



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,    *,
							    __a = 4,    __a = 768,    __a,    __a,    ):

														'''simple docstring'''

														super().__init__()

														_lowerCAmelCase  :							Optional[Any]				     =					nn.Parameter(torch.zeros(__a))

														# parameters for additional clip time embeddings
														_lowerCAmelCase  :							Dict				     =					nn.Linear(__a,    __a)
														_lowerCAmelCase  :							int				     =					nn.Linear(__a,    __a)

														# parameters for encoder hidden states
														_lowerCAmelCase  :							List[Any]				     =					clip_extra_context_tokens
														_lowerCAmelCase  :							int				     =					nn.Linear(
														    __a,    self.clip_extra_context_tokens * cross_attention_dim)
														_lowerCAmelCase  :							List[str]				     =					nn.Linear(__a,    __a)
														_lowerCAmelCase  :							Dict				     =					nn.LayerNorm(__a)







							def      snake_case__   (     self,    *, __a,    __a,    __a,    __a):

														'''simple docstring'''

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

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

														_lowerCAmelCase  :							str				     =					self.encoder_hidden_states_proj(__a)
														_lowerCAmelCase  :							Any				     =					self.text_encoder_hidden_states_norm(__a)
														_lowerCAmelCase  :							Optional[int]				     =					torch.cat([clip_extra_context_tokens, text_encoder_hidden_states],    dim=1)

														return text_encoder_hidden_states, additive_clip_time_embeddings





def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							def count_of_possible_combinations(_lowerCamelCase						) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														return sum(count_of_possible_combinations(target - item						) for item in array						)

							return count_of_possible_combinations(_lowerCamelCase						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							def count_of_possible_combinations_with_dp_array(
							    _lowerCamelCase       ,				_lowerCamelCase						) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														if dp_array[target] != -1:
																					return dp_array[target]
														_lowerCAmelCase  :							Optional[int]				     =					sum(
														    count_of_possible_combinations_with_dp_array(target - item       ,				_lowerCamelCase						)
														    for item in array						)
														_lowerCAmelCase  :							Any				     =					answer
														return answer

							_lowerCAmelCase  :							List[Any]				     =					[-1] * (target + 1)
							return count_of_possible_combinations_with_dp_array(_lowerCamelCase       ,				_lowerCamelCase						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[Any]				     =					[0] * (target + 1)
							_lowerCAmelCase  :							List[str]				     =					1

							for i in range(1       ,				target + 1						):
														for j in range(_lowerCamelCase						):
																					if i - array[j] >= 0:
																												dp_array[i] += dp_array[i - array[j]]

							return dp_array[target]


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						_snake_case	=						3
						_snake_case	=						5
						_snake_case	=						[1, 2, 5]
						print(combination_sum_iv(n, array, target))
