'''simple docstring'''

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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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







class 				_lowercase     (				UpperCAmelCase__  ,							UpperCAmelCase__			):


 '''simple docstring'''






 @register_to_config
 def __init__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	bool      ,       SCREAMING_SNAKE_CASE__   :	Optional[int] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[int] = None							)  ->    Optional[int]:
  super().__init__()

  __lowerCAmelCase											=						learnable

  if self.learnable:
   assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
   assert length is not None, "learnable=True requires `length` to be set"

   __lowerCAmelCase											=						torch.zeros(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
  else:
   __lowerCAmelCase											=						None

  __lowerCAmelCase											=						torch.nn.Parameter(SCREAMING_SNAKE_CASE__							)







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       VQModel
 _SCREAMING_SNAKE_CASE     :       CLIPTextModel
 _SCREAMING_SNAKE_CASE     :       CLIPTokenizer
 _SCREAMING_SNAKE_CASE     :       TransformeraDModel
 _SCREAMING_SNAKE_CASE     :       LearnedClassifierFreeSamplingEmbeddings
 _SCREAMING_SNAKE_CASE     :       VQDiffusionScheduler
 def __init__(      self   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	VQModel      ,       SCREAMING_SNAKE_CASE__   :	CLIPTextModel      ,       SCREAMING_SNAKE_CASE__   :	CLIPTokenizer      ,       SCREAMING_SNAKE_CASE__   :	TransformeraDModel      ,       SCREAMING_SNAKE_CASE__   :	VQDiffusionScheduler      ,       SCREAMING_SNAKE_CASE__   :	LearnedClassifierFreeSamplingEmbeddings      ,       )  ->    str:
  super().__init__()

  self.register_modules(
      vqvae=SCREAMING_SNAKE_CASE__      ,       transformer=SCREAMING_SNAKE_CASE__      ,       text_encoder=SCREAMING_SNAKE_CASE__      ,       tokenizer=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__      ,       learned_classifier_free_sampling_embeddings=SCREAMING_SNAKE_CASE__      ,       )
 def a					(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Tuple							)  ->    Tuple:
  __lowerCAmelCase											=						len(SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							) else 1

  # get prompt text embeddings
  __lowerCAmelCase											=						self.tokenizer(
      SCREAMING_SNAKE_CASE__      ,       padding="""max_length"""      ,       max_length=self.tokenizer.model_max_length      ,       return_tensors="""pt"""      ,       )
  __lowerCAmelCase											=						text_inputs.input_ids

  if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
   __lowerCAmelCase											=						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}"""							)
   __lowerCAmelCase											=						text_input_ids[:, : self.tokenizer.model_max_length]
  __lowerCAmelCase											=						self.text_encoder(text_input_ids.to(self.device							)							)[0]

  # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
  # While CLIP does normalize the pooled output of the text transformer when combining
  # the image and text embeddings, CLIP does not directly normalize the last hidden state.
  #
  # CLIP normalizing the pooled output.
  # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
  __lowerCAmelCase											=						prompt_embeds / prompt_embeds.norm(dim=-1      ,       keepdim=SCREAMING_SNAKE_CASE__							)

  # duplicate text embeddings for each generation per prompt
  __lowerCAmelCase											=						prompt_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__      ,       dim=0							)

  if do_classifier_free_guidance:
   if self.learned_classifier_free_sampling_embeddings.learnable:
    __lowerCAmelCase											=						self.learned_classifier_free_sampling_embeddings.embeddings
    __lowerCAmelCase											=						negative_prompt_embeds.unsqueeze(0							).repeat(SCREAMING_SNAKE_CASE__      ,       1      ,       1							)
   else:
    __lowerCAmelCase											=						[""""""] * batch_size

    __lowerCAmelCase											=						text_input_ids.shape[-1]
    __lowerCAmelCase											=						self.tokenizer(
        SCREAMING_SNAKE_CASE__      ,       padding="""max_length"""      ,       max_length=SCREAMING_SNAKE_CASE__      ,       truncation=SCREAMING_SNAKE_CASE__      ,       return_tensors="""pt"""      ,       )
    __lowerCAmelCase											=						self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
    # See comment for normalizing text embeddings
    __lowerCAmelCase											=						negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1      ,       keepdim=SCREAMING_SNAKE_CASE__							)

   # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
   __lowerCAmelCase											=						negative_prompt_embeds.shape[1]
   __lowerCAmelCase											=						negative_prompt_embeds.repeat(1      ,       SCREAMING_SNAKE_CASE__      ,       1							)
   __lowerCAmelCase											=						negative_prompt_embeds.view(batch_size * num_images_per_prompt      ,       SCREAMING_SNAKE_CASE__      ,       -1							)

   # 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
   __lowerCAmelCase											=						torch.cat([negative_prompt_embeds, prompt_embeds]							)

  return prompt_embeds
 @torch.no_grad()
 def __call__(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, List[str]]      ,       SCREAMING_SNAKE_CASE__   :	int = 1_00      ,       SCREAMING_SNAKE_CASE__   :	float = 5.0      ,       SCREAMING_SNAKE_CASE__   :	float = 1.0      ,       SCREAMING_SNAKE_CASE__   :	int = 1      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[torch.FloatTensor] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[str] = "pil"      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,       SCREAMING_SNAKE_CASE__   :	int = 1      ,       )  ->    Union[ImagePipelineOutput, Tuple]:
  if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase											=						1
  elif isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase											=						len(SCREAMING_SNAKE_CASE__							)
  else:
   raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE__							)}"""							)

  __lowerCAmelCase											=						batch_size * num_images_per_prompt

  __lowerCAmelCase											=						guidance_scale > 1.0

  __lowerCAmelCase											=						self._encode_prompt(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

  if (callback_steps is None) or (
      callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							) or callback_steps <= 0)
  ):
   raise ValueError(
       f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
       f""" {type(SCREAMING_SNAKE_CASE__							)}."""							)

  # get the initial completely masked latents unless the user supplied it

  __lowerCAmelCase											=						(batch_size, self.transformer.num_latent_pixels)
  if latents is None:
   __lowerCAmelCase											=						self.transformer.num_vector_embeds - 1
   __lowerCAmelCase											=						torch.full(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							).to(self.device							)
  else:
   if latents.shape != latents_shape:
    raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""							)
   if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
    raise ValueError(
        """Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"""
        f""" {self.transformer.num_vector_embeds - 1} (inclusive)."""							)
   __lowerCAmelCase											=						latents.to(self.device							)

  # set timesteps
  self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__      ,       device=self.device							)

  __lowerCAmelCase											=						self.scheduler.timesteps.to(self.device							)

  __lowerCAmelCase											=						latents

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

   # predict the un-noised image
   # model_output == `log_p_x_0`
   __lowerCAmelCase											=						self.transformer(SCREAMING_SNAKE_CASE__      ,       encoder_hidden_states=SCREAMING_SNAKE_CASE__      ,       timestep=SCREAMING_SNAKE_CASE__							).sample

   if do_classifier_free_guidance:
    __lowerCAmelCase							,       __lowerCAmelCase											=						model_output.chunk(2							)
    __lowerCAmelCase											=						model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
    model_output -= torch.logsumexp(SCREAMING_SNAKE_CASE__      ,       dim=1      ,       keepdim=SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						self.truncate(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

   # remove `log(0)`'s (`-inf`s)
   __lowerCAmelCase											=						model_output.clamp(-70							)

   # compute the previous noisy sample x_t -> x_t-1
   __lowerCAmelCase											=						self.scheduler.step(SCREAMING_SNAKE_CASE__      ,       timestep=SCREAMING_SNAKE_CASE__      ,       sample=SCREAMING_SNAKE_CASE__      ,       generator=SCREAMING_SNAKE_CASE__							).prev_sample

   # call the callback, if provided
   if callback is not None and i % callback_steps == 0:
    callback(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						self.vqvae.config.vq_embed_dim
  __lowerCAmelCase											=						(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
  __lowerCAmelCase											=						self.vqvae.quantize.get_codebook_entry(SCREAMING_SNAKE_CASE__      ,       shape=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.vqvae.decode(SCREAMING_SNAKE_CASE__      ,       force_not_quantize=SCREAMING_SNAKE_CASE__							).sample

  __lowerCAmelCase											=						(image / 2 + 0.5).clamp(0      ,       1							)
  __lowerCAmelCase											=						image.cpu().permute(0      ,       2      ,       3      ,       1							).numpy()

  if output_type == "pil":
   __lowerCAmelCase											=						self.numpy_to_pil(SCREAMING_SNAKE_CASE__							)

  if not return_dict:
   return (image,)

  return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__							)





 def a					(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	torch.FloatTensor      ,       SCREAMING_SNAKE_CASE__   :	float							)  ->    torch.FloatTensor:
  __lowerCAmelCase							,       __lowerCAmelCase											=						torch.sort(SCREAMING_SNAKE_CASE__      ,       1      ,       descending=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.exp(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						sorted_p_x_0.cumsum(dim=1							) < truncation_rate

  # Ensure that at least the largest probability is not zeroed out
  __lowerCAmelCase											=						torch.full_like(keep_mask[:, 0:1, :]      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.cat((all_true, keep_mask)      ,       dim=1							)
  __lowerCAmelCase											=						keep_mask[:, :-1, :]

  __lowerCAmelCase											=						keep_mask.gather(1      ,       indices.argsort(1							)							)

  __lowerCAmelCase											=						log_p_x_0.clone()

  __lowerCAmelCase											=						-torch.inf  # -inf = log(0)

  return rv







'''simple docstring'''

from __future__ import annotations

from dataclasses import dataclass







@dataclass
class 				_lowercase     :


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       float
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None







def      UpperCamelCase_						(					snake_case_       :       TreeNode | None      )					->     bool:






 '''simple docstring'''

 def is_valid_tree(snake_case_       :       TreeNode | None      ) -> bool:
  if node is None:
   return True

  if not isinstance(snake_case_						,		snake_case_      ):
   return False

  try:
   float(node.data      )
  except (TypeError, ValueError):
   return False

  return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

 if not is_valid_tree(snake_case_      ):
  raise ValueError(
      """Each node should be type of TreeNode and data should be float."""      )

 def is_binary_search_tree_recursive_check(
     snake_case_       :       TreeNode | None						,		snake_case_       :       float						,		snake_case_       :       float      ) -> bool:

  if node is None:
   return True

  return (
      left_bound < node.data < right_bound
      and is_binary_search_tree_recursive_check(node.left						,		snake_case_						,		node.data      )
      and is_binary_search_tree_recursive_check(
          node.right						,		node.data						,		snake_case_      )
  )

 return is_binary_search_tree_recursive_check(snake_case_						,		-float("""inf"""      )						,		float("""inf"""      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






