

from __future__ import annotations

import unittest
from collections.abc import Iterator



class 						lowercase_					:



						def __init__(    self   :       Tuple    ,      A__   :       List[str]    ,      A__   :       Dict	)					->  Tuple:
								_snake_case		=							label
								_snake_case		=							parent
								_snake_case		=							None
								_snake_case		=							None



class 						lowercase_					:



						def __init__(    self   :       List[Any]	)					->  Any:
								_snake_case		=							None



						def 		UpperCamelCase_	(    self   :       List[Any]	)					->  List[Any]:
								_snake_case		=							None



						def 		UpperCamelCase_	(    self   :       Dict	)					->  int:
								return self.root is None



						def 		UpperCamelCase_	(    self   :       List[Any]    ,      A__   :       Union[str, Any]	)					->  Dict:
								_snake_case		=							self._put(self.root    ,      __snake_case	)



						def 		UpperCamelCase_	(    self   :       int    ,      A__   :       Any    ,      A__   :       List[Any]    ,      A__   :       str = None	)					->  Any:
								if node is None:
										_snake_case		=							Node(__snake_case    ,      __snake_case	)
								else:
										if label < node.label:
												_snake_case		=							self._put(node.left    ,      __snake_case    ,      __snake_case	)
										elif label > node.label:
												_snake_case		=							self._put(node.right    ,      __snake_case    ,      __snake_case	)
										else:
												_snake_case		=							f"""Node with label {label} already exists"""
												raise Exception(__snake_case	)

								return node



						def 		UpperCamelCase_	(    self   :       Tuple    ,      A__   :       Dict	)					->  Optional[Any]:
								return self._search(self.root    ,      __snake_case	)



						def 		UpperCamelCase_	(    self   :       Dict    ,      A__   :       Optional[int]    ,      A__   :       str	)					->  List[str]:
								if node is None:
										_snake_case		=							f"""Node with label {label} does not exist"""
										raise Exception(__snake_case	)
								else:
										if label < node.label:
												_snake_case		=							self._search(node.left    ,      __snake_case	)
										elif label > node.label:
												_snake_case		=							self._search(node.right    ,      __snake_case	)

								return node



						def 		UpperCamelCase_	(    self   :       Optional[int]    ,      A__   :       Dict	)					->  Union[str, Any]:
								_snake_case		=							self.search(__snake_case	)
								if node.right and node.left:
										_snake_case		=							self._get_lowest_node(node.right	)
										_snake_case		=							node.left
										_snake_case		=							node.right
										_snake_case		=							lowest_node
										if node.right:
												_snake_case		=							lowest_node
										self._reassign_nodes(__snake_case    ,      __snake_case	)
								elif not node.right and node.left:
										self._reassign_nodes(__snake_case    ,      node.left	)
								elif node.right and not node.left:
										self._reassign_nodes(__snake_case    ,      node.right	)
								else:
										self._reassign_nodes(__snake_case    ,      __snake_case	)



						def 		UpperCamelCase_	(    self   :       Optional[int]    ,      A__   :       Optional[Any]    ,      A__   :       int	)					->  Optional[int]:
								if new_children:
										_snake_case		=							node.parent

								if node.parent:
										if node.parent.right == node:
												_snake_case		=							new_children
										else:
												_snake_case		=							new_children
								else:
										_snake_case		=							new_children



						def 		UpperCamelCase_	(    self   :       List[str]    ,      A__   :       Tuple	)					->  Optional[int]:
								if node.left:
										_snake_case		=							self._get_lowest_node(node.left	)
								else:
										_snake_case		=							node
										self._reassign_nodes(__snake_case    ,      node.right	)

								return lowest_node



						def 		UpperCamelCase_	(    self   :       Union[str, Any]    ,      A__   :       Union[str, Any]	)					->  List[Any]:
								try:
										self.search(__snake_case	)
										return True
								except Exception:
										return False



						def 		UpperCamelCase_	(    self   :       Tuple	)					->  Tuple:
								if self.root is None:
										raise Exception('''Binary search tree is empty'''	)

								_snake_case		=							self.root
								while node.right is not None:
										_snake_case		=							node.right

								return node.label



						def 		UpperCamelCase_	(    self   :       str	)					->  Dict:
								if self.root is None:
										raise Exception('''Binary search tree is empty'''	)

								_snake_case		=							self.root
								while node.left is not None:
										_snake_case		=							node.left

								return node.label



						def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  int:
								return self._inorder_traversal(self.root	)



						def 		UpperCamelCase_	(    self   :       Dict    ,      A__   :       Union[str, Any]	)					->  str:
								if node is not None:
										yield from self._inorder_traversal(node.left	)
										yield node
										yield from self._inorder_traversal(node.right	)



						def 		UpperCamelCase_	(    self   :       Tuple	)					->  Optional[int]:
								return self._preorder_traversal(self.root	)







						def 		UpperCamelCase_	(    self   :       Union[str, Any]    ,      A__   :       int	)					->  Optional[Any]:
								if node is not None:
										yield node
										yield from self._preorder_traversal(node.left	)
										yield from self._preorder_traversal(node.right	)



class 						lowercase_					(						unittest.TestCase  ):



						@staticmethod
						def 		UpperCamelCase_	(    )					->  Any:
								_snake_case		=							BinarySearchTree()
								t.put(8	)
								t.put(3	)
								t.put(6	)
								t.put(1	)
								t.put(10	)
								t.put(14	)
								t.put(13	)
								t.put(4	)
								t.put(7	)
								t.put(5	)

								return t



						def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  List[str]:
								_snake_case		=							BinarySearchTree()
								assert t.is_empty()

								t.put(8	)
								assert t.root is not None
								assert t.root.parent is None
								assert t.root.label == 8

								t.put(10	)
								assert t.root.right is not None
								assert t.root.right.parent == t.root
								assert t.root.right.label == 10

								t.put(3	)
								assert t.root.left is not None
								assert t.root.left.parent == t.root
								assert t.root.left.label == 3

								t.put(6	)
								assert t.root.left.right is not None
								assert t.root.left.right.parent == t.root.left
								assert t.root.left.right.label == 6

								t.put(1	)
								assert t.root.left.left is not None
								assert t.root.left.left.parent == t.root.left
								assert t.root.left.left.label == 1

								with self.assertRaises(__snake_case	):  # noqa: B017
										t.put(1	)



						def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  Dict:
								_snake_case		=							self._get_binary_search_tree()

								_snake_case		=							t.search(6	)
								assert node.label == 6

								_snake_case		=							t.search(13	)
								assert node.label == 13

								with self.assertRaises(__snake_case	):  # noqa: B017
										t.search(2	)



						def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  int:
								_snake_case		=							self._get_binary_search_tree()

								t.remove(13	)
								assert t.root is not None
								assert t.root.right is not None
								assert t.root.right.right is not None
								assert t.root.right.right.right is None
								assert t.root.right.right.left is None

								t.remove(7	)
								assert t.root.left is not None
								assert t.root.left.right is not None
								assert t.root.left.right.left is not None
								assert t.root.left.right.right is None
								assert t.root.left.right.left.label == 4

								t.remove(6	)
								assert t.root.left.left is not None
								assert t.root.left.right.right is not None
								assert t.root.left.left.label == 1
								assert t.root.left.right.label == 4
								assert t.root.left.right.right.label == 5
								assert t.root.left.right.left is None
								assert t.root.left.left.parent == t.root.left
								assert t.root.left.right.parent == t.root.left

								t.remove(3	)
								assert t.root is not None
								assert t.root.left.label == 4
								assert t.root.left.right.label == 5
								assert t.root.left.left.label == 1
								assert t.root.left.parent == t.root
								assert t.root.left.left.parent == t.root.left
								assert t.root.left.right.parent == t.root.left

								t.remove(4	)
								assert t.root.left is not None
								assert t.root.left.left is not None
								assert t.root.left.label == 5
								assert t.root.left.right is None
								assert t.root.left.left.label == 1
								assert t.root.left.parent == t.root
								assert t.root.left.left.parent == t.root.left



						def 		UpperCamelCase_	(    self   :       int	)					->  Union[str, Any]:
								_snake_case		=							self._get_binary_search_tree()

								t.remove(3	)
								assert t.root is not None
								assert t.root.left is not None
								assert t.root.left.left is not None
								assert t.root.left.right is not None
								assert t.root.left.right.left is not None
								assert t.root.left.right.right is not None
								assert t.root.left.label == 4
								assert t.root.left.right.label == 6
								assert t.root.left.left.label == 1
								assert t.root.left.right.right.label == 7
								assert t.root.left.right.left.label == 5
								assert t.root.left.parent == t.root
								assert t.root.left.right.parent == t.root.left
								assert t.root.left.left.parent == t.root.left
								assert t.root.left.right.left.parent == t.root.left.right



						def 		UpperCamelCase_	(    self   :       Dict	)					->  List[str]:
								_snake_case		=							self._get_binary_search_tree()
								t.empty()
								assert t.root is None



						def 		UpperCamelCase_	(    self   :       Dict	)					->  Optional[Any]:
								_snake_case		=							self._get_binary_search_tree()
								assert not t.is_empty()

								t.empty()
								assert t.is_empty()



						def 		UpperCamelCase_	(    self   :       List[str]	)					->  List[str]:
								_snake_case		=							self._get_binary_search_tree()

								assert t.exists(6	)
								assert not t.exists(-1	)



						def 		UpperCamelCase_	(    self   :       Tuple	)					->  Optional[int]:
								_snake_case		=							self._get_binary_search_tree()

								assert t.get_max_label() == 14

								t.empty()
								with self.assertRaises(__snake_case	):  # noqa: B017
										t.get_max_label()



						def 		UpperCamelCase_	(    self   :       List[Any]	)					->  Tuple:
								_snake_case		=							self._get_binary_search_tree()

								assert t.get_min_label() == 1

								t.empty()
								with self.assertRaises(__snake_case	):  # noqa: B017
										t.get_min_label()



						def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  List[str]:
								_snake_case		=							self._get_binary_search_tree()

								_snake_case		=							[i.label for i in t.inorder_traversal()]
								assert inorder_traversal_nodes == [1, 3, 4, 5, 6, 7, 8, 10, 13, 14]







						def 		UpperCamelCase_	(    self   :       Dict	)					->  Dict:
								_snake_case		=							self._get_binary_search_tree()

								_snake_case		=							[i.label for i in t.preorder_traversal()]
								assert preorder_traversal_nodes == [8, 3, 1, 6, 4, 5, 7, 10, 14, 13]



def    snake_case_()							->			Optional[Any]:



		"""simple docstring"""






		_snake_case		=							BinarySearchTree()
		t.put(8  )
		t.put(3  )
		t.put(6  )
		t.put(1  )
		t.put(10  )
		t.put(14  )
		t.put(13  )
		t.put(4  )
		t.put(7  )
		t.put(5  )

		print(
		    '''
            8
           / \\
          3   10
         / \\    \\
        1   6    14
           / \\   /
          4   7 13
           \\
            5
        '''  )

		print('''Label 6 exists:'''				, t.exists(6  )  )
		print('''Label 13 exists:'''				, t.exists(13  )  )
		print('''Label -1 exists:'''				, t.exists(-1  )  )
		print('''Label 12 exists:'''				, t.exists(12  )  )

		# Prints all the elements of the list in inorder traversal
		_snake_case		=							[i.label for i in t.inorder_traversal()]
		print('''Inorder traversal:'''				, UpperCamelCase_  )

		# Prints all the elements of the list in preorder traversal
		_snake_case		=							[i.label for i in t.preorder_traversal()]
		print('''Preorder traversal:'''				, UpperCamelCase_  )

		print('''Max. label:'''				, t.get_max_label()  )
		print('''Min. label:'''				, t.get_min_label()  )

		# Delete elements
		print('''\nDeleting elements 13, 10, 8, 3, 6, 14'''  )
		print(
		    '''
          4
         / \\
        1   7
             \\
              5
        '''  )
		t.remove(13  )
		t.remove(10  )
		t.remove(8  )
		t.remove(3  )
		t.remove(6  )
		t.remove(14  )

		# Prints all the elements of the list in inorder traversal after delete
		_snake_case		=							[i.label for i in t.inorder_traversal()]
		print('''Inorder traversal after delete:'''				, UpperCamelCase_  )

		# Prints all the elements of the list in preorder traversal after delete
		_snake_case		=							[i.label for i in t.preorder_traversal()]
		print('''Preorder traversal after delete:'''				, UpperCamelCase_  )

		print('''Max. label:'''				, t.get_max_label()  )
		print('''Min. label:'''				, t.get_min_label()  )


if __name__ == "__main__":
	binary_search_tree_example()




from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


__A    	=			'''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''



class 						lowercase_					(						__lowercase  ):



      def __init__(    self   :       str    ,      A__   :       UNetaDModel    ,      A__   :       CMStochasticIterativeScheduler	)					->  None:
        super().__init__()

        self.register_modules(
            unet=A__    ,      scheduler=A__    ,      )

        _snake_case		=							None



      def 		UpperCamelCase_	(    self   :       Union[str, Any]    ,      A__   :       Optional[int]=0	)					->  List[str]:
        if is_accelerate_available() and is_accelerate_version('''>='''    ,      '''0.14.0'''	):
          from accelerate import cpu_offload
        else:
          raise ImportError('''`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'''	)

        _snake_case		=							torch.device(f"""cuda:{gpu_id}"""	)

        if self.device.type != "cpu":
          self.to('''cpu'''    ,      silence_dtype_warnings=A__	)
          torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        for cpu_offloaded_model in [self.unet]:
          cpu_offload(A__    ,      A__	)

        if self.safety_checker is not None:
          cpu_offload(self.safety_checker    ,      execution_device=A__    ,      offload_buffers=A__	)



      def 		UpperCamelCase_	(    self   :       Optional[int]    ,      A__   :       Optional[Any]=0	)					->  Union[str, Any]:
        if is_accelerate_available() and is_accelerate_version('''>='''    ,      '''0.17.0.dev0'''	):
          from accelerate import cpu_offload_with_hook
        else:
          raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''	)

        _snake_case		=							torch.device(f"""cuda:{gpu_id}"""	)

        if self.device.type != "cpu":
          self.to('''cpu'''    ,      silence_dtype_warnings=A__	)
          torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        _snake_case		=							None
        for cpu_offloaded_model in [self.unet]:
          _snake_case,  _snake_case		=							cpu_offload_with_hook(A__    ,      A__    ,      prev_module_hook=A__	)

        if self.safety_checker is not None:
          _snake_case,  _snake_case		=							cpu_offload_with_hook(self.safety_checker    ,      A__    ,      prev_module_hook=A__	)

        # We'll offload the last model manually.
        _snake_case		=							hook



      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 		UpperCamelCase_	(    self   :       int	)					->  List[str]:
        if not hasattr(self.unet    ,      '''_hf_hook'''	):
          return self.device
        for module in self.unet.modules():
          if (
              hasattr(A__    ,      '''_hf_hook'''	)
              and hasattr(module._hf_hook    ,      '''execution_device'''	)
              and module._hf_hook.execution_device is not None
          ):
            return torch.device(module._hf_hook.execution_device	)
        return self.device



      def 		UpperCamelCase_	(    self   :       List[str]    ,      A__   :       Tuple    ,      A__   :       str    ,      A__   :       Tuple    ,      A__   :       Union[str, Any]    ,      A__   :       Union[str, Any]    ,      A__   :       Union[str, Any]    ,      A__   :       Tuple    ,      A__   :       Union[str, Any]=None	)					->  Optional[int]:
        _snake_case		=							(batch_size, num_channels, height, width)
        if isinstance(A__    ,      A__	) and len(A__	) != batch_size:
          raise ValueError(
              f"""You have passed a list of generators of length {len(A__	)}, but requested an effective batch"""
              f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""	)

        if latents is None:
          _snake_case		=							randn_tensor(A__    ,      generator=A__    ,      device=A__    ,      dtype=A__	)
        else:
          _snake_case		=							latents.to(device=A__    ,      dtype=A__	)

        # scale the initial noise by the standard deviation required by the scheduler
        _snake_case		=							latents * self.scheduler.init_noise_sigma
        return latents



      def 		UpperCamelCase_	(    self   :       Optional[Any]    ,      A__   :       torch.FloatTensor    ,      A__   :       str = "pil"	)					->  Optional[int]:
        if output_type not in ["pt", "np", "pil"]:
          raise ValueError(
              f"""output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"""	)

        # Equivalent to diffusers.VaeImageProcessor.denormalize
        _snake_case		=							(sample / 2 + 0.5).clamp(0    ,      1	)
        if output_type == "pt":
          return sample

        # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
        _snake_case		=							sample.cpu().permute(0    ,      2    ,      3    ,      1	).numpy()
        if output_type == "np":
          return sample

        # Output_type must be 'pil'
        _snake_case		=							self.numpy_to_pil(A__	)
        return sample



      def 		UpperCamelCase_	(    self   :       Any    ,      A__   :       Optional[Any]    ,      A__   :       Dict    ,      A__   :       int=None	)					->  Dict:
        if self.unet.config.num_class_embeds is not None:
          if isinstance(A__    ,      A__	):
            _snake_case		=							torch.tensor(A__    ,      dtype=torch.int	)
          elif isinstance(A__    ,      A__	):
            assert batch_size == 1, "Batch size must be 1 if classes is an int"
            _snake_case		=							torch.tensor([class_labels]    ,      dtype=torch.int	)
          elif class_labels is None:
            # Randomly generate batch_size class labels
            # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
            _snake_case		=							torch.randint(0    ,      self.unet.config.num_class_embeds    ,      size=(batch_size,)	)
          _snake_case		=							class_labels.to(A__	)
        else:
          _snake_case		=							None
        return class_labels



      def 		UpperCamelCase_	(    self   :       List[str]    ,      A__   :       List[str]    ,      A__   :       List[str]    ,      A__   :       str    ,      A__   :       List[Any]    ,      A__   :       Optional[Any]    ,      A__   :       Dict	)					->  Optional[Any]:
        if num_inference_steps is None and timesteps is None:
          raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''	)

        if num_inference_steps is not None and timesteps is not None:
          logger.warning(
              f"""Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"""
              ''' `timesteps` will be used over `num_inference_steps`.'''	)

        if latents is not None:
          _snake_case		=							(batch_size, 3, img_size, img_size)
          if latents.shape != expected_shape:
            raise ValueError(f"""The shape of latents is {latents.shape} but is expected to be {expected_shape}."""	)

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(A__    ,      A__	) or callback_steps <= 0)
        ):
          raise ValueError(
              f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
              f""" {type(A__	)}."""	)







      @torch.no_grad()
      @replace_example_docstring(A__	)
      def __call__(    self   :       Any    ,      A__   :       int = 1    ,      A__   :       Optional[Union[torch.Tensor, List[int], int]] = None    ,      A__   :       int = 1    ,      A__   :       List[int] = None    ,      A__   :       Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,      A__   :       Optional[torch.FloatTensor] = None    ,      A__   :       Optional[str] = "pil"    ,      A__   :       bool = True    ,      A__   :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None    ,      A__   :       int = 1    ,      )					->  Dict:
        _snake_case		=							self.unet.config.sample_size
        _snake_case		=							self._execution_device

        # 1. Check inputs
        self.check_inputs(A__    ,      A__    ,      A__    ,      A__    ,      A__    ,      A__	)

        # 2. Prepare image latents
        # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
        _snake_case		=							self.prepare_latents(
            batch_size=A__    ,      num_channels=self.unet.config.in_channels    ,      height=A__    ,      width=A__    ,      dtype=self.unet.dtype    ,      device=A__    ,      generator=A__    ,      latents=A__    ,      )

        # 3. Handle class_labels for class-conditional models
        _snake_case		=							self.prepare_class_labels(A__    ,      A__    ,      class_labels=A__	)

        # 4. Prepare timesteps
        if timesteps is not None:
          self.scheduler.set_timesteps(timesteps=A__    ,      device=A__	)
          _snake_case		=							self.scheduler.timesteps
          _snake_case		=							len(A__	)
        else:
          self.scheduler.set_timesteps(A__	)
          _snake_case		=							self.scheduler.timesteps

        # 5. Denoising loop
        # Multistep sampling: implements Algorithm 1 in the paper
        with self.progress_bar(total=A__	) as progress_bar:
          for i, t in enumerate(A__	):
            _snake_case		=							self.scheduler.scale_model_input(A__    ,      A__	)
            _snake_case		=							self.unet(A__    ,      A__    ,      class_labels=A__    ,      return_dict=A__	)[0]

            _snake_case		=							self.scheduler.step(A__    ,      A__    ,      A__    ,      generator=A__	)[0]

            # call the callback, if provided
            progress_bar.update()
            if callback is not None and i % callback_steps == 0:
              callback(A__    ,      A__    ,      A__	)

        # 6. Post-process image sample
        _snake_case		=							self.postprocess_image(A__    ,      output_type=A__	)

        # Offload last model to CPU
        if hasattr(self    ,      '''final_offload_hook'''	) and self.final_offload_hook is not None:
          self.final_offload_hook.offload()

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=A__	)

