

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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class  __UpperCAmelCase					(	lowerCamelCase__						):



   def __init__(							self							:					int,		__A							:					TransformeraDModel,		__A							:					AutoencoderKL,		__A							:					KarrasDiffusionSchedulers,		__A							:					Optional[Dict[int, str]] = None,		):
     super().__init__()
     self.register_modules(transformer=__A,		vae=__A,		scheduler=__A						)

     # create a imagenet -> id dictionary for easier use
     UpperCAmelCase	:							Union[str, Any]						=			{}
     if idalabel is not None:
       for key, value in idalabel.items():
         for label in value.split(''','''						):
           UpperCAmelCase	:							Optional[Any]						=			int(__A						)
       UpperCAmelCase	:							Union[str, Any]						=			dict(sorted(self.labels.items()						)						)



   def 	__magic_name__   (							self							:					Dict,		__A							:					Union[str, List[str]]						):

     if not isinstance(__A,		__A						):
       UpperCAmelCase	:							List[Any]						=			list(__A						)

     for l in label:
       if l not in self.labels:
         raise ValueError(
             F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''						)

     return [self.labels[l] for l in label]





   @torch.no_grad()
   def __call__(							self							:					Optional[int],		__A							:					List[int],		__A							:					float = 4.0,		__A							:					Optional[Union[torch.Generator, List[torch.Generator]]] = None,		__A							:					int = 5_0,		__A							:					Optional[str] = "pil",		__A							:					bool = True,		):
     UpperCAmelCase	:							str						=			len(__A						)
     UpperCAmelCase	:							Dict						=			self.transformer.config.sample_size
     UpperCAmelCase	:							List[Any]						=			self.transformer.config.in_channels

     UpperCAmelCase	:							Union[str, Any]						=			randn_tensor(
         shape=(batch_size, latent_channels, latent_size, latent_size),		generator=__A,		device=self.device,		dtype=self.transformer.dtype,		)
     UpperCAmelCase	:							Tuple						=			torch.cat([latents] * 2						) if guidance_scale > 1 else latents

     UpperCAmelCase	:							Any						=			torch.tensor(__A,		device=self.device						).reshape(-1						)
     UpperCAmelCase	:							Union[str, Any]						=			torch.tensor([1_0_0_0] * batch_size,		device=self.device						)
     UpperCAmelCase	:							Optional[int]						=			torch.cat([class_labels, class_null],		0						) if guidance_scale > 1 else class_labels

     # set step values
     self.scheduler.set_timesteps(__A						)

     for t in self.progress_bar(self.scheduler.timesteps						):
       if guidance_scale > 1:
         UpperCAmelCase	:							Dict						=			latent_model_input[: len(__A						) // 2]
         UpperCAmelCase	:							Union[str, Any]						=			torch.cat([half, half],		dim=0						)
       UpperCAmelCase	:							Tuple						=			self.scheduler.scale_model_input(__A,		__A						)

       UpperCAmelCase	:							int						=			t
       if not torch.is_tensor(__A						):
         # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
         # This would be a good case for the `match` statement (Python 3.10+)
         UpperCAmelCase	:							Optional[Any]						=			latent_model_input.device.type == '''mps'''
         if isinstance(__A,		__A						):
           UpperCAmelCase	:							List[str]						=			torch.floataa if is_mps else torch.floataa
         else:
           UpperCAmelCase	:							Dict						=			torch.intaa if is_mps else torch.intaa
         UpperCAmelCase	:							Optional[Any]						=			torch.tensor([timesteps],		dtype=__A,		device=latent_model_input.device						)
       elif len(timesteps.shape						) == 0:
         UpperCAmelCase	:							Optional[int]						=			timesteps[None].to(latent_model_input.device						)
       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       UpperCAmelCase	:							Any						=			timesteps.expand(latent_model_input.shape[0]						)
       # predict noise model_output
       UpperCAmelCase	:							Any						=			self.transformer(
           __A,		timestep=__A,		class_labels=__A						).sample

       # perform guidance
       if guidance_scale > 1:
         UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
         UpperCAmelCase			,						UpperCAmelCase	:							str						=			torch.split(__A,		len(__A						) // 2,		dim=0						)

         UpperCAmelCase	:							Optional[Any]						=			uncond_eps + guidance_scale * (cond_eps - uncond_eps)
         UpperCAmelCase	:							Union[str, Any]						=			torch.cat([half_eps, half_eps],		dim=0						)

         UpperCAmelCase	:							Optional[Any]						=			torch.cat([eps, rest],		dim=1						)

       # learned sigma
       if self.transformer.config.out_channels // 2 == latent_channels:
         UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			torch.split(__A,		__A,		dim=1						)
       else:
         UpperCAmelCase	:							int						=			noise_pred

       # compute previous image: x_t -> x_t-1
       UpperCAmelCase	:							List[str]						=			self.scheduler.step(__A,		__A,		__A						).prev_sample

     if guidance_scale > 1:
       UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			latent_model_input.chunk(2,		dim=0						)
     else:
       UpperCAmelCase	:							int						=			latent_model_input

     UpperCAmelCase	:							List[Any]						=			1 / self.vae.config.scaling_factor * latents
     UpperCAmelCase	:							Dict						=			self.vae.decode(__A						).sample

     UpperCAmelCase	:							Optional[Any]						=			(samples / 2 + 0.5).clamp(0,		1						)

     # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
     UpperCAmelCase	:							Union[str, Any]						=			samples.cpu().permute(0,		2,		3,		1						).float().numpy()

     if output_type == "pil":
       UpperCAmelCase	:							List[str]						=			self.numpy_to_pil(__A						)

     if not return_dict:
       return (samples,)

     return ImagePipelineOutput(images=__A						)








from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

_lowerCamelCase    :       Dict        =							logging.get_logger(__name__)





@add_end_docstrings(lowerCamelCase__						)
class  __UpperCAmelCase					(	lowerCamelCase__						):



   def __init__(							self							:					Optional[Any],		*__A							:					Tuple,		**__A							:					Tuple						):
     super().__init__(*__A,		**__A						)
     self.check_model_type(__A						)



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					int=None,		__A							:					Tuple=None,		__A							:					Any=None,		**__A							:					Optional[int]						):
     UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			{}, {}
     if padding is not None:
       UpperCAmelCase	:							Optional[int]						=			padding
     if truncation is not None:
       UpperCAmelCase	:							Optional[int]						=			truncation
     if top_k is not None:
       UpperCAmelCase	:							Tuple						=			top_k
     return preprocess_params, {}, postprocess_params



   def __call__(							self							:					Union[str, Any],		__A							:					Union["Image.Image", str],		__A							:					str = None,		**__A							:					Optional[int]						):
     if isinstance(__A,		(Image.Image, str)						) and isinstance(__A,		__A						):
       UpperCAmelCase	:							int						=			{'''image''': image, '''question''': question}
     else:
       UpperCAmelCase	:							str						=			image
     UpperCAmelCase	:							Union[str, Any]						=			super().__call__(__A,		**__A						)
     return results



   def 	__magic_name__   (							self							:					List[str],		__A							:					Union[str, Any],		__A							:					Tuple=False,		__A							:					List[Any]=False						):
     UpperCAmelCase	:							int						=			load_image(inputs['''image''']						)
     UpperCAmelCase	:							List[str]						=			self.tokenizer(
         inputs['''question'''],		return_tensors=self.framework,		padding=__A,		truncation=__A						)
     UpperCAmelCase	:							Union[str, Any]						=			self.image_processor(images=__A,		return_tensors=self.framework						)
     model_inputs.update(__A						)
     return model_inputs



   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					List[Any]						):
     UpperCAmelCase	:							Optional[int]						=			self.model(**__A						)
     return model_outputs





   def 	__magic_name__   (							self							:					Any,		__A							:					List[str],		__A							:					Union[str, Any]=5						):
     if top_k > self.model.config.num_labels:
       UpperCAmelCase	:							Any						=			self.model.config.num_labels

     if self.framework == "pt":
       UpperCAmelCase	:							Any						=			model_outputs.logits.sigmoid()[0]
       UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			probs.topk(__A						)
     else:
       raise ValueError(F'''Unsupported framework: {self.framework}'''						)

     UpperCAmelCase	:							str						=			scores.tolist()
     UpperCAmelCase	:							Tuple						=			ids.tolist()
     return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__A,		__A						)]





