
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


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





def 		SCREAMING_SNAKE_CASE__      ( self      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
				snake_case__  =			image.to(device=_SCREAMING_SNAKE_CASE							)
				snake_case__  =			image  # take image as image_embeddings
				snake_case__  =			image_embeddings.unsqueeze(1							)

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

				if do_classifier_free_guidance:
								snake_case__  =			torch.zeros_like(_SCREAMING_SNAKE_CASE							)

								# 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
								snake_case__  =			torch.cat([uncond_embeddings, image_embeddings]							)

				return image_embeddings





class    _SCREAMING_SNAKE_CASE					( lowerCAmelCase__	):
						def __init__(     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase = None						,		):
										super().__init__()

										snake_case__  =			{"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

										snake_case__  =			decoder_pipe_kwargs.get("torch_dtype"						,		UpperCamelCase__	) or prior.dtype

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

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

										self.register_modules(
										    prior=UpperCamelCase__						,		tokenizer=UpperCamelCase__						,		text_encoder=UpperCamelCase__						,		prior_scheduler=UpperCamelCase__						,		)
						def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase = None						,		lowerCamelCase = None						,		):
										if text_model_output is None:
														snake_case__  =			len(UpperCamelCase__	) if isinstance(UpperCamelCase__						,		UpperCamelCase__	) else 1
														# get prompt text embeddings
														snake_case__  =			self.tokenizer(
														    UpperCamelCase__						,		padding="max_length"						,		max_length=self.tokenizer.model_max_length						,		return_tensors="pt"						,		)
														snake_case__  =			text_inputs.input_ids
														snake_case__  =			text_inputs.attention_mask.bool().to(UpperCamelCase__	)

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

														snake_case__  =			self.text_encoder(text_input_ids.to(UpperCamelCase__	)	)

														snake_case__  =			text_encoder_output.text_embeds
														snake_case__  =			text_encoder_output.last_hidden_state

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

										snake_case__  =			text_embeddings.repeat_interleave(UpperCamelCase__						,		dim=0	)
										snake_case__  =			text_encoder_hidden_states.repeat_interleave(UpperCamelCase__						,		dim=0	)
										snake_case__  =			text_mask.repeat_interleave(UpperCamelCase__						,		dim=0	)

										if do_classifier_free_guidance:
														snake_case__  =			[""] * batch_size

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

														snake_case__  =			uncond_embeddings_text_encoder_output.text_embeds
														snake_case__  =			uncond_embeddings_text_encoder_output.last_hidden_state

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

														snake_case__  =			uncond_embeddings.shape[1]
														snake_case__  =			uncond_embeddings.repeat(1						,		UpperCamelCase__	)
														snake_case__  =			uncond_embeddings.view(batch_size * num_images_per_prompt						,		UpperCamelCase__	)

														snake_case__  =			uncond_text_encoder_hidden_states.shape[1]
														snake_case__  =			uncond_text_encoder_hidden_states.repeat(1						,		UpperCamelCase__						,		1	)
														snake_case__  =			uncond_text_encoder_hidden_states.view(
														    batch_size * num_images_per_prompt						,		UpperCamelCase__						,		-1	)
														snake_case__  =			uncond_text_mask.repeat_interleave(UpperCamelCase__						,		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
														snake_case__  =			torch.cat([uncond_embeddings, text_embeddings]	)
														snake_case__  =			torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]	)

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

										return text_embeddings, text_encoder_hidden_states, text_mask
						@property
						def A_     (     self	):
										if self.device != torch.device("meta"	) or not hasattr(self.prior						,		"_hf_hook"	):
														return self.device
										for module in self.prior.modules():
														if (
														    hasattr(UpperCamelCase__						,		"_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						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	):
										if latents is None:
														snake_case__  =			randn_tensor(UpperCamelCase__						,		generator=UpperCamelCase__						,		device=UpperCamelCase__						,		dtype=UpperCamelCase__	)
										else:
														if latents.shape != shape:
																		raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""	)
														snake_case__  =			latents.to(UpperCamelCase__	)

										snake_case__  =			latents * scheduler.init_noise_sigma
										return latents
						def A_     (     self						,		lowerCamelCase = None	):
										self.decoder_pipe.to(UpperCamelCase__	)
										super().to(UpperCamelCase__	)



						@torch.no_grad()
						def __call__(     self						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase = 1						,		lowerCamelCase = 25						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase = 4.0						,		lowerCamelCase = 8.0						,		lowerCamelCase = 50						,		lowerCamelCase = 1						,		lowerCamelCase = 0.0						,		lowerCamelCase = "pil"						,		lowerCamelCase = True						,		):
										if prompt is not None:
														if isinstance(UpperCamelCase__						,		UpperCamelCase__	):
																		snake_case__  =			1
														elif isinstance(UpperCamelCase__						,		UpperCamelCase__	):
																		snake_case__  =			len(UpperCamelCase__	)
														else:
																		raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase__	)}"""	)
										else:
														snake_case__  =			text_model_output[0].shape[0]

										snake_case__  =			self._execution_device

										snake_case__  =			batch_size * num_images_per_prompt

										snake_case__  =			prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

										snake_case__  =			self._encode_prompt(
										    UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__	)

										# prior

										self.prior_scheduler.set_timesteps(UpperCamelCase__						,		device=UpperCamelCase__	)
										snake_case__  =			self.prior_scheduler.timesteps

										snake_case__  =			self.prior.config.embedding_dim

										snake_case__  =			self.prepare_latents(
										    (batch_size, embedding_dim)						,		text_embeddings.dtype						,		UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__						,		self.prior_scheduler						,		)

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

														snake_case__  =			self.prior(
														    UpperCamelCase__						,		timestep=UpperCamelCase__						,		proj_embedding=UpperCamelCase__						,		encoder_hidden_states=UpperCamelCase__						,		attention_mask=UpperCamelCase__						,		).predicted_image_embedding

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

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

														snake_case__  =			self.prior_scheduler.step(
														    UpperCamelCase__						,		timestep=UpperCamelCase__						,		sample=UpperCamelCase__						,		generator=UpperCamelCase__						,		prev_timestep=UpperCamelCase__						,		).prev_sample

										snake_case__  =			self.prior.post_process_latents(UpperCamelCase__	)

										snake_case__  =			prior_latents

										snake_case__  =			self.decoder_pipe(
										    image=UpperCamelCase__						,		height=UpperCamelCase__						,		width=UpperCamelCase__						,		num_inference_steps=UpperCamelCase__						,		guidance_scale=UpperCamelCase__						,		generator=UpperCamelCase__						,		output_type=UpperCamelCase__						,		return_dict=UpperCamelCase__						,		num_images_per_prompt=UpperCamelCase__						,		eta=UpperCamelCase__						,		)
										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					( __UpperCamelCase	):
      @abstractmethod
      def __lt__(     self						,		lowerCamelCase	):
          pass
      @abstractmethod
      def __gt__(     self						,		lowerCamelCase	):
          pass



      @abstractmethod
      def __eq__(     self						,		lowerCamelCase	):
          pass





__magic_name__           =					TypeVar('''T''', bound=Comparable)




class    _SCREAMING_SNAKE_CASE					( Generic[T]	):
      def __init__(     self	):
          snake_case__  =			[]
          snake_case__  =			0
      def __repr__(     self	):
          return str(self.h	)
      def A_     (     self						,		lowerCamelCase	):
          if child_idx > 0:
              return (child_idx - 1) // 2
          return None
      def A_     (     self						,		lowerCamelCase	):
          snake_case__  =			2 * parent_idx + 1
          if left_child_index < self.heap_size:
              return left_child_index
          return None
      def A_     (     self						,		lowerCamelCase	):
          snake_case__  =			2 * parent_idx + 2
          if right_child_index < self.heap_size:
              return right_child_index
          return None
      def A_     (     self						,		lowerCamelCase	):
          if index < self.heap_size:
              snake_case__  =			index
              snake_case__  =			self.left_child_idx(lowerCamelCase	)
              snake_case__  =			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]:
                  snake_case__  =			left_child
              if right_child is not None and self.h[right_child] > self.h[violation]:
                  snake_case__  =			right_child
              # if violation indeed exists
              if violation != index:
                  # swap to fix the violation
                  snake_case__  ,   snake_case__  =			self.h[index], self.h[violation]
                  # fix the subsequent violation recursively if any
                  self.max_heapify(lowerCamelCase	)
      def A_     (     self						,		lowerCamelCase	):
          snake_case__  =			list(lowerCamelCase	)
          snake_case__  =			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	):
          if self.heap_size >= 2:
              snake_case__  =			self.h[0]
              snake_case__  =			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						,		lowerCamelCase	):
          self.h.append(lowerCamelCase	)
          snake_case__  =			(self.heap_size - 1) // 2
          self.heap_size += 1
          while idx >= 0:
              self.max_heapify(lowerCamelCase	)
              snake_case__  =			(idx - 1) // 2



      def A_     (     self	):
          snake_case__  =			self.heap_size
          for j in range(size - 1						,		0						,		-1	):
              snake_case__  ,   snake_case__  =			self.h[j], self.h[0]
              self.heap_size -= 1
              self.max_heapify(0	)
          snake_case__  =			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],
         [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
         [-45, -2, -5],
     ]:
          print(F'''unsorted array: {unsorted}''')

          __magic_name__           =					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(100)
          print(F'''after new value 100 inserted: {heap}''')

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



