



'''simple docstring'''





import torch
from torch import nn

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






class __A			(  A   ,   A						):



 '''simple docstring'''







 @register_to_config
 def __init__(self ,   *,
     A = 4 ,   A = 768 ,   A ,   A ,   )    ->     Any:


      """simple docstring"""



      super().__init__()

      _a      =  nn.Parameter(torch.zeros(A       )       )

      # parameters for additional clip time embeddings
      _a      =  nn.Linear(A ,   A       )
      _a      =  nn.Linear(A ,   A       )

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



 def 					a__  (self ,   *, A ,   A ,   A ,   A       )    ->     Optional[int]:


      """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(
               A ,   -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(A       )
      _a      =  self.clip_image_embeddings_project_to_time_embeddings(A       )
      _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(A       )
      _a      =  clip_extra_context_tokens.reshape(A ,   -1 ,   self.clip_extra_context_tokens       )
      _a      =  clip_extra_context_tokens.permute(0 ,   2 ,   1       )

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

      return text_encoder_hidden_states, additive_clip_time_embeddings








'''simple docstring'''





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_		       =  {"configuration_mmbt": ["MMBTConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_		       =  ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]


if TYPE_CHECKING:
  from .configuration_mmbt import MMBTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
  import sys

  lowercase_		       =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



