
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class      _UpperCAmelCase							(		_snake_case     ,			_snake_case							):




      @register_to_config
      def __init__(							self   ,       *,
          lowercase_ = 4   ,       lowercase_ = 7_6_8   ,       lowercase_   ,       lowercase_   ,       )						->					Optional[int]:
          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     a_      (							self   ,       *, lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_							)						->					List[Any]:
          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"""

from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE_    					=						Lock()




def     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Optional[Any] ,				lowerCAmelCase				:					str ,				lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					Optional[Any] ) ->      Optional[int]:






    """simple docstring"""




    global process_lock

    # we perform n swaps since after n swaps we know we are sorted
    # we *could* stop early if we are sorted already, but it takes as long to
    # find out we are sorted as it does to sort the list with this algorithm
    for i in range(0 ,				10 ):
        if (i + position) % 2 == 0 and r_send is not None:
            # send your value to your right neighbor
            process_lock.acquire()
            r_send[1].send(lowerCAmelCase )
            process_lock.release()

            # receive your right neighbor's value
            process_lock.acquire()
            UpperCAmelCase									=					rr_cv[0].recv()
            process_lock.release()

            # take the lower value since you are on the left
            UpperCAmelCase									=					min(lowerCAmelCase ,				lowerCAmelCase )
        elif (i + position) % 2 != 0 and l_send is not None:
            # send your value to your left neighbor
            process_lock.acquire()
            l_send[1].send(lowerCAmelCase )
            process_lock.release()

            # receive your left neighbor's value
            process_lock.acquire()
            UpperCAmelCase									=					lr_cv[0].recv()
            process_lock.release()

            # take the higher value since you are on the right
            UpperCAmelCase									=					max(lowerCAmelCase ,				lowerCAmelCase )
    # after all swaps are performed, send the values back to main
    result_pipe[1].send(lowerCAmelCase )




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ) ->      List[str]:






    """simple docstring"""




    UpperCAmelCase									=					[]
    UpperCAmelCase									=					[]
    # initialize the list of pipes where the values will be retrieved
    for _ in arr:
        result_pipe.append(Pipe() )
    # creates the processes
    # the first and last process only have one neighbor so they are made outside
    # of the loop
    UpperCAmelCase									=					Pipe()
    UpperCAmelCase									=					Pipe()
    process_array_.append(
        Process(
            target=lowerCAmelCase ,				args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) ,				) )
    UpperCAmelCase									=					temp_rs
    UpperCAmelCase									=					temp_rr

    for i in range(1 ,				len(lowerCAmelCase ) - 1 ):
        UpperCAmelCase									=					Pipe()
        UpperCAmelCase									=					Pipe()
        process_array_.append(
            Process(
                target=lowerCAmelCase ,				args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) ,				) )
        UpperCAmelCase									=					temp_rs
        UpperCAmelCase									=					temp_rr

    process_array_.append(
        Process(
            target=lowerCAmelCase ,				args=(
                len(lowerCAmelCase ) - 1,
                arr[len(lowerCAmelCase ) - 1],
                temp_ls,
                None,
                temp_lr,
                None,
                result_pipe[len(lowerCAmelCase ) - 1],
            ) ,				) )

    # start the processes
    for p in process_array_:
        p.start()

    # wait for the processes to end and write their values to the list
    for p in range(0 ,				len(lowerCAmelCase ) ):
        UpperCAmelCase									=					result_pipe[p][0].recv()
        process_array_[p].join()
    return arr




def     lowercase__						(					) ->      Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase									=					list(range(10 ,				0 ,				-1 ) )
    print('Initial List' )
    print(*lowerCAmelCase )
    UpperCAmelCase									=					odd_even_transposition(lowerCAmelCase )
    print('Sorted List\n' )
    print(*lowerCAmelCase )


if __name__ == "__main__":
   main()



