
from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__lowerCAmelCase					:			Dict			      =		TypeVar("T")


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:
    return (position - 1) // 2


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:
    return (2 * position) + 1


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:
    return (2 * position) + 2


class 						__lowerCAmelCase	(	Generic[T]      ):



      """simple docstring"""







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




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




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




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




      def 		snake_case_			(      self		:       Optional[Any]				,       _snake_case		:       T				,       _snake_case		:       int				):
          # Add an element with given priority to the queue
          self.heap.append((elem, weight)				)
          __lowercase		:			Optional[Any]	    =       self.elements
          self.elements += 1
          self._bubble_up(_snake_case				)




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




      def 		snake_case_			(      self		:       Union[str, Any]				,       _snake_case		:       T				,       _snake_case		:       int				):
          # Update the weight of the given key
          __lowercase		:			List[Any]	    =       self.position_map[elem]
          __lowercase		:			Any	    =       (elem, weight)
          if position > 0:
              __lowercase		:			Dict	    =       get_parent_position(_snake_case				)
              __lowercase    ,      __lowercase		:			Tuple	    =       self.heap[parent_position]
              if parent_weight > weight:
                  self._bubble_up(_snake_case				)
              else:
                  self._bubble_down(_snake_case				)
          else:
              self._bubble_down(_snake_case				)




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




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




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       int				,       _snake_case		:       int				):
          # Swap the nodes at the given positions
          __lowercase		:			Union[str, Any]	    =       self.heap[nodea_pos][0]
          __lowercase		:			Tuple	    =       self.heap[nodea_pos][0]
          __lowercase    ,      __lowercase		:			str	    =       (
              self.heap[nodea_pos],
              self.heap[nodea_pos],
          )
          __lowercase		:			List[Any]	    =       nodea_pos
          __lowercase		:			List[str]	    =       nodea_pos






class 						__lowerCAmelCase	(	Generic[T]      ):



      """simple docstring"""







      def __init__(      self		:       Optional[Any]				):
          __lowercase		:			dict[T, dict[T, int]]	    =       {}
          __lowercase		:			int	    =       0




      def __repr__(      self		:       Optional[int]				):
          return str(self.connections				)




      def __len__(      self		:       Dict				):
          return self.nodes




      def 		snake_case_			(      self		:       List[str]				,       _snake_case		:       T				):
          # Add a node in the graph if it is not in the graph
          if node not in self.connections:
              __lowercase		:			Union[str, Any]	    =       {}
              self.nodes += 1




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






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

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

    if priority_queue.is_empty():
        return dist, parent

    # initialization
    __lowercase		:			int	    =       priority_queue.extract_min()
    __lowercase		:			Tuple	    =       0
    for neighbour in graph.connections[node]:
        if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            __lowercase		:			Tuple	    =       dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(__lowerCAmelCase	,					dist[neighbour]	)
            __lowercase		:			Union[str, Any]	    =       node

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




import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np


class 						__lowerCAmelCase	:



      """simple docstring"""







      def 		snake_case_			(      self		:       str				):
          torch.manual_seed(0				)
          __lowercase		:			Optional[int]	    =       TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

          torch.manual_seed(0				)
          __lowercase		:			Optional[Any]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

          torch.manual_seed(0				)
          __lowercase		:			Tuple	    =       UNetaDConditionModel(
              sample_size=32				,       layers_per_block=1				,       block_out_channels=[32, 64]				,       down_block_types=[
                  '''ResnetDownsampleBlock2D''',
                  '''SimpleCrossAttnDownBlock2D''',
              ]				,       mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''				,       up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']				,       in_channels=3				,       out_channels=6				,       cross_attention_dim=32				,       encoder_hid_dim=32				,       attention_head_dim=8				,       addition_embed_type='''text'''				,       addition_embed_type_num_heads=2				,       cross_attention_norm='''group_norm'''				,       resnet_time_scale_shift='''scale_shift'''				,       act_fn='''gelu'''				,       )
          unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

          torch.manual_seed(0				)
          __lowercase		:			List[str]	    =       DDPMScheduler(
              num_train_timesteps=1000				,       beta_schedule='''squaredcos_cap_v2'''				,       beta_start=0.00_01				,       beta_end=0.02				,       thresholding=_snake_case				,       dynamic_thresholding_ratio=0.95				,       sample_max_value=1.0				,       prediction_type='''epsilon'''				,       variance_type='''learned_range'''				,       )

          torch.manual_seed(0				)
          __lowercase		:			str	    =       IFWatermarker()

          return {
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "unet": unet,
              "scheduler": scheduler,
              "watermarker": watermarker,
              "safety_checker": None,
              "feature_extractor": None,
          }




      def 		snake_case_			(      self		:       Optional[int]				):
          torch.manual_seed(0				)
          __lowercase		:			Optional[int]	    =       TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

          torch.manual_seed(0				)
          __lowercase		:			Tuple	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

          torch.manual_seed(0				)
          __lowercase		:			Dict	    =       UNetaDConditionModel(
              sample_size=32				,       layers_per_block=[1, 2]				,       block_out_channels=[32, 64]				,       down_block_types=[
                  '''ResnetDownsampleBlock2D''',
                  '''SimpleCrossAttnDownBlock2D''',
              ]				,       mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''				,       up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']				,       in_channels=6				,       out_channels=6				,       cross_attention_dim=32				,       encoder_hid_dim=32				,       attention_head_dim=8				,       addition_embed_type='''text'''				,       addition_embed_type_num_heads=2				,       cross_attention_norm='''group_norm'''				,       resnet_time_scale_shift='''scale_shift'''				,       act_fn='''gelu'''				,       class_embed_type='''timestep'''				,       mid_block_scale_factor=1.4_14				,       time_embedding_act_fn='''gelu'''				,       time_embedding_dim=32				,       )
          unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

          torch.manual_seed(0				)
          __lowercase		:			Dict	    =       DDPMScheduler(
              num_train_timesteps=1000				,       beta_schedule='''squaredcos_cap_v2'''				,       beta_start=0.00_01				,       beta_end=0.02				,       thresholding=_snake_case				,       dynamic_thresholding_ratio=0.95				,       sample_max_value=1.0				,       prediction_type='''epsilon'''				,       variance_type='''learned_range'''				,       )

          torch.manual_seed(0				)
          __lowercase		:			Optional[int]	    =       DDPMScheduler(
              num_train_timesteps=1000				,       beta_schedule='''squaredcos_cap_v2'''				,       beta_start=0.00_01				,       beta_end=0.02				,       )

          torch.manual_seed(0				)
          __lowercase		:			Any	    =       IFWatermarker()

          return {
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "unet": unet,
              "scheduler": scheduler,
              "image_noising_scheduler": image_noising_scheduler,
              "watermarker": watermarker,
              "safety_checker": None,
              "feature_extractor": None,
          }




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			Tuple	    =       self.get_dummy_components()
          __lowercase		:			Any	    =       self.pipeline_class(**_snake_case				)
          pipe.to(_snake_case				)
          pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			List[str]	    =       self.get_dummy_inputs(_snake_case				)

          __lowercase		:			List[Any]	    =       inputs['''prompt''']
          __lowercase		:			int	    =       inputs['''generator''']
          __lowercase		:			Dict	    =       inputs['''num_inference_steps''']
          __lowercase		:			Optional[int]	    =       inputs['''output_type''']

          if "image" in inputs:
              __lowercase		:			Tuple	    =       inputs['''image''']
          else:
              __lowercase		:			Dict	    =       None

          if "mask_image" in inputs:
              __lowercase		:			Tuple	    =       inputs['''mask_image''']
          else:
              __lowercase		:			List[Any]	    =       None

          if "original_image" in inputs:
              __lowercase		:			List[Any]	    =       inputs['''original_image''']
          else:
              __lowercase		:			Optional[int]	    =       None

          __lowercase    ,      __lowercase		:			Optional[int]	    =       pipe.encode_prompt(_snake_case				)

          # inputs with prompt converted to embeddings
          __lowercase		:			int	    =       {
              '''prompt_embeds''': prompt_embeds,
              '''negative_prompt_embeds''': negative_prompt_embeds,
              '''generator''': generator,
              '''num_inference_steps''': num_inference_steps,
              '''output_type''': output_type,
          }

          if image is not None:
              __lowercase		:			Tuple	    =       image

          if mask_image is not None:
              __lowercase		:			List[str]	    =       mask_image

          if original_image is not None:
              __lowercase		:			List[Any]	    =       original_image

          # set all optional components to None
          for optional_component in pipe._optional_components:
              setattr(_snake_case				,       _snake_case				,       _snake_case				)

          __lowercase		:			List[str]	    =       pipe(**_snake_case				)[0]

          with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(_snake_case				)
              __lowercase		:			List[Any]	    =       self.pipeline_class.from_pretrained(_snake_case				)
              pipe_loaded.to(_snake_case				)
              pipe_loaded.set_progress_bar_config(disable=_snake_case				)

          pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

          for optional_component in pipe._optional_components:
              self.assertTrue(
                  getattr(_snake_case				,       _snake_case				) is None				,       F'`{optional_component}` did not stay set to None after loading.'				,       )

          __lowercase		:			int	    =       self.get_dummy_inputs(_snake_case				)

          __lowercase		:			Union[str, Any]	    =       inputs['''generator''']
          __lowercase		:			Any	    =       inputs['''num_inference_steps''']
          __lowercase		:			Tuple	    =       inputs['''output_type''']

          # inputs with prompt converted to embeddings
          __lowercase		:			List[str]	    =       {
              '''prompt_embeds''': prompt_embeds,
              '''negative_prompt_embeds''': negative_prompt_embeds,
              '''generator''': generator,
              '''num_inference_steps''': num_inference_steps,
              '''output_type''': output_type,
          }

          if image is not None:
              __lowercase		:			Dict	    =       image

          if mask_image is not None:
              __lowercase		:			Tuple	    =       mask_image

          if original_image is not None:
              __lowercase		:			Any	    =       original_image

          __lowercase		:			List[str]	    =       pipe_loaded(**_snake_case				)[0]

          __lowercase		:			Optional[int]	    =       np.abs(to_np(_snake_case				) - to_np(_snake_case				)				).max()
          self.assertLess(_snake_case				,       1E-4				)




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Union[str, Any]	    =       self.get_dummy_components()
          __lowercase		:			Dict	    =       self.pipeline_class(**_snake_case				)
          pipe.to(_snake_case				)
          pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Dict	    =       self.get_dummy_inputs(_snake_case				)
          __lowercase		:			Any	    =       pipe(**_snake_case				)[0]

          with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(_snake_case				)
              __lowercase		:			Dict	    =       self.pipeline_class.from_pretrained(_snake_case				)
              pipe_loaded.to(_snake_case				)
              pipe_loaded.set_progress_bar_config(disable=_snake_case				)

          pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

          __lowercase		:			Optional[Any]	    =       self.get_dummy_inputs(_snake_case				)
          __lowercase		:			str	    =       pipe_loaded(**_snake_case				)[0]

          __lowercase		:			int	    =       np.abs(to_np(_snake_case				) - to_np(_snake_case				)				).max()
          self.assertLess(_snake_case				,       1E-4				)


