




"""simple docstring"""






import torch
from torch import nn

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






class lowerCAmelCase__			(  lowercase,			lowercase		):






			'''simple docstring'''



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

							_lowerCamelCase  :    Dict					=					nn.Parameter(torch.zeros(lowercase						)						)

							# parameters for additional clip time embeddings
							_lowerCamelCase  :    List[str]					=					nn.Linear(lowercase						,   lowercase						)
							_lowerCamelCase  :    int					=					nn.Linear(lowercase						,   lowercase						)

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






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

							_lowerCamelCase  :    Optional[int]					=					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, ...
							_lowerCamelCase  :    Any					=					self.embedding_proj(lowercase						)
							_lowerCamelCase  :    str					=					self.clip_image_embeddings_project_to_time_embeddings(lowercase						)
							_lowerCamelCase  :    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"
							_lowerCamelCase  :    Optional[Any]					=					self.clip_extra_context_tokens_proj(lowercase						)
							_lowerCamelCase  :    str					=					clip_extra_context_tokens.reshape(lowercase						,   -1						,   self.clip_extra_context_tokens						)
							_lowerCamelCase  :    Any					=					clip_extra_context_tokens.permute(0						,   2						,   1						)

							_lowerCamelCase  :    int					=					self.encoder_hidden_states_proj(lowercase						)
							_lowerCamelCase  :    List[str]					=					self.text_encoder_hidden_states_norm(lowercase						)
							_lowerCamelCase  :    Union[str, Any]					=					torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]						,   dim=1						)

							return text_encoder_hidden_states, additive_clip_time_embeddings





"""simple docstring"""






def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    Optional[int]					=					int(lowercase__	)
				if decimal in (0, 1):  # Exit cases for the recursion
								return str(lowercase__	)
				_lowerCamelCase,				_lowerCamelCase  :    Dict					=					divmod(lowercase__							,					2	)
				return binary_recursive(lowercase__	) + str(lowercase__	)







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[Any]					=					str(lowercase__	).strip()
				if not number:
								raise ValueError('No input value was provided'	)
				_lowerCamelCase  :    str					=					'-' if number.startswith('-'	) else ''
				_lowerCamelCase  :    Union[str, Any]					=					number.lstrip('-'	)
				if not number.isnumeric():
								raise ValueError('Input value is not an integer'	)
				return f'''{negative}0b{binary_recursive(int(lowercase__	)	)}'''


if __name__ == "__main__":
			from doctest import testmod

			testmod()