



"""simple docstring"""




from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

_lowerCAmelCase							:   Union[str, Any]				=				TypeVar("T")

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     return (position - 1) // 2

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     return (2 * position) + 1

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     return (2 * position) + 2






class       UpperCAmelCase_    (							Generic[T]		):



      def __init__(	self	:      Any		):
           _UpperCAmelCase     :      list[tuple[T, int]]														=       []
           _UpperCAmelCase     :      dict[T, int]														=       {}
           _UpperCAmelCase     :      int														=       0



      def __len__(	self	:      str		):
           return self.elements



      def __repr__(	self	:      Any		):
           return str(self.heap		)



      def 		snake_case_   (	self	:      Dict		):
           # Check if the priority queue is empty
           return self.elements == 0



      def 		snake_case_   (	self	:      Dict	,      A	:      T	,      A	:      int		):
           # Add an element with given priority to the queue
           self.heap.append((elem, weight)		)
           _UpperCAmelCase     :      Any														=       self.elements
           self.elements += 1
           self._bubble_up(A		)



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



      def 		snake_case_   (	self	:      int	,      A	:      T	,      A	:      int		):
           # Update the weight of the given key
           _UpperCAmelCase     :      List[Any]														=       self.position_map[elem]
           _UpperCAmelCase     :      Optional[int]														=       (elem, weight)
           if position > 0:
                _UpperCAmelCase     :      List[Any]														=       get_parent_position(A		)
                _UpperCAmelCase							,			_UpperCAmelCase     :      List[Any]														=       self.heap[parent_position]
                if parent_weight > weight:
                     self._bubble_up(A		)
                else:
                     self._bubble_down(A		)
           else:
                self._bubble_down(A		)



      def 		snake_case_   (	self	:      List[str]	,      A	:      T		):
           # Place a node at the proper position (upward movement) [to be used internally
           # only]
           _UpperCAmelCase     :      str														=       self.position_map[elem]
           if curr_pos == 0:
                return None
           _UpperCAmelCase     :      List[Any]														=       get_parent_position(A		)
           _UpperCAmelCase							,			_UpperCAmelCase     :      Tuple														=       self.heap[curr_pos]
           _UpperCAmelCase							,			_UpperCAmelCase     :      Any														=       self.heap[parent_position]
           if parent_weight > weight:
                self._swap_nodes(A	,      A		)
                return self._bubble_up(A		)
           return None



      def 		snake_case_   (	self	:      Dict	,      A	:      T		):
           # Place a node at the proper position (downward movement) [to be used
           # internally only]
           _UpperCAmelCase     :      List[Any]														=       self.position_map[elem]
           _UpperCAmelCase							,			_UpperCAmelCase     :      Union[str, Any]														=       self.heap[curr_pos]
           _UpperCAmelCase     :      Tuple														=       get_child_left_position(A		)
           _UpperCAmelCase     :      List[str]														=       get_child_right_position(A		)
           if child_left_position < self.elements and child_right_position < self.elements:
                _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[int]														=       self.heap[child_left_position]
                _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       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:
                _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       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:
                _UpperCAmelCase							,			_UpperCAmelCase     :      Tuple														=       self.heap[child_right_position]
                if child_right_weight < weight:
                     self._swap_nodes(A	,      A		)
                     return self._bubble_down(A		)
           return None



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      int	,      A	:      int		):
           # Swap the nodes at the given positions
           _UpperCAmelCase     :      List[Any]														=       self.heap[nodea_pos][0]
           _UpperCAmelCase     :      int														=       self.heap[nodea_pos][0]
           _UpperCAmelCase							,			_UpperCAmelCase     :      Any														=       (
               self.heap[nodea_pos],
               self.heap[nodea_pos],
           )
           _UpperCAmelCase     :      str														=       nodea_pos
           _UpperCAmelCase     :      List[Any]														=       nodea_pos







class       UpperCAmelCase_    (							Generic[T]		):



      def __init__(	self	:      List[str]		):
           _UpperCAmelCase     :      dict[T, dict[T, int]]														=       {}
           _UpperCAmelCase     :      int														=       0



      def __repr__(	self	:      Dict		):
           return str(self.connections		)



      def __len__(	self	:      List[Any]		):
           return self.nodes



      def 		snake_case_   (	self	:      Tuple	,      A	:      T		):
           # Add a node in the graph if it is not in the graph
           if node not in self.connections:
                _UpperCAmelCase     :      str														=       {}
                self.nodes += 1



      def 		snake_case_   (	self	:      str	,      A	:      T	,      A	:      T	,      A	:      int		):
           # Add an edge between 2 nodes in the graph
           self.add_node(A		)
           self.add_node(A		)
           _UpperCAmelCase     :      int														=       weight
           _UpperCAmelCase     :      Union[str, Any]														=       weight







def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		GraphUndirectedWeighted[T]		,    )    ->  tuple[dict[T, int], dict[T, T | None]]:





     '''simple docstring'''

     _UpperCAmelCase     :      dict[T, int]														=       {node: maxsize for node in graph.connections}
     _UpperCAmelCase     :      dict[T, T | None]														=       {node: None for node in graph.connections}

     _UpperCAmelCase     :      MinPriorityQueue[T]														=       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
     _UpperCAmelCase     :      Optional[Any]														=       priority_queue.extract_min()
     _UpperCAmelCase     :      Optional[int]														=       0
     for neighbour in graph.connections[node]:
          if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
               _UpperCAmelCase     :      Union[str, Any]														=       dist[node] + graph.connections[node][neighbour]
               priority_queue.update_key(SCREAMING_SNAKE_CASE__		,    dist[neighbour]							)
               _UpperCAmelCase     :      Dict														=       node

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





"""simple docstring"""




import os
import tempfile
import unittest

import numpy as np

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


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

 from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline






@require_flax
class       UpperCAmelCase_    (							unittest.TestCase		):



      def 		snake_case_   (	self	:      Optional[int]		):
           with tempfile.TemporaryDirectory() as tmpdirname:
                # pipeline has Flax weights
                _UpperCAmelCase     :      Union[str, Any]														=       FlaxDiffusionPipeline.from_pretrained(
                    "hf-internal-testing/tiny-stable-diffusion-pipe"	,      safety_checker=A	,      cache_dir=A		)

                _UpperCAmelCase     :      List[str]														=       [t[-1] for t in os.walk(os.path.join(A	,      os.listdir(A		)[0]	,      "snapshots"		)		)]
                _UpperCAmelCase     :      Union[str, Any]														=       [item for sublist in all_root_files for item in sublist]

                # None of the downloaded files should be a PyTorch file even if we have some here:
                # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
                assert not any(f.endswith(".bin"		) for f in files		)







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



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      int														=       FlaxStableDiffusionPipeline.from_pretrained(
               "hf-internal-testing/tiny-stable-diffusion-pipe"	,      safety_checker=A		)

           _UpperCAmelCase     :      str														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      int														=       jax.random.PRNGKey(0		)
           _UpperCAmelCase     :      List[str]														=       4

           _UpperCAmelCase     :      Any														=       jax.device_count()
           _UpperCAmelCase     :      Tuple														=       num_samples * [prompt]
           _UpperCAmelCase     :      Optional[int]														=       pipeline.prepare_inputs(A		)

           # shard inputs and rng
           _UpperCAmelCase     :      Union[str, Any]														=       replicate(A		)
           _UpperCAmelCase     :      List[Any]														=       jax.random.split(A	,      A		)
           _UpperCAmelCase     :      List[Any]														=       shard(A		)

           _UpperCAmelCase     :      Tuple														=       pipeline(A	,      A	,      A	,      A	,      jit=A		).images

           assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
           if jax.device_count() == 8:
                assert np.abs(np.abs(images[0, 0, :2, :2, -2:]	,      dtype=np.floataa		).sum() - 4.1_514_745		) < 1e-3
                assert np.abs(np.abs(A	,      dtype=np.floataa		).sum() - 49_947.875		) < 5e-1

           _UpperCAmelCase     :      Optional[int]														=       pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]		)		)		)
           assert len(A		) == num_samples



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="flax"	,      safety_checker=A		)

           _UpperCAmelCase     :      Dict														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      Dict														=       jax.random.PRNGKey(0		)
           _UpperCAmelCase     :      str														=       5_0

           _UpperCAmelCase     :      List[str]														=       jax.device_count()
           _UpperCAmelCase     :      Any														=       num_samples * [prompt]
           _UpperCAmelCase     :      Dict														=       pipeline.prepare_inputs(A		)

           # shard inputs and rng
           _UpperCAmelCase     :      Dict														=       replicate(A		)
           _UpperCAmelCase     :      str														=       jax.random.split(A	,      A		)
           _UpperCAmelCase     :      Optional[Any]														=       shard(A		)

           _UpperCAmelCase     :      List[Any]														=       pipeline(A	,      A	,      A	,      A	,      jit=A		).images

           assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           if jax.device_count() == 8:
                assert np.abs((np.abs(images[0, 0, :2, :2, -2:]	,      dtype=np.floataa		).sum() - 0.05_652_401)		) < 1e-3
                assert np.abs((np.abs(A	,      dtype=np.floataa		).sum() - 2_383_808.2)		) < 5e-1



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      List[Any]														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="bf16"	,      dtype=jnp.bfloataa	,      safety_checker=A		)

           _UpperCAmelCase     :      Any														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      List[Any]														=       jax.random.PRNGKey(0		)
           _UpperCAmelCase     :      List[Any]														=       5_0

           _UpperCAmelCase     :      Union[str, Any]														=       jax.device_count()
           _UpperCAmelCase     :      Optional[int]														=       num_samples * [prompt]
           _UpperCAmelCase     :      Union[str, Any]														=       pipeline.prepare_inputs(A		)

           # shard inputs and rng
           _UpperCAmelCase     :      int														=       replicate(A		)
           _UpperCAmelCase     :      Optional[int]														=       jax.random.split(A	,      A		)
           _UpperCAmelCase     :      str														=       shard(A		)

           _UpperCAmelCase     :      Union[str, Any]														=       pipeline(A	,      A	,      A	,      A	,      jit=A		).images

           assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           if jax.device_count() == 8:
                assert np.abs((np.abs(images[0, 0, :2, :2, -2:]	,      dtype=np.floataa		).sum() - 0.04_003_906)		) < 1e-3
                assert np.abs((np.abs(A	,      dtype=np.floataa		).sum() - 2_373_516.75)		) < 5e-1



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="bf16"	,      dtype=jnp.bfloataa		)

           _UpperCAmelCase     :      str														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      Optional[int]														=       jax.random.PRNGKey(0		)
           _UpperCAmelCase     :      Dict														=       5_0

           _UpperCAmelCase     :      Union[str, Any]														=       jax.device_count()
           _UpperCAmelCase     :      str														=       num_samples * [prompt]
           _UpperCAmelCase     :      int														=       pipeline.prepare_inputs(A		)

           # shard inputs and rng
           _UpperCAmelCase     :      Optional[int]														=       replicate(A		)
           _UpperCAmelCase     :      Tuple														=       jax.random.split(A	,      A		)
           _UpperCAmelCase     :      Dict														=       shard(A		)

           _UpperCAmelCase     :      Optional[Any]														=       pipeline(A	,      A	,      A	,      A	,      jit=A		).images

           assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           if jax.device_count() == 8:
                assert np.abs((np.abs(images[0, 0, :2, :2, -2:]	,      dtype=np.floataa		).sum() - 0.04_003_906)		) < 1e-3
                assert np.abs((np.abs(A	,      dtype=np.floataa		).sum() - 2_373_516.75)		) < 5e-1



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       FlaxDDIMScheduler(
               beta_start=0.00_085	,      beta_end=0.012	,      beta_schedule="scaled_linear"	,      set_alpha_to_one=A	,      steps_offset=1	,      )

           _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="bf16"	,      dtype=jnp.bfloataa	,      scheduler=A	,      safety_checker=A	,      )
           _UpperCAmelCase     :      Union[str, Any]														=       scheduler.create_state()

           _UpperCAmelCase     :      str														=       scheduler_state

           _UpperCAmelCase     :      Union[str, Any]														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      str														=       jax.random.PRNGKey(0		)
           _UpperCAmelCase     :      List[str]														=       5_0

           _UpperCAmelCase     :      Optional[int]														=       jax.device_count()
           _UpperCAmelCase     :      int														=       num_samples * [prompt]
           _UpperCAmelCase     :      Optional[int]														=       pipeline.prepare_inputs(A		)

           # shard inputs and rng
           _UpperCAmelCase     :      List[Any]														=       replicate(A		)
           _UpperCAmelCase     :      Tuple														=       jax.random.split(A	,      A		)
           _UpperCAmelCase     :      Union[str, Any]														=       shard(A		)

           _UpperCAmelCase     :      Dict														=       pipeline(A	,      A	,      A	,      A	,      jit=A		).images

           assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           if jax.device_count() == 8:
                assert np.abs((np.abs(images[0, 0, :2, :2, -2:]	,      dtype=np.floataa		).sum() - 0.045_043_945)		) < 1e-3
                assert np.abs((np.abs(A	,      dtype=np.floataa		).sum() - 2_347_693.5)		) < 5e-1



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Optional[Any]														=       (
               "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
               " field, close up, split lighting, cinematic"
           )

           _UpperCAmelCase     :      Optional[Any]														=       jax.device_count()
           _UpperCAmelCase     :      Union[str, Any]														=       num_samples * [prompt]
           _UpperCAmelCase     :      Optional[Any]														=       jax.random.split(jax.random.PRNGKey(0		)	,      A		)

           _UpperCAmelCase							,			_UpperCAmelCase     :      str														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="bf16"	,      dtype=jnp.bfloataa	,      safety_checker=A	,      )

           _UpperCAmelCase     :      int														=       replicate(A		)
           _UpperCAmelCase     :      Union[str, Any]														=       pipeline.prepare_inputs(A		)
           _UpperCAmelCase     :      int														=       shard(A		)
           _UpperCAmelCase     :      Dict														=       pipeline(A	,      A	,      A	,      jit=A		).images
           assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           _UpperCAmelCase     :      List[str]														=       images[2, 0, 2_5_6, 1_0:1_7, 1]

           # With memory efficient attention
           _UpperCAmelCase							,			_UpperCAmelCase     :      str														=       FlaxStableDiffusionPipeline.from_pretrained(
               "CompVis/stable-diffusion-v1-4"	,      revision="bf16"	,      dtype=jnp.bfloataa	,      safety_checker=A	,      use_memory_efficient_attention=A	,      )

           _UpperCAmelCase     :      Any														=       replicate(A		)
           _UpperCAmelCase     :      str														=       pipeline.prepare_inputs(A		)
           _UpperCAmelCase     :      Tuple														=       shard(A		)
           _UpperCAmelCase     :      Tuple														=       pipeline(A	,      A	,      A	,      jit=A		).images
           assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
           _UpperCAmelCase     :      str														=       images[2, 0, 2_5_6, 1_0:1_7, 1]

           # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
           # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
           assert abs(slice_eff - slice		).max() < 1e-2
