





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     =    logging.get_logger(__name__)

enable_full_determinism()



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      UNetaDModel
 lowercase_		=      "sample"

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Any:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =4
   lowerCamelCase__:				Optional[Any]        =3
   lowerCamelCase__:				Any        =(32, 32)

   lowerCamelCase__:				str        =floats_tensor((batch_size, num_channels) + sizes).to(UpperCAmelCase_)
   lowerCamelCase__:				Optional[int]        =torch.tensor([10]).to(UpperCAmelCase_)

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

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->int:



   '''simple docstring'''



   return (3, 32, 32)

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



   '''simple docstring'''



   return (3, 32, 32)







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				List[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,
   }
   lowerCamelCase__:				Dict        =self.dummy_input
   return init_dict, inputs_dict



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      UNetaDModel
 lowercase_		=      "sample"

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =4
   lowerCamelCase__:				Optional[Any]        =4
   lowerCamelCase__:				Union[str, Any]        =(32, 32)

   lowerCamelCase__:				int        =floats_tensor((batch_size, num_channels) + sizes).to(UpperCAmelCase_)
   lowerCamelCase__:				List[str]        =torch.tensor([10]).to(UpperCAmelCase_)

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

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->str:



   '''simple docstring'''



   return (4, 32, 32)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->List[str]:



   '''simple docstring'''



   return (4, 32, 32)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->str:



   '''simple docstring'''



   lowerCamelCase__:				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"),
   }
   lowerCamelCase__:				int        =self.dummy_input
   return init_dict, inputs_dict

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Tuple        =UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"	,			output_loading_info=UpperCAmelCase_)

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

   model.to(UpperCAmelCase_)
   lowerCamelCase__:				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 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Any:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				List[Any]        =UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"	,			output_loading_info=UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =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 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->List[str]:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Tuple        =UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"	,			output_loading_info=UpperCAmelCase_)
   model_accelerate.to(UpperCAmelCase_)
   model_accelerate.eval()

   lowerCamelCase__:				List[Any]        =torch.randn(
       1	,			model_accelerate.config.in_channels	,			model_accelerate.config.sample_size	,			model_accelerate.config.sample_size	,			generator=torch.manual_seed(0)	,			)
   lowerCamelCase__:				List[str]        =noise.to(UpperCAmelCase_)
   lowerCamelCase__:				Dict        =torch.tensor([10] * noise.shape[0]).to(UpperCAmelCase_)

   lowerCamelCase__:				List[str]        =model_accelerate(UpperCAmelCase_	,			UpperCAmelCase_)["sample"]

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

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

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







 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Any        =UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update")
   model.eval()
   model.to(UpperCAmelCase_)

   lowerCamelCase__:				Union[str, Any]        =torch.randn(
       1	,			model.config.in_channels	,			model.config.sample_size	,			model.config.sample_size	,			generator=torch.manual_seed(0)	,			)
   lowerCamelCase__:				int        =noise.to(UpperCAmelCase_)
   lowerCamelCase__:				Dict        =torch.tensor([10] * noise.shape[0]).to(UpperCAmelCase_)

   with torch.no_grad():
     lowerCamelCase__:				int        =model(UpperCAmelCase_	,			UpperCAmelCase_).sample

   lowerCamelCase__:				List[str]        =output[0, -1, -3:, -3:].flatten().cpu()
   # fmt: off
   lowerCamelCase__:				List[str]        =torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800])
   # fmt: on

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



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      UNetaDModel
 lowercase_		=      "sample"

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       Any=(32, 32))	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:				int        =4
   lowerCamelCase__:				str        =3

   lowerCamelCase__:				Dict        =floats_tensor((batch_size, num_channels) + sizes).to(UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =torch.tensor(batch_size * [10]).to(dtype=torch.intaa	,			device=UpperCAmelCase_)

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

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->List[Any]:



   '''simple docstring'''



   return (3, 32, 32)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Optional[Any]:



   '''simple docstring'''



   return (3, 32, 32)

 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        ={
       "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",
       ],
   }
   lowerCamelCase__:				str        =self.dummy_input
   return init_dict, inputs_dict

 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Dict        =UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"	,			output_loading_info=UpperCAmelCase_)
   self.assertIsNotNone(UpperCAmelCase_)
   self.assertEqual(len(loading_info["missing_keys"])	,			0)

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

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

 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				Any        =UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256")
   model.to(UpperCAmelCase_)

   lowerCamelCase__:				Dict        =4
   lowerCamelCase__:				Optional[Any]        =3
   lowerCamelCase__:				str        =(256, 256)

   lowerCamelCase__:				Dict        =torch.ones((batch_size, num_channels) + sizes).to(UpperCAmelCase_)
   lowerCamelCase__:				List[Any]        =torch.tensor(batch_size * [1E-4]).to(UpperCAmelCase_)

   with torch.no_grad():
     lowerCamelCase__:				List[Any]        =model(UpperCAmelCase_	,			UpperCAmelCase_).sample

   lowerCamelCase__:				str        =output[0, -3:, -3:, -1].flatten().cpu()
   # fmt: off
   lowerCamelCase__:				Dict        =torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -1_0980.7129, -2_0028.8535, 8148.2822, 2342.2905, 567.7608])
   # fmt: on

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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
   model.to(UpperCAmelCase_)

   lowerCamelCase__:				Optional[int]        =4
   lowerCamelCase__:				Optional[int]        =3
   lowerCamelCase__:				Optional[Any]        =(32, 32)

   lowerCamelCase__:				int        =torch.ones((batch_size, num_channels) + sizes).to(UpperCAmelCase_)
   lowerCamelCase__:				Optional[int]        =torch.tensor(batch_size * [1E-4]).to(UpperCAmelCase_)

   with torch.no_grad():
     lowerCamelCase__:				int        =model(UpperCAmelCase_	,			UpperCAmelCase_).sample

   lowerCamelCase__:				Optional[int]        =output[0, -3:, -3:, -1].flatten().cpu()
   # fmt: off
   lowerCamelCase__:				Dict        =torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256])
   # fmt: on

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







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Dict:



   '''simple docstring'''



   pass








def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Tuple:







  """simple docstring"""





  assert x is not None
  assert y is not None

  lowerCamelCase__:				Any        =len(__a		)
  lowerCamelCase__:				int        =len(__a		)

  # declaring the array for storing the dp values
  lowerCamelCase__:				List[Any]        =[[0] * (n + 1) for _ in range(m + 1		)]  # noqa: E741

  for i in range(1     ,   m + 1		):
    for j in range(1     ,   n + 1		):
      lowerCamelCase__:				str        =1 if x[i - 1] == y[j - 1] else 0

      lowerCamelCase__:				str        =max(l[i - 1][j]     ,   l[i][j - 1]     ,   l[i - 1][j - 1] + match		)

  lowerCamelCase__:				Any        =""
  lowerCamelCase__  , lowerCamelCase__:				str        =m, n
  while i > 0 and j > 0:
    lowerCamelCase__:				Union[str, Any]        =1 if x[i - 1] == y[j - 1] else 0

    if l[i][j] == l[i - 1][j - 1] + match:
      if match == 1:
        lowerCamelCase__:				Any        =x[i - 1] + seq
      i -= 1
      j -= 1
    elif l[i][j] == l[i - 1][j]:
      i -= 1
    else:
      j -= 1

  return l[m][n], seq


if __name__ == "__main__":
     __A     =    "AGGTAB"
     __A     =    "GXTXAYB"
     __A     =    4
     __A     =    "GTAB"

     __A ,     __A     =    longest_common_subsequence(a, b)
     print("len =", ln, ", sub-sequence =", subseq)
     import doctest

     doctest.testmod()

