





"""simple docstring"""


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

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


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






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A=7_6_8    ):






									'''simple docstring'''







									super().__init__(_A    )
									UpperCamelCase				:		Tuple				   =			proj_size

									UpperCamelCase				:		Optional[int]				   =			CLIPVisionModel(_A    )
									UpperCamelCase				:		Union[str, Any]				   =			PaintByExampleMapper(_A    )
									UpperCamelCase				:		int				   =			nn.LayerNorm(config.hidden_size    )
									UpperCamelCase				:		str				   =			nn.Linear(config.hidden_size						,   self.proj_size    )

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







			def   _a      (      self						,   _A						,   _A=False    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			self.model(pixel_values=_A    )
									UpperCamelCase				:		Tuple				   =			clip_output.pooler_output
									UpperCamelCase				:		List[Any]				   =			self.mapper(latent_states[:, None]    )
									UpperCamelCase				:		List[Any]				   =			self.final_layer_norm(_A    )
									UpperCamelCase				:		Union[str, Any]				   =			self.proj_out(_A    )
									if return_uncond_vector:
															return latent_states, self.uncond_vector

									return latent_states






class 							lowercase__			(   nn.Module	):

			"""simple docstring"""







			def __init__(      self						,   _A    ):






									'''simple docstring'''







									super().__init__()
									UpperCamelCase				:		Any				   =			(config.num_hidden_layers + 1) // 5
									UpperCamelCase				:		Optional[Any]				   =			config.hidden_size
									UpperCamelCase				:		int				   =			1
									UpperCamelCase				:		Optional[int]				   =			nn.ModuleList(
									    [
									        BasicTransformerBlock(_A						,   _A						,   _A						,   activation_fn="""gelu"""						,   attention_bias=_A    )
									        for _ in range(_A    )
									    ]    )







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									for block in self.blocks:
															UpperCamelCase				:		Union[str, Any]				   =			block(_A    )

									return hidden_states










"""simple docstring"""


from math import ceil, sqrt

def      UpperCamelCase							(SCREAMING_SNAKE_CASE = 100_0000     ):
						UpperCamelCase				:		int				   =			0

						for outer_width in range(3							,     (limit // 4) + 2     ):
												if outer_width**2 > limit:
																		UpperCamelCase				:		Optional[Any]				   =			max(ceil(sqrt(outer_width**2 - limit     )     )							,     1     )
												else:
																		UpperCamelCase				:		str				   =			1
												if (outer_width - hole_width_lower_bound) % 2:
																		hole_width_lower_bound += 1

												answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

						return answer


if __name__ == "__main__":
		print(f'''{solution() = }''')



