





'''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


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






class    UpperCAmelCase	(       UpperCamelCase__			,       UpperCamelCase__				):
 @register_to_config
 def __init__(				self      :Tuple						,       lowercase_      :bool						,       lowercase_      :Optional[int] = None						,       lowercase_      :Optional[int] = None							)->							Optional[int]:
     super().__init__()

     A__				=			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"

         A__				=			torch.zeros(lowercase_						,       lowercase_							)
     else:
         A__				=			None

     A__				=			torch.nn.Parameter(lowercase_							)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				42
 __lowercase   		=				42
 __lowercase   		=				42
 __lowercase   		=				42
 __lowercase   		=				42
 __lowercase   		=				42
 def __init__(				self      :Any						,       lowercase_      :VQModel						,       lowercase_      :CLIPTextModel						,       lowercase_      :CLIPTokenizer						,       lowercase_      :TransformeraDModel						,       lowercase_      :VQDiffusionScheduler						,       lowercase_      :LearnedClassifierFreeSamplingEmbeddings						,       )->							Tuple:
     super().__init__()

     self.register_modules(
         vqvae=lowercase_						,       transformer=lowercase_						,       text_encoder=lowercase_						,       tokenizer=lowercase_						,       scheduler=lowercase_						,       learned_classifier_free_sampling_embeddings=lowercase_						,       )
 def      UpperCAmelCase_							(				self      :Any						,       lowercase_      :Dict						,       lowercase_      :Tuple						,       lowercase_      :Optional[Any]							)->							Dict:
     A__				=			len(lowercase_							) if isinstance(lowercase_						,       lowercase_							) else 1

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

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
         A__				=			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}"							)
         A__				=			text_input_ids[:, : self.tokenizer.model_max_length]
     A__				=			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
     A__				=			prompt_embeds / prompt_embeds.norm(dim=-1						,       keepdim=lowercase_							)

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

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

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

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

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

     A__				=			batch_size * num_images_per_prompt

     A__				=			guidance_scale > 1.0

     A__				=			self._encode_prompt(lowercase_						,       lowercase_						,       lowercase_							)

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

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

     A__				=			(batch_size, self.transformer.num_latent_pixels)
     if latents is None:
         A__				=			self.transformer.num_vector_embeds - 1
         A__				=			torch.full(lowercase_						,       lowercase_							).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)."							)
         A__				=			latents.to(self.device							)

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

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

     A__				=			latents

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

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

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

         A__				=			self.truncate(lowercase_						,       lowercase_							)

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

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

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

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

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

     if output_type == "pil":
         A__				=			self.numpy_to_pil(lowercase_							)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=lowercase_							)



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

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

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

     A__				=			log_p_x_0.clone()

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

     return rv










'''simple docstring'''

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    "stable diffusion controlnet",
    "0.22.0",
    "Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.",
    standard_warn=False,
    stacklevel=3,
)



