

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   lowercase_   (  __lowercase       ,							__lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Optional[Any]										=    UNetaDModel
      UpperCamelCase_      :		List[Any]										=    "sample"



      @property
      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Dict:
        _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 						UpperCamelCase_		(      self				:    int		)   ->					List[Any]:
        return (3, 32, 32)



      @property
      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					str:
        return (3, 32, 32)







      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Tuple:
        _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   lowercase_   (  __lowercase       ,							__lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Optional[int]										=    UNetaDModel
      UpperCamelCase_      :		List[str]										=    "sample"



      @property
      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					int:
        _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 						UpperCamelCase_		(      self				:    str		)   ->					Optional[Any]:
        return (4, 32, 32)



      @property
      def 						UpperCamelCase_		(      self				:    Any		)   ->					Union[str, Any]:
        return (4, 32, 32)



      def 						UpperCamelCase_		(      self				:    Any		)   ->					List[Any]:
        _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 						UpperCamelCase_		(      self				:    List[str]		)   ->					int:
        _snake_case,					_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 						UpperCamelCase_		(      self				:    str		)   ->					int:
        _snake_case,					_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 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[int]:
        # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
        _snake_case,					_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,					_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 						UpperCamelCase_		(      self				:    Any		)   ->					Union[str, Any]:
        _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([-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(A__      , A__      , rtol=1e-3		)		)



class   lowercase_   (  __lowercase       ,							__lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Union[str, Any]										=    UNetaDModel
      UpperCamelCase_      :		int										=    "sample"



      @property
      def 						UpperCamelCase_		(      self				:    str      , A__				:    Optional[Any]=(32, 32)		)   ->					Tuple:
        _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 						UpperCamelCase_		(      self				:    Any		)   ->					List[str]:
        return (3, 32, 32)



      @property
      def 						UpperCamelCase_		(      self				:    str		)   ->					List[str]:
        return (3, 32, 32)



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					int:
        _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 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					int:
        _snake_case,					_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 						UpperCamelCase_		(      self				:    List[str]		)   ->					int:
        _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([-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(A__      , A__      , rtol=1e-2		)		)



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					List[Any]:
        _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([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]		)
        # fmt: on

        self.assertTrue(torch_all_close(A__      , A__      , rtol=1e-2		)		)







      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Union[str, Any]:
        # not required for this model
        pass




from __future__ import annotations



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     bool:



  """simple docstring"""






  _snake_case						=							get_failure_array(_UpperCamelCase					)

  # 2) Step through text searching for pattern
  _snake_case,					_snake_case						=							0, 0  # index into text, pattern
  while i < len(_UpperCamelCase					):
    if pattern[j] == text[i]:
      if j == (len(_UpperCamelCase					) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _snake_case						=							failure[j - 1]
      continue
    i += 1
  return False



def 				snake_case_(_UpperCamelCase					)							->     list[int]:



  """simple docstring"""






  _snake_case						=							[0]
  _snake_case						=							0
  _snake_case						=							1
  while j < len(_UpperCamelCase					):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _snake_case						=							failure[i - 1]
      continue
    j += 1
    failure.append(_UpperCamelCase					)
  return failure


if __name__ == "__main__":
 # Test 1)
 __A      		=     '''abc1abc12'''
 __A      		=     '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 __A      		=     '''alskfjaldsk23adsfabcabc'''
 assert kmp(pattern, texta) and not kmp(pattern, texta)

 # Test 2)
 __A      		=     '''ABABX'''
 __A      		=     '''ABABZABABYABABX'''
 assert kmp(pattern, text)

 # Test 3)
 __A      		=     '''AAAB'''
 __A      		=     '''ABAAAAAB'''
 assert kmp(pattern, text)

 # Test 4)
 __A      		=     '''abcdabcy'''
 __A      		=     '''abcxabcdabxabcdabcdabcy'''
 assert kmp(pattern, text)

 # Test 5)
 __A      		=     '''aabaabaaa'''
 assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]

