





from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowercase_					     =	TypeVar("""T""")




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     int:
  return (position - 1) // 2




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     int:
  return (2 * position) + 1




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     int:
  return (2 * position) + 2
class        SCREAMING_SNAKE_CASE     (Generic[T]   ):


   def __init__(					self    :       Optional[Any] )->     None:





     """simple docstring"""







     lowercase__			    =						[]
     lowercase__			    =						{}
     lowercase__			    =						0


   def __len__(					self    :       List[Any] )->     int:





     """simple docstring"""







     return self.elements


   def __repr__(					self    :       str )->     str:





     """simple docstring"""







     return str(self.heap )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     bool:





     """simple docstring"""







     return self.elements == 0


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  a    :       T    ,  a    :       int )->     None:





     """simple docstring"""







     self.heap.append((elem, weight) )
     lowercase__			    =						self.elements
     self.elements += 1
     self._bubble_up(a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     T:





     """simple docstring"""







     if self.elements > 1:
       self._swap_nodes(0    ,  self.elements - 1 )
     lowercase__    ,					lowercase__			    =						self.heap.pop()
     del self.position_map[elem]
     self.elements -= 1
     if self.elements > 0:
       lowercase__    ,					lowercase__			    =						self.heap[0]
       self._bubble_down(a )
     return elem


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any    ,  a    :       T    ,  a    :       int )->     None:





     """simple docstring"""







     lowercase__			    =						self.position_map[elem]
     lowercase__			    =						(elem, weight)
     if position > 0:
       lowercase__			    =						get_parent_position(a )
       lowercase__    ,					lowercase__			    =						self.heap[parent_position]
       if parent_weight > weight:
         self._bubble_up(a )
       else:
         self._bubble_down(a )
     else:
       self._bubble_down(a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  a    :       T )->     None:





     """simple docstring"""







     lowercase__			    =						self.position_map[elem]
     if curr_pos == 0:
       return None
     lowercase__			    =						get_parent_position(a )
     lowercase__    ,					lowercase__			    =						self.heap[curr_pos]
     lowercase__    ,					lowercase__			    =						self.heap[parent_position]
     if parent_weight > weight:
       self._swap_nodes(a    ,  a )
       return self._bubble_up(a )
     return None


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  a    :       T )->     None:





     """simple docstring"""







     lowercase__			    =						self.position_map[elem]
     lowercase__    ,					lowercase__			    =						self.heap[curr_pos]
     lowercase__			    =						get_child_left_position(a )
     lowercase__			    =						get_child_right_position(a )
     if child_left_position < self.elements and child_right_position < self.elements:
       lowercase__    ,					lowercase__			    =						self.heap[child_left_position]
       lowercase__    ,					lowercase__			    =						self.heap[child_right_position]
       if child_right_weight < child_left_weight and child_right_weight < weight:
         self._swap_nodes(a    ,  a )
         return self._bubble_down(a )
     if child_left_position < self.elements:
       lowercase__    ,					lowercase__			    =						self.heap[child_left_position]
       if child_left_weight < weight:
         self._swap_nodes(a    ,  a )
         return self._bubble_down(a )
     else:
       return None
     if child_right_position < self.elements:
       lowercase__    ,					lowercase__			    =						self.heap[child_right_position]
       if child_right_weight < weight:
         self._swap_nodes(a    ,  a )
         return self._bubble_down(a )
     return None


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  a    :       int    ,  a    :       int )->     None:





     """simple docstring"""







     lowercase__			    =						self.heap[nodea_pos][0]
     lowercase__			    =						self.heap[nodea_pos][0]
     lowercase__    ,					lowercase__			    =						(
         self.heap[nodea_pos],
         self.heap[nodea_pos],
     )
     lowercase__			    =						nodea_pos
     lowercase__			    =						nodea_pos






class        SCREAMING_SNAKE_CASE     (Generic[T]   ):


   def __init__(					self    :       Union[str, Any] )->     None:





     """simple docstring"""







     lowercase__			    =						{}
     lowercase__			    =						0


   def __repr__(					self    :       Dict )->     str:





     """simple docstring"""







     return str(self.connections )


   def __len__(					self    :       Tuple )->     int:





     """simple docstring"""







     return self.nodes


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any    ,  a    :       T )->     None:





     """simple docstring"""







     if node not in self.connections:
       lowercase__			    =						{}
       self.nodes += 1


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       T    ,  a    :       T    ,  a    :       int )->     None:





     """simple docstring"""







     self.add_node(a )
     self.add_node(a )
     lowercase__			    =						weight
     lowercase__			    =						weight






def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		)       ->     tuple[dict[T, int], dict[T, T | None]]:
  lowercase__			    =						{node: maxsize for node in graph.connections}
  lowercase__			    =						{node: None for node in graph.connections}

  lowercase__			    =						MinPriorityQueue()
  for node, weight in dist.items():
    priority_queue.push(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)

  if priority_queue.is_empty():
    return dist, parent

  # initialization
  lowercase__			    =						priority_queue.extract_min()
  lowercase__			    =						0
  for neighbour in graph.connections[node]:
    if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
      lowercase__			    =						dist[node] + graph.connections[node][neighbour]
      priority_queue.update_key(_SCREAMING_SNAKE_CASE	,		dist[neighbour]	)
      lowercase__			    =						node

    # running prim's algorithm
  while not priority_queue.is_empty():
    lowercase__			    =						priority_queue.extract_min()
    for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
        lowercase__			    =						dist[node] + graph.connections[node][neighbour]
        priority_queue.update_key(_SCREAMING_SNAKE_CASE	,		dist[neighbour]	)
        lowercase__			    =						node
  return dist, parent









import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, 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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	List[Any]				      = StableDiffusionSAGPipeline
   _UpperCamelCase					:	str				      = TEXT_TO_IMAGE_PARAMS
   _UpperCamelCase					:	Dict				      = TEXT_TO_IMAGE_BATCH_PARAMS
   _UpperCamelCase					:	str				      = TEXT_TO_IMAGE_IMAGE_PARAMS
   _UpperCamelCase					:	Optional[int]				      = TEXT_TO_IMAGE_IMAGE_PARAMS
   _UpperCamelCase					:	Union[str, Any]				      = False


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





     """simple docstring"""







     torch.manual_seed(0 )
     lowercase__			    =						UNetaDConditionModel(
         block_out_channels=(32, 64)    ,  layers_per_block=2    ,  sample_size=32    ,  in_channels=4    ,  out_channels=4    ,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,  cross_attention_dim=32    ,  )
     lowercase__			    =						DDIMScheduler(
         beta_start=0.00085    ,  beta_end=0.012    ,  beta_schedule='scaled_linear'    ,  clip_sample=a    ,  set_alpha_to_one=a    ,  )
     torch.manual_seed(0 )
     lowercase__			    =						AutoencoderKL(
         block_out_channels=[32, 64]    ,  in_channels=3    ,  out_channels=3    ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,  latent_channels=4    ,  )
     torch.manual_seed(0 )
     lowercase__			    =						CLIPTextConfig(
         bos_token_id=0    ,  eos_token_id=2    ,  hidden_size=32    ,  intermediate_size=37    ,  layer_norm_eps=1E-0_5    ,  num_attention_heads=4    ,  num_hidden_layers=5    ,  pad_token_id=1    ,  vocab_size=1_000    ,  )
     lowercase__			    =						CLIPTextModel(a )
     lowercase__			    =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

     lowercase__			    =						{
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'safety_checker': None,
         'feature_extractor': None,
     }
     return components


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       Optional[int]    ,  a    :       Any=0 )->     Union[str, Any]:





     """simple docstring"""







     if str(a ).startswith('mps' ):
       lowercase__			    =						torch.manual_seed(a )
     else:
       lowercase__			    =						torch.Generator(device=a ).manual_seed(a )
     lowercase__			    =						{
         'prompt': '.',
         'generator': generator,
         'num_inference_steps': 2,
         'guidance_scale': 1.0,
         'sag_scale': 1.0,
         'output_type': 'numpy',
     }
     return inputs


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     Any:





     """simple docstring"""







     super().test_inference_batch_single_identical(expected_max_diff=3E-3 )






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


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     int:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' )
     lowercase__			    =						sag_pipe.to(a )
     sag_pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						'.'
     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						sag_pipe(
         [prompt]    ,  generator=a    ,  guidance_scale=7.5    ,  sag_scale=1.0    ,  num_inference_steps=20    ,  output_type='np' )

     lowercase__			    =						output.images

     lowercase__			    =						image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
     lowercase__			    =						sag_pipe.to(a )
     sag_pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						'.'
     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						sag_pipe(
         [prompt]    ,  generator=a    ,  guidance_scale=7.5    ,  sag_scale=1.0    ,  num_inference_steps=20    ,  output_type='np' )

     lowercase__			    =						output.images

     lowercase__			    =						image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2


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





     """simple docstring"""







     lowercase__			    =						StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
     lowercase__			    =						sag_pipe.to(a )
     sag_pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						'.'
     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						sag_pipe(
         [prompt]    ,  width=768    ,  height=512    ,  generator=a    ,  guidance_scale=7.5    ,  sag_scale=1.0    ,  num_inference_steps=20    ,  output_type='np'    ,  )

     lowercase__			    =						output.images

     assert image.shape == (1, 512, 768, 3)


