

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 			A						:







    """simple docstring"""





    @property
    def 						snake_case__					(				self			:	int		)->   str:





        '''simple docstring'''







        return self.get_dummy_input()


    @property
    def 						snake_case__					(				self			:	List[Any]		)->   int:





        '''simple docstring'''







        if self.block_type == "down":
            return (4, 3_2, 1_6, 1_6)
        elif self.block_type == "mid":
            return (4, 3_2, 3_2, 3_2)
        elif self.block_type == "up":
            return (4, 3_2, 6_4, 6_4)

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


    def 						snake_case__					(				self			:	List[str],lowercase_			:	Dict=True,lowercase_			:	List[str]=False,lowercase_			:	Union[str, Any]=False,lowercase_			:	Union[str, Any]=False,)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       4
        A__   				=       3_2
        A__   				=       (3_2, 3_2)

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

        if include_temb:
            A__   				=       1_2_8
            A__   				=       randn_tensor((batch_size, temb_channels),generator=lowercase_,device=lowercase_		)

        if include_res_hidden_states_tuple:
            A__   				=       torch.manual_seed(1		)
            A__   				=       (randn_tensor(lowercase_,generator=lowercase_,device=lowercase_		),)

        if include_encoder_hidden_states:
            A__   				=       floats_tensor((batch_size, 3_2, 3_2)		).to(lowercase_		)

        if include_skip_sample:
            A__   				=       randn_tensor(((batch_size, 3) + sizes),generator=lowercase_,device=lowercase_		)

        return dummy_input


    def 						snake_case__					(				self			:	Dict		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       {
            'in_channels': 3_2,
            'out_channels': 3_2,
            'temb_channels': 1_2_8,
        }
        if self.block_type == "up":
            A__   				=       3_2

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

        A__   				=       self.dummy_input
        return init_dict, inputs_dict


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	int		)->   Optional[int]:





        '''simple docstring'''







        A__				,						A__   				=       self.prepare_init_args_and_inputs_for_common()
        A__   				=       self.block_class(**lowercase_		)
        unet_block.to(lowercase_		)
        unet_block.eval()

        with torch.no_grad():
            A__   				=       unet_block(**lowercase_		)

        if isinstance(lowercase_,lowercase_		):
            A__   				=       output[0]

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

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


    @unittest.skipIf(torch_device == 'mps','Training is not supported in mps'		)
    def 						snake_case__					(				self			:	Dict		)->   Any:





        '''simple docstring'''







        A__				,						A__   				=       self.prepare_init_args_and_inputs_for_common()
        A__   				=       self.block_class(**lowercase_		)
        model.to(lowercase_		)
        model.train()
        A__   				=       model(**lowercase_		)

        if isinstance(lowercase_,lowercase_		):
            A__   				=       output[0]

        A__   				=       torch.device(lowercase_		)
        A__   				=       randn_tensor(output.shape,device=lowercase_		)
        A__   				=       torch.nn.functional.mse_loss(lowercase_,lowercase_		)
        loss.backward()




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int = 1000				)							->				int:





    '''simple docstring'''






    A__   				=       3
    A__   				=       0
    while a < n:
        if a % 3 == 0 or a % 5 == 0:
            result += a
        elif a % 15 == 0:
            result -= a
        a += 1
    return result


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

