
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__a         =   logging.get_logger(__name__)  # pylint: disable=invalid-name
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	def __init__(			self    :     str			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     str=7_6_8			)			->	Dict:
					super().__init__(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   proj_size

					lowercase_	    =   CLIPVisionModel(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   PaintByExampleMapper(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   nn.LayerNorm(config.hidden_size			)
					lowercase_	    =   nn.Linear(config.hidden_size			,				self.proj_size			)

					# uncondition for scaling
					lowercase_	    =   nn.Parameter(torch.randn((1, 1, self.proj_size)			)			)




	def 		_lowercase    (			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     int=False			)			->	List[str]:
					lowercase_	    =   self.model(pixel_values=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   clip_output.pooler_output
					lowercase_	    =   self.mapper(latent_states[:, None]			)
					lowercase_	    =   self.final_layer_norm(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   self.proj_out(SCREAMING_SNAKE_CASE_			)
					if return_uncond_vector:
									return latent_states, self.uncond_vector

					return latent_states

class 					lowercase__(							nn.Module				):



	"""simple docstring"""




	def __init__(			self    :     str			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Dict:
					super().__init__()
					lowercase_	    =   (config.num_hidden_layers + 1) // 5
					lowercase_	    =   config.hidden_size
					lowercase_	    =   1
					lowercase_	    =   nn.ModuleList(
					    [
					        BasicTransformerBlock(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				activation_fn='''gelu'''			,				attention_bias=SCREAMING_SNAKE_CASE_			)
					        for _ in range(SCREAMING_SNAKE_CASE_			)
					    ]			)




	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	List[Any]:
					for block in self.blocks:
									lowercase_	    =   block(SCREAMING_SNAKE_CASE_			)

					return hidden_states






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()




