



"""simple docstring"""






from __future__ import annotations

from random import random






class        __snake_case       :




  def __init__( self     :						Dict						,					__lowerCAmelCase     :						int | None = None		):






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= value
         _lowerCamelCase						:				Any    			= random()
         _lowerCamelCase						:				Node | None    			= None
         _lowerCamelCase						:				Node | None    			= None




  def __repr__( self     :						Any		):






         """simple docstring"""






         from pprint import pformat

         if self.left is None and self.right is None:
                return f'''\'{self.value}: {self.prior:.5}\''''
         else:
                return pformat(
                    {f'''{self.value}: {self.prior:.5}''': (self.left, self.right)}						,					indent=1		)




  def __str__( self     :						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, Any]    			= str(self.value		) + ''' '''
         _lowerCamelCase						:				Any    			= str(self.left or ''''''		)
         _lowerCamelCase						:				Any    			= str(self.right or ''''''		)
         return value + left + right






def        snake_case_  (      A_					:		Node | None,				A_					:		int						):


       '''simple docstring'''





       if root is None:  # None tree is split into 2 Nones
              return None, None
       elif root.value is None:
              return None, None
       else:
              if value < root.value:
                     _lowerCamelCase			,    _lowerCamelCase						:				str    			= split(root.left,				A_						)
                     return left, root
              else:
                     _lowerCamelCase			,    _lowerCamelCase						:				Dict    			= split(root.right,				A_						)
                     return root, right






def        snake_case_  (      A_					:		Node | None,				A_					:		Node | None						):


       '''simple docstring'''





       if (not left) or (not right):  # If one node is None, return the other
              return left or right
       elif left.prior < right.prior:
              _lowerCamelCase						:				str    			= merge(left.right,				A_						)
              return left
       else:
              _lowerCamelCase						:				str    			= merge(A_,				right.left						)
              return right






def        snake_case_  (      A_					:		Node | None,				A_					:		int						):


       '''simple docstring'''





       _lowerCamelCase						:				Union[str, Any]    			= Node(A_						)
       _lowerCamelCase			,    _lowerCamelCase						:				List[Any]    			= split(A_,				A_						)
       return merge(merge(A_,				A_						),				A_						)






def        snake_case_  (      A_					:		Node | None,				A_					:		int						):


       '''simple docstring'''





       _lowerCamelCase			,    _lowerCamelCase						:				Any    			= split(A_,				value - 1						)
       _lowerCamelCase			,    _lowerCamelCase						:				Any    			= split(A_,				A_						)
       return merge(A_,				A_						)






def        snake_case_  (      A_					:		Node | None						):


       '''simple docstring'''





       if not root:  # None
              return
       else:
              inorder(root.left						)
              print(root.value,				end=''','''						)
              inorder(root.right						)






def        snake_case_  (      A_					:		Node | None,				A_					:		str						):


       '''simple docstring'''





       for arg in args.split():
              if arg[0] == "+":
                     _lowerCamelCase						:				Any    			= insert(A_,				int(arg[1:]						)						)

              elif arg[0] == "-":
                     _lowerCamelCase						:				Optional[Any]    			= erase(A_,				int(arg[1:]						)						)

              else:
                     print('''Unknown command'''						)

       return root






def        snake_case_  (      ):


       '''simple docstring'''





       _lowerCamelCase						:				Union[str, Any]    			= None
       print(
           '''enter numbers to create a tree, + value to add value into treap, '''
           '''- value to erase all nodes with value. \'q\' to quit. '''						)

       _lowerCamelCase						:				Optional[int]    			= input()
       while args != "q":
              _lowerCamelCase						:				List[str]    			= interact_treap(A_,				A_						)
              print(A_						)
              _lowerCamelCase						:				Optional[Any]    			= input()

       print('''good by!'''						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


lowercase_       		=  False


@skip_mps
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							StableDiffusionAttendAndExcitePipeline
    lowerCamelCase			=							False
    lowerCamelCase			=							TEXT_TO_IMAGE_PARAMS
    lowerCamelCase			=							TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'} )
    lowerCamelCase			=							TEXT_TO_IMAGE_IMAGE_PARAMS
    lowerCamelCase			=							TEXT_TO_IMAGE_IMAGE_PARAMS


    @classmethod
    def 						snake_case__     (      cls :       Any		)->    Optional[Any]:





        '''simple docstring'''







        super().setUpClass()
        torch.use_deterministic_algorithms(lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       Optional[Any]		)->    Dict:





        '''simple docstring'''







        super().tearDownClass()
        torch.use_deterministic_algorithms(lowercase_		)


    def 						snake_case__     (      self :       List[str]		)->    int:





        '''simple docstring'''







        torch.manual_seed(0		)
        A__          =   UNetaDConditionModel(
            block_out_channels=(3_2, 6_4),layers_per_block=1,sample_size=3_2,in_channels=4,out_channels=4,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'),up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'),cross_attention_dim=3_2,attention_head_dim=(2, 4),use_linear_projection=lowercase_,)
        A__          =   DDIMScheduler(
            beta_start=0.00_085,beta_end=0.012,beta_schedule='scaled_linear',clip_sample=lowercase_,set_alpha_to_one=lowercase_,)
        torch.manual_seed(0		)
        A__          =   AutoencoderKL(
            block_out_channels=[3_2, 6_4],in_channels=3,out_channels=3,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],latent_channels=4,sample_size=1_2_8,)
        torch.manual_seed(0		)
        A__          =   CLIPTextConfig(
            bos_token_id=0,eos_token_id=2,hidden_size=3_2,intermediate_size=3_7,layer_norm_eps=1E-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1_0_0_0,hidden_act='gelu',projection_dim=5_1_2,)
        A__          =   CLIPTextModel(lowercase_		)
        A__          =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

        A__          =   {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }

        return components


    def 						snake_case__     (      self :       Tuple,lowercase_ :       str,lowercase_ :       List[Any]=0		)->    int:





        '''simple docstring'''







        if str(lowercase_		).startswith('mps'		):
            A__          =   torch.manual_seed(lowercase_		)
        else:
            A__          =   torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
        A__          =   A__          =   {
            'prompt': 'a cat and a frog',
            'token_indices': [2, 5],
            'generator': generator,
            'num_inference_steps': 1,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
            'max_iter_to_alter': 2,
            'thresholds': {0: 0.7},
        }
        return inputs


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





        '''simple docstring'''







        A__          =   'cpu'

        A__          =   self.get_dummy_components()
        A__          =   self.pipeline_class(**lowercase_		)
        pipe.to(lowercase_		)
        pipe.set_progress_bar_config(disable=lowercase_		)

        A__          =   self.get_dummy_inputs(lowercase_		)
        A__          =   pipe(**lowercase_		).images
        A__          =   image[0, -3:, -3:, -1]

        self.assertEqual(image.shape,(1, 6_4, 6_4, 3)		)
        A__          =   np.array(
            [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]		)
        A__          =   np.abs(image_slice.flatten() - expected_slice		).max()
        self.assertLessEqual(lowercase_,1E-3		)


    def 						snake_case__     (      self :       str		)->    Optional[Any]:





        '''simple docstring'''







        super().test_cpu_offload_forward_pass(expected_max_diff=5E-4		)


    def 						snake_case__     (      self :       str		)->    int:





        '''simple docstring'''







        self._test_inference_batch_consistent(batch_sizes=[1, 2]		)


    def 						snake_case__     (      self :       str		)->    Optional[int]:





        '''simple docstring'''







        self._test_inference_batch_single_identical(batch_size=2,expected_max_diff=7E-4		)


    def 						snake_case__     (      self :       Optional[Any]		)->    int:





        '''simple docstring'''







        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)


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





        '''simple docstring'''







        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4		)


    def 						snake_case__     (      self :       Dict		)->    Any:





        '''simple docstring'''







        super().test_save_load_local(expected_max_difference=5E-4		)


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





        '''simple docstring'''







        super().test_save_load_optional_components(expected_max_difference=4E-4		)




@require_torch_gpu
@slow
class  A						(							unittest.TestCase ):







    """simple docstring"""





    @classmethod
    def 						snake_case__     (      cls :       Any		)->    Optional[int]:





        '''simple docstring'''







        super().setUpClass()
        torch.use_deterministic_algorithms(lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       int		)->    List[Any]:





        '''simple docstring'''







        super().tearDownClass()
        torch.use_deterministic_algorithms(lowercase_		)


    def 						snake_case__     (      self :       List[Any]		)->    Any:





        '''simple docstring'''







        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


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





        '''simple docstring'''







        A__          =   torch.manual_seed(5_1		)

        A__          =   StableDiffusionAttendAndExcitePipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4',safety_checker=lowercase_,torch_dtype=torch.floataa		)
        pipe.to('cuda'		)

        A__          =   'a painting of an elephant with glasses'
        A__          =   [5, 7]

        A__          =   pipe(
            prompt=lowercase_,token_indices=lowercase_,guidance_scale=7.5,generator=lowercase_,num_inference_steps=5,max_iter_to_alter=5,output_type='numpy',).images[0]

        A__          =   load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'		)
        assert np.abs((expected_image - image).max()		) < 5E-1

