
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 					lowercase__(							UpperCAmelCase					,		UpperCAmelCase				):



	"""simple docstring"""




	@register_to_config
	def __init__(			self    :     str			,				*,
	    SCREAMING_SNAKE_CASE_    :     int = 4			,				SCREAMING_SNAKE_CASE_    :     int = 7_6_8			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				)			->	Tuple:
					super().__init__()

					lowercase_	    =   nn.Parameter(torch.zeros(SCREAMING_SNAKE_CASE_			)			)

					# parameters for additional clip time embeddings
					lowercase_	    =   nn.Linear(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   nn.Linear(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

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




	def 		_lowercase    (			self    :     Optional[int]			,				*, SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Dict			)			->	List[Any]:
					if do_classifier_free_guidance:
									# Add the classifier free guidance embeddings to the image embeddings
									lowercase_	    =   image_embeddings.shape[0]
									lowercase_	    =   self.learned_classifier_free_guidance_embeddings.unsqueeze(0			)
									lowercase_	    =   classifier_free_guidance_embeddings.expand(
									    SCREAMING_SNAKE_CASE_			,				-1			)
									lowercase_	    =   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]

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

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

					return text_encoder_hidden_states, additive_clip_time_embeddings






from __future__ import annotations




def 							a		(				snake_case__:   list[list[int]]   ):


				'''simple docstring'''

				# preprocessing the first row
				for i in range(1		,  len(matrix[0]   )   ):
								matrix[0][i] += matrix[0][i - 1]

				# preprocessing the first column
				for i in range(1		,  len(snake_case__   )   ):
								matrix[i][0] += matrix[i - 1][0]

				# updating the path cost for current position
				for i in range(1		,  len(snake_case__   )   ):
								for j in range(1		,  len(matrix[0]   )   ):
												matrix[i][j] += min(matrix[i - 1][j]		,  matrix[i][j - 1]   )

				return matrix[-1][-1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()




