





import torch
from torch import nn

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



class        _lowerCAmelCase    (			UpperCAmelCase_							,    UpperCAmelCase_							):


  '''simple docstring'''







  @register_to_config
  def __init__(      self      :					int      ,				*,
      UpperCamelCase      :					int = 4      ,				UpperCamelCase      :					int = 7_68      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					str      ,				):


    '''simple docstring'''





    super().__init__()

    _snake_case   :				Tuple														=	nn.Parameter(torch.zeros(UpperCamelCase							)							)

    # parameters for additional clip time embeddings
    _snake_case   :				Optional[Any]														=	nn.Linear(UpperCamelCase      ,				UpperCamelCase							)
    _snake_case   :				int														=	nn.Linear(UpperCamelCase      ,				UpperCamelCase							)

    # parameters for encoder hidden states
    _snake_case   :				Optional[Any]														=	clip_extra_context_tokens
    _snake_case   :				Optional[int]														=	nn.Linear(
        UpperCamelCase      ,				self.clip_extra_context_tokens * cross_attention_dim							)
    _snake_case   :				List[Any]														=	nn.Linear(UpperCamelCase      ,				UpperCamelCase							)
    _snake_case   :				str														=	nn.LayerNorm(UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Optional[Any]      ,				*, UpperCamelCase      :					Any      ,				UpperCamelCase      :					Union[str, Any]      ,				UpperCamelCase      :					Dict      ,				UpperCamelCase      :					List[str]							):


    '''simple docstring'''





    if do_classifier_free_guidance:
      # Add the classifier free guidance embeddings to the image embeddings
      _snake_case   :				str														=	image_embeddings.shape[0]
      _snake_case   :				Union[str, Any]														=	self.learned_classifier_free_guidance_embeddings.unsqueeze(0							)
      _snake_case   :				str														=	classifier_free_guidance_embeddings.expand(
          UpperCamelCase      ,				-1							)
      _snake_case   :				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]

    _snake_case   :				Tuple														=	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, ...
    _snake_case   :				int														=	self.embedding_proj(UpperCamelCase							)
    _snake_case   :				Union[str, Any]														=	self.clip_image_embeddings_project_to_time_embeddings(UpperCamelCase							)
    _snake_case   :				Any														=	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"
    _snake_case   :				Optional[Any]														=	self.clip_extra_context_tokens_proj(UpperCamelCase							)
    _snake_case   :				List[Any]														=	clip_extra_context_tokens.reshape(UpperCamelCase      ,				-1      ,				self.clip_extra_context_tokens							)
    _snake_case   :				Union[str, Any]														=	clip_extra_context_tokens.permute(0      ,				2      ,				1							)

    _snake_case   :				List[Any]														=	self.encoder_hidden_states_proj(UpperCamelCase							)
    _snake_case   :				List[str]														=	self.text_encoder_hidden_states_norm(UpperCamelCase							)
    _snake_case   :				List[str]														=	torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]      ,				dim=1							)

    return text_encoder_hidden_states, additive_clip_time_embeddings











from functools import reduce

lowerCAmelCase_						   =    (
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)


def     lowerCamelCase_	(      lowerCAmelCase:	str = N				)->		int:
  return max(
      # mypy cannot properly interpret reduce
      int(reduce(lambda lowerCAmelCase    ,							lowerCAmelCase				: str(int(lowerCAmelCase				) * int(lowerCAmelCase				)				)    ,							n[i : i + 13]				)				)
      for i in range(len(lowerCAmelCase				) - 12				)				)


if __name__ == "__main__":
      print(F"""{solution() = }""")




