import torch
from torch import nn

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





class      A							(				a__		,				a__     ):


   @register_to_config
   def __init__(self     :						Optional[int]       ,  *,
       __UpperCAmelCase     :						int = 4       ,  __UpperCAmelCase     :						List[str] = 7_6_8       ,  __UpperCAmelCase     :						Optional[int]       ,  __UpperCAmelCase     :						Tuple       ,  )						->				Optional[Any]:
         """simple docstring"""



         super().__init__()

         UpperCAmelCase__								=  nn.Parameter(torch.zeros(lowerCAmelCase__		)		)

         # parameters for additional clip time embeddings
         UpperCAmelCase__								=  nn.Linear(lowerCAmelCase__       ,  lowerCAmelCase__		)
         UpperCAmelCase__								=  nn.Linear(lowerCAmelCase__       ,  lowerCAmelCase__		)

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





   def 			lowercase_      (self     :						int       ,  *, __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						Optional[int]       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				Union[str, Any]:
         """simple docstring"""



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

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

         return text_encoder_hidden_states, additive_clip_time_embeddings










"""simple docstring"""




import torch
from torch import nn

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


class        __UpperCamelCase				(						a__		,       a__						):






							@register_to_config
							def __init__(   self      ,					*,
							    lowerCAmelCase__ = 4      ,					lowerCAmelCase__ = 768      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> Optional[Any]:
								super().__init__()

								a      :		Tuple	=				nn.Parameter(torch.zeros(lowerCAmelCase__      )      )

								# parameters for additional clip time embeddings
								a      :		str	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )

								# parameters for encoder hidden states
								a      :		int	=				clip_extra_context_tokens
								a      :		int	=				nn.Linear(
								    lowerCAmelCase__      ,					self.clip_extra_context_tokens * cross_attention_dim      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		str	=				nn.LayerNorm(lowerCAmelCase__      )






							def     __a   (   self      ,					*, lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								if do_classifier_free_guidance:
									# Add the classifier free guidance embeddings to the image embeddings
									a      :		str	=				image_embeddings.shape[0]
									a      :		Optional[int]	=				self.learned_classifier_free_guidance_embeddings.unsqueeze(0      )
									a      :		Any	=				classifier_free_guidance_embeddings.expand(
									    lowerCAmelCase__      ,					-1      )
									a      :		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]

								a      :		List[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, ...
								a      :		Dict	=				self.embedding_proj(lowerCAmelCase__      )
								a      :		List[str]	=				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__      )
								a      :		Dict	=				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"
								a      :		Union[str, Any]	=				self.clip_extra_context_tokens_proj(lowerCAmelCase__      )
								a      :		List[str]	=				clip_extra_context_tokens.reshape(lowerCAmelCase__      ,					-1      ,					self.clip_extra_context_tokens      )
								a      :		Optional[Any]	=				clip_extra_context_tokens.permute(0      ,					2      ,					1      )

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

								return text_encoder_hidden_states, additive_clip_time_embeddings


