



"""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


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


def 	lowercase__  (      self						,   _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					)  ->      Any:






      '''simple docstring'''


      lowercase	:   Dict											= image.to(device=_UpperCAmelCase					)
      lowercase	:   List[Any]											= image  # take image as image_embeddings
      lowercase	:   Optional[int]											= image_embeddings.unsqueeze(1					)

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

      if do_classifier_free_guidance:
            lowercase	:   str											= 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
            lowercase	:   Tuple											= torch.cat([uncond_embeddings, image_embeddings]					)

      return image_embeddings





class 	a__		(    SCREAMING_SNAKE_CASE__  ):


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

         lowercase	:   Union[str, Any]											= {'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

         lowercase	:   Any											= decoder_pipe_kwargs.get('torch_dtype',  lowerCAmelCase      ) or prior.dtype

         lowercase	:   List[Any]											= StableDiffusionImageVariationPipeline.from_pretrained(
             'lambdalabs/sd-image-variations-diffusers',  **lowerCAmelCase      )

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

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


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

               if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                     lowercase	:   Any											= 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}'''      )
                     lowercase	:   List[Any]											= text_input_ids[:, : self.tokenizer.model_max_length]

               lowercase	:   List[Any]											= self.text_encoder(text_input_ids.to(lowerCAmelCase      )      )

               lowercase	:   Optional[Any]											= text_encoder_output.text_embeds
               lowercase	:   Optional[Any]											= text_encoder_output.last_hidden_state

         else:
               lowercase	:   Optional[Any]											= text_model_output[0].shape[0]
               lowercase						,     lowercase	:   Optional[Any]											= text_model_output[0], text_model_output[1]
               lowercase	:   Union[str, Any]											= text_attention_mask

         lowercase	:   Any											= text_embeddings.repeat_interleave(lowerCAmelCase,  dim=0      )
         lowercase	:   str											= text_encoder_hidden_states.repeat_interleave(lowerCAmelCase,  dim=0      )
         lowercase	:   Dict											= text_mask.repeat_interleave(lowerCAmelCase,  dim=0      )

         if do_classifier_free_guidance:
               lowercase	:   List[Any]											= [''] * batch_size

               lowercase	:   Union[str, Any]											= self.tokenizer(
                   lowerCAmelCase,  padding='max_length',  max_length=self.tokenizer.model_max_length,  truncation=lowerCAmelCase,  return_tensors='pt',  )
               lowercase	:   Optional[int]											= uncond_input.attention_mask.bool().to(lowerCAmelCase      )
               lowercase	:   Any											= self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase      )      )

               lowercase	:   Optional[int]											= uncond_embeddings_text_encoder_output.text_embeds
               lowercase	:   Any											= uncond_embeddings_text_encoder_output.last_hidden_state

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

               lowercase	:   Optional[int]											= uncond_embeddings.shape[1]
               lowercase	:   Union[str, Any]											= uncond_embeddings.repeat(1,  lowerCAmelCase      )
               lowercase	:   str											= uncond_embeddings.view(batch_size * num_images_per_prompt,  lowerCAmelCase      )

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

               lowercase	:   List[str]											= torch.cat([uncond_text_mask, text_mask]      )

         return text_embeddings, text_encoder_hidden_states, text_mask


   @property
   def 							lowercase					(				self   :				List[str]      )     ->							Tuple:
         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   :				str,  lowerCAmelCase   :				Any,  lowerCAmelCase   :				Dict,  lowerCAmelCase   :				Optional[Any],  lowerCAmelCase   :				Dict,  lowerCAmelCase   :				Optional[int],  lowerCAmelCase   :				Tuple      )     ->							Tuple:
         if latents is None:
               lowercase	:   Optional[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}'''      )
               lowercase	:   List[Any]											= latents.to(lowerCAmelCase      )

         lowercase	:   Optional[Any]											= latents * scheduler.init_noise_sigma
         return latents


   def 							lowercase					(				self   :				List[Any],  lowerCAmelCase   :				Optional[Union[str, torch.device]] = None      )     ->							Any:
         self.decoder_pipe.to(lowerCAmelCase      )
         super().to(lowerCAmelCase      )






   @torch.no_grad()
   def __call__(				self   :				int,  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,  )     ->							Optional[Any]:
         if prompt is not None:
               if isinstance(lowerCAmelCase,  lowerCAmelCase      ):
                     lowercase	:   int											= 1
               elif isinstance(lowerCAmelCase,  lowerCAmelCase      ):
                     lowercase	:   Any											= len(lowerCAmelCase      )
               else:
                     raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase      )}'''      )
         else:
               lowercase	:   Tuple											= text_model_output[0].shape[0]

         lowercase	:   Optional[int]											= self._execution_device

         lowercase	:   List[str]											= batch_size * num_images_per_prompt

         lowercase	:   List[Any]											= prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

         lowercase						,     lowercase						,     lowercase	:   Union[str, Any]											= self._encode_prompt(
             lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase      )

         # prior

         self.prior_scheduler.set_timesteps(lowerCAmelCase,  device=lowerCAmelCase      )
         lowercase	:   Dict											= self.prior_scheduler.timesteps

         lowercase	:   List[Any]											= self.prior.config.embedding_dim

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

               lowercase	:   Dict											= self.prior(
                   lowerCAmelCase,  timestep=lowerCAmelCase,  proj_embedding=lowerCAmelCase,  encoder_hidden_states=lowerCAmelCase,  attention_mask=lowerCAmelCase,  ).predicted_image_embedding

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

               if i + 1 == prior_timesteps_tensor.shape[0]:
                     lowercase	:   str											= None
               else:
                     lowercase	:   Union[str, Any]											= prior_timesteps_tensor[i + 1]

               lowercase	:   List[Any]											= self.prior_scheduler.step(
                   lowerCAmelCase,  timestep=lowerCAmelCase,  sample=lowerCAmelCase,  generator=lowerCAmelCase,  prev_timestep=lowerCAmelCase,  ).prev_sample

         lowercase	:   Optional[int]											= self.prior.post_process_latents(lowerCAmelCase      )

         lowercase	:   int											= prior_latents

         lowercase	:   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





"""simple docstring"""




from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass


@dataclass
class 	a__		:
   _lowerCamelCase       				=					42
   _lowerCamelCase       				=					None
   _lowerCamelCase       				=					None




def 	lowercase__  (      )  ->      TreeNode:






      '''simple docstring'''


      return TreeNode(3						,   TreeNode(9					)						,   TreeNode(20						,   TreeNode(15					)						,   TreeNode(7					)					)					)


def 	lowercase__  (      _UpperCAmelCase					)  ->      list[int]:






      '''simple docstring'''


      def depth_first_search(
          _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					) -> None:
            if not root:
                  return

            if depth == len(_UpperCAmelCase					):
                  right_view.append(root.val					)

            depth_first_search(root.right						,   depth + 1						,   _UpperCAmelCase					)
            depth_first_search(root.left						,   depth + 1						,   _UpperCAmelCase					)

      lowercase	:   list											= []
      if not root:
            return right_view

      depth_first_search(_UpperCAmelCase						,   0						,   _UpperCAmelCase					)
      return right_view


def 	lowercase__  (      _UpperCAmelCase					)  ->      list[int]:






      '''simple docstring'''


      def depth_first_search(
          _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					) -> None:
            if not root:
                  return

            if depth == len(_UpperCAmelCase					):
                  left_view.append(root.val					)

            depth_first_search(root.left						,   depth + 1						,   _UpperCAmelCase					)
            depth_first_search(root.right						,   depth + 1						,   _UpperCAmelCase					)

      lowercase	:   list											= []
      if not root:
            return left_view

      depth_first_search(_UpperCAmelCase						,   0						,   _UpperCAmelCase					)
      return left_view


def 	lowercase__  (      _UpperCAmelCase					)  ->      list[int]:






      '''simple docstring'''


      def breadth_first_search(_UpperCAmelCase						,   _UpperCAmelCase					) -> None:
            lowercase	:   List[str]											= [(root, 0)]
            lowercase	:   List[Any]											= defaultdict(_UpperCAmelCase					)

            while queue:
                  lowercase	:   Union[str, Any]											= queue.pop(0					)
                  lowercase						,     lowercase	:   str											= first

                  lookup[hd].append(node.val					)

                  if node.left:
                        queue.append((node.left, hd - 1)					)
                  if node.right:
                        queue.append((node.right, hd + 1)					)

            for pair in sorted(lookup.items()						,   key=lambda _UpperCAmelCase					: each[0]					):
                  top_view.append(pair[1][0]					)

      lowercase	:   list											= []
      if not root:
            return top_view

      breadth_first_search(_UpperCAmelCase						,   _UpperCAmelCase					)
      return top_view


def 	lowercase__  (      _UpperCAmelCase					)  ->      list[int]:






      '''simple docstring'''


      from collections import defaultdict

      def breadth_first_search(_UpperCAmelCase						,   _UpperCAmelCase					) -> None:
            lowercase	:   Any											= [(root, 0)]
            lowercase	:   Dict											= defaultdict(_UpperCAmelCase					)

            while queue:
                  lowercase	:   str											= queue.pop(0					)
                  lowercase						,     lowercase	:   List[Any]											= first
                  lookup[hd].append(node.val					)

                  if node.left:
                        queue.append((node.left, hd - 1)					)
                  if node.right:
                        queue.append((node.right, hd + 1)					)

            for pair in sorted(lookup.items()						,   key=lambda _UpperCAmelCase					: each[0]					):
                  bottom_view.append(pair[1][-1]					)

      lowercase	:   list											= []
      if not root:
            return bottom_view

      breadth_first_search(_UpperCAmelCase						,   _UpperCAmelCase					)
      return bottom_view


if __name__ == "__main__":
     import doctest

     doctest.testmod()
