
"""simple docstring"""



import torch
from torch import nn

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





class 				a			(					_UpperCamelCase						,		_UpperCamelCase      ):



      @register_to_config
      def __init__(   self     ,	*,
          UpperCamelCase_ = 4     ,	UpperCamelCase_ = 768     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	):
          super().__init__()

          UpperCAmelCase__	:      Tuple     							=					nn.Parameter(torch.zeros(UpperCamelCase_      )      )

          # parameters for additional clip time embeddings
          UpperCAmelCase__	:      List[Any]     							=					nn.Linear(UpperCamelCase_     ,	UpperCamelCase_      )
          UpperCAmelCase__	:      Dict     							=					nn.Linear(UpperCamelCase_     ,	UpperCamelCase_      )

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



      def  __snake_case							(   self     ,	*, UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):





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

          UpperCAmelCase__	:      Tuple     							=					self.encoder_hidden_states_proj(UpperCamelCase_      )
          UpperCAmelCase__	:      List[str]     							=					self.text_encoder_hidden_states_norm(UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[int]     							=					torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]     ,	dim=1      )

          return text_encoder_hidden_states, additive_clip_time_embeddings








# Function to print upper half of diamond (pyramid)






def    _A	(   lowerCamelCase ):
   for i in range(0				,			lowerCamelCase ):
      for _ in range(0				,			n - i - 1 ):  # printing spaces
         print(" "				,			end="" )
      for _ in range(0				,			i + 1 ):  # printing stars
         print("* "				,			end="" )
      print()






def    _A	(   lowerCamelCase ):
   for i in range(lowerCamelCase				,			0				,			-1 ):
      for _ in range(lowerCamelCase				,			0				,			-1 ):  # printing stars
         print("* "				,			end="" )
      print()
      for _ in range(n - i + 1				,			0				,			-1 ):  # printing spaces
         print(" "				,			end="" )






def    _A	(   lowerCamelCase ):
   if n <= 0:
      print("       ...       ....        nothing printing :(" )
      return
   floyd(lowerCamelCase )  # upper half
   reverse_floyd(lowerCamelCase )  # lower half


if __name__ == "__main__":
  print(R"""| /\ | |- |  |-  |--| |\  /| |-""")
  print(R"""|/  \| |- |_ |_  |__| | \/ | |_""")
  SCREAMING_SNAKE_CASE__							:      Dict         =    1
  while K:
    SCREAMING_SNAKE_CASE__							:      str         =    int(input("""enter the number and , and see the magic : """))
    print()
    pretty_print(user_number)
    SCREAMING_SNAKE_CASE__							:      Tuple         =    int(input("""press 0 to exit... and 1 to continue..."""))

  print("""Good Bye...""")

