
"""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


UpperCAmelCase__        =					logging.get_logger(__name__)

enable_full_determinism()

class  lowerCAmelCase__							(		UpperCamelCase_					,      UpperCamelCase_					,      unittest.TestCase      ):
     __a         =   UNetaDModel
     __a         =   """sample"""







     @property
     def 	lowercase      (				self     :     Tuple     ):
       _snake_case   	=			4
       _snake_case   	=			3
       _snake_case   	=			(32, 32)

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

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







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







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







     def 	lowercase      (				self     :     List[str]     ):
       _snake_case   	=			{
           "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,
       }
       _snake_case   	=			self.dummy_input
       return init_dict, inputs_dict






class  lowerCAmelCase__							(		UpperCamelCase_					,      UpperCamelCase_					,      unittest.TestCase      ):
     __a         =   UNetaDModel
     __a         =   """sample"""







     @property
     def 	lowercase      (				self     :     str     ):
       _snake_case   	=			4
       _snake_case   	=			4
       _snake_case   	=			(32, 32)

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

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







     @property
     def 	lowercase      (				self     :     Any     ):
       return (4, 32, 32)







     @property
     def 	lowercase      (				self     :     Any     ):
       return (4, 32, 32)







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







     def 	lowercase      (				self     :     List[str]     ):
       _snake_case   	=			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__     )
       _snake_case   	=			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 	lowercase      (				self     :     Union[str, Any]     ):
       _snake_case   	=			UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''       , output_loading_info=a__     )
       model.to(a__     )
       _snake_case   	=			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 	lowercase      (				self     :     str     ):
       _snake_case   	=			UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''       , output_loading_info=a__     )
       model_accelerate.to(a__     )
       model_accelerate.eval()

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

       _snake_case   	=			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()

       _snake_case   	=			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()
       _snake_case   	=			model_normal_load(a__       , a__     )["sample"]

       assert torch_all_close(a__       , a__       , rtol=1e-3     )







     def 	lowercase      (				self     :     int     ):
       _snake_case   	=			UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''     )
       model.eval()
       model.to(a__     )

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

       with torch.no_grad():
         _snake_case   	=			model(a__       , a__     ).sample

       _snake_case   	=			output[0, -1, -3:, -3:].flatten().cpu()
       # fmt: off
       _snake_case   	=			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(a__       , a__       , rtol=1e-3     )     )






class  lowerCAmelCase__							(		UpperCamelCase_					,      UpperCamelCase_					,      unittest.TestCase      ):
     __a         =   UNetaDModel
     __a         =   """sample"""







     @property
     def 	lowercase      (				self     :     str       , _lowerCamelCase     :     List[Any]=(32, 32)     ):
       _snake_case   	=			4
       _snake_case   	=			3

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

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







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







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







     def 	lowercase      (				self     :     Optional[Any]     ):
       _snake_case   	=			{
           "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",
           ],
       }
       _snake_case   	=			self.dummy_input
       return init_dict, inputs_dict







     @slow
     def 	lowercase      (				self     :     Tuple     ):
       _snake_case   	=			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__     )
       _snake_case   	=			self.dummy_input
       _snake_case   	=			floats_tensor((4, 3) + (256, 256)     ).to(a__     )
       _snake_case   	=			noise
       _snake_case   	=			model(**a__     )

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







     @slow
     def 	lowercase      (				self     :     Tuple     ):
       _snake_case   	=			UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''     )
       model.to(a__     )

       _snake_case   	=			4
       _snake_case   	=			3
       _snake_case   	=			(256, 256)

       _snake_case   	=			torch.ones((batch_size, num_channels) + sizes     ).to(a__     )
       _snake_case   	=			torch.tensor(batch_size * [1e-4]     ).to(a__     )

       with torch.no_grad():
         _snake_case   	=			model(a__       , a__     ).sample

       _snake_case   	=			output[0, -3:, -3:, -1].flatten().cpu()
       # fmt: off
       _snake_case   	=			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(a__       , a__       , rtol=1e-2     )     )







     def 	lowercase      (				self     :     List[str]     ):
       _snake_case   	=			UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''     )
       model.to(a__     )

       _snake_case   	=			4
       _snake_case   	=			3
       _snake_case   	=			(32, 32)

       _snake_case   	=			torch.ones((batch_size, num_channels) + sizes     ).to(a__     )
       _snake_case   	=			torch.tensor(batch_size * [1e-4]     ).to(a__     )

       with torch.no_grad():
         _snake_case   	=			model(a__       , a__     ).sample

       _snake_case   	=			output[0, -3:, -3:, -1].flatten().cpu()
       # fmt: off
       _snake_case   	=			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(a__       , a__       , rtol=1e-2     )     )







     def 	lowercase      (				self     :     List[str]     ):
       pass





'''simple docstring'''

def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   str     		=	len(lowerCamelCase_			)
 lowerCAmelCase__     :   Optional[Any]     		=	len(matrix[0]			)
 lowerCAmelCase__     :   Any     		=	min(lowerCamelCase_      ,			lowerCamelCase_			)

 for row in range(lowerCamelCase_			):
  # Check if diagonal element is not zero
  if matrix[row][row] != 0:
   # Eliminate all the elements below the diagonal
   for col in range(row + 1      ,			lowerCamelCase_			):
    lowerCAmelCase__     :   Tuple     		=	matrix[col][row] / matrix[row][row]
    for i in range(lowerCamelCase_      ,			lowerCamelCase_			):
     matrix[col][i] -= multiplier * matrix[row][i]
  else:
   # Find a non-zero diagonal element to swap rows
   lowerCAmelCase__     :   Dict     		=	True
   for i in range(row + 1      ,			lowerCamelCase_			):
    if matrix[i][row] != 0:
     lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[Any]     		=	matrix[i], matrix[row]
     lowerCAmelCase__     :   Optional[Any]     		=	False
     break
   if reduce:
    rank -= 1
    for i in range(lowerCamelCase_			):
     lowerCAmelCase__     :   Union[str, Any]     		=	matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
   row -= 1

 return rank


if __name__ == "__main__":
     import doctest

     doctest.testmod()





