





'''simple docstring'''




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			:       int      ,     *,
     snake_case_			:       int = 4      ,     snake_case_			:       int = 7_6_8      ,     snake_case_			:       int      ,     snake_case_			:       Tuple      ,     ):
      super().__init__()

      _UpperCAmelCase					=  nn.Parameter(torch.zeros(snake_case_       )       )

      # parameters for additional clip time embeddings
      _UpperCAmelCase					=  nn.Linear(snake_case_      ,     snake_case_       )
      _UpperCAmelCase					=  nn.Linear(snake_case_      ,     snake_case_       )

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




 def 	lowercase      (   self			:       Optional[int]      ,     *, snake_case_			:       List[Any]      ,     snake_case_			:       int      ,     snake_case_			:       Tuple      ,     snake_case_			:       Optional[int]       ):

      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(
               snake_case_      ,     -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(snake_case_       )
      _UpperCAmelCase					=  self.clip_image_embeddings_project_to_time_embeddings(snake_case_       )
      _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(snake_case_       )
      _UpperCAmelCase					=  clip_extra_context_tokens.reshape(snake_case_      ,     -1      ,     self.clip_extra_context_tokens       )
      _UpperCAmelCase					=  clip_extra_context_tokens.permute(0      ,     2      ,     1       )

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

      return text_encoder_hidden_states, additive_clip_time_embeddings








from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A				:       Optional[Any]      =  {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A				:       Optional[Any]      =  [
	    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'VanForImageClassification',
	    'VanModel',
	    'VanPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_van import (
		    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VanForImageClassification,
		    VanModel,
		    VanPreTrainedModel,
		)

else:
	import sys

	A				:       Union[str, Any]      =  _LazyModule(__name__, globals()['__file__'], _import_structure)





