





"""simple docstring"""
class    _SCREAMING_SNAKE_CASE:




       def __init__(		self						,SCREAMING_SNAKE_CASE__						,SCREAMING_SNAKE_CASE__				)     -> Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE  :Any													=    name
             __SCREAMING_SNAKE_CASE  :List[Any]													=    val




       def __str__(		self				)     -> Dict:





             """simple docstring"""






             return f'''{self.__class__.__name__}({self.name}, {self.val})'''




       def __lt__(		self						,SCREAMING_SNAKE_CASE__				)     -> Any:





             """simple docstring"""






             return self.val < other.val






class    _SCREAMING_SNAKE_CASE:




       def __init__(		self						,SCREAMING_SNAKE_CASE__				)     -> Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE  :List[str]													=    {}
             __SCREAMING_SNAKE_CASE  :str													=    {}
             __SCREAMING_SNAKE_CASE  :Optional[int]													=    self.build_heap(A_				)




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





             """simple docstring"""






             return self.get_value(A_				)




       def        _UpperCamelCase	(		self						,SCREAMING_SNAKE_CASE__				)     -> List[Any]:





             """simple docstring"""






             return (idx - 1) // 2




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





             """simple docstring"""






             return idx * 2 + 1




       def        _UpperCamelCase	(		self						,SCREAMING_SNAKE_CASE__				)     -> Tuple:





             """simple docstring"""






             return idx * 2 + 2




       def        _UpperCamelCase	(		self						,SCREAMING_SNAKE_CASE__				)     -> Tuple:





             """simple docstring"""






             return self.heap_dict[key]




       def        _UpperCamelCase	(		self						,SCREAMING_SNAKE_CASE__				)     -> int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE  :List[Any]													=    len(A_				) - 1
             __SCREAMING_SNAKE_CASE  :Any													=    self.get_parent_idx(A_				)

             for idx, i in enumerate(A_				):
                   __SCREAMING_SNAKE_CASE  :str													=    idx
                   __SCREAMING_SNAKE_CASE  :Dict													=    i.val

             for i in range(A_						,-1						,-1				):
                   self.sift_down(A_						,A_				)
             return array




       def        _UpperCamelCase	(		self						,SCREAMING_SNAKE_CASE__						,SCREAMING_SNAKE_CASE__				)     -> Dict:





             """simple docstring"""






             while True:
                   __SCREAMING_SNAKE_CASE  :List[Any]													=    self.get_left_child_idx(A_				)  # noqa: E741
                   __SCREAMING_SNAKE_CASE  :List[Any]													=    self.get_right_child_idx(A_				)

                   __SCREAMING_SNAKE_CASE  :Dict													=    idx
                   if l < len(A_				) and array[l] < array[idx]:
                         __SCREAMING_SNAKE_CASE  :int													=    l
                   if r < len(A_				) and array[r] < array[smallest]:
                         __SCREAMING_SNAKE_CASE  :Optional[int]													=    r

                   if smallest != idx:
                         __SCREAMING_SNAKE_CASE							,__SCREAMING_SNAKE_CASE  :Optional[Any]													=    array[smallest], array[idx]
                         (
                             (
                             __SCREAMING_SNAKE_CASE		
                         )							,(
                             __SCREAMING_SNAKE_CASE		
                         )							,		
                         )  :Tuple													=    (
                             self.idx_of_element[array[smallest]],
                             self.idx_of_element[array[idx]],
                         )
                         __SCREAMING_SNAKE_CASE  :Optional[Any]													=    smallest
                   else:
                         break




       def        _UpperCamelCase	(		self						,SCREAMING_SNAKE_CASE__				)     -> Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE  :Union[str, Any]													=    self.get_parent_idx(A_				)
             while p >= 0 and self.heap[p] > self.heap[idx]:
                   __SCREAMING_SNAKE_CASE							,__SCREAMING_SNAKE_CASE  :Optional[Any]													=    self.heap[idx], self.heap[p]
                   __SCREAMING_SNAKE_CASE							,__SCREAMING_SNAKE_CASE  :List[Any]													=    (
                       self.idx_of_element[self.heap[idx]],
                       self.idx_of_element[self.heap[p]],
                   )
                   __SCREAMING_SNAKE_CASE  :Optional[Any]													=    p
                   __SCREAMING_SNAKE_CASE  :List[str]													=    self.get_parent_idx(A_				)




       def        _UpperCamelCase	(		self				)     -> List[str]:





             """simple docstring"""






             return self.heap[0]




       def        _UpperCamelCase	(		self				)     -> Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE							,__SCREAMING_SNAKE_CASE  :str													=    self.heap[-1], self.heap[0]
             __SCREAMING_SNAKE_CASE							,__SCREAMING_SNAKE_CASE  :Tuple													=    (
                 self.idx_of_element[self.heap[-1]],
                 self.idx_of_element[self.heap[0]],
             )

             __SCREAMING_SNAKE_CASE  :str													=    self.heap.pop()
             del self.idx_of_element[x]
             self.sift_down(0						,self.heap				)
             return x




       def        _UpperCamelCase	(		self						,SCREAMING_SNAKE_CASE__				)     -> int:





             """simple docstring"""






             self.heap.append(A_				)
             __SCREAMING_SNAKE_CASE  :Tuple													=    len(self.heap				) - 1
             __SCREAMING_SNAKE_CASE  :Any													=    node.val
             self.sift_up(len(self.heap				) - 1				)




       def        _UpperCamelCase	(		self				)     -> Any:





             """simple docstring"""






             return len(self.heap				) == 0




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





             """simple docstring"""






             assert (
                 self.heap[self.idx_of_element[node]].val > new_value
             ), "newValue must be less that current value"
             __SCREAMING_SNAKE_CASE  :Optional[int]													=    new_value
             __SCREAMING_SNAKE_CASE  :str													=    new_value
             self.sift_up(self.idx_of_element[node]				)




lowerCamelCase_        =						Node("R", -1)
lowerCamelCase_        =						Node("B", 6)
lowerCamelCase_        =						Node("A", 3)
lowerCamelCase_        =						Node("X", 1)
lowerCamelCase_        =						Node("E", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
lowerCamelCase_        =						MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("Min Heap - before decrease key")
for i in my_min_heap.heap:
  print(i)

print("Min Heap - After decrease key of node [B -> -17]")
my_min_heap.decrease_key(b, -1_7)

# After
for i in my_min_heap.heap:
  print(i)

if __name__ == "__main__":
  import doctest

  doctest.testmod()



"""simple docstring"""

import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin





@skip_mps
class  lowerCamelCase__    (		_a      , _a      , unittest.TestCase     ):
      a			:  Any           =		IFPipeline
      a			:  str           =		TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
      a			:  Dict           =		TEXT_TO_IMAGE_BATCH_PARAMS
      a			:  int           =		PipelineTesterMixin.required_optional_params - {"""latents"""}
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return self._get_dummy_components()
      def SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						int   ,							A_						:						Dict=0				):






       '''simple docstring'''


       if str(A_				).startswith("""mps"""				):
        __lowercase						=						torch.manual_seed(A_				)
       else:
        __lowercase						=						torch.Generator(device=A_				).manual_seed(A_				)

       __lowercase						=						{
           """prompt""": """A painting of a squirrel eating a burger""",
           """generator""": generator,
           """num_inference_steps""": 2,
           """output_type""": """numpy""",
       }

       return inputs
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       self._test_save_load_optional_components()
      @unittest.skipIf(torch_device != """cuda"""   ,							reason="""float16 requires CUDA"""				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       super().test_save_load_floataa(expected_max_diff=1e-1				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       self._test_save_load_local()
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2   ,							)






      @unittest.skipIf(
          torch_device != """cuda""" or not is_xformers_available()   ,							reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,							)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)






@slow
@require_torch_gpu
class  lowerCamelCase__    (		unittest.TestCase     ):
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""   ,							variant="""fp16"""   ,							torch_dtype=torch.floataa				)

       __lowercase						=						IFSuperResolutionPipeline.from_pretrained(
           """DeepFloyd/IF-II-L-v1.0"""   ,							variant="""fp16"""   ,							torch_dtype=torch.floataa   ,							text_encoder=A_   ,							tokenizer=A_				)

       # pre compute text embeddings and remove T5 to save memory

       pipe_a.text_encoder.to("""cuda"""				)

       __lowercase				,		__lowercase						=						pipe_a.encode_prompt("""anime turtle"""   ,							device="""cuda"""				)

       del pipe_a.tokenizer
       del pipe_a.text_encoder
       gc.collect()

       __lowercase						=						None
       __lowercase						=						None

       pipe_a.enable_model_cpu_offload()
       pipe_a.enable_model_cpu_offload()

       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)
       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)

       self._test_if(A_   ,							A_   ,							A_   ,							A_				)

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # img2img

       __lowercase						=						IFImgaImgPipeline(**pipe_a.components				)
       __lowercase						=						IFImgaImgSuperResolutionPipeline(**pipe_a.components				)

       pipe_a.enable_model_cpu_offload()
       pipe_a.enable_model_cpu_offload()

       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)
       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)

       self._test_if_imgaimg(A_   ,							A_   ,							A_   ,							A_				)

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # inpainting

       __lowercase						=						IFInpaintingPipeline(**pipe_a.components				)
       __lowercase						=						IFInpaintingSuperResolutionPipeline(**pipe_a.components				)

       pipe_a.enable_model_cpu_offload()
       pipe_a.enable_model_cpu_offload()

       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)
       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)

       self._test_if_inpainting(A_   ,							A_   ,							A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Any   ,							A_						:						int   ,							A_						:						str   ,							A_						:						Dict				):






       '''simple docstring'''


       _start_torch_memory_measurement()

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)
       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							num_inference_steps=2   ,							generator=A_   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (6_4, 6_4, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 1_3 * 1_0**9

       __lowercase						=						load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"""				)
       assert_mean_pixel_difference(A_   ,							A_				)

       # pipeline 2

       _start_torch_memory_measurement()

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)

       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							generator=A_   ,							num_inference_steps=2   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 1_0**9

       __lowercase						=						load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"""				)
       assert_mean_pixel_difference(A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						Tuple   ,							A_						:						List[Any]   ,							A_						:						List[Any]   ,							A_						:						Any				):






       '''simple docstring'''


       _start_torch_memory_measurement()

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							num_inference_steps=2   ,							generator=A_   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (6_4, 6_4, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 1_0 * 1_0**9

       __lowercase						=						load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"""				)
       assert_mean_pixel_difference(A_   ,							A_				)

       # pipeline 2

       _start_torch_memory_measurement()

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)

       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							original_image=A_   ,							generator=A_   ,							num_inference_steps=2   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 1_0**9

       __lowercase						=						load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"""				)
       assert_mean_pixel_difference(A_   ,							A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							A_						:						List[Any]   ,							A_						:						str   ,							A_						:						List[Any]   ,							A_						:						List[Any]				):






       '''simple docstring'''


       _start_torch_memory_measurement()

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(1				)				).to(A_				)

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)
       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							mask_image=A_   ,							num_inference_steps=2   ,							generator=A_   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (6_4, 6_4, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 1_0 * 1_0**9

       __lowercase						=						load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"""				)
       assert_mean_pixel_difference(A_   ,							A_				)

       # pipeline 2

       _start_torch_memory_measurement()

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,							rng=random.Random(1				)				).to(A_				)

       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							mask_image=A_   ,							original_image=A_   ,							generator=A_   ,							num_inference_steps=2   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 1_0**9

       __lowercase						=						load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"""				)
       assert_mean_pixel_difference(A_   ,							A_				)






def lowerCAmelCase_   (					):






 """simple docstring"""







 torch.cuda.empty_cache()
 torch.cuda.reset_max_memory_allocated()
 torch.cuda.reset_peak_memory_stats()
