

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__ :     str				    =    logging.get_logger(__name__)

enable_full_determinism()







class        a__       (   UpperCamelCase_	,			UpperCamelCase_	,			unittest.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Tuple			       =UNetaDModel
  UpperCAmelCase__      : Tuple			       ="""sample"""


  @property
  def  _lowercase      (			self    : Union[str, Any]		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   4
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   3
        SCREAMING_SNAKE_CASE :      Any       =   (3_2, 3_2)

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   floats_tensor((batch_size, num_channels) + sizes		).to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   torch.tensor([1_0]		).to(UpperCAmelCase__		)

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


  @property
  def  _lowercase      (			self    : List[Any]		)					->Optional[Any]:
        """simple docstring"""







        return (3, 3_2, 3_2)


  @property
  def  _lowercase      (			self    : Dict		)					->List[str]:
        """simple docstring"""







        return (3, 3_2, 3_2)




  def  _lowercase      (			self    : Optional[int]		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   {
            '''block_out_channels''': (3_2, 6_4),
            '''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''': 3_2,
        }
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.dummy_input
        return init_dict, inputs_dict







class        a__       (   UpperCamelCase_	,			UpperCamelCase_	,			unittest.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : List[str]			       =UNetaDModel
  UpperCAmelCase__      : Tuple			       ="""sample"""


  @property
  def  _lowercase      (			self    : List[str]		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   4
        SCREAMING_SNAKE_CASE :      Tuple       =   4
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (3_2, 3_2)

        SCREAMING_SNAKE_CASE :      Tuple       =   floats_tensor((batch_size, num_channels) + sizes		).to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   torch.tensor([1_0]		).to(UpperCAmelCase__		)

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


  @property
  def  _lowercase      (			self    : Optional[int]		)					->int:
        """simple docstring"""







        return (4, 3_2, 3_2)


  @property
  def  _lowercase      (			self    : int		)					->Dict:
        """simple docstring"""







        return (4, 3_2, 3_2)


  def  _lowercase      (			self    : Any		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   {
            '''sample_size''': 3_2,
            '''in_channels''': 4,
            '''out_channels''': 4,
            '''layers_per_block''': 2,
            '''block_out_channels''': (3_2, 6_4),
            '''attention_head_dim''': 3_2,
            '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
            '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
        }
        SCREAMING_SNAKE_CASE :      int       =   self.dummy_input
        return init_dict, inputs_dict


  def  _lowercase      (			self    : str		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   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__		)
        SCREAMING_SNAKE_CASE :      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  _lowercase      (			self    : Dict		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""				,				output_loading_info=UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      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  _lowercase      (			self    : List[Any]		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""				,				output_loading_info=UpperCAmelCase__		)
        model_accelerate.to(UpperCAmelCase__		)
        model_accelerate.eval()

        SCREAMING_SNAKE_CASE :      int       =   torch.randn(
            1				,				model_accelerate.config.in_channels				,				model_accelerate.config.sample_size				,				model_accelerate.config.sample_size				,				generator=torch.manual_seed(0		)				,				)
        SCREAMING_SNAKE_CASE :      Tuple       =   noise.to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[str]       =   torch.tensor([1_0] * noise.shape[0]		).to(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      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()

        SCREAMING_SNAKE_CASE :      Dict       =   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()
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   model_normal_load(UpperCAmelCase__				,				UpperCAmelCase__		)['''sample''']

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




  def  _lowercase      (			self    : Optional[int]		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""		)
        model.eval()
        model.to(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      int       =   torch.randn(
            1				,				model.config.in_channels				,				model.config.sample_size				,				model.config.sample_size				,				generator=torch.manual_seed(0		)				,				)
        SCREAMING_SNAKE_CASE :      Dict       =   noise.to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[Any]       =   torch.tensor([1_0] * noise.shape[0]		).to(UpperCAmelCase__		)

        with torch.no_grad():
              SCREAMING_SNAKE_CASE :      Dict       =   model(UpperCAmelCase__				,				UpperCAmelCase__		).sample

        SCREAMING_SNAKE_CASE :      List[str]       =   output[0, -1, -3:, -3:].flatten().cpu()
        # fmt: off
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   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(UpperCAmelCase__				,				UpperCAmelCase__				,				rtol=1e-3		)		)







class        a__       (   UpperCamelCase_	,			UpperCamelCase_	,			unittest.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Any			       =UNetaDModel
  UpperCAmelCase__      : List[str]			       ="""sample"""


  @property
  def  _lowercase      (			self    : Union[str, Any]				,				UpperCAmelCase__    : Any=(3_2, 3_2)		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   4
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   3

        SCREAMING_SNAKE_CASE :      List[str]       =   floats_tensor((batch_size, num_channels) + sizes		).to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   torch.tensor(batch_size * [1_0]		).to(dtype=torch.intaa				,				device=UpperCAmelCase__		)

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


  @property
  def  _lowercase      (			self    : Dict		)					->Any:
        """simple docstring"""







        return (3, 3_2, 3_2)


  @property
  def  _lowercase      (			self    : Optional[Any]		)					->Dict:
        """simple docstring"""







        return (3, 3_2, 3_2)


  def  _lowercase      (			self    : List[Any]		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   {
            '''block_out_channels''': [3_2, 6_4, 6_4, 6_4],
            '''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''',
            ],
        }
        SCREAMING_SNAKE_CASE :      str       =   self.dummy_input
        return init_dict, inputs_dict


  @slow
  def  _lowercase      (			self    : List[str]		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   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__		)
        SCREAMING_SNAKE_CASE :      str       =   self.dummy_input
        SCREAMING_SNAKE_CASE :      Optional[int]       =   floats_tensor((4, 3) + (2_5_6, 2_5_6)		).to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   noise
        SCREAMING_SNAKE_CASE :      Optional[int]       =   model(**UpperCAmelCase__		)

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


  @slow
  def  _lowercase      (			self    : Optional[Any]		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""		)
        model.to(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      List[str]       =   4
        SCREAMING_SNAKE_CASE :      Dict       =   3
        SCREAMING_SNAKE_CASE :      Optional[int]       =   (2_5_6, 2_5_6)

        SCREAMING_SNAKE_CASE :      Tuple       =   torch.ones((batch_size, num_channels) + sizes		).to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   torch.tensor(batch_size * [1e-4]		).to(UpperCAmelCase__		)

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

        SCREAMING_SNAKE_CASE :      Tuple       =   output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        SCREAMING_SNAKE_CASE :      Optional[int]       =   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(UpperCAmelCase__				,				UpperCAmelCase__				,				rtol=1e-2		)		)


  def  _lowercase      (			self    : int		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""		)
        model.to(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      int       =   4
        SCREAMING_SNAKE_CASE :      Dict       =   3
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (3_2, 3_2)

        SCREAMING_SNAKE_CASE :      Dict       =   torch.ones((batch_size, num_channels) + sizes		).to(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Tuple       =   torch.tensor(batch_size * [1e-4]		).to(UpperCAmelCase__		)

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

        SCREAMING_SNAKE_CASE :      str       =   output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   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(UpperCAmelCase__				,				UpperCAmelCase__				,				rtol=1e-2		)		)




  def  _lowercase      (			self    : Union[str, Any]		)					->Optional[int]:
        """simple docstring"""







        pass







def 					__lowercase      (     _A			)     ->						list[int]:
      SCREAMING_SNAKE_CASE :      int       =   [0 for i in range(len(_A			)			)]

      # initialize interval's left pointer and right pointer
      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Dict       =   0, 0

      for i in range(1						,    len(_A			)			):
            # case when current index is inside the interval
            if i <= right_pointer:
                  SCREAMING_SNAKE_CASE :      Any       =   min(right_pointer - i + 1						,    z_result[i - left_pointer]			)
                  SCREAMING_SNAKE_CASE :      Optional[Any]       =   min_edge

            while go_next(_A						,    _A						,    _A			):
                  z_result[i] += 1

            # if new index's result gives us more right interval,
            # we've to update left_pointer and right_pointer
            if i + z_result[i] - 1 > right_pointer:
                  SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[Any]       =   i, i + z_result[i] - 1

      return z_result






def 					__lowercase      (     _A						,    _A						,    _A			)     ->						bool:
      return i + z_result[i] < len(_A			) and s[z_result[i]] == s[i + z_result[i]]






def 					__lowercase      (     _A						,    _A			)     ->						int:
      SCREAMING_SNAKE_CASE :      Any       =   0
      # concatenate 'pattern' and 'input_str' and call z_function
      # with concatenated string
      SCREAMING_SNAKE_CASE :      List[Any]       =   z_function(pattern + input_str			)

      for val in z_result:
            # if value is greater then length of the pattern string
            # that means this index is starting position of substring
            # which is equal to pattern string
            if val >= len(_A			):
                  answer += 1

      return answer


if __name__ == "__main__":
  import doctest

  doctest.testmod()




