

import torch
from torch import nn

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






class 	UpperCAmelCase__		(	A__						,					A__	):






  """simple docstring"""


  @register_to_config
  def __init__(							self						:		Tuple     ,       *,
      __lowerCamelCase						:		int = 4     ,       __lowerCamelCase						:		int = 768     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		Optional[int]     ,       )		->							Tuple:
       super().__init__()

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

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

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







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

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

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

       return text_encoder_hidden_states, additive_clip_time_embeddings





def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ):
          raise ValueError('''multiplicative_persistence() only accepts integral values'''    )
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values'''    )

     SCREAMING_SNAKE_CASE__   =   0
     SCREAMING_SNAKE_CASE__   =   str(_A    )

     while len(_A    ) != 1:
          SCREAMING_SNAKE_CASE__   =   [int(_A    ) for i in num_string]

          SCREAMING_SNAKE_CASE__   =   1
          for i in range(0				,		len(_A    )    ):
               total *= numbers[i]

          SCREAMING_SNAKE_CASE__   =   str(_A    )

          steps += 1
     return steps
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ):
          raise ValueError('''additive_persistence() only accepts integral values'''    )
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values'''    )

     SCREAMING_SNAKE_CASE__   =   0
     SCREAMING_SNAKE_CASE__   =   str(_A    )

     while len(_A    ) != 1:
          SCREAMING_SNAKE_CASE__   =   [int(_A    ) for i in num_string]

          SCREAMING_SNAKE_CASE__   =   0
          for i in range(0				,		len(_A    )    ):
               total += numbers[i]

          SCREAMING_SNAKE_CASE__   =   str(_A    )

          steps += 1
     return steps


if __name__ == "__main__":
       import doctest

       doctest.testmod()


