
"""simple docstring"""


from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

_lowercase							      = TypeVar('''T''')






def 					_snake_case (						snake_case__      :   int   ):
	return (position - 1) // 2






def 					_snake_case (						snake_case__      :   int   ):
	return (2 * position) + 1






def 					_snake_case (						snake_case__      :   int   ):
	return (2 * position) + 2







class  lowerCAmelCase_ (					Generic[T]			):



						'''simple docstring'''


						def __init__(       self   :					Optional[Any]					)   ->      None:
							A        =      []
							A        =      {}
							A        =      0


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


						def __repr__(       self   :					Optional[Any]					)   ->      str:
							return str(self.heap					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      bool:
							# Check if the priority queue is empty
							return self.elements == 0


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]     ,A_   :					T     ,A_   :					int					)   ->      None:
							# Add an element with given priority to the queue
							self.heap.append((elem, weight)					)
							A        =      self.elements
							self.elements += 1
							self._bubble_up(A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      T:
							# Remove and return the element with lowest weight (highest priority)
							if self.elements > 1:
								self._swap_nodes(0     ,self.elements - 1					)
							A      ,					A        =      self.heap.pop()
							del self.position_map[elem]
							self.elements -= 1
							if self.elements > 0:
								A      ,					A        =      self.heap[0]
								self._bubble_down(A_					)
							return elem


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict     ,A_   :					T     ,A_   :					int					)   ->      None:
							# Update the weight of the given key
							A        =      self.position_map[elem]
							A        =      (elem, weight)
							if position > 0:
								A        =      get_parent_position(A_					)
								A      ,					A        =      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   :					Optional[Any]     ,A_   :					T					)   ->      None:
							# Place a node at the proper position (upward movement) [to be used internally
							# only]
							A        =      self.position_map[elem]
							if curr_pos == 0:
								return None
							A        =      get_parent_position(A_					)
							A      ,					A        =      self.heap[curr_pos]
							A      ,					A        =      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   :					List[Any]     ,A_   :					T					)   ->      None:
							# Place a node at the proper position (downward movement) [to be used
							# internally only]
							A        =      self.position_map[elem]
							A      ,					A        =      self.heap[curr_pos]
							A        =      get_child_left_position(A_					)
							A        =      get_child_right_position(A_					)
							if child_left_position < self.elements and child_right_position < self.elements:
								A      ,					A        =      self.heap[child_left_position]
								A      ,					A        =      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:
								A      ,					A        =      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:
								A      ,					A        =      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   :					Optional[Any]     ,A_   :					int     ,A_   :					int					)   ->      None:
							# Swap the nodes at the given positions
							A        =      self.heap[nodea_pos][0]
							A        =      self.heap[nodea_pos][0]
							A      ,					A        =      (
							    self.heap[nodea_pos],
							    self.heap[nodea_pos],
							)
							A        =      nodea_pos
							A        =      nodea_pos







class  lowerCAmelCase_ (					Generic[T]			):



						'''simple docstring'''


						def __init__(       self   :					Union[str, Any]					)   ->      None:
							A        =      {}
							A        =      0


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


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


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]     ,A_   :					T					)   ->      None:
							# Add a node in the graph if it is not in the graph
							if node not in self.connections:
								A        =      {}
								self.nodes += 1




						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,A_   :					T     ,A_   :					T     ,A_   :					int					)   ->      None:
							# Add an edge between 2 nodes in the graph
							self.add_node(A_					)
							self.add_node(A_					)
							A        =      weight
							A        =      weight






def 					_snake_case (						snake_case__      :   GraphUndirectedWeighted[T]		,					):
	A        =      {node: maxsize for node in graph.connections}
	A        =      {node: None for node in graph.connections}

	A        =      MinPriorityQueue()
	for node, weight in dist.items():
		priority_queue.push(snake_case__		,					snake_case__   )

	if priority_queue.is_empty():
		return dist, parent

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

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

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
   import jax
   import jax.numpy as jnp
   from flax.jax_utils import replicate
   from flax.training.common_utils import shard





@slow
@require_flax
class 		_lowercase						(					unittest.TestCase       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						str:

  """simple docstring"""





  super().tearDown()
  gc.collect()

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

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						str =					FlaxStableDiffusionPipeline.from_pretrained(
      'stabilityai/stable-diffusion-2'   ,      revision='bf16'   ,      dtype=jnp.bfloataa   ,      )

  UpperCamelCase_					:						str =					'A painting of a squirrel eating a burger'
  UpperCamelCase_					:						Any =					jax.device_count()
  UpperCamelCase_					:						List[str] =					num_samples * [prompt]
  UpperCamelCase_					:						List[Any] =					sd_pipe.prepare_inputs(snake_case			)

  UpperCamelCase_					:						Dict =					replicate(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					shard(snake_case			)

  UpperCamelCase_					:						Dict =					jax.random.PRNGKey(0			)
  UpperCamelCase_					:						Tuple =					jax.random.split(snake_case   ,      jax.device_count()			)

  UpperCamelCase_					:						Optional[Any] =					sd_pipe(snake_case   ,      snake_case   ,      snake_case   ,      num_inference_steps=2_5   ,      jit=snake_case			)[0]
  assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

  UpperCamelCase_					:						int =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
  UpperCamelCase_					:						Tuple =					images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

  UpperCamelCase_					:						str =					jnp.asarray(jax.device_get(image_slice.flatten()			)			)
  UpperCamelCase_					:						List[str] =					jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]			)
  print(f"output_slice: {output_slice}"			)
  assert jnp.abs(output_slice - expected_slice			).max() < 1e-2


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

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'stabilityai/stable-diffusion-2'
  UpperCamelCase_, UpperCamelCase_					:						Tuple =					FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case   ,      subfolder='scheduler'			)
  UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					FlaxStableDiffusionPipeline.from_pretrained(
      snake_case   ,      scheduler=snake_case   ,      revision='bf16'   ,      dtype=jnp.bfloataa   ,      )
  UpperCamelCase_					:						Optional[int] =					scheduler_params

  UpperCamelCase_					:						Optional[Any] =					'A painting of a squirrel eating a burger'
  UpperCamelCase_					:						Union[str, Any] =					jax.device_count()
  UpperCamelCase_					:						Union[str, Any] =					num_samples * [prompt]
  UpperCamelCase_					:						Tuple =					sd_pipe.prepare_inputs(snake_case			)

  UpperCamelCase_					:						List[Any] =					replicate(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					shard(snake_case			)

  UpperCamelCase_					:						Tuple =					jax.random.PRNGKey(0			)
  UpperCamelCase_					:						str =					jax.random.split(snake_case   ,      jax.device_count()			)

  UpperCamelCase_					:						str =					sd_pipe(snake_case   ,      snake_case   ,      snake_case   ,      num_inference_steps=2_5   ,      jit=snake_case			)[0]
  assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

  UpperCamelCase_					:						str =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
  UpperCamelCase_					:						int =					images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

  UpperCamelCase_					:						int =					jnp.asarray(jax.device_get(image_slice.flatten()			)			)
  UpperCamelCase_					:						Union[str, Any] =					jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]			)
  print(f"output_slice: {output_slice}"			)
  assert jnp.abs(output_slice - expected_slice			).max() < 1e-2



