



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

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


A__    :    List[str]        =   logging.get_logger(__name__)  # pylint: disable=invalid-name



class 							__snake_case		(	UpperCamelCase_		):




							def __init__(			self	:			str							,							A_	:			List[Any]							,							A_	:			List[str]=7_6_8):
									super().__init__(__a)
									lowerCAmelCase_     :	int							   =					proj_size

									lowerCAmelCase_     :	str							   =					CLIPVisionModel(__a)
									lowerCAmelCase_     :	Tuple							   =					PaintByExampleMapper(__a)
									lowerCAmelCase_     :	int							   =					nn.LayerNorm(config.hidden_size)
									lowerCAmelCase_     :	Optional[Any]							   =					nn.Linear(config.hidden_size							,							self.proj_size)

									# uncondition for scaling
									lowerCAmelCase_     :	Optional[Any]							   =					nn.Parameter(torch.randn((1, 1, self.proj_size)))







							def   UpperCAmelCase__     (			self	:			Any							,							A_	:			int							,							A_	:			int=False):
									lowerCAmelCase_     :	Optional[Any]							   =					self.model(pixel_values=__a)
									lowerCAmelCase_     :	Any							   =					clip_output.pooler_output
									lowerCAmelCase_     :	List[str]							   =					self.mapper(latent_states[:, None])
									lowerCAmelCase_     :	Optional[int]							   =					self.final_layer_norm(__a)
									lowerCAmelCase_     :	List[Any]							   =					self.proj_out(__a)
									if return_uncond_vector:
											return latent_states, self.uncond_vector

									return latent_states





class 							__snake_case		(	nn.Module		):




							def __init__(			self	:			List[Any]							,							A_	:			Dict):
									super().__init__()
									lowerCAmelCase_     :	int							   =					(config.num_hidden_layers + 1) // 5
									lowerCAmelCase_     :	Any							   =					config.hidden_size
									lowerCAmelCase_     :	Union[str, Any]							   =					1
									lowerCAmelCase_     :	Optional[Any]							   =					nn.ModuleList(
									    [
									        BasicTransformerBlock(__a							,							__a							,							__a							,							activation_fn='''gelu'''							,							attention_bias=__a)
									        for _ in range(__a)
									    ])







							def   UpperCAmelCase__     (			self	:			Union[str, Any]							,							A_	:			Union[str, Any]):
									for block in self.blocks:
											lowerCAmelCase_     :	Tuple							   =					block(__a)

									return hidden_states






'''simple docstring'''


from collections.abc import Sequence





def    lowerCAmelCase       (		UpperCamelCase__					: Sequence[float]     ,       UpperCamelCase__					: bool = False							):


	"""simple docstring"""
	if not arr:
		return 0

	__UpperCAmelCase      					=      0 if allow_empty_subarrays else float('''-inf'''							)
	__UpperCAmelCase      					=      0.0
	for num in arr:
		__UpperCAmelCase      					=      max(0 if allow_empty_subarrays else num     ,       curr_sum + num							)
		__UpperCAmelCase      					=      max(UpperCamelCase__     ,       UpperCamelCase__							)

	return max_sum


if __name__ == "__main__":
					from doctest import testmod

					testmod()

					__lowerCAmelCase						:							int    							=						[-2, 1, -3, 4, -1, 2, 1, -5, 4]
					print(F"""{max_subarray_sum(nums) = }""")
