      :	bool = True							,			snake_case__      :	bool = True							,			):


        '''simple docstring'''
        super().__init__()
        UpperCAmelCase__						:    Any							=							num_attention_heads
        UpperCAmelCase__						:    Tuple							=							attention_head_dim
        UpperCAmelCase__						:    Any							=							num_attention_heads * attention_head_dim

        UpperCAmelCase__						:    Dict							=							in_channels

        UpperCAmelCase__						:    Dict							=							torch.nn.GroupNorm(num_groups=snake_case__							,			num_channels=snake_case__							,			eps=1e-6							,			affine=snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							nn.Linear(snake_case__							,			snake_case__							)

        # 3. Define transformers blocks
        UpperCAmelCase__						:    Optional[Any]							=							nn.ModuleList(
            [
                BasicTransformerBlock(
                    snake_case__							,			snake_case__							,			snake_case__							,			dropout=snake_case__							,			cross_attention_dim=snake_case__							,			activation_fn=snake_case__							,			attention_bias=snake_case__							,			double_self_attention=snake_case__							,			norm_elementwise_affine=snake_case__							,			)
                for d in range(snake_case__							)
            ]							)

        UpperCAmelCase__						:    List[str]							=							nn.Linear(snake_case__							,			snake_case__							)




   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	Dict							,			snake_case__      :	Any=None							,			snake_case__      :	List[Any]=None							,			snake_case__      :	Tuple=None							,			snake_case__      :	Dict=1							,			snake_case__      :	int=None							,			snake_case__      :	bool = True							,			):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							hidden_states.shape
        UpperCAmelCase__						:    List[str]							=							batch_frames // num_frames

        UpperCAmelCase__						:    Optional[Any]							=							hidden_states

        UpperCAmelCase__						:    Optional[Any]							=							hidden_states[None, :].reshape(snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    int							=							hidden_states.permute(0							,			2							,			1							,			3							,			4							)

        UpperCAmelCase__						:    Optional[Any]							=							self.norm(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							hidden_states.permute(0							,			3							,			4							,			2							,			1							).reshape(batch_size * height * width							,			snake_case__							,			snake_case__							)

        UpperCAmelCase__						:    int							=							self.proj_in(snake_case__							)

        # 2. Blocks
        for block in self.transformer_blocks:
             UpperCAmelCase__						:    Any							=							block(
                 snake_case__							,			encoder_hidden_states=snake_case__							,			timestep=snake_case__							,			cross_attention_kwargs=snake_case__							,			class_labels=snake_case__							,			)

        # 3. Output
        UpperCAmelCase__						:    Union[str, Any]							=							self.proj_out(snake_case__							)
        UpperCAmelCase__						:    Any							=							(
            hidden_states[None, None, :]
            .reshape(snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
            .permute(0							,			3							,			4							,			1							,			2							)
            .contiguous()
        )
        UpperCAmelCase__						:    Union[str, Any]							=							hidden_states.reshape(snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							hidden_states + residual

        if not return_dict:
             return (output,)

        return TransformerTemporalModelOutput(sample=snake_case__							)










"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Tuple				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    List[str]							=							len(snake_case				)
     for i in range(length - 1				):
          UpperCAmelCase__						:    Any							=							i
          for k in range(i + 1			,					snake_case				):
               if collection[k] < collection[least]:
                    UpperCAmelCase__						:    Union[str, Any]							=							k
          if least != i:
               UpperCAmelCase__							,				UpperCAmelCase__						:    Union[str, Any]							=							(collection[i], collection[least])
     return collection


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


