






"""simple docstring"""



import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__   ):
   @register_to_config
   def __init__(		self      :	Tuple							,			*,
       snake_case__      :	int = 4							,			snake_case__      :	int = 7_6_8							,			snake_case__      :	int							,			snake_case__      :	List[Any]							,			):


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

        UpperCAmelCase__						:    str							=							nn.Parameter(torch.zeros(snake_case__							)							)

        # parameters for additional clip time embeddings
        UpperCAmelCase__						:    List[str]							=							nn.Linear(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Union[str, Any]							=							nn.Linear(snake_case__							,			snake_case__							)

        # parameters for encoder hidden states
        UpperCAmelCase__						:    Union[str, Any]							=							clip_extra_context_tokens
        UpperCAmelCase__						:    Any							=							nn.Linear(
            snake_case__							,			self.clip_extra_context_tokens * cross_attention_dim							)
        UpperCAmelCase__						:    Any							=							nn.Linear(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							nn.LayerNorm(snake_case__							)




   def       __a							(		self      :	List[str]							,			*, snake_case__      :	Optional[Any]							,			snake_case__      :	Dict							,			snake_case__      :	Optional[Any]							,			snake_case__      :	Union[str, Any]							):


        '''simple docstring'''
        if do_classifier_free_guidance:
             # Add the classifier free guidance embeddings to the image embeddings
             UpperCAmelCase__						:    Optional[Any]							=							image_embeddings.shape[0]
             UpperCAmelCase__						:    Optional[Any]							=							self.learned_classifier_free_guidance_embeddings.unsqueeze(0							)
             UpperCAmelCase__						:    Union[str, Any]							=							classifier_free_guidance_embeddings.expand(
                 snake_case__							,			-1							)
             UpperCAmelCase__						:    List[str]							=							torch.cat([classifier_free_guidance_embeddings, image_embeddings]							,			dim=0							)

        # The image embeddings batch size and the text embeddings batch size are equal
        assert image_embeddings.shape[0] == prompt_embeds.shape[0]

        UpperCAmelCase__						:    str							=							prompt_embeds.shape[0]

        # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
        # adding CLIP embeddings to the existing timestep embedding, ...
        UpperCAmelCase__						:    Dict							=							self.embedding_proj(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							self.clip_image_embeddings_project_to_time_embeddings(snake_case__							)
        UpperCAmelCase__						:    List[str]							=							time_projected_image_embeddings + time_projected_prompt_embeds

        # ... and by projecting CLIP embeddings into four
        # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
        UpperCAmelCase__						:    List[Any]							=							self.clip_extra_context_tokens_proj(snake_case__							)
        UpperCAmelCase__						:    Union[str, Any]							=							clip_extra_context_tokens.reshape(snake_case__							,			-1							,			self.clip_extra_context_tokens							)
        UpperCAmelCase__						:    str							=							clip_extra_context_tokens.permute(0							,			2							,			1							)

        UpperCAmelCase__						:    List[Any]							=							self.encoder_hidden_states_proj(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							self.text_encoder_hidden_states_norm(snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]							,			dim=1							)

        return text_encoder_hidden_states, additive_clip_time_embeddings










"""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))


