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			:						Tuple				  =		logging.get_logger(__name__)

enable_full_determinism()
class  A_			(							__snake_case      ,							__snake_case      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      UNetaDModel
  _SCREAMING_SNAKE_CASE     					=      'sample'

  @property
  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	4
   __a							=	3
   __a							=	(32, 32)

   __a							=	floats_tensor((batch_size, num_channels) + sizes							).to(A_							)
   __a							=	torch.tensor([10]							).to(A_							)

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

  @property
  def 		_UpperCAmelCase (		self			:   List[Any]							):
   return (3, 32, 32)

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

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	{
       "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,
   }
   __a							=	self.dummy_input
   return init_dict, inputs_dict
class  A_			(							__snake_case      ,							__snake_case      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      UNetaDModel
  _SCREAMING_SNAKE_CASE     					=      'sample'

  @property
  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	4
   __a							=	4
   __a							=	(32, 32)

   __a							=	floats_tensor((batch_size, num_channels) + sizes							).to(A_							)
   __a							=	torch.tensor([10]							).to(A_							)

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

  @property
  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   return (4, 32, 32)

  @property
  def 		_UpperCAmelCase (		self			:   List[Any]							):
   return (4, 32, 32)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	{
       "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"),
   }
   __a							=	self.dummy_input
   return init_dict, inputs_dict

  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	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_							)
   __a							=	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 		_UpperCAmelCase (		self			:   Tuple							):
   __a							=	UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"  ,				output_loading_info=A_							)
   model.to(A_							)
   __a							=	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 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"  ,				output_loading_info=A_							)
   model_accelerate.to(A_							)
   model_accelerate.eval()

   __a							=	torch.randn(
       1  ,				model_accelerate.config.in_channels  ,				model_accelerate.config.sample_size  ,				model_accelerate.config.sample_size  ,				generator=torch.manual_seed(0							)  ,				)
   __a							=	noise.to(A_							)
   __a							=	torch.tensor([10] * noise.shape[0]							).to(A_							)

   __a							=	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()

   __a							=	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()
   __a							=	model_normal_load(A_  ,				A_							)["sample"]

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

  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"							)
   model.eval()
   model.to(A_							)

   __a							=	torch.randn(
       1  ,				model.config.in_channels  ,				model.config.sample_size  ,				model.config.sample_size  ,				generator=torch.manual_seed(0							)  ,				)
   __a							=	noise.to(A_							)
   __a							=	torch.tensor([10] * noise.shape[0]							).to(A_							)

   with torch.no_grad():
    __a							=	model(A_  ,				A_							).sample

   __a							=	output[0, -1, -3:, -3:].flatten().cpu()
   # fmt: off
   __a							=	torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]							)
   # fmt: on

   self.assertTrue(torch_all_close(A_  ,				A_  ,				rtol=1E-3							)							)
class  A_			(							__snake_case      ,							__snake_case      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      UNetaDModel
  _SCREAMING_SNAKE_CASE     					=      'sample'

  @property
  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=(32, 32)							):
   __a							=	4
   __a							=	3

   __a							=	floats_tensor((batch_size, num_channels) + sizes							).to(A_							)
   __a							=	torch.tensor(batch_size * [10]							).to(dtype=torch.intaa  ,				device=A_							)

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

  @property
  def 		_UpperCAmelCase (		self			:   List[str]							):
   return (3, 32, 32)

  @property
  def 		_UpperCAmelCase (		self			:   Tuple							):
   return (3, 32, 32)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	{
       "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",
       ],
   }
   __a							=	self.dummy_input
   return init_dict, inputs_dict

  @slow
  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	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_							)
   __a							=	self.dummy_input
   __a							=	floats_tensor((4, 3) + (2_56, 2_56)							).to(A_							)
   __a							=	noise
   __a							=	model(**A_							)

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

  @slow
  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"							)
   model.to(A_							)

   __a							=	4
   __a							=	3
   __a							=	(2_56, 2_56)

   __a							=	torch.ones((batch_size, num_channels) + sizes							).to(A_							)
   __a							=	torch.tensor(batch_size * [1E-4]							).to(A_							)

   with torch.no_grad():
    __a							=	model(A_  ,				A_							).sample

   __a							=	output[0, -3:, -3:, -1].flatten().cpu()
   # fmt: off
   __a							=	torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]							)
   # fmt: on

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

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update"							)
   model.to(A_							)

   __a							=	4
   __a							=	3
   __a							=	(32, 32)

   __a							=	torch.ones((batch_size, num_channels) + sizes							).to(A_							)
   __a							=	torch.tensor(batch_size * [1E-4]							).to(A_							)

   with torch.no_grad():
    __a							=	model(A_  ,				A_							).sample

   __a							=	output[0, -3:, -3:, -1].flatten().cpu()
   # fmt: off
   __a							=	torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]							)
   # fmt: on

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

  def 		_UpperCAmelCase (		self			:   int							):
   pass









def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					Optional[int]:
 UpperCamelCase      :   List[Any]			      =  [1]
 for i in range(2				,  _lowerCAmelCase	):
  factorials.append(factorials[-1] * i	)
 assert 0 <= k < factorials[-1] * n, "k out of bounds"

 UpperCamelCase      :   Tuple			      =  []
 UpperCamelCase      :   str			      =  list(range(_lowerCAmelCase	)	)

 # Find permutation
 while factorials:
  UpperCamelCase      :   int			      =  factorials.pop()
  UpperCamelCase      ,       UpperCamelCase      :   int			      =  divmod(_lowerCAmelCase				,  _lowerCAmelCase	)
  permutation.append(elements[number]	)
  elements.remove(elements[number]	)
 permutation.append(elements[0]	)

 return permutation


if __name__ == "__main__":
    import doctest

    doctest.testmod()
