

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

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


lowerCAmelCase__							=			logging.get_logger(__name__)  # pylint: disable=invalid-name
def   _lowerCAmelCase(						self ,							__A ,							__A ,							__A ,							__A      ):
  UpperCAmelCase          =	image.to(device=_UpperCAmelCase      )
  UpperCAmelCase          =	image  # take image as image_embeddings
  UpperCAmelCase          =	image_embeddings.unsqueeze(1      )

  # duplicate image embeddings for each generation per prompt, using mps friendly method
  UpperCAmelCase          =	image_embeddings.shape
  UpperCAmelCase          =	image_embeddings.repeat(1 ,							_UpperCAmelCase ,							1      )
  UpperCAmelCase          =	image_embeddings.view(bs_embed * num_images_per_prompt ,							_UpperCAmelCase ,							-1      )

  if do_classifier_free_guidance:
    UpperCAmelCase          =	torch.zeros_like(_UpperCAmelCase      )

    # For classifier free guidance, we need to do two forward passes.
    # Here we concatenate the unconditional and text embeddings into a single batch
    # to avoid doing two forward passes
    UpperCAmelCase          =	torch.cat([uncond_embeddings, image_embeddings]      )

  return image_embeddings






class     __magic_name__     (							__UpperCamelCase     ):


 def __init__(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			PriorTransformer      ,    lowerCAmelCase__		:			CLIPTokenizer      ,    lowerCAmelCase__		:			CLIPTextModelWithProjection      ,    lowerCAmelCase__		:			UnCLIPScheduler      ,    lowerCAmelCase__		:			Optional[dict] = None      ,    )     ->   str:
   super().__init__()

   UpperCAmelCase          =	{'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

   UpperCAmelCase          =	decoder_pipe_kwargs.get("torch_dtype"      ,    a_     ) or prior.dtype

   UpperCAmelCase          =	StableDiffusionImageVariationPipeline.from_pretrained(
       "lambdalabs/sd-image-variations-diffusers"      ,    **a_     )

   # replace `_encode_image` method
   UpperCAmelCase          =	types.MethodType(_encode_image      ,    self.decoder_pipe     )

   self.register_modules(
       prior=a_      ,    tokenizer=a_      ,    text_encoder=a_      ,    prior_scheduler=a_      ,    )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			Optional[int]      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			int      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			Optional[Union[CLIPTextModelOutput, Tuple]] = None      ,    lowerCAmelCase__		:			Optional[torch.Tensor] = None      ,    )     ->   Optional[Any]:





   if text_model_output is None:
     UpperCAmelCase          =	len(a_     ) if isinstance(a_      ,    a_     ) else 1
     # get prompt text embeddings
     UpperCAmelCase          =	self.tokenizer(
         a_      ,    padding="max_length"      ,    max_length=self.tokenizer.model_max_length      ,    return_tensors="pt"      ,    )
     UpperCAmelCase          =	text_inputs.input_ids
     UpperCAmelCase          =	text_inputs.attention_mask.bool().to(a_     )

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
       UpperCAmelCase          =	self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]     )
       logger.warning(
           "The following part of your input was truncated because CLIP can only handle sequences up to"
           f" {self.tokenizer.model_max_length} tokens: {removed_text}"     )
       UpperCAmelCase          =	text_input_ids[:, : self.tokenizer.model_max_length]

     UpperCAmelCase          =	self.text_encoder(text_input_ids.to(a_     )     )

     UpperCAmelCase          =	text_encoder_output.text_embeds
     UpperCAmelCase          =	text_encoder_output.last_hidden_state

   else:
     UpperCAmelCase          =	text_model_output[0].shape[0]
     UpperCAmelCase          =	text_model_output[0], text_model_output[1]
     UpperCAmelCase          =	text_attention_mask

   UpperCAmelCase          =	text_embeddings.repeat_interleave(a_      ,    dim=0     )
   UpperCAmelCase          =	text_encoder_hidden_states.repeat_interleave(a_      ,    dim=0     )
   UpperCAmelCase          =	text_mask.repeat_interleave(a_      ,    dim=0     )

   if do_classifier_free_guidance:
     UpperCAmelCase          =	[''''''] * batch_size

     UpperCAmelCase          =	self.tokenizer(
         a_      ,    padding="max_length"      ,    max_length=self.tokenizer.model_max_length      ,    truncation=a_      ,    return_tensors="pt"      ,    )
     UpperCAmelCase          =	uncond_input.attention_mask.bool().to(a_     )
     UpperCAmelCase          =	self.text_encoder(uncond_input.input_ids.to(a_     )     )

     UpperCAmelCase          =	uncond_embeddings_text_encoder_output.text_embeds
     UpperCAmelCase          =	uncond_embeddings_text_encoder_output.last_hidden_state

     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

     UpperCAmelCase          =	uncond_embeddings.shape[1]
     UpperCAmelCase          =	uncond_embeddings.repeat(1      ,    a_     )
     UpperCAmelCase          =	uncond_embeddings.view(batch_size * num_images_per_prompt      ,    a_     )

     UpperCAmelCase          =	uncond_text_encoder_hidden_states.shape[1]
     UpperCAmelCase          =	uncond_text_encoder_hidden_states.repeat(1      ,    a_      ,    1     )
     UpperCAmelCase          =	uncond_text_encoder_hidden_states.view(
         batch_size * num_images_per_prompt      ,    a_      ,    -1     )
     UpperCAmelCase          =	uncond_text_mask.repeat_interleave(a_      ,    dim=0     )

     # done duplicates

     # For classifier free guidance, we need to do two forward passes.
     # Here we concatenate the unconditional and text embeddings into a single batch
     # to avoid doing two forward passes
     UpperCAmelCase          =	torch.cat([uncond_embeddings, text_embeddings]     )
     UpperCAmelCase          =	torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

     UpperCAmelCase          =	torch.cat([uncond_text_mask, text_mask]     )

   return text_embeddings, text_encoder_hidden_states, text_mask


 @property
 def _UpperCamelCase						(    self		:			Dict     )     ->   Dict:





   if self.device != torch.device("meta"     ) or not hasattr(self.prior      ,    "_hf_hook"     ):
     return self.device
   for module in self.prior.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		:			Tuple      ,    lowerCAmelCase__		:			str      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			Dict      ,    lowerCAmelCase__		:			Optional[int]      ,    lowerCAmelCase__		:			Optional[int]      ,    lowerCAmelCase__		:			Optional[Any]     )     ->   int:





   if latents is None:
     UpperCAmelCase          =	randn_tensor(a_      ,    generator=a_      ,    device=a_      ,    dtype=a_     )
   else:
     if latents.shape != shape:
       raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"     )
     UpperCAmelCase          =	latents.to(a_     )

   UpperCAmelCase          =	latents * scheduler.init_noise_sigma
   return latents


 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			Optional[Union[str, torch.device]] = None     )     ->   List[str]:
   self.decoder_pipe.to(a_     )
   super().to(a_     )


 @torch.no_grad()
 def __call__(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			Optional[Union[str, List[str]]] = None      ,    lowerCAmelCase__		:			Optional[int] = None      ,    lowerCAmelCase__		:			Optional[int] = None      ,    lowerCAmelCase__		:			int = 1      ,    lowerCAmelCase__		:			int = 2_5      ,    lowerCAmelCase__		:			Optional[torch.Generator] = None      ,    lowerCAmelCase__		:			Optional[torch.FloatTensor] = None      ,    lowerCAmelCase__		:			Optional[Union[CLIPTextModelOutput, Tuple]] = None      ,    lowerCAmelCase__		:			Optional[torch.Tensor] = None      ,    lowerCAmelCase__		:			float = 4.0      ,    lowerCAmelCase__		:			float = 8.0      ,    lowerCAmelCase__		:			int = 5_0      ,    lowerCAmelCase__		:			Optional[int] = 1      ,    lowerCAmelCase__		:			float = 0.0      ,    lowerCAmelCase__		:			Optional[str] = "pil"      ,    lowerCAmelCase__		:			bool = True      ,    )     ->   List[str]:





   if prompt is not None:
     if isinstance(a_      ,    a_     ):
       UpperCAmelCase          =	1
     elif isinstance(a_      ,    a_     ):
       UpperCAmelCase          =	len(a_     )
     else:
       raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(a_     )}"     )
   else:
     UpperCAmelCase          =	text_model_output[0].shape[0]

   UpperCAmelCase          =	self._execution_device

   UpperCAmelCase          =	batch_size * num_images_per_prompt

   UpperCAmelCase          =	prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

   UpperCAmelCase          =	self._encode_prompt(
       a_      ,    a_      ,    a_      ,    a_      ,    a_      ,    a_     )

   # prior

   self.prior_scheduler.set_timesteps(a_      ,    device=a_     )
   UpperCAmelCase          =	self.prior_scheduler.timesteps

   UpperCAmelCase          =	self.prior.config.embedding_dim

   UpperCAmelCase          =	self.prepare_latents(
       (batch_size, embedding_dim)      ,    text_embeddings.dtype      ,    a_      ,    a_      ,    a_      ,    self.prior_scheduler      ,    )

   for i, t in enumerate(self.progress_bar(a_     )     ):
     # expand the latents if we are doing classifier free guidance
     UpperCAmelCase          =	torch.cat([prior_latents] * 2     ) if do_classifier_free_guidance else prior_latents

     UpperCAmelCase          =	self.prior(
         a_      ,    timestep=a_      ,    proj_embedding=a_      ,    encoder_hidden_states=a_      ,    attention_mask=a_      ,    ).predicted_image_embedding

     if do_classifier_free_guidance:
       UpperCAmelCase          =	predicted_image_embedding.chunk(2     )
       UpperCAmelCase          =	predicted_image_embedding_uncond + prior_guidance_scale * (
           predicted_image_embedding_text - predicted_image_embedding_uncond
       )

     if i + 1 == prior_timesteps_tensor.shape[0]:
       UpperCAmelCase          =	None
     else:
       UpperCAmelCase          =	prior_timesteps_tensor[i + 1]

     UpperCAmelCase          =	self.prior_scheduler.step(
         a_      ,    timestep=a_      ,    sample=a_      ,    generator=a_      ,    prev_timestep=a_      ,    ).prev_sample

   UpperCAmelCase          =	self.prior.post_process_latents(a_     )

   UpperCAmelCase          =	prior_latents

   UpperCAmelCase          =	self.decoder_pipe(
       image=a_      ,    height=a_      ,    width=a_      ,    num_inference_steps=a_      ,    guidance_scale=a_      ,    generator=a_      ,    output_type=a_      ,    return_dict=a_      ,    num_images_per_prompt=a_      ,    eta=a_      ,    )
   return output








class 						UpperCAmelCase__     :




			'''simple docstring'''






			def __init__(  self				:					List[Any]			,			a_				:					int					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      data
					__UpperCAmelCase       :	BinaryTreeNode | None						=      None
					__UpperCAmelCase       :	BinaryTreeNode | None						=      None

def 			a		( _UpperCAmelCase				:   BinaryTreeNode | None					,  _UpperCAmelCase				:   int		):







		'''simple docstring'''





		if node is None:
				__UpperCAmelCase       :	List[Any]						=      BinaryTreeNode(_UpperCAmelCase		)
				return node

		# binary search tree is not empty,
		# so we will insert it into the tree
		# if new_value is less than value of data in node,
		#  add it to left subtree and proceed recursively
		if new_value < node.data:
				__UpperCAmelCase       :	int						=      insert(node.left_child					,  _UpperCAmelCase		)
		else:
				# if new_value is greater than value of data in node,
				#  add it to right subtree and proceed recursively
				__UpperCAmelCase       :	str						=      insert(node.right_child					,  _UpperCAmelCase		)
		return node

def 			a		( _UpperCAmelCase				:   None | BinaryTreeNode		):  # if node is None,return







		'''simple docstring'''





		if node:
				__UpperCAmelCase       :	Optional[int]						=      inorder(node.left_child		)
				__UpperCAmelCase       :	int						=      [*inorder_array, node.data]
				__UpperCAmelCase       :	Optional[Any]						=      inorder_array + inorder(node.right_child		)
		else:
				__UpperCAmelCase       :	List[Any]						=      []
		return inorder_array

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Any						=      insert(_UpperCAmelCase					,  15		)
		insert(_UpperCAmelCase					,  10		)
		insert(_UpperCAmelCase					,  25		)
		insert(_UpperCAmelCase					,  6		)
		insert(_UpperCAmelCase					,  14		)
		insert(_UpperCAmelCase					,  20		)
		insert(_UpperCAmelCase					,  60		)
		return root

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      make_tree()
		print('''Printing values of binary search tree in Inorder Traversal.'''		)
		inorder(_UpperCAmelCase		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()





