
"""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


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





class 				a			(					lowercase						,		lowercase      ):



      @register_to_config
      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None      ):
          super().__init__()

          UpperCAmelCase__	:      Any     							=					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"

              UpperCAmelCase__	:      Union[str, Any]     							=					torch.zeros(UpperCamelCase_     ,	UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      Dict     							=					None

          UpperCAmelCase__	:      List[Any]     							=					torch.nn.Parameter(UpperCamelCase_      )





class 				a			(					lowercase      ):
      UpperCamelCase							:   VQModel
      UpperCamelCase							:   CLIPTextModel
      UpperCamelCase							:   CLIPTokenizer
      UpperCamelCase							:   TransformeraDModel
      UpperCamelCase							:   LearnedClassifierFreeSamplingEmbeddings
      UpperCamelCase							:   VQDiffusionScheduler



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	):
          super().__init__()

          self.register_modules(
              vqvae=UpperCamelCase_     ,	transformer=UpperCamelCase_     ,	text_encoder=UpperCamelCase_     ,	tokenizer=UpperCamelCase_     ,	scheduler=UpperCamelCase_     ,	learned_classifier_free_sampling_embeddings=UpperCamelCase_     ,	)



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					len(UpperCamelCase_      ) if isinstance(UpperCamelCase_     ,	UpperCamelCase_      ) else 1

          # get prompt text embeddings
          UpperCAmelCase__	:      str     							=					self.tokenizer(
              UpperCamelCase_     ,	padding='max_length'     ,	max_length=self.tokenizer.model_max_length     ,	return_tensors='pt'     ,	)
          UpperCAmelCase__	:      int     							=					text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
              UpperCAmelCase__	:      Dict     							=					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__	:      List[str]     							=					text_input_ids[:, : self.tokenizer.model_max_length]
          UpperCAmelCase__	:      Dict     							=					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
          UpperCAmelCase__	:      List[str]     							=					prompt_embeds / prompt_embeds.norm(dim=-1     ,	keepdim=UpperCamelCase_      )

          # duplicate text embeddings for each generation per prompt
          UpperCAmelCase__	:      int     							=					prompt_embeds.repeat_interleave(UpperCamelCase_     ,	dim=0      )

          if do_classifier_free_guidance:
              if self.learned_classifier_free_sampling_embeddings.learnable:
                  UpperCAmelCase__	:      str     							=					self.learned_classifier_free_sampling_embeddings.embeddings
                  UpperCAmelCase__	:      Optional[Any]     							=					negative_prompt_embeds.unsqueeze(0      ).repeat(UpperCamelCase_     ,	1     ,	1      )
              else:
                  UpperCAmelCase__	:      Tuple     							=					[''] * batch_size

                  UpperCAmelCase__	:      List[Any]     							=					text_input_ids.shape[-1]
                  UpperCAmelCase__	:      Union[str, Any]     							=					self.tokenizer(
                      UpperCamelCase_     ,	padding='max_length'     ,	max_length=UpperCamelCase_     ,	truncation=UpperCamelCase_     ,	return_tensors='pt'     ,	)
                  UpperCAmelCase__	:      Union[str, Any]     							=					self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]
                  # See comment for normalizing text embeddings
                  UpperCAmelCase__	:      Any     							=					negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1     ,	keepdim=UpperCamelCase_      )

              # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
              UpperCAmelCase__	:      List[str]     							=					negative_prompt_embeds.shape[1]
              UpperCAmelCase__	:      int     							=					negative_prompt_embeds.repeat(1     ,	UpperCamelCase_     ,	1      )
              UpperCAmelCase__	:      Tuple     							=					negative_prompt_embeds.view(batch_size * num_images_per_prompt     ,	UpperCamelCase_     ,	-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
              UpperCAmelCase__	:      Dict     							=					torch.cat([negative_prompt_embeds, prompt_embeds]      )

          return prompt_embeds



      @torch.no_grad()
      def __call__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = 100     ,	UpperCamelCase_ = 5.0     ,	UpperCamelCase_ = 1.0     ,	UpperCamelCase_ = 1     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = "pil"     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = 1     ,	):
          if isinstance(UpperCamelCase_     ,	UpperCamelCase_      ):
              UpperCAmelCase__	:      Union[str, Any]     							=					1
          elif isinstance(UpperCamelCase_     ,	UpperCamelCase_      ):
              UpperCAmelCase__	:      List[str]     							=					len(UpperCamelCase_      )
          else:
              raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase_      )}'''      )

          UpperCAmelCase__	:      List[Any]     							=					batch_size * num_images_per_prompt

          UpperCAmelCase__	:      Any     							=					guidance_scale > 1.0

          UpperCAmelCase__	:      str     							=					self._encode_prompt(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )

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

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

          UpperCAmelCase__	:      Dict     							=					(batch_size, self.transformer.num_latent_pixels)
          if latents is None:
              UpperCAmelCase__	:      List[str]     							=					self.transformer.num_vector_embeds - 1
              UpperCAmelCase__	:      Any     							=					torch.full(UpperCamelCase_     ,	UpperCamelCase_      ).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).'''      )
              UpperCAmelCase__	:      str     							=					latents.to(self.device      )

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

          UpperCAmelCase__	:      Optional[Any]     							=					self.scheduler.timesteps.to(self.device      )

          UpperCAmelCase__	:      List[str]     							=					latents

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

              # predict the un-noised image
              # model_output == `log_p_x_0`
              UpperCAmelCase__	:      Optional[int]     							=					self.transformer(UpperCamelCase_     ,	encoder_hidden_states=UpperCamelCase_     ,	timestep=UpperCamelCase_      ).sample

              if do_classifier_free_guidance:
                  UpperCAmelCase__							,							UpperCAmelCase__	:      Union[str, Any]     							=					model_output.chunk(2      )
                  UpperCAmelCase__	:      Dict     							=					model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                  model_output -= torch.logsumexp(UpperCamelCase_     ,	dim=1     ,	keepdim=UpperCamelCase_      )

              UpperCAmelCase__	:      Union[str, Any]     							=					self.truncate(UpperCamelCase_     ,	UpperCamelCase_      )

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

              # compute the previous noisy sample x_t -> x_t-1
              UpperCAmelCase__	:      List[str]     							=					self.scheduler.step(UpperCamelCase_     ,	timestep=UpperCamelCase_     ,	sample=UpperCamelCase_     ,	generator=UpperCamelCase_      ).prev_sample

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

          UpperCAmelCase__	:      str     							=					self.vqvae.config.vq_embed_dim
          UpperCAmelCase__	:      Optional[Any]     							=					(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
          UpperCAmelCase__	:      Optional[int]     							=					self.vqvae.quantize.get_codebook_entry(UpperCamelCase_     ,	shape=UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					self.vqvae.decode(UpperCamelCase_     ,	force_not_quantize=UpperCamelCase_      ).sample

          UpperCAmelCase__	:      Union[str, Any]     							=					(image / 2 + 0.5).clamp(0     ,	1      )
          UpperCAmelCase__	:      Union[str, Any]     							=					image.cpu().permute(0     ,	2     ,	3     ,	1      ).numpy()

          if output_type == "pil":
              UpperCAmelCase__	:      List[Any]     							=					self.numpy_to_pil(UpperCamelCase_      )

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[int]     							=					torch.sort(UpperCamelCase_     ,	1     ,	descending=UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					torch.exp(UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					sorted_p_x_0.cumsum(dim=1      ) < truncation_rate

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

          UpperCAmelCase__	:      Union[str, Any]     							=					keep_mask.gather(1     ,	indices.argsort(1      )      )

          UpperCAmelCase__	:      List[str]     							=					log_p_x_0.clone()

          UpperCAmelCase__	:      Optional[Any]     							=					-torch.inf  # -inf = log(0)

          return rv



"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					value
          UpperCAmelCase__	:      Node | None     							=					None
          UpperCAmelCase__	:      Node | None     							=					None





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					tree



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left      ) + self.depth_first_search(node.right      )
          )



      def __iter__(   self      ):
          yield self.depth_first_search(self.tree      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

