'''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


__SCREAMING_SNAKE_CASE       :     int     =  logging.get_logger(__name__)

enable_full_determinism()

class 	lowerCamelCase_	(snake_case__							, snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						UNetaDModel
      __UpperCamelCase:  str						=						"sample"




      @property
      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  int							      =    4
             _UpperCAmelCase     :  Any							      =    3
             _UpperCAmelCase     :  List[str]							      =    (32, 32)

             _UpperCAmelCase     :  Optional[int]							      =    floats_tensor((batch_size, num_channels) + sizes       ).to(A       )
             _UpperCAmelCase     :  Optional[Any]							      =    torch.tensor([10]       ).to(A       )

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




      @property
      def      _A	(       self						:				Optional[int]       ):
             return (3, 32, 32)




      @property
      def      _A	(       self						:				Optional[Any]       ):
             return (3, 32, 32)





      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Optional[Any]							      =    {
                 "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,
             }
             _UpperCAmelCase     :  Union[str, Any]							      =    self.dummy_input
             return init_dict, inputs_dict

class 	lowerCamelCase_	(snake_case__							, snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Union[str, Any]						=						UNetaDModel
      __UpperCamelCase:  List[Any]						=						"sample"




      @property
      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  List[Any]							      =    4
             _UpperCAmelCase     :  Union[str, Any]							      =    4
             _UpperCAmelCase     :  Optional[Any]							      =    (32, 32)

             _UpperCAmelCase     :  str							      =    floats_tensor((batch_size, num_channels) + sizes       ).to(A       )
             _UpperCAmelCase     :  Tuple							      =    torch.tensor([10]       ).to(A       )

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




      @property
      def      _A	(       self						:				Dict       ):
             return (4, 32, 32)




      @property
      def      _A	(       self						:				Dict       ):
             return (4, 32, 32)




      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  Tuple							      =    {
                 "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"),
             }
             _UpperCAmelCase     :  str							      =    self.dummy_input
             return init_dict, inputs_dict




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  List[str]							      =    UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"						,					output_loading_info=A       )

             self.assertIsNotNone(A       )
             self.assertEqual(len(loading_info["missing_keys"]       )						,					0       )

             model.to(A       )
             _UpperCAmelCase     :  Optional[int]							      =    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      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  List[str]							      =    UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"						,					output_loading_info=A       )
             model.to(A       )
             _UpperCAmelCase     :  Optional[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      _A	(       self						:				Dict       ):
             # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
             _UpperCAmelCase      ,  _UpperCAmelCase     :  List[Any]							      =    UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"						,					output_loading_info=A       )
             model_accelerate.to(A       )
             model_accelerate.eval()

             _UpperCAmelCase     :  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       )						,					)
             _UpperCAmelCase     :  str							      =    noise.to(A       )
             _UpperCAmelCase     :  Optional[Any]							      =    torch.tensor([10] * noise.shape[0]       ).to(A       )

             _UpperCAmelCase     :  Dict							      =    model_accelerate(A						,					A       )["sample"]

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

             _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[int]							      =    UNetaDModel.from_pretrained(
                 "fusing/unet-ldm-dummy-update"						,					output_loading_info=A						,					low_cpu_mem_usage=A       )
             model_normal_load.to(A       )
             model_normal_load.eval()
             _UpperCAmelCase     :  Any							      =    model_normal_load(A						,					A       )["sample"]

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





      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  List[str]							      =    UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"       )
             model.eval()
             model.to(A       )

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

             with torch.no_grad():
                    _UpperCAmelCase     :  Dict							      =    model(A						,					A       ).sample

             _UpperCAmelCase     :  Dict							      =    output[0, -1, -3:, -3:].flatten().cpu()
             # fmt: off
             _UpperCAmelCase     :  int							      =    torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]       )
             # fmt: on

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

class 	lowerCamelCase_	(snake_case__							, snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  List[Any]						=						UNetaDModel
      __UpperCamelCase:  Dict						=						"sample"




      @property
      def      _A	(       self						:				Optional[Any]						,					A						:				Optional[Any]=(32, 32)       ):
             _UpperCAmelCase     :  Optional[Any]							      =    4
             _UpperCAmelCase     :  int							      =    3

             _UpperCAmelCase     :  Union[str, Any]							      =    floats_tensor((batch_size, num_channels) + sizes       ).to(A       )
             _UpperCAmelCase     :  Optional[Any]							      =    torch.tensor(batch_size * [10]       ).to(dtype=torch.intaa						,					device=A       )

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




      @property
      def      _A	(       self						:				str       ):
             return (3, 32, 32)




      @property
      def      _A	(       self						:				str       ):
             return (3, 32, 32)




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  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",
                 ],
             }
             _UpperCAmelCase     :  str							      =    self.dummy_input
             return init_dict, inputs_dict




      @slow
      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Tuple							      =    UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"						,					output_loading_info=A       )
             self.assertIsNotNone(A       )
             self.assertEqual(len(loading_info["missing_keys"]       )						,					0       )

             model.to(A       )
             _UpperCAmelCase     :  Optional[int]							      =    self.dummy_input
             _UpperCAmelCase     :  Optional[int]							      =    floats_tensor((4, 3) + (256, 256)       ).to(A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    noise
             _UpperCAmelCase     :  Optional[Any]							      =    model(**A       )

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




      @slow
      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  List[str]							      =    UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"       )
             model.to(A       )

             _UpperCAmelCase     :  List[Any]							      =    4
             _UpperCAmelCase     :  List[str]							      =    3
             _UpperCAmelCase     :  str							      =    (256, 256)

             _UpperCAmelCase     :  List[str]							      =    torch.ones((batch_size, num_channels) + sizes       ).to(A       )
             _UpperCAmelCase     :  Tuple							      =    torch.tensor(batch_size * [1E-4]       ).to(A       )

             with torch.no_grad():
                    _UpperCAmelCase     :  Any							      =    model(A						,					A       ).sample

             _UpperCAmelCase     :  Tuple							      =    output[0, -3:, -3:, -1].flatten().cpu()
             # fmt: off
             _UpperCAmelCase     :  Union[str, Any]							      =    torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]       )
             # fmt: on

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




      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  Optional[int]							      =    UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update"       )
             model.to(A       )

             _UpperCAmelCase     :  Optional[int]							      =    4
             _UpperCAmelCase     :  Dict							      =    3
             _UpperCAmelCase     :  Dict							      =    (32, 32)

             _UpperCAmelCase     :  List[Any]							      =    torch.ones((batch_size, num_channels) + sizes       ).to(A       )
             _UpperCAmelCase     :  Optional[int]							      =    torch.tensor(batch_size * [1E-4]       ).to(A       )

             with torch.no_grad():
                    _UpperCAmelCase     :  Optional[Any]							      =    model(A						,					A       ).sample

             _UpperCAmelCase     :  List[str]							      =    output[0, -3:, -3:, -1].flatten().cpu()
             # fmt: off
             _UpperCAmelCase     :  List[str]							      =    torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]       )
             # fmt: on

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





      def      _A	(       self						:				List[Any]       ):
             # not required for this model
             pass


"""simple docstring"""




def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int = 50	)       ->      int:
  _lowerCAmelCase						:       int  			=     [1] * (length + 1)

  for row_length in range(3			,length + 1	):
    for block_length in range(3			,row_length + 1	):
      for block_start in range(row_length - block_length	):
        ways_number[row_length] += ways_number[
            row_length - block_start - block_length - 1
        ]

      ways_number[row_length] += 1

  return ways_number[length]


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




