

import torch
from torch import nn

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







class 			lowercase    (	lowercase_     ,  lowercase_      ):




				@register_to_config
				def __init__( self				,       *,
				    snake_case = 4				,       snake_case = 768				,       snake_case				,       snake_case				,       ):
											super().__init__()

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

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

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




				def    a							( self				,       *, snake_case				,       snake_case				,       snake_case				,       snake_case	):
											if do_classifier_free_guidance:
																		# Add the classifier free guidance embeddings to the image embeddings
																		snake_case_          =   image_embeddings.shape[0]
																		snake_case_          =   self.learned_classifier_free_guidance_embeddings.unsqueeze(0	)
																		snake_case_          =   classifier_free_guidance_embeddings.expand(
																		    snake_case				,       -1	)
																		snake_case_          =   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]

											snake_case_          =   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, ...
											snake_case_          =   self.embedding_proj(snake_case	)
											snake_case_          =   self.clip_image_embeddings_project_to_time_embeddings(snake_case	)
											snake_case_          =   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"
											snake_case_          =   self.clip_extra_context_tokens_proj(snake_case	)
											snake_case_          =   clip_extra_context_tokens.reshape(snake_case				,       -1				,       self.clip_extra_context_tokens	)
											snake_case_          =   clip_extra_context_tokens.permute(0				,       2				,       1	)

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

											return text_encoder_hidden_states, additive_clip_time_embeddings







def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							global f  # a global dp table for knapsack
							if f[i][j] < 0:
														if j < wt[i - 1]:
																					snake_case_          =   mf_knapsack(i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
														else:
																					snake_case_          =   max(
																					    mf_knapsack(i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)       ,							mf_knapsack(i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							j - wt[i - 1]				) + val[i - 1]       ,							)
														snake_case_          =   val
							return f[i][j]





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   [[0] * (w + 1) for _ in range(n + 1				)]

							for i in range(1       ,							n + 1				):
														for w_ in range(1       ,							w + 1				):
																					if wt[i - 1] <= w_:
																												snake_case_          =   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]       ,							dp[i - 1][w_]				)
																					else:
																												snake_case_          =   dp[i - 1][w_]

							return dp[n][w_], dp





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							if not (isinstance(UpperCamelCase__       ,							(list, tuple)				) and isinstance(UpperCamelCase__       ,							(list, tuple)				)):
														raise ValueError(
														    'Both the weights and values vectors must be either lists or tuples'				)

							snake_case_          =   len(UpperCamelCase__				)
							if num_items != len(UpperCamelCase__				):
														snake_case_          =   (
														    'The number of weights must be the same as the number of values.\n'
														    F'''But got {num_items} weights and {len(UpperCamelCase__				)} values'''
														)
														raise ValueError(UpperCamelCase__				)
							for i in range(UpperCamelCase__				):
														if not isinstance(wt[i]       ,							UpperCamelCase__				):
																					snake_case_          =   (
																					    'All weights must be integers but got weight of '
																					    F'''type {type(wt[i]				)} at index {i}'''
																					)
																					raise TypeError(UpperCamelCase__				)

							snake_case_    ,   snake_case_          =   knapsack(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
							snake_case_          =   set()
							_construct_solution(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)

							return optimal_val, example_optional_set





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							if i > 0 and j > 0:
														if dp[i - 1][j] == dp[i][j]:
																					_construct_solution(UpperCamelCase__       ,							UpperCamelCase__       ,							i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__				)
														else:
																					optimal_set.add(UpperCamelCase__				)
																					_construct_solution(UpperCamelCase__       ,							UpperCamelCase__       ,							i - 1       ,							j - wt[i - 1]       ,							UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       int						    =	[3, 2, 4, 4]
					_UpperCAmelCase		:       Tuple						    =	[4, 3, 2, 3]
					_UpperCAmelCase		:       Dict						    =	4
					_UpperCAmelCase		:       int						    =	6
					_UpperCAmelCase		:       Union[str, Any]						    =	[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					_UpperCAmelCase    ,						_UpperCAmelCase		:       Optional[int]						    =	knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					_UpperCAmelCase    ,						_UpperCAmelCase		:       Optional[int]						    =	knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)




