



import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCAmelCase_		 =					logging.get_logger(__name__)  # pylint: disable=invalid-name

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Union[List, PIL.Image.Image, torch.Tensor]				)  ->     List[Any]:
   warnings.warn(
       '''The preprocess method is deprecated and will be removed in a future version. Please'''
       ''' use VaeImageProcessor.preprocess instead'''	,   _snake_case	,   )
   if isinstance(_snake_case	,   torch.Tensor				):
      return image
   elif isinstance(_snake_case	,   PIL.Image.Image				):
      _A      =       [image]

   if isinstance(image[0]	,   PIL.Image.Image				):
      _A  ,    _A      =       image[0].size
      _A  ,    _A      =       (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

      _A      =       [np.array(i.resize((w, h)	,   resample=PIL_INTERPOLATION['''lanczos''']				)				)[None, :] for i in image]
      _A      =       np.concatenate(_snake_case	,   axis=0				)
      _A      =       np.array(_snake_case				).astype(np.floataa				) / 255.0
      _A      =       image.transpose(0	,   3	,   1	,   2				)
      _A      =       2.0 * image - 1.0
      _A      =       torch.from_numpy(_snake_case				)
   elif isinstance(image[0]	,   torch.Tensor				):
      _A      =       torch.cat(_snake_case	,   dim=0				)
   return image

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Union[List, PIL.Image.Image, torch.Tensor]				)  ->     Optional[Any]:
   if isinstance(_snake_case	,   torch.Tensor				):
      return mask
   elif isinstance(_snake_case	,   PIL.Image.Image				):
      _A      =       [mask]

   if isinstance(mask[0]	,   PIL.Image.Image				):
      _A  ,    _A      =       mask[0].size
      _A  ,    _A      =       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
      _A      =       [np.array(m.convert('''L'''				).resize((w, h)	,   resample=PIL_INTERPOLATION['''nearest''']				)				)[None, :] for m in mask]
      _A      =       np.concatenate(_snake_case	,   axis=0				)
      _A      =       mask.astype(np.floataa				) / 255.0
      _A      =       0
      _A      =       1
      _A      =       torch.from_numpy(_snake_case				)
   elif isinstance(mask[0]	,   torch.Tensor				):
      _A      =       torch.cat(_snake_case	,   dim=0				)
   return mask


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: UNetaDModel
 a__					: RePaintScheduler

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Any      )   ->	Optional[Any]:
    super().__init__()
    self.register_modules(unet=__lowerCAmelCase			,						scheduler=__lowerCAmelCase      )

 @torch.no_grad()
 def __call__(    self					:					Optional[int]			,						__lowerCAmelCase					:					Union[torch.Tensor, PIL.Image.Image]			,						__lowerCAmelCase					:					Union[torch.Tensor, PIL.Image.Image]			,						__lowerCAmelCase					:					int = 2_50			,						__lowerCAmelCase					:					float = 0.0			,						__lowerCAmelCase					:					int = 10			,						__lowerCAmelCase					:					int = 10			,						__lowerCAmelCase					:					Optional[Union[torch.Generator, List[torch.Generator]]] = None			,						__lowerCAmelCase					:					Optional[str] = "pil"			,						__lowerCAmelCase					:					bool = True			,						)   ->	Union[ImagePipelineOutput, Tuple]:
    _A      =       image

    _A      =       _preprocess_image(__lowerCAmelCase      )
    _A      =       original_image.to(device=self.device			,						dtype=self.unet.dtype      )
    _A      =       _preprocess_mask(__lowerCAmelCase      )
    _A      =       mask_image.to(device=self.device			,						dtype=self.unet.dtype      )

    _A      =       original_image.shape[0]

    # sample gaussian noise to begin the loop
    if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ) and len(__lowerCAmelCase      ) != batch_size:
       raise ValueError(
           f'''You have passed a list of generators of length {len(__lowerCAmelCase      )}, but requested an effective batch'''
           f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

    _A      =       original_image.shape
    _A      =       randn_tensor(__lowerCAmelCase			,						generator=__lowerCAmelCase			,						device=self.device			,						dtype=self.unet.dtype      )

    # set step values
    self.scheduler.set_timesteps(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						self.device      )
    _A      =       eta

    _A      =       self.scheduler.timesteps[0] + 1
    _A      =       generator[0] if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ) else generator
    for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
       if t < t_last:
          # predict the noise residual
          _A      =       self.unet(__lowerCAmelCase			,						__lowerCAmelCase      ).sample
          # compute previous image: x_t -> x_t-1
          _A      =       self.scheduler.step(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      ).prev_sample

       else:
          # compute the reverse: x_t-1 -> x_t
          _A      =       self.scheduler.undo_step(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )
       _A      =       t

    _A      =       (image / 2 + 0.5).clamp(0			,						1      )
    _A      =       image.cpu().permute(0			,						2			,						3			,						1      ).numpy()
    if output_type == "pil":
       _A      =       self.numpy_to_pil(__lowerCAmelCase      )

    if not return_dict:
       return (image,)

    return ImagePipelineOutput(images=__lowerCAmelCase      )








from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

UpperCAmelCase_		 =					TypeVar("""T""")

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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (2 * position) + 1

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (2 * position) + 2


class 	lowerCamelCase__	(       Generic[T]):



 """simple docstring"""

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

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

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

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

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					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(__lowerCAmelCase      )

 def     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(__lowerCAmelCase      )
    return elem

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Update the weight of the given key
    _A      =       self.position_map[elem]
    _A      =       (elem, weight)
    if position > 0:
       _A      =       get_parent_position(__lowerCAmelCase      )
       _A  ,    _A      =       self.heap[parent_position]
       if parent_weight > weight:
          self._bubble_up(__lowerCAmelCase      )
       else:
          self._bubble_down(__lowerCAmelCase      )
    else:
       self._bubble_down(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					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(__lowerCAmelCase      )
    _A  ,    _A      =       self.heap[curr_pos]
    _A  ,    _A      =       self.heap[parent_position]
    if parent_weight > weight:
       self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
       return self._bubble_up(__lowerCAmelCase      )
    return None

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					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(__lowerCAmelCase      )
    _A      =       get_child_right_position(__lowerCAmelCase      )
    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(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    if child_left_position < self.elements:
       _A  ,    _A      =       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      =       self.heap[child_right_position]
       if child_right_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    return None

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					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					:					str      )   ->	None:
    _A      =       {}
    _A      =       0

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

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

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					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     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Add an edge between 2 nodes in the graph
    self.add_node(__lowerCAmelCase      )
    self.add_node(__lowerCAmelCase      )
    _A      =       weight
    _A      =       weight

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:GraphUndirectedWeighted[T]	,   )  ->     tuple[dict[T, int], dict[T, T | None]]:
   _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



