



import torch
from torch import nn

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







class 		snake_case						(	SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   @register_to_config
   def __init__(					self							:			List[str]				,		*,
       lowerCAmelCase							:			int = 4				,		lowerCAmelCase							:			int = 768				,		lowerCAmelCase							:			int				,		lowerCAmelCase							:			List[Any]				,		)				->				Optional[Any]:

    """simple docstring"""







    super().__init__()

    _snake_case    :  str					=					nn.Parameter(torch.zeros(lowerCAmelCase))

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

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


   def      UpperCamelCase_  (					self							:			str				,		*, lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			Union[str, Any]				,		lowerCAmelCase							:			Dict)				->				int:

    """simple docstring"""







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

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

    return text_encoder_hidden_states, additive_clip_time_embeddings





import sys

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



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str = N				)  ->     int:
 _snake_case    :  int					=					-sys.maxsize - 1
 for i in range(len(SCREAMING_SNAKE_CASE__				) - 12				):
  _snake_case    :  Optional[int]					=					1
  for j in range(13				):
   product *= int(n[i + j]				)
  if product > largest_product:
   _snake_case    :  List[str]					=					product
 return largest_product


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