


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

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


snake_case							:       Optional[Any]     			=      logging.get_logger(__name__)  # pylint: disable=invalid-name







class        snake_case_   (_snake_case      ):
					def __init__(      self   :str			,__snake_case   :List[Any]			,__snake_case   :List[Any]=7_68      )					->    Optional[Any]:
												super().__init__(__snake_case      )
												a__             =     proj_size

												a__             =     CLIPVisionModel(__snake_case      )
												a__             =     PaintByExampleMapper(__snake_case      )
												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   :Dict			,__snake_case   :Tuple			,__snake_case   :Union[str, Any]=False      )					->    Union[str, Any]:
												a__             =     self.model(pixel_values=__snake_case      )
												a__             =     clip_output.pooler_output
												a__             =     self.mapper(latent_states[:, None]      )
												a__             =     self.final_layer_norm(__snake_case      )
												a__             =     self.proj_out(__snake_case      )
												if return_uncond_vector:
																			return latent_states, self.uncond_vector

												return latent_states







class        snake_case_   (nn.Module      ):
					def __init__(      self   :List[str]			,__snake_case   :Tuple      )					->    str:
												super().__init__()
												a__             =     (config.num_hidden_layers + 1) // 5
												a__             =     config.hidden_size
												a__             =     1
												a__             =     nn.ModuleList(
												    [
												        BasicTransformerBlock(__snake_case			,__snake_case			,__snake_case			,activation_fn='gelu'			,attention_bias=__snake_case      )
												        for _ in range(__snake_case      )
												    ]      )



					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :int      )					->    Optional[Any]:
												for block in self.blocks:
																			a__             =     block(__snake_case      )

												return hidden_states








'''simple docstring'''


from collections import defaultdict
from math import ceil, sqrt







def 			snake_case_   (  SCREAMING_SNAKE_CASE__ = 100_0000							,      SCREAMING_SNAKE_CASE__ = 10     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       defaultdict     		=      defaultdict(SCREAMING_SNAKE_CASE__     )

				for outer_width in range(3							,      (t_limit // 4) + 2     ):
								if outer_width * outer_width > t_limit:
												_SCREAMING_SNAKE_CASE :       int     		=      max(
												    ceil(sqrt(outer_width * outer_width - t_limit     )     )							,      1     )
								else:
												_SCREAMING_SNAKE_CASE :       List[str]     		=      1

								hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

								for hole_width in range(SCREAMING_SNAKE_CASE__							,      outer_width - 1							,      2     ):
												count[outer_width * outer_width - hole_width * hole_width] += 1

				return sum(1 for n in count.values() if 1 <= n <= 10     )


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




