






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


UpperCAmelCase_					:		List[Any]           = logging.get_logger(__name__)  # pylint: disable=invalid-name

def   SCREAMING_SNAKE_CASE_				(				self   :    Any      ,     __magic_name__   :    Optional[Any]      ,     __magic_name__   :    Optional[Any]      ,     __magic_name__   :    Union[str, Any]      ,     __magic_name__   :    Tuple   )						->     int:






	"""simple docstring"""



	UpperCamelCase	:List[str]		=      image.to(device=__magic_name__   )
	UpperCamelCase	:int		=      image  # take image as image_embeddings
	UpperCamelCase	:Optional[int]		=      image_embeddings.unsqueeze(1   )

	# duplicate image embeddings for each generation per prompt, using mps friendly method
	UpperCamelCase	, UpperCamelCase	, UpperCamelCase	:Any		=      image_embeddings.shape
	UpperCamelCase	:Optional[Any]		=      image_embeddings.repeat(1      ,     __magic_name__      ,     1   )
	UpperCamelCase	:Optional[Any]		=      image_embeddings.view(bs_embed * num_images_per_prompt      ,     __magic_name__      ,     -1   )

	if do_classifier_free_guidance:
		UpperCamelCase	:Optional[Any]		=      torch.zeros_like(__magic_name__   )

		# 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	:Optional[Any]		=      torch.cat([uncond_embeddings, image_embeddings]   )

	return image_embeddings

class        _SCREAMING_SNAKE_CASE			(     _a ):





	def __init__(						self							:   Dict					,    __lowerCamelCase							:   PriorTransformer					,    __lowerCamelCase							:   CLIPTokenizer					,    __lowerCamelCase							:   CLIPTextModelWithProjection					,    __lowerCamelCase							:   UnCLIPScheduler					,    __lowerCamelCase							:   Optional[dict] = None					,    ):
		super().__init__()

		UpperCamelCase	:str		=      {"""image_encoder""": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

		UpperCamelCase	:Tuple		=      decoder_pipe_kwargs.get("""torch_dtype"""					,    __lowerCamelCase      ) or prior.dtype

		UpperCamelCase	:Union[str, Any]		=      StableDiffusionImageVariationPipeline.from_pretrained(
		    """lambdalabs/sd-image-variations-diffusers"""					,    **__lowerCamelCase      )

		# replace `_encode_image` method
		UpperCamelCase	:Dict		=      types.MethodType(_encode_image					,    self.decoder_pipe      )

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





	def 		_A					(						self							:   List[Any]					,    __lowerCamelCase							:   Optional[int]					,    __lowerCamelCase							:   Dict					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   Union[str, Any]					,    __lowerCamelCase							:   Optional[Union[CLIPTextModelOutput, Tuple]] = None					,    __lowerCamelCase							:   Optional[torch.Tensor] = None					,    ):
		if text_model_output is None:
			UpperCamelCase	:int		=      len(__lowerCamelCase      ) if isinstance(__lowerCamelCase					,    __lowerCamelCase      ) else 1
			# get prompt text embeddings
			UpperCamelCase	:Union[str, Any]		=      self.tokenizer(
			    __lowerCamelCase					,    padding="""max_length"""					,    max_length=self.tokenizer.model_max_length					,    return_tensors="""pt"""					,    )
			UpperCamelCase	:Optional[int]		=      text_inputs.input_ids
			UpperCamelCase	:Union[str, Any]		=      text_inputs.attention_mask.bool().to(__lowerCamelCase      )

			if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
				UpperCamelCase	:Tuple		=      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	:Tuple		=      text_input_ids[:, : self.tokenizer.model_max_length]

			UpperCamelCase	:str		=      self.text_encoder(text_input_ids.to(__lowerCamelCase      )      )

			UpperCamelCase	:Tuple		=      text_encoder_output.text_embeds
			UpperCamelCase	:List[str]		=      text_encoder_output.last_hidden_state

		else:
			UpperCamelCase	:Tuple		=      text_model_output[0].shape[0]
			UpperCamelCase	, UpperCamelCase	:Optional[int]		=      text_model_output[0], text_model_output[1]
			UpperCamelCase	:Dict		=      text_attention_mask

		UpperCamelCase	:str		=      text_embeddings.repeat_interleave(__lowerCamelCase					,    dim=0      )
		UpperCamelCase	:Any		=      text_encoder_hidden_states.repeat_interleave(__lowerCamelCase					,    dim=0      )
		UpperCamelCase	:Optional[int]		=      text_mask.repeat_interleave(__lowerCamelCase					,    dim=0      )

		if do_classifier_free_guidance:
			UpperCamelCase	:int		=      [""""""] * batch_size

			UpperCamelCase	:Optional[Any]		=      self.tokenizer(
			    __lowerCamelCase					,    padding="""max_length"""					,    max_length=self.tokenizer.model_max_length					,    truncation=__lowerCamelCase					,    return_tensors="""pt"""					,    )
			UpperCamelCase	:List[Any]		=      uncond_input.attention_mask.bool().to(__lowerCamelCase      )
			UpperCamelCase	:int		=      self.text_encoder(uncond_input.input_ids.to(__lowerCamelCase      )      )

			UpperCamelCase	:List[str]		=      uncond_embeddings_text_encoder_output.text_embeds
			UpperCamelCase	:List[str]		=      uncond_embeddings_text_encoder_output.last_hidden_state

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

			UpperCamelCase	:Tuple		=      uncond_embeddings.shape[1]
			UpperCamelCase	:Optional[int]		=      uncond_embeddings.repeat(1					,    __lowerCamelCase      )
			UpperCamelCase	:Dict		=      uncond_embeddings.view(batch_size * num_images_per_prompt					,    __lowerCamelCase      )

			UpperCamelCase	:List[str]		=      uncond_text_encoder_hidden_states.shape[1]
			UpperCamelCase	:Optional[int]		=      uncond_text_encoder_hidden_states.repeat(1					,    __lowerCamelCase					,    1      )
			UpperCamelCase	:Optional[Any]		=      uncond_text_encoder_hidden_states.view(
			    batch_size * num_images_per_prompt					,    __lowerCamelCase					,    -1      )
			UpperCamelCase	:List[Any]		=      uncond_text_mask.repeat_interleave(__lowerCamelCase					,    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	:Optional[int]		=      torch.cat([uncond_embeddings, text_embeddings]      )
			UpperCamelCase	:Any		=      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]      )

			UpperCamelCase	:Optional[Any]		=      torch.cat([uncond_text_mask, text_mask]      )

		return text_embeddings, text_encoder_hidden_states, text_mask





	@property
	def 		_A					(						self							:   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(__lowerCamelCase					,    """_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 		_A					(						self							:   Tuple					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   Optional[Any]					,    __lowerCamelCase							:   Tuple					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   List[Any]					,    __lowerCamelCase							:   List[Any]      ):
		if latents is None:
			UpperCamelCase	:int		=      randn_tensor(__lowerCamelCase					,    generator=__lowerCamelCase					,    device=__lowerCamelCase					,    dtype=__lowerCamelCase      )
		else:
			if latents.shape != shape:
				raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""      )
			UpperCamelCase	:Union[str, Any]		=      latents.to(__lowerCamelCase      )

		UpperCamelCase	:Tuple		=      latents * scheduler.init_noise_sigma
		return latents





	def 		_A					(						self							:   Tuple					,    __lowerCamelCase							:   Optional[Union[str, torch.device]] = None      ):
		self.decoder_pipe.to(__lowerCamelCase      )
		super().to(__lowerCamelCase      )





	@torch.no_grad()
	def __call__(						self							:   Union[str, Any]					,    __lowerCamelCase							:   Optional[Union[str, List[str]]] = None					,    __lowerCamelCase							:   Optional[int] = None					,    __lowerCamelCase							:   Optional[int] = None					,    __lowerCamelCase							:   int = 1					,    __lowerCamelCase							:   int = 25					,    __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 = 50					,    __lowerCamelCase							:   Optional[int] = 1					,    __lowerCamelCase							:   float = 0.0					,    __lowerCamelCase							:   Optional[str] = "pil"					,    __lowerCamelCase							:   bool = True					,    ):
		if prompt is not None:
			if isinstance(__lowerCamelCase					,    __lowerCamelCase      ):
				UpperCamelCase	:Optional[Any]		=      1
			elif isinstance(__lowerCamelCase					,    __lowerCamelCase      ):
				UpperCamelCase	:Any		=      len(__lowerCamelCase      )
			else:
				raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__lowerCamelCase      )}"""      )
		else:
			UpperCamelCase	:str		=      text_model_output[0].shape[0]

		UpperCamelCase	:Any		=      self._execution_device

		UpperCamelCase	:int		=      batch_size * num_images_per_prompt

		UpperCamelCase	:Optional[Any]		=      prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

		UpperCamelCase	, UpperCamelCase	, UpperCamelCase	:Dict		=      self._encode_prompt(
		    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase      )

		# prior

		self.prior_scheduler.set_timesteps(__lowerCamelCase					,    device=__lowerCamelCase      )
		UpperCamelCase	:Tuple		=      self.prior_scheduler.timesteps

		UpperCamelCase	:Union[str, Any]		=      self.prior.config.embedding_dim

		UpperCamelCase	:List[str]		=      self.prepare_latents(
		    (batch_size, embedding_dim)					,    text_embeddings.dtype					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    self.prior_scheduler					,    )

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

			UpperCamelCase	:Union[str, Any]		=      self.prior(
			    __lowerCamelCase					,    timestep=__lowerCamelCase					,    proj_embedding=__lowerCamelCase					,    encoder_hidden_states=__lowerCamelCase					,    attention_mask=__lowerCamelCase					,    ).predicted_image_embedding

			if do_classifier_free_guidance:
				UpperCamelCase	, UpperCamelCase	:List[str]		=      predicted_image_embedding.chunk(2      )
				UpperCamelCase	:List[Any]		=      predicted_image_embedding_uncond + prior_guidance_scale * (
				    predicted_image_embedding_text - predicted_image_embedding_uncond
				)

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

			UpperCamelCase	:Union[str, Any]		=      self.prior_scheduler.step(
			    __lowerCamelCase					,    timestep=__lowerCamelCase					,    sample=__lowerCamelCase					,    generator=__lowerCamelCase					,    prev_timestep=__lowerCamelCase					,    ).prev_sample

		UpperCamelCase	:List[str]		=      self.prior.post_process_latents(__lowerCamelCase      )

		UpperCamelCase	:Optional[int]		=      prior_latents

		UpperCamelCase	:Optional[int]		=      self.decoder_pipe(
		    image=__lowerCamelCase					,    height=__lowerCamelCase					,    width=__lowerCamelCase					,    num_inference_steps=__lowerCamelCase					,    guidance_scale=__lowerCamelCase					,    generator=__lowerCamelCase					,    output_type=__lowerCamelCase					,    return_dict=__lowerCamelCase					,    num_images_per_prompt=__lowerCamelCase					,    eta=__lowerCamelCase					,    )
		return output








class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   Tuple      ):
		UpperCamelCase	:List[str]		=      [0]
		UpperCamelCase	:Optional[int]		=      0





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   int      ):
		UpperCamelCase	:List[str]		=      self.__heap[i]
		while i // 2 > 0:
			if self.__heap[i] > self.__heap[i // 2]:
				UpperCamelCase	:Any		=      self.__heap[i // 2]
				UpperCamelCase	:Optional[Any]		=      temporary
			i //= 2





	def 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   int      ):
		self.__heap.append(__lowerCamelCase      )
		self.__size += 1
		self.__swap_up(self.__size      )





	def 		_A					(						self							:   Optional[Any]					,    __lowerCamelCase							:   int      ):
		while self.__size >= 2 * i:
			if 2 * i + 1 > self.__size:
				UpperCamelCase	:List[Any]		=      2 * i
			else:
				if self.__heap[2 * i] > self.__heap[2 * i + 1]:
					UpperCamelCase	:Union[str, Any]		=      2 * i
				else:
					UpperCamelCase	:Dict		=      2 * i + 1
			UpperCamelCase	:Optional[Any]		=      self.__heap[i]
			if self.__heap[i] < self.__heap[bigger_child]:
				UpperCamelCase	:List[str]		=      self.__heap[bigger_child]
				UpperCamelCase	:List[str]		=      temporary
			UpperCamelCase	:Optional[int]		=      bigger_child





	def 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	:str		=      self.__heap[1]
		UpperCamelCase	:Tuple		=      self.__heap[self.__size]
		self.__size -= 1
		self.__heap.pop()
		self.__swap_down(1      )
		return max_value





	@property
	def 		_A					(						self							:   int      ):
		return self.__heap[1:]





	def __len__(						self							:   str      ):
		return self.__size


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# create an instance of BinaryHeap
	UpperCAmelCase_					:		Optional[int]           = BinaryHeap()
	binary_heap.insert(6)
	binary_heap.insert(10)
	binary_heap.insert(15)
	binary_heap.insert(12)
	# pop root(max-values because it is max heap)
	print(binary_heap.pop())  # 15
	print(binary_heap.pop())  # 12
	# get the list and size after operations
	print(binary_heap.get_list)
	print(len(binary_heap))
