

from typing import List

from .keymap import KEYMAP, get_character




def 					_lowercase     (				a_				:					str				)				->	Dict:




					'''simple docstring'''







					def decorator(a_				:					Any				):
										__magic_name__							=    getattr(a_					,'handle_key'					,[]				)
										handle += [key]
										setattr(a_					,'handle_key'					,a_				)
										return func

					return decorator




def 					_lowercase     (				*a_				:					List[str]				)				->	List[Any]:




					'''simple docstring'''







					def decorator(a_				:					Tuple				):
										__magic_name__							=    getattr(a_					,'handle_key'					,[]				)
										handle += keys
										setattr(a_					,'handle_key'					,a_				)
										return func

					return decorator




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):







	def __new__(   cls: Optional[int]  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    super().__new__(cls  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )
						if not hasattr(__UpperCamelCase  , 'key_handler'      ):
											setattr(__UpperCamelCase  , 'key_handler'  , {}      )
						setattr(__UpperCamelCase  , 'handle_input'  , KeyHandler.handle_input      )

						for value in attrs.values():
											__magic_name__							=    getattr(__UpperCamelCase  , 'handle_key'  , []      )
											for key in handled_keys:
																__magic_name__							=    value
						return new_cls







	@staticmethod
	def 	_SCREAMING_SNAKE_CASE							(   cls: Any      ):

						'''simple docstring'''





						__magic_name__							=    get_character()
						if char != KEYMAP["undefined"]:
											__magic_name__							=    ord(__UpperCamelCase      )
						__magic_name__							=    cls.key_handler.get(__UpperCamelCase      )
						if handler:
											__magic_name__							=    char
											return handler(cls      )
						else:
											return None




def 					_lowercase     (				cls				:					str				)				->	str:




					'''simple docstring'''







					return KeyHandler(cls.__name__					,cls.__bases__					,cls.__dict__.copy()				)







import torch
from torch import nn

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

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			):







	@register_to_config
	def __init__(   self: Optional[Any]  , *,
	    __UpperCamelCase: int = 4  , __UpperCamelCase: int = 7_68  , __UpperCamelCase: int  , __UpperCamelCase: Any  , ):

						'''simple docstring'''





						super().__init__()

						__magic_name__							=    nn.Parameter(torch.zeros(__UpperCamelCase      )      )

						# parameters for additional clip time embeddings
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )

						# parameters for encoder hidden states
						__magic_name__							=    clip_extra_context_tokens
						__magic_name__							=    nn.Linear(
						    __UpperCamelCase  , self.clip_extra_context_tokens * cross_attention_dim      )
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    nn.LayerNorm(__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , *, __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Any      ):

						'''simple docstring'''





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

						__magic_name__							=    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, ...
						__magic_name__							=    self.embedding_proj(__UpperCamelCase      )
						__magic_name__							=    self.clip_image_embeddings_project_to_time_embeddings(__UpperCamelCase      )
						__magic_name__							=    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"
						__magic_name__							=    self.clip_extra_context_tokens_proj(__UpperCamelCase      )
						__magic_name__							=    clip_extra_context_tokens.reshape(__UpperCamelCase  , -1  , self.clip_extra_context_tokens      )
						__magic_name__							=    clip_extra_context_tokens.permute(0  , 2  , 1      )

						__magic_name__							=    self.encoder_hidden_states_proj(__UpperCamelCase      )
						__magic_name__							=    self.text_encoder_hidden_states_norm(__UpperCamelCase      )
						__magic_name__							=    torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]  , dim=1      )

						return text_encoder_hidden_states, additive_clip_time_embeddings




