

import torch
from torch import nn

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





class        lowerCAmelCase__  (					__magic_name__				,							__magic_name__			):



       '''simple docstring'''







       @register_to_config
       def __init__(							self ,   *,
           lowercase__ = 4 ,   lowercase__ = 7_6_8 ,   lowercase__ ,   lowercase__ ,   ):







             '''simple docstring'''



             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 ,   *, lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''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(
                       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





def      A__					(			__A  :						str     ,				__A  :						str			)					->str:

      if not (isinstance(__A     ,				__A			) and isinstance(__A     ,				__A			)):
            raise ValueError('''longest_common_substring() takes two strings for inputs'''			)

      __A    =len(__A			)
      __A    =len(__A			)

      __A    =[[0] * (texta_length + 1) for _ in range(texta_length + 1			)]
      __A    =0
      __A    =0

      for i in range(1     ,				texta_length + 1			):
            for j in range(1     ,				texta_length + 1			):
                  if texta[i - 1] == texta[j - 1]:
                        __A    =1 + dp[i - 1][j - 1]
                        if dp[i][j] > ans_length:
                              __A    =i
                              __A    =dp[i][j]

      return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


