






import torch
from torch import nn

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


class   __SCREAMING_SNAKE_CASE(	a_    ,  a_	):
							@register_to_config
							def __init__(     self:      List[Any]       ,	*,
							    UpperCamelCase:      int = 4       ,	UpperCamelCase:      int = 7_68       ,	UpperCamelCase:      int       ,	UpperCamelCase:      List[str]       ,	)		->		Union[str, Any]:
											super().__init__()

											snake_case__									=     nn.Parameter(torch.zeros(UpperCamelCase						)						)

											# parameters for additional clip time embeddings
											snake_case__									=     nn.Linear(UpperCamelCase       ,	UpperCamelCase						)
											snake_case__									=     nn.Linear(UpperCamelCase       ,	UpperCamelCase						)

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


							def lowerCAmelCase_ (     self:      List[str]       ,	*, UpperCamelCase:      List[str]       ,	UpperCamelCase:      List[Any]       ,	UpperCamelCase:      Union[str, Any]       ,	UpperCamelCase:      str						)		->		Dict:
											if do_classifier_free_guidance:
															# Add the classifier free guidance embeddings to the image embeddings
															snake_case__									=     image_embeddings.shape[0]
															snake_case__									=     self.learned_classifier_free_guidance_embeddings.unsqueeze(0						)
															snake_case__									=     classifier_free_guidance_embeddings.expand(
															    UpperCamelCase       ,	-1						)
															snake_case__									=     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]

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

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

											return text_encoder_hidden_states, additive_clip_time_embeddings












from __future__ import annotations
def      a_ (      _A  )			->			bool:







				"""simple docstring"""
				return len(set(_A  )  ) == len(_A  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




