





'''simple docstring'''




import torch
from torch import nn

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


class 			SCREAMING_SNAKE_CASE   (a__ ,		a__							):


     @register_to_config
     def __init__(						self						,     *,
         _UpperCAmelCase = 4						,     _UpperCAmelCase = 768						,     _UpperCAmelCase						,     _UpperCAmelCase						,     ):

          '''simple docstring'''






          super().__init__()

          __A						:			Tuple								=	nn.Parameter(torch.zeros(_UpperCAmelCase))

          # parameters for additional clip time embeddings
          __A						:			int								=	nn.Linear(_UpperCAmelCase						,     _UpperCAmelCase)
          __A						:			List[str]								=	nn.Linear(_UpperCAmelCase						,     _UpperCAmelCase)

          # parameters for encoder hidden states
          __A						:			str								=	clip_extra_context_tokens
          __A						:			Optional[int]								=	nn.Linear(
              _UpperCAmelCase						,     self.clip_extra_context_tokens * cross_attention_dim)
          __A						:			Union[str, Any]								=	nn.Linear(_UpperCAmelCase						,     _UpperCAmelCase)
          __A						:			Tuple								=	nn.LayerNorm(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     *, _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          if do_classifier_free_guidance:
               # Add the classifier free guidance embeddings to the image embeddings
               __A						:			str								=	image_embeddings.shape[0]
               __A						:			Union[str, Any]								=	self.learned_classifier_free_guidance_embeddings.unsqueeze(0)
               __A						:			Optional[Any]								=	classifier_free_guidance_embeddings.expand(
                   _UpperCAmelCase						,     -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						:			int								=	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						:			int								=	self.embedding_proj(_UpperCAmelCase)
          __A						:			Union[str, Any]								=	self.clip_image_embeddings_project_to_time_embeddings(_UpperCAmelCase)
          __A						:			Any								=	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						:			Tuple								=	self.clip_extra_context_tokens_proj(_UpperCAmelCase)
          __A						:			List[Any]								=	clip_extra_context_tokens.reshape(_UpperCAmelCase						,     -1						,     self.clip_extra_context_tokens)
          __A						:			List[str]								=	clip_extra_context_tokens.permute(0						,     2						,     1)

          __A						:			int								=	self.encoder_hidden_states_proj(_UpperCAmelCase)
          __A						:			Union[str, Any]								=	self.text_encoder_hidden_states_norm(_UpperCAmelCase)
          __A						:			int								=	torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]						,     dim=1)

          return text_encoder_hidden_states, additive_clip_time_embeddings






'''simple docstring'''




from __future__ import annotations






def 	_lowerCAmelCase      (  __snake_case  :			list[int | str]      )					->					None:
     create_state_space_tree(__snake_case			,							[]			,							0			,							[0 for i in range(len(__snake_case      )      )]      )






def 	_lowerCAmelCase      (  __snake_case  :			list[int | str]			,							__snake_case  :			list[int | str]			,							__snake_case  :			int			,							__snake_case  :			list[int]			,							)					->					None:

     if index == len(__snake_case      ):
          print(__snake_case      )
          return

     for i in range(len(__snake_case      )      ):
          if not index_used[i]:
               current_sequence.append(sequence[i]      )
               __A						:			Any								=	True
               create_state_space_tree(__snake_case			,							__snake_case			,							index + 1			,							__snake_case      )
               current_sequence.pop()
               __A						:			Any								=	False
lowercase__   :				list[int | str]      =   [3, 1, 2, 4]
generate_all_permutations(sequence)

lowercase__   :				list[int | str]      =   ["A", "B", "C"]







generate_all_permutations(sequence_a)