'''simple docstring'''

import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


__a: str          =   logging.get_logger(__name__)

enable_full_determinism()



class 			UpperCAmelCase  (						lowercase__    ,						lowercase__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    UNetaDModel
       SCREAMING_SNAKE_CASE							=    '''sample'''

       @property
       def _lowerCAmelCase(						self )  ->  int:
        lowercase__				:    Optional[Any]  =					4
        lowercase__				:    Tuple  =					3
        lowercase__				:    Dict  =					(32, 32)

        lowercase__				:    List[Any]  =					floats_tensor((batch_size, num_channels) + sizes ).to(SCREAMING_SNAKE_CASE__ )
        lowercase__				:    str  =					torch.tensor([10] ).to(SCREAMING_SNAKE_CASE__ )

        return {"sample": noise, "timestep": time_step}

       @property
       def _lowerCAmelCase(						self )  ->  List[Any]:
        return (3, 32, 32)

       @property
       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        return (3, 32, 32)


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    List[str]  =					{
            'block_out_channels': (32, 64),
            'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
            'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
            'attention_head_dim': 3,
            'out_channels': 3,
            'in_channels': 3,
            'layers_per_block': 2,
            'sample_size': 32,
        }
        lowercase__				:    List[Any]  =					self.dummy_input
        return init_dict, inputs_dict



class 			UpperCAmelCase  (						lowercase__    ,						lowercase__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    UNetaDModel
       SCREAMING_SNAKE_CASE							=    '''sample'''

       @property
       def _lowerCAmelCase(						self )  ->  List[Any]:
        lowercase__				:    Tuple  =					4
        lowercase__				:    Any  =					4
        lowercase__				:    Dict  =					(32, 32)

        lowercase__				:    str  =					floats_tensor((batch_size, num_channels) + sizes ).to(SCREAMING_SNAKE_CASE__ )
        lowercase__				:    List[str]  =					torch.tensor([10] ).to(SCREAMING_SNAKE_CASE__ )

        return {"sample": noise, "timestep": time_step}

       @property
       def _lowerCAmelCase(						self )  ->  str:
        return (4, 32, 32)

       @property
       def _lowerCAmelCase(						self )  ->  List[Any]:
        return (4, 32, 32)

       def _lowerCAmelCase(						self )  ->  List[Any]:
        lowercase__				:    Any  =					{
            'sample_size': 32,
            'in_channels': 4,
            'out_channels': 4,
            'layers_per_block': 2,
            'block_out_channels': (32, 64),
            'attention_head_dim': 32,
            'down_block_types': ('DownBlock2D', 'DownBlock2D'),
            'up_block_types': ('UpBlock2D', 'UpBlock2D'),
        }
        lowercase__				:    int  =					self.dummy_input
        return init_dict, inputs_dict

       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        lowercase__				:    Dict  =					UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''	,   output_loading_info=SCREAMING_SNAKE_CASE__ )

        self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
        self.assertEqual(len(loading_info['''missing_keys'''] )	,   0 )

        model.to(SCREAMING_SNAKE_CASE__ )
        lowercase__				:    List[Any]  =					model(**self.dummy_input ).sample

        assert image is not None, "Make sure output is not None"

       @unittest.skipIf(torch_device != '''cuda'''	,   '''This test is supposed to run on GPU''' )
       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    List[Any]  =					UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''	,   output_loading_info=SCREAMING_SNAKE_CASE__ )
        model.to(SCREAMING_SNAKE_CASE__ )
        lowercase__				:    str  =					model(**self.dummy_input ).sample

        assert image is not None, "Make sure output is not None"

       @unittest.skipIf(torch_device != '''cuda'''	,   '''This test is supposed to run on GPU''' )
       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
        lowercase__				:    Optional[int]  =					UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''	,   output_loading_info=SCREAMING_SNAKE_CASE__ )
        model_accelerate.to(SCREAMING_SNAKE_CASE__ )
        model_accelerate.eval()

        lowercase__				:    Optional[Any]  =					torch.randn(
            1	,   model_accelerate.config.in_channels	,   model_accelerate.config.sample_size	,   model_accelerate.config.sample_size	,   generator=torch.manual_seed(0 )	,   )
        lowercase__				:    Union[str, Any]  =					noise.to(SCREAMING_SNAKE_CASE__ )
        lowercase__				:    Any  =					torch.tensor([10] * noise.shape[0] ).to(SCREAMING_SNAKE_CASE__ )

        lowercase__				:    Dict  =					model_accelerate(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__ )['sample']

        # two models don't need to stay in the device at the same time
        del model_accelerate
        torch.cuda.empty_cache()
        gc.collect()

        lowercase__				:    Optional[Any]  =					UNetaDModel.from_pretrained(
            '''fusing/unet-ldm-dummy-update'''	,   output_loading_info=SCREAMING_SNAKE_CASE__	,   low_cpu_mem_usage=SCREAMING_SNAKE_CASE__ )
        model_normal_load.to(SCREAMING_SNAKE_CASE__ )
        model_normal_load.eval()
        lowercase__				:    List[Any]  =					model_normal_load(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__ )['sample']

        assert torch_all_close(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   rtol=1E-3 )


       def _lowerCAmelCase(						self )  ->  Optional[int]:
        lowercase__				:    Dict  =					UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''' )
        model.eval()
        model.to(SCREAMING_SNAKE_CASE__ )

        lowercase__				:    Optional[Any]  =					torch.randn(
            1	,   model.config.in_channels	,   model.config.sample_size	,   model.config.sample_size	,   generator=torch.manual_seed(0 )	,   )
        lowercase__				:    Dict  =					noise.to(SCREAMING_SNAKE_CASE__ )
        lowercase__				:    Any  =					torch.tensor([10] * noise.shape[0] ).to(SCREAMING_SNAKE_CASE__ )

        with torch.no_grad():
         lowercase__				:    Union[str, Any]  =					model(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__ ).sample

        lowercase__				:    Optional[int]  =					output[0, -1, -3:, -3:].flatten().cpu()
        # fmt: off
        lowercase__				:    List[Any]  =					torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0] )
        # fmt: on

        self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   rtol=1E-3 ) )



class 			UpperCAmelCase  (						lowercase__    ,						lowercase__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    UNetaDModel
       SCREAMING_SNAKE_CASE							=    '''sample'''

       @property
       def _lowerCAmelCase(						self	,   __lowerCAmelCase=(32, 32) )  ->  List[str]:
        lowercase__				:    Any  =					4
        lowercase__				:    Any  =					3

        lowercase__				:    int  =					floats_tensor((batch_size, num_channels) + sizes ).to(SCREAMING_SNAKE_CASE__ )
        lowercase__				:    Tuple  =					torch.tensor(batch_size * [10] ).to(dtype=torch.intaa	,   device=SCREAMING_SNAKE_CASE__ )

        return {"sample": noise, "timestep": time_step}

       @property
       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        return (3, 32, 32)

       @property
       def _lowerCAmelCase(						self )  ->  Tuple:
        return (3, 32, 32)

       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    Optional[Any]  =					{
            'block_out_channels': [32, 64, 64, 64],
            'in_channels': 3,
            'layers_per_block': 1,
            'out_channels': 3,
            'time_embedding_type': 'fourier',
            'norm_eps': 1E-6,
            'mid_block_scale_factor': math.sqrt(2.0 ),
            'norm_num_groups': None,
            'down_block_types': [
                'SkipDownBlock2D',
                'AttnSkipDownBlock2D',
                'SkipDownBlock2D',
                'SkipDownBlock2D',
            ],
            'up_block_types': [
                'SkipUpBlock2D',
                'SkipUpBlock2D',
                'AttnSkipUpBlock2D',
                'SkipUpBlock2D',
            ],
        }
        lowercase__				:    List[str]  =					self.dummy_input
        return init_dict, inputs_dict

       @slow
       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        lowercase__				:    List[Any]  =					UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''	,   output_loading_info=SCREAMING_SNAKE_CASE__ )
        self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
        self.assertEqual(len(loading_info['''missing_keys'''] )	,   0 )

        model.to(SCREAMING_SNAKE_CASE__ )
        lowercase__				:    Union[str, Any]  =					self.dummy_input
        lowercase__				:    Dict  =					floats_tensor((4, 3) + (256, 256) ).to(SCREAMING_SNAKE_CASE__ )
        lowercase__				:    Optional[Any]  =					noise
        lowercase__				:    List[str]  =					model(**SCREAMING_SNAKE_CASE__ )

        assert image is not None, "Make sure output is not None"

       @slow
       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    List[str]  =					UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256''' )
        model.to(SCREAMING_SNAKE_CASE__ )

        lowercase__				:    int  =					4
        lowercase__				:    Union[str, Any]  =					3
        lowercase__				:    List[str]  =					(256, 256)

        lowercase__				:    Union[str, Any]  =					torch.ones((batch_size, num_channels) + sizes ).to(SCREAMING_SNAKE_CASE__ )
        lowercase__				:    Union[str, Any]  =					torch.tensor(batch_size * [1E-4] ).to(SCREAMING_SNAKE_CASE__ )

        with torch.no_grad():
         lowercase__				:    Dict  =					model(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__ ).sample

        lowercase__				:    Union[str, Any]  =					output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        lowercase__				:    str  =					torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8] )
        # fmt: on

        self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   rtol=1E-2 ) )

       def _lowerCAmelCase(						self )  ->  Any:
        lowercase__				:    str  =					UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update''' )
        model.to(SCREAMING_SNAKE_CASE__ )

        lowercase__				:    List[str]  =					4
        lowercase__				:    List[str]  =					3
        lowercase__				:    Union[str, Any]  =					(32, 32)

        lowercase__				:    Dict  =					torch.ones((batch_size, num_channels) + sizes ).to(SCREAMING_SNAKE_CASE__ )
        lowercase__				:    List[str]  =					torch.tensor(batch_size * [1E-4] ).to(SCREAMING_SNAKE_CASE__ )

        with torch.no_grad():
         lowercase__				:    str  =					model(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__ ).sample

        lowercase__				:    int  =					output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        lowercase__				:    str  =					torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6] )
        # fmt: on

        self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   rtol=1E-2 ) )


       def _lowerCAmelCase(						self )  ->  Optional[int]:
        # not required for this model
        pass





from __future__ import annotations

UpperCAmelCase_      : Tuple  					=       []







def        SCREAMING_SNAKE_CASE_						(			__A :			list[list[int]]  ,    __A :			int  ,    __A :			int				)	->		bool:

       """simple docstring"""

       for i in range(len(__A				)				):
              if board[row][i] == 1:
                     return False
       for i in range(len(__A				)				):
              if board[i][column] == 1:
                     return False
       for i, j in zip(range(__A  ,    -1  ,    -1				)  ,    range(__A  ,    -1  ,    -1				)				):
              if board[i][j] == 1:
                     return False
       for i, j in zip(range(__A  ,    -1  ,    -1				)  ,    range(__A  ,    len(__A				)				)				):
              if board[i][j] == 1:
                     return False
       return True







def        SCREAMING_SNAKE_CASE_						(			__A :			list[list[int]]  ,    __A :			int				)	->		bool:

       """simple docstring"""

       if row >= len(__A				):
              solution.append(__A				)
              printboard(__A				)
              print()
              return True
       for i in range(len(__A				)				):
              if is_safe(__A  ,    __A  ,    __A				):
                     a_					:		Any							=  1
                     solve(__A  ,    row + 1				)
                     a_					:		Tuple							=  0
       return False







def        SCREAMING_SNAKE_CASE_						(			__A :			list[list[int]]				)	->		None:

       """simple docstring"""

       for i in range(len(__A				)				):
              for j in range(len(__A				)				):
                     if board[i][j] == 1:
                            print('Q'  ,    end=' '				)
                     else:
                            print('.'  ,    end=' '				)
              print()


# n=int(input("The no. of queens"))
UpperCAmelCase_      : List[str]  					=       8
UpperCAmelCase_      : str  					=       [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('The total no. of solutions are :', len(solution))


