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






class      _UpperCAmelCase				(					_snake_case		,					_snake_case):
    @register_to_config
    def __init__(    self						,    snake_case_						,    snake_case_ = None						,    snake_case_ = None     ):
      super().__init__()

      _snake_case     :						int						=   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"

        _snake_case     :						Union[str, Any]						=   torch.zeros(lowerCAmelCase__						,    lowerCAmelCase__     )
      else:
        _snake_case     :						Optional[Any]						=   None

      _snake_case     :						Dict						=   torch.nn.Parameter(lowerCAmelCase__     )






class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: Any  =					4_2
    __lowercase							: List[Any]  =					4_2
    __lowercase							: int  =					4_2
    __lowercase							: Optional[Any]  =					4_2
    __lowercase							: Union[str, Any]  =					4_2
    __lowercase							: List[Any]  =					4_2
    def __init__(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    ):
      super().__init__()

      self.register_modules(
          vqvae=lowerCAmelCase__						,    transformer=lowerCAmelCase__						,    text_encoder=lowerCAmelCase__						,    tokenizer=lowerCAmelCase__						,    scheduler=lowerCAmelCase__						,    learned_classifier_free_sampling_embeddings=lowerCAmelCase__						,    )
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_     ):
      _snake_case     :						List[str]						=   len(lowerCAmelCase__     ) if isinstance(lowerCAmelCase__						,    lowerCAmelCase__     ) else 1

      # get prompt text embeddings
      _snake_case     :						Optional[Any]						=   self.tokenizer(
          lowerCAmelCase__						,    padding="max_length"						,    max_length=self.tokenizer.model_max_length						,    return_tensors="pt"						,    )
      _snake_case     :						Optional[int]						=   text_inputs.input_ids

      if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
        _snake_case     :						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}'     )
        _snake_case     :						List[Any]						=   text_input_ids[:, : self.tokenizer.model_max_length]
      _snake_case     :						Tuple						=   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
      _snake_case     :						List[Any]						=   prompt_embeds / prompt_embeds.norm(dim=-1						,    keepdim=lowerCAmelCase__     )

      # duplicate text embeddings for each generation per prompt
      _snake_case     :						List[str]						=   prompt_embeds.repeat_interleave(lowerCAmelCase__						,    dim=0     )

      if do_classifier_free_guidance:
        if self.learned_classifier_free_sampling_embeddings.learnable:
          _snake_case     :						Dict						=   self.learned_classifier_free_sampling_embeddings.embeddings
          _snake_case     :						Union[str, Any]						=   negative_prompt_embeds.unsqueeze(0     ).repeat(lowerCAmelCase__						,    1						,    1     )
        else:
          _snake_case     :						Union[str, Any]						=   [""] * batch_size

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

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        _snake_case     :						Any						=   negative_prompt_embeds.shape[1]
        _snake_case     :						Optional[int]						=   negative_prompt_embeds.repeat(1						,    lowerCAmelCase__						,    1     )
        _snake_case     :						Any						=   negative_prompt_embeds.view(batch_size * num_images_per_prompt						,    lowerCAmelCase__						,    -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
        _snake_case     :						Tuple						=   torch.cat([negative_prompt_embeds, prompt_embeds]     )

      return prompt_embeds
    @torch.no_grad()
    def __call__(    self						,    snake_case_						,    snake_case_ = 1_00						,    snake_case_ = 5.0						,    snake_case_ = 1.0						,    snake_case_ = 1						,    snake_case_ = None						,    snake_case_ = None						,    snake_case_ = "pil"						,    snake_case_ = True						,    snake_case_ = None						,    snake_case_ = 1						,    ):
      if isinstance(lowerCAmelCase__						,    lowerCAmelCase__     ):
        _snake_case     :						List[Any]						=   1
      elif isinstance(lowerCAmelCase__						,    lowerCAmelCase__     ):
        _snake_case     :						Any						=   len(lowerCAmelCase__     )
      else:
        raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__     )}'     )

      _snake_case     :						List[Any]						=   batch_size * num_images_per_prompt

      _snake_case     :						Optional[int]						=   guidance_scale > 1.0

      _snake_case     :						str						=   self._encode_prompt(lowerCAmelCase__						,    lowerCAmelCase__						,    lowerCAmelCase__     )

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

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

      _snake_case     :						Optional[int]						=   (batch_size, self.transformer.num_latent_pixels)
      if latents is None:
        _snake_case     :						str						=   self.transformer.num_vector_embeds - 1
        _snake_case     :						Optional[Any]						=   torch.full(lowerCAmelCase__						,    lowerCAmelCase__     ).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).'     )
        _snake_case     :						str						=   latents.to(self.device     )

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

      _snake_case     :						str						=   self.scheduler.timesteps.to(self.device     )

      _snake_case     :						Any						=   latents

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

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

        if do_classifier_free_guidance:
          _snake_case			,					_snake_case     :						Optional[int]						=   model_output.chunk(2     )
          _snake_case     :						Optional[Any]						=   model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
          model_output -= torch.logsumexp(lowerCAmelCase__						,    dim=1						,    keepdim=lowerCAmelCase__     )

        _snake_case     :						Any						=   self.truncate(lowerCAmelCase__						,    lowerCAmelCase__     )

        # remove `log(0)`'s (`-inf`s)
        _snake_case     :						Union[str, Any]						=   model_output.clamp(-70     )

        # compute the previous noisy sample x_t -> x_t-1
        _snake_case     :						str						=   self.scheduler.step(lowerCAmelCase__						,    timestep=lowerCAmelCase__						,    sample=lowerCAmelCase__						,    generator=lowerCAmelCase__     ).prev_sample

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

      _snake_case     :						Dict						=   self.vqvae.config.vq_embed_dim
      _snake_case     :						Tuple						=   (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
      _snake_case     :						Tuple						=   self.vqvae.quantize.get_codebook_entry(lowerCAmelCase__						,    shape=lowerCAmelCase__     )
      _snake_case     :						int						=   self.vqvae.decode(lowerCAmelCase__						,    force_not_quantize=lowerCAmelCase__     ).sample

      _snake_case     :						Tuple						=   (image / 2 + 0.5).clamp(0						,    1     )
      _snake_case     :						List[str]						=   image.cpu().permute(0						,    2						,    3						,    1     ).numpy()

      if output_type == "pil":
        _snake_case     :						Tuple						=   self.numpy_to_pil(lowerCAmelCase__     )

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=lowerCAmelCase__     )


    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_     ):
      _snake_case			,					_snake_case     :						Dict						=   torch.sort(lowerCAmelCase__						,    1						,    descending=lowerCAmelCase__     )
      _snake_case     :						List[Any]						=   torch.exp(lowerCAmelCase__     )
      _snake_case     :						Any						=   sorted_p_x_0.cumsum(dim=1     ) < truncation_rate

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

      _snake_case     :						Tuple						=   keep_mask.gather(1						,    indices.argsort(1     )     )

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

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

      return rv



"""simple docstring"""
from __future__ import annotations

from collections import namedtuple
def 				a__   (   a   :				float       ,     a   :				float       ,     a   :				float		):

  """simple docstring"""







  _snake_case     :						Optional[Any]						=   namedtuple("result"       ,     "name value"		)
  if (voltage, current, power).count(0		) != 1:
    raise ValueError("Only one argument must be 0"		)
  elif power < 0:
    raise ValueError(
        "Power cannot be negative in any electrical/electronics system"		)
  elif voltage == 0:
    return result("voltage"       ,     power / current		)
  elif current == 0:
    return result("current"       ,     power / voltage		)
  elif power == 0:
    return result("power"       ,     float(round(abs(voltage * current		)       ,     2		)		)		)
  else:
    raise ValueError("Exactly one argument must be 0"		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

