


from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase__      		= TypeVar('T')
def 							_UpperCAmelCase				(UpperCamelCase__     :  int						):
       return (position - 1) // 2
def 							_UpperCAmelCase				(UpperCamelCase__     :  int						):
       return (2 * position) + 1
def 							_UpperCAmelCase				(UpperCamelCase__     :  int						):
       return (2 * position) + 2







class      lowerCAmelCase__       (				Generic[T]):




     '''simple docstring'''


     def __init__(						self)			->				None:
            _A      :       list[tuple[T, int]]				    = []
            _A      :       dict[T, int]				    = {}
            _A      :       int				    = 0


     def __len__(						self)			->				int:
            return self.elements


     def __repr__(						self)			->				str:
            return str(self.heap)


     def  _lowerCamelCase (						self)			->				bool:
            # Check if the priority queue is empty
            return self.elements == 0


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				None:
            # Add an element with given priority to the queue
            self.heap.append((elem, weight))
            _A      :       int				    = self.elements
            self.elements += 1
            self._bubble_up(__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				T:
            # Remove and return the element with lowest weight (highest priority)
            if self.elements > 1:
                   self._swap_nodes(0 ,						self.elements - 1)
            _A    ,			_A      :       Optional[Any]				    = self.heap.pop()
            del self.position_map[elem]
            self.elements -= 1
            if self.elements > 0:
                   _A    ,			_A      :       str				    = self.heap[0]
                   self._bubble_down(__lowerCamelCase)
            return elem


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				None:
            # Update the weight of the given key
            _A      :       Optional[Any]				    = self.position_map[elem]
            _A      :       Any				    = (elem, weight)
            if position > 0:
                   _A      :       Any				    = get_parent_position(__lowerCamelCase)
                   _A    ,			_A      :       Tuple				    = self.heap[parent_position]
                   if parent_weight > weight:
                          self._bubble_up(__lowerCamelCase)
                   else:
                          self._bubble_down(__lowerCamelCase)
            else:
                   self._bubble_down(__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            # Place a node at the proper position (upward movement) [to be used internally
            # only]
            _A      :       Optional[int]				    = self.position_map[elem]
            if curr_pos == 0:
                   return None
            _A      :       Optional[Any]				    = get_parent_position(__lowerCamelCase)
            _A    ,			_A      :       Any				    = self.heap[curr_pos]
            _A    ,			_A      :       str				    = self.heap[parent_position]
            if parent_weight > weight:
                   self._swap_nodes(__lowerCamelCase ,						__lowerCamelCase)
                   return self._bubble_up(__lowerCamelCase)
            return None


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            # Place a node at the proper position (downward movement) [to be used
            # internally only]
            _A      :       Any				    = self.position_map[elem]
            _A    ,			_A      :       Optional[int]				    = self.heap[curr_pos]
            _A      :       Optional[int]				    = get_child_left_position(__lowerCamelCase)
            _A      :       List[str]				    = get_child_right_position(__lowerCamelCase)
            if child_left_position < self.elements and child_right_position < self.elements:
                   _A    ,			_A      :       str				    = self.heap[child_left_position]
                   _A    ,			_A      :       List[str]				    = self.heap[child_right_position]
                   if child_right_weight < child_left_weight and child_right_weight < weight:
                          self._swap_nodes(__lowerCamelCase ,						__lowerCamelCase)
                          return self._bubble_down(__lowerCamelCase)
            if child_left_position < self.elements:
                   _A    ,			_A      :       int				    = self.heap[child_left_position]
                   if child_left_weight < weight:
                          self._swap_nodes(__lowerCamelCase ,						__lowerCamelCase)
                          return self._bubble_down(__lowerCamelCase)
            else:
                   return None
            if child_right_position < self.elements:
                   _A    ,			_A      :       int				    = self.heap[child_right_position]
                   if child_right_weight < weight:
                          self._swap_nodes(__lowerCamelCase ,						__lowerCamelCase)
                          return self._bubble_down(__lowerCamelCase)
            return None


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				None:
            # Swap the nodes at the given positions
            _A      :       str				    = self.heap[nodea_pos][0]
            _A      :       List[Any]				    = self.heap[nodea_pos][0]
            _A    ,			_A      :       Dict				    = (
                self.heap[nodea_pos],
                self.heap[nodea_pos],
            )
            _A      :       Tuple				    = nodea_pos
            _A      :       int				    = nodea_pos







class      lowerCAmelCase__       (				Generic[T]):




     '''simple docstring'''


     def __init__(						self)			->				None:
            _A      :       dict[T, dict[T, int]]				    = {}
            _A      :       int				    = 0


     def __repr__(						self)			->				str:
            return str(self.connections)


     def __len__(						self)			->				int:
            return self.nodes


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            # Add a node in the graph if it is not in the graph
            if node not in self.connections:
                   _A      :       Optional[int]				    = {}
                   self.nodes += 1


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				None:
            # Add an edge between 2 nodes in the graph
            self.add_node(__lowerCamelCase)
            self.add_node(__lowerCamelCase)
            _A      :       Optional[int]				    = weight
            _A      :       str				    = weight




def 							_UpperCAmelCase				(UpperCamelCase__     :  GraphUndirectedWeighted[T]				,							):
       _A      :       dict[T, int]				    = {node: maxsize for node in graph.connections}
       _A      :       dict[T, T | None]				    = {node: None for node in graph.connections}

       _A      :       MinPriorityQueue[T]				    = MinPriorityQueue()
       for node, weight in dist.items():
              priority_queue.push(UpperCamelCase__				,							UpperCamelCase__						)

       if priority_queue.is_empty():
              return dist, parent

       # initialization
       _A      :       List[str]				    = priority_queue.extract_min()
       _A      :       Tuple				    = 0
       for neighbour in graph.connections[node]:
              if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                     _A      :       Tuple				    = dist[node] + graph.connections[node][neighbour]
                     priority_queue.update_key(UpperCamelCase__				,							dist[neighbour]						)
                     _A      :       int				    = node

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




'''simple docstring'''




import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

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





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  int:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Any:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_euler'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe([prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=9.0    ,      num_inference_steps=2_0    ,      output_type='np'    )

        lowerCAmelCase     =			output.images

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

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Dict:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_euler'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe([prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=9.0    ,      num_inference_steps=2_0    ,      output_type='np'    )

        lowerCAmelCase     =			output.images

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

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5E-1





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_dpmpp_2m'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe(
            [prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=7.5    ,      num_inference_steps=1_5    ,      output_type='np'    ,      use_karras_sigmas=UpperCAmelCase__    ,      )

        lowerCAmelCase     =			output.images

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

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array(
            [0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





