


'''simple docstring'''



import torch
from torch import nn

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



class      a    (		__snake_case     ,  __snake_case	):


     """simple docstring"""






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

          __UpperCAmelCase     :			List[Any]  							=      nn.Parameter(torch.zeros(UpperCamelCase__      )      )

          # parameters for additional clip time embeddings
          __UpperCAmelCase     :			Any  							=      nn.Linear(UpperCamelCase__							,			UpperCamelCase__      )
          __UpperCAmelCase     :			Optional[int]  							=      nn.Linear(UpperCamelCase__							,			UpperCamelCase__      )

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






     def 			lowerCamelCase__			(    self					:		List[Any]							,			*, snake_case					:		Union[str, Any]							,			snake_case					:		List[str]							,			snake_case					:		Any							,			snake_case					:		List[str]      )						->							Optional[Any]:


          if do_classifier_free_guidance:
               # Add the classifier free guidance embeddings to the image embeddings
               __UpperCAmelCase     :			Dict  							=      image_embeddings.shape[0]
               __UpperCAmelCase     :			Optional[Any]  							=      self.learned_classifier_free_guidance_embeddings.unsqueeze(0      )
               __UpperCAmelCase     :			int  							=      classifier_free_guidance_embeddings.expand(
                   UpperCamelCase__							,			-1      )
               __UpperCAmelCase     :			List[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]

          __UpperCAmelCase     :			List[Any]  							=      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     :			Tuple  							=      self.clip_image_embeddings_project_to_time_embeddings(UpperCamelCase__      )
          __UpperCAmelCase     :			int  							=      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     :			Union[str, Any]  							=      self.clip_extra_context_tokens_proj(UpperCamelCase__      )
          __UpperCAmelCase     :			Optional[Any]  							=      clip_extra_context_tokens.reshape(UpperCamelCase__							,			-1							,			self.clip_extra_context_tokens      )
          __UpperCAmelCase     :			Dict  							=      clip_extra_context_tokens.permute(0							,			2							,			1      )

          __UpperCAmelCase     :			Dict  							=      self.encoder_hidden_states_proj(UpperCamelCase__      )
          __UpperCAmelCase     :			Dict  							=      self.text_encoder_hidden_states_norm(UpperCamelCase__      )
          __UpperCAmelCase     :			Tuple  							=      torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]							,			dim=1      )

          return text_encoder_hidden_states, additive_clip_time_embeddings



'''simple docstring'''



from argparse import ArgumentParser

from .env import EnvironmentCommand




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[Any]  							=      ArgumentParser('''Diffusers CLI tool'''      , usage='''diffusers-cli <command> [<args>]'''  )
     __UpperCAmelCase     :			Union[str, Any]  							=      parser.add_subparsers(help='''diffusers-cli command helpers'''  )

     # Register commands
     EnvironmentCommand.register_subcommand(_lowercase  )

     # Let's go
     __UpperCAmelCase     :			Union[str, Any]  							=      parser.parse_args()

     if not hasattr(_lowercase      , '''func'''  ):
          parser.print_help()
          exit(1  )

     # Run
     __UpperCAmelCase     :			Any  							=      args.func(_lowercase  )
     service.run()


if __name__ == "__main__":
      main()