





'''simple docstring'''




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
  lowercase__   :				Optional[Any]      =   argparse.ArgumentParser()

  parser.add_argument(
      '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
  )
  # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
  parser.add_argument(
      '''--original_config_file''',
      default=None,
      type=str,
      help='''The YAML config file corresponding to the original architecture.''',
  )
  parser.add_argument(
      '''--num_in_channels''',
      default=None,
      type=int,
      help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
  )
  parser.add_argument(
      '''--scheduler_type''',
      default='''pndm''',
      type=str,
      help='''Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']''',
  )
  parser.add_argument(
      '''--pipeline_type''',
      default=None,
      type=str,
      help=(
          '''The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''''
          '''. If `None` pipeline will be automatically inferred.'''
      ),
  )
  parser.add_argument(
      '''--image_size''',
      default=None,
      type=int,
      help=(
          '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
          ''' Base. Use 768 for Stable Diffusion v2.'''
      ),
  )
  parser.add_argument(
      '''--prediction_type''',
      default=None,
      type=str,
      help=(
          '''The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'''
          ''' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'''
      ),
  )
  parser.add_argument(
      '''--extract_ema''',
      action='''store_true''',
      help=(
          '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
          ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
          ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
      ),
  )
  parser.add_argument(
      '''--upcast_attention''',
      action='''store_true''',
      help=(
          '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
          ''' diffusion 2.1.'''
      ),
  )
  parser.add_argument(
      '''--from_safetensors''',
      action='''store_true''',
      help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
  )
  parser.add_argument(
      '''--to_safetensors''',
      action='''store_true''',
      help='''Whether to store pipeline in safetensors format or not.''',
  )
  parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
  parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
  parser.add_argument(
      '''--stable_unclip''',
      type=str,
      default=None,
      required=False,
      help='''Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.''',
  )
  parser.add_argument(
      '''--stable_unclip_prior''',
      type=str,
      default=None,
      required=False,
      help='''Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.''',
  )
  parser.add_argument(
      '''--clip_stats_path''',
      type=str,
      help='''Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.''',
      required=False,
  )
  parser.add_argument(
      '''--controlnet''', action='''store_true''', default=None, help='''Set flag if this is a controlnet checkpoint.'''
  )
  parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
  parser.add_argument(
      '''--vae_path''',
      type=str,
      default=None,
      required=False,
      help='''Set to a path, hub id to an already converted vae to not convert it again.''',
  )
  lowercase__   :				Tuple      =   parser.parse_args()

  lowercase__   :				Any      =   download_from_original_stable_diffusion_ckpt(
      checkpoint_path=args.checkpoint_path,
      original_config_file=args.original_config_file,
      image_size=args.image_size,
      prediction_type=args.prediction_type,
      model_type=args.pipeline_type,
      extract_ema=args.extract_ema,
      scheduler_type=args.scheduler_type,
      num_in_channels=args.num_in_channels,
      upcast_attention=args.upcast_attention,
      from_safetensors=args.from_safetensors,
      device=args.device,
      stable_unclip=args.stable_unclip,
      stable_unclip_prior=args.stable_unclip_prior,
      clip_stats_path=args.clip_stats_path,
      controlnet=args.controlnet,
      vae_path=args.vae_path,
  )

  if args.half:
    pipe.to(torch_dtype=torch.floataa)

  if args.controlnet:
    # only save the controlnet model
    pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
  else:
    pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)













'''simple docstring'''




from collections.abc import Iterable
from typing import Any


class 			SCREAMING_SNAKE_CASE   :


     def __init__(						self						,     _UpperCAmelCase = None):

          '''simple docstring'''






          __A						:			str								=	value
          __A						:			Node | None								=	None  # Added in order to delete a node easier
          __A						:			Node | None								=	None
          __A						:			Node | None								=	None


     def __repr__(						self):

          '''simple docstring'''






          from pprint import pformat

          if self.left is None and self.right is None:
               return str(self.value)
          return pformat({F'{self.value}': (self.left, self.right)}						,     indent=1)




class 			SCREAMING_SNAKE_CASE   :


     def __init__(						self						,     _UpperCAmelCase = None):

          '''simple docstring'''






          __A						:			int								=	root


     def __str__(						self):

          '''simple docstring'''






          return str(self.root)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          if new_children is not None:  # reset its kids
               __A						:			Optional[int]								=	node.parent
          if node.parent is not None:  # reset its parent
               if self.is_right(_UpperCAmelCase):  # If it is the right children
                    __A						:			int								=	new_children
               else:
                    __A						:			Union[str, Any]								=	new_children
          else:
               __A						:			Tuple								=	new_children


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          if node.parent and node.parent.right:
               return node == node.parent.right
          return False


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.root is None


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Dict								=	Node(_UpperCAmelCase)  # create a new Node
          if self.empty():  # if Tree is empty
               __A						:			Union[str, Any]								=	new_node  # set its root
          else:  # Tree is not empty
               __A						:			Dict								=	self.root  # from root
               if parent_node is None:
                    return
               while True:  # While we don't get to a leaf
                    if value < parent_node.value:  # We go left
                         if parent_node.left is None:
                              __A						:			Tuple								=	new_node  # We insert the new node in a leaf
                              break
                         else:
                              __A						:			str								=	parent_node.left
                    else:
                         if parent_node.right is None:
                              __A						:			List[str]								=	new_node
                              break
                         else:
                              __A						:			int								=	parent_node.right
               __A						:			int								=	parent_node


     def 							SCREAMING_SNAKE_CASE  (						self						,     *_UpperCAmelCase):

          '''simple docstring'''






          for value in values:
               self.__insert(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          if self.empty():
               raise IndexError('Warning: Tree is empty! please use another.')
          else:
               __A						:			Any								=	self.root
               # use lazy evaluation here to avoid NoneType Attribute error
               while node is not None and node.value is not value:
                    __A						:			List[Any]								=	node.left if value < node.value else node.right
               return node


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase = None):

          '''simple docstring'''






          if node is None:
               if self.root is None:
                    return None
               __A						:			str								=	self.root

          if not self.empty():
               while node.right is not None:
                    __A						:			Dict								=	node.right
          return node


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase = None):

          '''simple docstring'''






          if node is None:
               __A						:			Optional[Any]								=	self.root
          if self.root is None:
               return None
          if not self.empty():
               __A						:			Optional[int]								=	self.root
               while node.left is not None:
                    __A						:			Tuple								=	node.left
          return node


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.search(_UpperCAmelCase)  # Look for the node with that label
          if node is not None:
               if node.left is None and node.right is None:  # If it has no children
                    self.__reassign_nodes(_UpperCAmelCase						,     _UpperCAmelCase)
               elif node.left is None:  # Has only right children
                    self.__reassign_nodes(_UpperCAmelCase						,     node.right)
               elif node.right is None:  # Has only left children
                    self.__reassign_nodes(_UpperCAmelCase						,     node.left)
               else:
                    __A						:			str								=	self.get_max(
                        node.left)  # Gets the max value of the left branch
                    self.remove(tmp_node.value)  # type: ignore
                    __A						:			List[Any]								=	(
                        tmp_node.value  # type: ignore
                    )  # Assigns the value to the node to delete and keep tree structure


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          if node is not None:
               yield node  # Preorder Traversal
               yield from self.preorder_traverse(node.left)
               yield from self.preorder_traverse(node.right)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase=None):

          '''simple docstring'''






          if traversal_function is None:
               return self.preorder_traverse(self.root)
          else:
               return traversal_function(self.root)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          if node:
               self.inorder(_UpperCAmelCase						,     node.left)
               arr.append(node.value)
               self.inorder(_UpperCAmelCase						,     node.right)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			list[int]								=	[]
          self.inorder(_UpperCAmelCase						,     _UpperCAmelCase)  # append all values to list using inorder traversal
          return arr[k - 1]






def 	_lowerCAmelCase      (  __snake_case  :			Node | None      )					->					list[Node]:
     __A						:			Tuple								=	[]
     if curr_node is not None:
          __A						:			Optional[Any]								=	postorder(curr_node.left      ) + postorder(curr_node.right      ) + [curr_node]
     return node_list






def 	_lowerCAmelCase      (  )					->					None:
     __A						:			Optional[int]								=	(8, 3, 6, 1, 10, 14, 13, 4, 7)
     __A						:			str								=	BinarySearchTree()
     for i in testlist:
          t.insert(__snake_case      )

     # Prints all the elements of the list in order traversal
     print(__snake_case      )

     if t.search(6      ) is not None:
          print('The value 6 exists'      )
     else:
          print('The value 6 doesn\'t exist'      )

     if t.search(-1      ) is not None:
          print('The value -1 exists'      )
     else:
          print('The value -1 doesn\'t exist'      )

     if not t.empty():
          print('Max Value: '			,							t.get_max().value      )  # type: ignore
          print('Min Value: '			,							t.get_min().value      )  # type: ignore

     for i in testlist:
          t.remove(__snake_case      )
          print(__snake_case      )


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)