




def    UpperCAmelCase_				( __UpperCAmelCase		:      int = 4_00_00_00				)   ->					int:
      SCREAMING_SNAKE_CASE_      		=     []
      SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     0, 1
      while b <= n:
            if b % 2 == 0:
                  even_fibs.append(__UpperCAmelCase				)
            SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     b, a + b
      return sum(__UpperCAmelCase				)


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





import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch





@require_torch
class 			lowerCamelCase_				:


 '''simple docstring'''







 @property
 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       return self.get_dummy_input()



 @property
 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       if self.block_type == "down":
             return (4, 32, 16, 16)
       elif self.block_type == "mid":
             return (4, 32, 32, 32)
       elif self.block_type == "up":
             return (4, 32, 64, 64)

       raise ValueError(F"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."		)



 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						Optional[Any]=True							,							_lowerCAmelCase    :						str=False							,							_lowerCAmelCase    :						Optional[int]=False							,							_lowerCAmelCase    :						Dict=False							,							):
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     32
       SCREAMING_SNAKE_CASE_      		=     (32, 32)

       SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     torch.device(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     (batch_size, num_channels) + sizes
       SCREAMING_SNAKE_CASE_      		=     randn_tensor(_lowerCAmelCase							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     {'hidden_states': hidden_states}

       if include_temb:
             SCREAMING_SNAKE_CASE_      		=     128
             SCREAMING_SNAKE_CASE_      		=     randn_tensor((batch_size, temb_channels)							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		)

       if include_res_hidden_states_tuple:
             SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(1		)
             SCREAMING_SNAKE_CASE_      		=     (randn_tensor(_lowerCAmelCase							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		),)

       if include_encoder_hidden_states:
             SCREAMING_SNAKE_CASE_      		=     floats_tensor((batch_size, 32, 32)		).to(_lowerCAmelCase		)

       if include_skip_sample:
             SCREAMING_SNAKE_CASE_      		=     randn_tensor(((batch_size, 3) + sizes)							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		)

       return dummy_input



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     {
           'in_channels': 32,
           'out_channels': 32,
           'temb_channels': 128,
       }
       if self.block_type == "up":
             SCREAMING_SNAKE_CASE_      		=     32

       if self.block_type == "mid":
             init_dict.pop('out_channels'		)

       SCREAMING_SNAKE_CASE_      		=     self.dummy_input
       return init_dict, inputs_dict



 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.prepare_init_args_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_      		=     self.block_class(**_lowerCAmelCase		)
       unet_block.to(_lowerCAmelCase		)
       unet_block.eval()

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     unet_block(**_lowerCAmelCase		)

       if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_      		=     output[0]

       self.assertEqual(output.shape							,							self.output_shape		)

       SCREAMING_SNAKE_CASE_      		=     output[0, -1, -3:, -3:]
       SCREAMING_SNAKE_CASE_      		=     torch.tensor(_lowerCAmelCase		).to(_lowerCAmelCase		)
       assert torch_all_close(output_slice.flatten()							,							_lowerCAmelCase							,							atol=5E-3		)




 @unittest.skipIf(torch_device == 'mps'							,							'Training is not supported in mps'		)
 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.prepare_init_args_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_      		=     self.block_class(**_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.train()
       SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)

       if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_      		=     output[0]

       SCREAMING_SNAKE_CASE_      		=     torch.device(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     randn_tensor(output.shape							,							device=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.functional.mse_loss(_lowerCAmelCase							,							_lowerCAmelCase		)
       loss.backward()