





'''simple docstring'''

import torch
from torch import nn

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






class    UpperCAmelCase	(       UpperCamelCase__			,       UpperCamelCase__				):
 @register_to_config
 def __init__(				self      :Tuple						,       *,
     lowercase_      :int = 4						,       lowercase_      :int = 7_68						,       lowercase_      :int						,       lowercase_      :Union[str, Any]						,       )->							List[Any]:
     super().__init__()

     A__				=			nn.Parameter(torch.zeros(lowercase_							)							)

     # parameters for additional clip time embeddings
     A__				=			nn.Linear(lowercase_						,       lowercase_							)
     A__				=			nn.Linear(lowercase_						,       lowercase_							)

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



 def      UpperCAmelCase_							(				self      :List[str]						,       *, lowercase_      :List[str]						,       lowercase_      :Dict						,       lowercase_      :Any						,       lowercase_      :str							)->							Any:
     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(
             lowercase_						,       -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(lowercase_							)
     A__				=			self.clip_image_embeddings_project_to_time_embeddings(lowercase_							)
     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(lowercase_							)
     A__				=			clip_extra_context_tokens.reshape(lowercase_						,       -1						,       self.clip_extra_context_tokens							)
     A__				=			clip_extra_context_tokens.permute(0						,       2						,       1							)

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

     return text_encoder_hidden_states, additive_clip_time_embeddings










'''simple docstring'''

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    "stable diffusion controlnet",
    "0.22.0",
    "Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.",
    standard_warn=False,
    stacklevel=3,
)



