import torch
from torch import nn

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


class 						lowercase    (     a				,       a     ):




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



												'''simple docstring'''

												super().__init__()

												SCREAMING_SNAKE_CASE    			=       nn.Parameter(torch.zeros(_UpperCamelCase      )      )

												# parameters for additional clip time embeddings
												SCREAMING_SNAKE_CASE    			=       nn.Linear(_UpperCamelCase	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       nn.Linear(_UpperCamelCase	,							_UpperCamelCase      )

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





							def 	__snake_case(						self   :					Optional[int]	,							*, _UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					str      )					->				List[str]:



												'''simple docstring'''

												if do_classifier_free_guidance:
																	# Add the classifier free guidance embeddings to the image embeddings
																	SCREAMING_SNAKE_CASE    			=       image_embeddings.shape[0]
																	SCREAMING_SNAKE_CASE    			=       self.learned_classifier_free_guidance_embeddings.unsqueeze(0      )
																	SCREAMING_SNAKE_CASE    			=       classifier_free_guidance_embeddings.expand(
																	    _UpperCamelCase	,							-1      )
																	SCREAMING_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]

												SCREAMING_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, ...
												SCREAMING_SNAKE_CASE    			=       self.embedding_proj(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       self.clip_image_embeddings_project_to_time_embeddings(_UpperCamelCase      )
												SCREAMING_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"
												SCREAMING_SNAKE_CASE    			=       self.clip_extra_context_tokens_proj(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       clip_extra_context_tokens.reshape(_UpperCamelCase	,							-1	,							self.clip_extra_context_tokens      )
												SCREAMING_SNAKE_CASE    			=       clip_extra_context_tokens.permute(0	,							2	,							1      )

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

												return text_encoder_hidden_states, additive_clip_time_embeddings





import functools





def 	__lowerCamelCase							(UpperCAmelCase__     :							list[int]      , UpperCAmelCase__     :							list[int]  ):

					# Validation
					if not isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for day in days  ):
										raise ValueError("The parameter days should be a list of integers"  )

					if len(UpperCAmelCase__  ) != 3 or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for cost in costs  ):
										raise ValueError("The parameter costs should be a list of three integers"  )

					if len(UpperCAmelCase__  ) == 0:
										return 0

					if min(UpperCAmelCase__  ) <= 0:
										raise ValueError("All days elements should be greater than 0"  )

					if max(UpperCAmelCase__  ) >= 3_6_6:
										raise ValueError("All days elements should be less than 366"  )

					SCREAMING_SNAKE_CASE    			=       set(UpperCAmelCase__  )

					@functools.cache
					def dynamic_programming(UpperCAmelCase__     :							int  ) -> int:
										if index > 3_6_5:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1  )

										return min(
										    costs[0] + dynamic_programming(index + 1  )      , costs[1] + dynamic_programming(index + 7  )      , costs[2] + dynamic_programming(index + 3_0  )      , )

					return dynamic_programming(1  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




