





'''simple docstring'''




import torch
from torch import nn

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


class 					SCREAMING_SNAKE_CASE       (a__   ,						a__				):


     @register_to_config
     def __init__(		self		,      *,
         _UpperCAmelCase = 4		,      _UpperCAmelCase = 768		,      _UpperCAmelCase		,      _UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__()

          __A		:					str						=			nn.Parameter(torch.zeros(_UpperCAmelCase))

          # parameters for additional clip time embeddings
          __A		:					List[str]						=			nn.Linear(_UpperCAmelCase		,      _UpperCAmelCase)
          __A		:					List[str]						=			nn.Linear(_UpperCAmelCase		,      _UpperCAmelCase)

          # parameters for encoder hidden states
          __A		:					Dict						=			clip_extra_context_tokens
          __A		:					List[Any]						=			nn.Linear(
              _UpperCAmelCase		,      self.clip_extra_context_tokens * cross_attention_dim)
          __A		:					Dict						=			nn.Linear(_UpperCAmelCase		,      _UpperCAmelCase)
          __A		:					List[Any]						=			nn.LayerNorm(_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      *, _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          if do_classifier_free_guidance:
               # Add the classifier free guidance embeddings to the image embeddings
               __A		:					Optional[int]						=			image_embeddings.shape[0]
               __A		:					List[Any]						=			self.learned_classifier_free_guidance_embeddings.unsqueeze(0)
               __A		:					Dict						=			classifier_free_guidance_embeddings.expand(
                   _UpperCAmelCase		,      -1)
               __A		:					Union[str, Any]						=			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		:					List[str]						=			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		:					Any						=			self.embedding_proj(_UpperCAmelCase)
          __A		:					Optional[Any]						=			self.clip_image_embeddings_project_to_time_embeddings(_UpperCAmelCase)
          __A		:					Tuple						=			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		:					Tuple						=			self.clip_extra_context_tokens_proj(_UpperCAmelCase)
          __A		:					int						=			clip_extra_context_tokens.reshape(_UpperCAmelCase		,      -1		,      self.clip_extra_context_tokens)
          __A		:					Optional[Any]						=			clip_extra_context_tokens.permute(0		,      2		,      1)

          __A		:					Union[str, Any]						=			self.encoder_hidden_states_proj(_UpperCAmelCase)
          __A		:					Optional[int]						=			self.text_encoder_hidden_states_norm(_UpperCAmelCase)
          __A		:					Any						=			torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]		,      dim=1)

          return text_encoder_hidden_states, additive_clip_time_embeddings






'''simple docstring'''




import math
import sys






def 			_lowerCAmelCase     (    __snake_case    :					int     )	->	int:
     if number != int(__snake_case     ):
          raise ValueError('the value of input must be a natural number'     )
     if number < 0:
          raise ValueError('the value of input must not be a negative number'     )
     if number == 0:
          return 1
     __A		:					str						=			[-1] * (number + 1)
     __A		:					Dict						=			0
     for i in range(1					,				number + 1     ):
          __A		:					int						=			sys.maxsize
          __A		:					int						=			int(math.sqrt(__snake_case     )     )
          for j in range(1					,				root + 1     ):
               __A		:					str						=			1 + answers[i - (j**2)]
               __A		:					Dict						=			min(__snake_case					,				__snake_case     )
          __A		:					Union[str, Any]						=			answer
     return answers[number]


if __name__ == "__main__":
  import doctest

  doctest.testmod()