



import torch
from torch import nn

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






class 						__magic_name__    (       _snake_case	,						_snake_case    ):


 @register_to_config
 def __init__(						self		: Optional[Any]			,						*,
     lowerCAmelCase__		: int = 4			,						lowerCAmelCase__		: int = 7_6_8			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: Optional[int]			,						)    ->      Optional[int]:
   super().__init__()

   UpperCAmelCase					      =  nn.Parameter(torch.zeros(lowerCAmelCase__    )    )

   # parameters for additional clip time embeddings
   UpperCAmelCase					      =  nn.Linear(lowerCAmelCase__			,						lowerCAmelCase__    )
   UpperCAmelCase					      =  nn.Linear(lowerCAmelCase__			,						lowerCAmelCase__    )

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


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

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

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

   return text_encoder_hidden_states, additive_clip_time_embeddings






def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):
  if n == 0:
    return 1

  elif n % 2 == 1:
    return (binary_exponentiation(__A   ,       n - 1   ,       __A			) * a) % mod

  else:
    UpperCAmelCase					      =  binary_exponentiation(__A   ,       n / 2   ,       __A			)
    return (b * b) % mod


# a prime number
lowerCAmelCase__       = 701

lowerCAmelCase__       = 1000000000
lowerCAmelCase__       = 10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)





print((a / b) % p == (a * b ** (p - 2)) % p)



