




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

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


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

class 						__lowerCAmelCase      (      __magic_name__					):
							def __init__(		self		:Dict						, __magic_name__		:Any						, __magic_name__		:List[Any]=768							):







								'''simple docstring'''







								super().__init__(__magic_name__							)
								a									=				proj_size

								a									=				CLIPVisionModel(__magic_name__							)
								a									=				PaintByExampleMapper(__magic_name__							)
								a									=				nn.LayerNorm(config.hidden_size							)
								a									=				nn.Linear(config.hidden_size						, self.proj_size							)

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





							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:List[str]						, __magic_name__		:List[Any]=False							):







								'''simple docstring'''







								a									=				self.model(pixel_values=__magic_name__							)
								a									=				clip_output.pooler_output
								a									=				self.mapper(latent_states[:, None]							)
								a									=				self.final_layer_norm(__magic_name__							)
								a									=				self.proj_out(__magic_name__							)
								if return_uncond_vector:
									return latent_states, self.uncond_vector

								return latent_states







class 						__lowerCAmelCase      (      nn.Module					):
							def __init__(		self		:Optional[Any]						, __magic_name__		:List[str]							):







								'''simple docstring'''







								super().__init__()
								a									=				(config.num_hidden_layers + 1) // 5
								a									=				config.hidden_size
								a									=				1
								a									=				nn.ModuleList(
								    [
								        BasicTransformerBlock(__magic_name__						, __magic_name__						, __magic_name__						, activation_fn="""gelu"""						, attention_bias=__magic_name__							)
								        for _ in range(__magic_name__							)
								    ]							)





							def  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:Dict							):







								'''simple docstring'''







								for block in self.blocks:
									a									=				block(__magic_name__							)

								return hidden_states







from math import factorial, radians





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase = 18				,     __lowerCamelCase = 10       ) ->			float:
	a									=				angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

	# Converting from degrees to radians
	a									=				radians(__lowerCamelCase       )

	a									=				angle_in_radians
	a									=				3
	a									=				-1

	for _ in range(__lowerCamelCase       ):
		result += (b * (angle_in_radians**a)) / factorial(__lowerCamelCase       )

		a									=				-b  # One positive term and the next will be negative and so on...
		a += 2  # Increased by 2 for every term.

	return round(__lowerCamelCase				,     __lowerCamelCase       )


if __name__ == "__main__":
		__import__("doctest").testmod()

