
"""simple docstring"""
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


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


def      lowerCamelCase_     (						self						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_			)  ->      int:


					'''simple docstring'''
					__a:    List[str]		     =       image.to(device=lowerCamelCase_			)
					__a:    int		     =       image  # take image as image_embeddings
					__a:    Optional[int]		     =       image_embeddings.unsqueeze(1			)

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

					if do_classifier_free_guidance:
										__a:    Optional[Any]		     =       torch.zeros_like(lowerCamelCase_			)

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

					return image_embeddings




class  _snake_case	(    _a							):






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






												"""simple docstring"""



												super().__init__()

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

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

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

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

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






							def 				__lowercase	(       self    :      List[Any]    ,			snake_case__    :      Optional[int]    ,			snake_case__    :      Dict    ,			snake_case__    :      int    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      Optional[Union[CLIPTextModelOutput, Tuple]] = None    ,			snake_case__    :      Optional[torch.Tensor] = None    ,			):






												"""simple docstring"""



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

																	if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																						__a:    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}"""     )
																						__a:    Tuple		     =       text_input_ids[:, : self.tokenizer.model_max_length]

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

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

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

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

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

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

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

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

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

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

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

												return text_embeddings, text_encoder_hidden_states, text_mask






							@property
							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												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 				__lowercase	(       self    :      Tuple    ,			snake_case__    :      int    ,			snake_case__    :      Optional[Any]    ,			snake_case__    :      Tuple    ,			snake_case__    :      str    ,			snake_case__    :      List[Any]    ,			snake_case__    :      List[Any]     ):






												"""simple docstring"""



												if latents is None:
																	__a:    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}"""     )
																	__a:    Union[str, Any]		     =       latents.to(__lowerCamelCase     )

												__a:    Tuple		     =       latents * scheduler.init_noise_sigma
												return latents






							def 				__lowercase	(       self    :      Tuple    ,			snake_case__    :      Optional[Union[str, torch.device]] = None     ):






												"""simple docstring"""



												self.decoder_pipe.to(__lowerCamelCase     )
												super().to(__lowerCamelCase     )






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






												"""simple docstring"""



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

												__a:    Any		     =       self._execution_device

												__a:    int		     =       batch_size * num_images_per_prompt

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

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

												# prior

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

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

												__a:    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
																	__a:    Union[str, Any]		     =       torch.cat([prior_latents] * 2     ) if do_classifier_free_guidance else prior_latents

																	__a:    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:
																						__a:    List[str]		     =       predicted_image_embedding.chunk(2     )
																						__a:    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]:
																						__a:    Tuple		     =       None
																	else:
																						__a:    Tuple		     =       prior_timesteps_tensor[i + 1]

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

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

												__a:    Optional[int]		     =       prior_latents

												__a:    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








from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar

class        _SCREAMING_SNAKE_CASE			(     _a ):





	@abstractmethod
	def __lt__(						self							:   T					,    __lowerCamelCase							:   T      ):
		pass





	@abstractmethod
	def __gt__(						self							:   T					,    __lowerCamelCase							:   T      ):
		pass





	@abstractmethod
	def __eq__(						self							:   T					,    __lowerCamelCase							:   object      ):
		pass
UpperCAmelCase_					:		str           = TypeVar('''T''', bound=Comparable)

class        _SCREAMING_SNAKE_CASE			(     Generic[T] ):





	def __init__(						self							:   Dict      ):
		UpperCamelCase	:list[T]		=      []
		UpperCamelCase	:int		=      0





	def __repr__(						self							:   List[str]      ):
		return str(self.h      )





	def 		_A					(						self							:   Tuple					,    __lowerCamelCase							:   int      ):
		if child_idx > 0:
			return (child_idx - 1) // 2
		return None





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   int      ):
		UpperCamelCase	:Optional[int]		=      2 * parent_idx + 1
		if left_child_index < self.heap_size:
			return left_child_index
		return None





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   int      ):
		UpperCamelCase	:Optional[Any]		=      2 * parent_idx + 2
		if right_child_index < self.heap_size:
			return right_child_index
		return None





	def 		_A					(						self							:   Tuple					,    __lowerCamelCase							:   int      ):
		if index < self.heap_size:
			UpperCamelCase	:int		=      index
			UpperCamelCase	:Dict		=      self.left_child_idx(__lowerCamelCase      )
			UpperCamelCase	:Any		=      self.right_child_idx(__lowerCamelCase      )
			# check which child is larger than its parent
			if left_child is not None and self.h[left_child] > self.h[violation]:
				UpperCamelCase	:Dict		=      left_child
			if right_child is not None and self.h[right_child] > self.h[violation]:
				UpperCamelCase	:Dict		=      right_child
			# if violation indeed exists
			if violation != index:
				# swap to fix the violation
				UpperCamelCase	, UpperCamelCase	:Optional[int]		=      self.h[index], self.h[violation]
				# fix the subsequent violation recursively if any
				self.max_heapify(__lowerCamelCase      )





	def 		_A					(						self							:   Any					,    __lowerCamelCase							:   Iterable[T]      ):
		UpperCamelCase	:Optional[Any]		=      list(__lowerCamelCase      )
		UpperCamelCase	:int		=      len(self.h      )
		if self.heap_size > 1:
			# max_heapify from right to left but exclude leaves (last level)
			for i in range(self.heap_size // 2 - 1					,    -1					,    -1      ):
				self.max_heapify(__lowerCamelCase      )





	def 		_A					(						self							:   Optional[int]      ):
		if self.heap_size >= 2:
			UpperCamelCase	:List[str]		=      self.h[0]
			UpperCamelCase	:str		=      self.h.pop(-1      )
			self.heap_size -= 1
			self.max_heapify(0      )
			return me
		elif self.heap_size == 1:
			self.heap_size -= 1
			return self.h.pop(-1      )
		else:
			raise Exception("""Empty heap"""      )





	def 		_A					(						self							:   Optional[Any]					,    __lowerCamelCase							:   T      ):
		self.h.append(__lowerCamelCase      )
		UpperCamelCase	:Any		=      (self.heap_size - 1) // 2
		self.heap_size += 1
		while idx >= 0:
			self.max_heapify(__lowerCamelCase      )
			UpperCamelCase	:List[str]		=      (idx - 1) // 2





	def 		_A					(						self							:   int      ):
		UpperCamelCase	:int		=      self.heap_size
		for j in range(size - 1					,    0					,    -1      ):
			UpperCamelCase	, UpperCamelCase	:Any		=      self.h[j], self.h[0]
			self.heap_size -= 1
			self.max_heapify(0      )
		UpperCamelCase	:List[str]		=      size


if __name__ == "__main__":
	import doctest

	# run doc test
	doctest.testmod()

	# demo
	for unsorted in [
	    [0],
	    [2],
	    [3, 5],
	    [5, 3],
	    [5, 5],
	    [0, 0, 0, 0],
	    [1, 1, 1, 1],
	    [2, 2, 3, 5],
	    [0, 2, 2, 3, 5],
	    [2, 5, 3, 0, 2, 3, 0, 3],
	    [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
	    [1_03, 9, 1, 7, 11, 15, 25, 2_01, 2_09, 1_07, 5],
	    [-45, -2, -5],
	]:
		print(F'''unsorted array: {unsorted}''')

		UpperCAmelCase_					:		Heap[int]           = Heap()
		heap.build_max_heap(unsorted)
		print(F'''after build heap: {heap}''')

		print(F'''max value: {heap.extract_max()}''')
		print(F'''after max value removed: {heap}''')

		heap.insert(1_00)
		print(F'''after new value 100 inserted: {heap}''')

		heap.heap_sort()
		print(F'''heap-sorted array: {heap}\n''')
