


'''simple docstring'''







import torch
from torch import nn

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


class  lowerCAmelCase_						(       snake_case__		,    snake_case__      ):





		"""simple docstring"""



		@register_to_config
		def __init__(					self     :							int				,							*,
		    SCREAMING_SNAKE_CASE__     :							int = 4				,							SCREAMING_SNAKE_CASE__     :							int = 7_6_8				,							SCREAMING_SNAKE_CASE__     :							int				,							SCREAMING_SNAKE_CASE__     :							Any				,							):





							'''simple docstring'''

							super().__init__()

							__a       =   nn.Parameter(torch.zeros(SCREAMING_SNAKE_CASE__						)						)

							# parameters for additional clip time embeddings
							__a       =   nn.Linear(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)
							__a       =   nn.Linear(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

							# parameters for encoder hidden states
							__a       =   clip_extra_context_tokens
							__a       =   nn.Linear(
							    SCREAMING_SNAKE_CASE__				,							self.clip_extra_context_tokens * cross_attention_dim						)
							__a       =   nn.Linear(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)
							__a       =   nn.LayerNorm(SCREAMING_SNAKE_CASE__						)


		def __a						(					self     :							Optional[Any]				,							*, SCREAMING_SNAKE_CASE__     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							Tuple						):





							'''simple docstring'''

							if do_classifier_free_guidance:
												# Add the classifier free guidance embeddings to the image embeddings
												__a       =   image_embeddings.shape[0]
												__a       =   self.learned_classifier_free_guidance_embeddings.unsqueeze(0						)
												__a       =   classifier_free_guidance_embeddings.expand(
												    SCREAMING_SNAKE_CASE__				,							-1						)
												__a       =   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]

							__a       =   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, ...
							__a       =   self.embedding_proj(SCREAMING_SNAKE_CASE__						)
							__a       =   self.clip_image_embeddings_project_to_time_embeddings(SCREAMING_SNAKE_CASE__						)
							__a       =   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"
							__a       =   self.clip_extra_context_tokens_proj(SCREAMING_SNAKE_CASE__						)
							__a       =   clip_extra_context_tokens.reshape(SCREAMING_SNAKE_CASE__				,							-1				,							self.clip_extra_context_tokens						)
							__a       =   clip_extra_context_tokens.permute(0				,							2				,							1						)

							__a       =   self.encoder_hidden_states_proj(SCREAMING_SNAKE_CASE__						)
							__a       =   self.text_encoder_hidden_states_norm(SCREAMING_SNAKE_CASE__						)
							__a       =   torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]				,							dim=1						)

							return text_encoder_hidden_states, additive_clip_time_embeddings










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     return " ".join(input_str.split()[::-1]		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






