
class        A_   :




	'''simple docstring'''


	def __init__(       self		,    snake_case       ):
		# we need a list not a string, so do something to change the type
		lowercase       =    arr.split(','       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    [int(self.array[0]       )] * len(self.array       )
		lowercase       =    [int(self.array[0]       )] * len(self.array       )
		for i in range(1		,    len(self.array       )       ):
			lowercase       =    max(
			    int(self.array[i]       ) + sum_value[i - 1]		,    int(self.array[i]       )       )
			lowercase       =    max(sum_value[i]		,    rear[i - 1]       )
		return rear[len(self.array       ) - 1]


if __name__ == "__main__":
	UpperCAmelCase	      = input('''please input some numbers:''')
	UpperCAmelCase	      = SubArray(whole_array)
	UpperCAmelCase	      = array.solve_sub_array()
	print(('''the results is:''', re))



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

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


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







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case=768       ):
		super().__init__(snake_case       )
		lowercase       =    proj_size

		lowercase       =    CLIPVisionModel(snake_case       )
		lowercase       =    PaintByExampleMapper(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 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=False       ):
		lowercase       =    self.model(pixel_values=snake_case       )
		lowercase       =    clip_output.pooler_output
		lowercase       =    self.mapper(latent_states[:, None]       )
		lowercase       =    self.final_layer_norm(snake_case       )
		lowercase       =    self.proj_out(snake_case       )
		if return_uncond_vector:
			return latent_states, self.uncond_vector

		return latent_states







class        A_   (			nn.Module   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case       ):
		super().__init__()
		lowercase       =    (config.num_hidden_layers + 1) // 5
		lowercase       =    config.hidden_size
		lowercase       =    1
		lowercase       =    nn.ModuleList(
		    [
		        BasicTransformerBlock(snake_case		,    snake_case		,    snake_case		,    activation_fn='gelu'		,    attention_bias=snake_case       )
		        for _ in range(snake_case       )
		    ]       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		for block in self.blocks:
			lowercase       =    block(snake_case       )

		return hidden_states

