import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


a_					      = logging.getLogger(__name__)





def      __lowercase  (		lowerCamelCase      :  Any			):
 random.seed(lowerCamelCase			)
 np.random.seed(lowerCamelCase			)
 torch.manual_seed(lowerCamelCase			)
 torch.cuda.manual_seed_all(lowerCamelCase			)





def      __lowercase  (		lowerCamelCase      :  List[Any]						,						lowerCamelCase      :  List[str]						,						lowerCamelCase      :  Optional[Any]			):
 model.eval()
 UpperCamelCase_      :		Tuple				=      next(model.parameters()			).device
 UpperCamelCase_      :		Tuple				=      1
 UpperCamelCase_      :		Tuple				=      torch.zeros((eval_batch_size, context_len)						,						dtype=torch.long						,						device=lowerCamelCase			)
 UpperCamelCase_      :		int				=      DataLoader(lowerCamelCase						,						shuffle=lowerCamelCase						,						batch_size=lowerCamelCase			)
 UpperCamelCase_      :		Union[str, Any]				=      torch.zeros(1						,						device=lowerCamelCase			)
 UpperCamelCase_      :		Union[str, Any]				=      0
 for batch in eval_dataloader:
  batch.to(lowerCamelCase			)
  # pad
  context.zero_()
  for i in range(lowerCamelCase			):
   UpperCamelCase_      :		List[Any]				=      batch[i]
  UpperCamelCase_      :		Any				=      model(lowerCamelCase						,						labels=lowerCamelCase			)
  eval_loss += outputs[0].sum().item()
  nb_eval_examples += batch.size(0			)
 UpperCamelCase_      :		Optional[int]				=      eval_loss / nb_eval_examples
 UpperCamelCase_      :		Optional[Any]				=      torch.exp(lowerCamelCase			)
 model.train()
 return perplexity





def      __lowercase  (		lowerCamelCase      :  Dict="gpt2"			):
 UpperCamelCase_      :		Any				=      GPTaLMHeadModel.from_pretrained(lowerCamelCase						,						output_hidden_states=lowerCamelCase			)
 torch.save(model.state_dict()						,						model_name + 'local.pt'			)
 return model





def      __lowercase  (		lowerCamelCase      :  Optional[int]						,						lowerCamelCase      :  Union[str, Any]						,						lowerCamelCase      :  Dict			):
 UpperCamelCase_      :		Union[str, Any]				=      copy.deepcopy(lowerCamelCase			)
 model.to(lowerCamelCase			)

 UpperCamelCase_      :		str				=      ['bias', 'LayerNorm.weight']
 UpperCamelCase_      :		Tuple				=      [
     {
         'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay			)],
         'weight_decay': 0.0,
     },
     {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay			)], 'weight_decay': 0.0},
 ]
 UpperCamelCase_      :		int				=      AdamW(lowerCamelCase						,						lr=5e-5						,						eps=1e-8			)
 UpperCamelCase_      :		int				=      get_linear_schedule_with_warmup(lowerCamelCase						,						0						,						lowerCamelCase			)
 torch.cuda.empty_cache()
 return model, lm_optimizer, lm_scheduler





def      __lowercase  (		lowerCamelCase      :  Tuple						,						lowerCamelCase      :  Optional[Any]						,						lowerCamelCase      :  Dict						,						lowerCamelCase      :  Union[str, Any]			):
 UpperCamelCase_      :		int				=      np.array(lowerCamelCase			).mean()
 UpperCamelCase_      :		str				=      np.array(lowerCamelCase			).std()
 UpperCamelCase_      :		Optional[Any]				=      (real_perps - avg) / std
 UpperCamelCase_      :		Optional[int]				=      list(zip(lowerCamelCase						,						lowerCamelCase						,						lowerCamelCase			)			)
 joblib.dump(lowerCamelCase						,						lowerCamelCase			)





def      __lowercase  (		lowerCamelCase      :  Any						,						lowerCamelCase      :  List[Any]						,						lowerCamelCase      :  List[str]						,						lowerCamelCase      :  int						,						lowerCamelCase      :  Optional[int]						,						lowerCamelCase      :  Dict						,						lowerCamelCase      :  Tuple						,						lowerCamelCase      :  Tuple="dev.jbl"						,						lowerCamelCase      :  Union[str, Any]=recopy_gpta						,						):
 UpperCamelCase_      :		Optional[Any]				=      []
 UpperCamelCase_      :		Optional[int]				=      []
 UpperCamelCase_      :		Any				=      []

 # Initialize the transformer model
 UpperCamelCase_      :		Tuple				=      copy.deepcopy(lowerCamelCase			)
 orig_model.to(device='cpu'			)
 torch.cuda.empty_cache()

 # Compute perplexity of initial transformer model for comparison
 model.train()
 UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_      :		Any				=      recopy_model(lowerCamelCase						,						lowerCamelCase						,						lowerCamelCase			)

 for step in tqdm(range(lowerCamelCase			)			):
  UpperCamelCase_      :		int				=      torch.zeros((1, context_len)						,						dtype=torch.long						,						device=lowerCamelCase			)
  UpperCamelCase_      :		str				=      random.choice(lowerCamelCase			)
  UpperCamelCase_      :		List[str]				=      random.randint(0						,						len(story[0]			) - context_len - 1			)
  UpperCamelCase_      :		List[str]				=      story[0][start : start + context_len]
  lm_optimizer.zero_grad()
  UpperCamelCase_      :		Tuple				=      model(lowerCamelCase						,						labels=lowerCamelCase			)
  UpperCamelCase_      :		Dict				=      outputs[0]
  UpperCamelCase_      :		Union[str, Any]				=      compute_perplexity(lowerCamelCase						,						lowerCamelCase						,						lowerCamelCase			)
  model.train()
  lm_loss.backward()
  # Do LM backprop
  torch.nn.utils.clip_grad_norm_(model.parameters()						,						3.0			)
  lm_optimizer.step()
  lm_scheduler.step()  # Update learning rate schedule

  # Compute perplexity after back-propagating on the selected context
  UpperCamelCase_      :		List[str]				=      compute_perplexity(lowerCamelCase						,						lowerCamelCase						,						lowerCamelCase			)

  # Periodically save the stored (X, IG(X)) pairs
  if step % 1000 == 0 and step > 1:
   intermittent_save(lowerCamelCase						,						lowerCamelCase						,						lowerCamelCase						,						lowerCamelCase			)

  # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
  UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_      :		Any				=      recopy_model(lowerCamelCase						,						lowerCamelCase						,						lowerCamelCase			)

  past_perps.append(past_perp.item()			)
  real_perps.append(orig_perp - real_perp.item()			)
  contexts.append(np.array(context.cpu()			)			)

 intermittent_save(lowerCamelCase						,						lowerCamelCase						,						lowerCamelCase						,						lowerCamelCase			)





def      __lowercase  (		lowerCamelCase      :  Any						,						lowerCamelCase      :  Dict="data/tokenized_stories_train_wikitext103.jbl"						,						lowerCamelCase      :  Optional[int]=100						,						lowerCamelCase      :  List[str]=1026						,						lowerCamelCase      :  Optional[int]=True			):
 UpperCamelCase_      :		Optional[int]				=      joblib.load(lowerCamelCase			)
 print('data loaded'			)
 UpperCamelCase_      :		Dict				=      []
 if trim:
  for i, example in enumerate(lowerCamelCase			):
   if len(example[0]			) > min_len:
    UpperCamelCase_      :		int				=      random.randint(0						,						len(example[0]			) - context_len - 1			)
    objective_set.append(example[0, start : start + context_len]			)
   if len(lowerCamelCase			) >= number:
    break
  UpperCamelCase_      :		Optional[Any]				=      []
  for j in range(i + 1						,						len(lowerCamelCase			)			):
   if len(data[j][0]			) > min_len:
    train_data.append(data[j]			)
 else:
  UpperCamelCase_      :		str				=      data[0:number]
  UpperCamelCase_      :		Any				=      data[number:]

 joblib.dump(lowerCamelCase						,						'objective_set.jbl'			)
 print('objective set saved'			)
 return train_data, objective_set





def      __lowercase  (		lowerCamelCase      :  str						,						lowerCamelCase      :  int						,						lowerCamelCase      :  Union[str, Any]						,						lowerCamelCase      :  List[Any]						,						lowerCamelCase      :  Dict=50						,						lowerCamelCase      :  Optional[int]="secondary_learner.pt"			):
 UpperCamelCase_      :		Optional[int]				=      torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'			)
 # We will use the first 512 pairs from our dataset as a test set for
 # our secondary learner and the rest to train
 UpperCamelCase_      :		Tuple				=      train_dataset[:512]
 UpperCamelCase_      :		Union[str, Any]				=      train_dataset[512:]
 UpperCamelCase_      :		Union[str, Any]				=      DataLoader(lowerCamelCase						,						shuffle=lowerCamelCase						,						batch_size=lowerCamelCase			)
 UpperCamelCase_      :		Tuple				=      DataLoader(lowerCamelCase						,						shuffle=lowerCamelCase						,						batch_size=lowerCamelCase			)

 # secondary learner model set up
 UpperCamelCase_      :		List[str]				=      nn.MSELoss()
 UpperCamelCase_      :		List[str]				=      nn.MSELoss(reduction='sum'			)
 secondary_learner.to(lowerCamelCase			)
 UpperCamelCase_      :		List[Any]				=      torch.optim.Adam(secondary_learner.parameters()						,						lr=0.0_0_0_0_1			)
 secondary_learner.train()

 # TODO in original code this is written as number of actual batches seen
 # not number of items seen but other places it is number of items instead.
 # improve consistency! changed this to epochs for clarity
 UpperCamelCase_      :		Optional[Any]				=      float('inf'			)
 # Iterate through batches until we've used max_steps batches
 for epoch in range(int(lowerCamelCase			)			):
  UpperCamelCase_      :		Tuple				=      0.0
  secondary_learner.train()
  for step, batch in enumerate(lowerCamelCase			):
   UpperCamelCase_      :		Optional[int]				=      batch[0].to(lowerCamelCase			)
   UpperCamelCase_      :		Union[str, Any]				=      batch[1].to(lowerCamelCase			)
   UpperCamelCase_      :		Dict				=      secondary_learner(lowerCamelCase			)
   q_optimizer.zero_grad()
   UpperCamelCase_      :		Tuple				=      loss(lowerCamelCase						,						real_q.float()			)
   q_loss.backward()
   q_optimizer.step()
   tr_q_loss += q_loss.item()

   # model trains fairly quickly so we won't wait for a full epoch
   # eval is triggered at eval_freq and end of epochs
   if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(lowerCamelCase			)):
    UpperCamelCase_      :		Dict				=      tr_q_loss / (step + 1)

    secondary_learner.eval()
    UpperCamelCase_      :		Any				=      0.0
    UpperCamelCase_      :		Union[str, Any]				=      0.0
    UpperCamelCase_      :		Union[str, Any]				=      []
    UpperCamelCase_      :		str				=      []
    # Compute performance of the secondary learner after this batch
    for stepa, batcha in enumerate(lowerCamelCase			):
     UpperCamelCase_      :		List[str]				=      batcha[0].to(lowerCamelCase			)
     UpperCamelCase_      :		str				=      batcha[1].to(lowerCamelCase			)
     UpperCamelCase_      :		Optional[int]				=      secondary_learner(lowerCamelCase			)
     q_lossa += test_loss(lowerCamelCase						,						lowerCamelCase			).item()
     sum_qa += torch.sum(lowerCamelCase			).item()
     for ei, i in enumerate(predicted_qa.cpu().detach().numpy()			):
      predicted.append(i.item()			)
     for ei, i in enumerate(real_qa.cpu().detach().numpy()			):
      actual.append(i.item()			)

    q_lossa /= len(lowerCamelCase			)
    print(
        'Epoch: '						,						lowerCamelCase						,						'step: '						,						lowerCamelCase						,						'Avg. q:'						,						sum_qa / len(lowerCamelCase			)						,						'Train Loss: '						,						lowerCamelCase						,						'Test Loss: '						,						lowerCamelCase						,						)
    if q_lossa < best_test_loss:
     joblib.dump((predicted, actual)						,						'pred_vs_actual.jbl'			)
     torch.save(secondary_learner.state_dict()						,						lowerCamelCase			)
     UpperCamelCase_      :		int				=      q_lossa

   secondary_learner.train()
 return secondary_learner






class 	_lowercase		(      nn.Module ):

 def __init__(				self			:							Optional[int]     ,   snake_case			:							Optional[Any]    )     ->		str:

  """simple docstring"""





  super(snake_case     ,   self    ).__init__()
  UpperCamelCase_      :		Optional[Any]				=      model.transformer.wte
  UpperCamelCase_      :		List[Any]				=      copy.deepcopy(model.transformer.wte.weight    )
  UpperCamelCase_      :		Dict				=      nn.Convad(self.embeddings.weight.size(1    )     ,   2_5_6     ,   3     ,   padding=1    )
  UpperCamelCase_      :		str				=      nn.Sequential(nn.Linear(2_5_6     ,   3_2    )     ,   nn.Dropout(p=0.1    )     ,   nn.Linear(3_2     ,   3_2    )     ,   nn.Linear(3_2     ,   1    )    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]     ,   snake_case			:							Any    )     ->		List[str]:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      torch.max(self.conv(self.embeddings(snake_case    ).squeeze(1    ).transpose(1     ,   2    )    )     ,   2    )[0]
  UpperCamelCase_      :		str				=      self.fc(snake_case    )
  return qs.squeeze(1    )


 @classmethod
 def 		SCREAMING_SNAKE_CASE__     (				cls			:							Tuple     ,   snake_case			:							Optional[int]     ,   snake_case			:							Any    )     ->		Optional[Any]:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      cls(snake_case    )  # this calls __init__
  UpperCamelCase_      :		Tuple				=      torch.load(snake_case    )
  secondary_learner.load_state_dict(snake_case    )
  UpperCamelCase_      :		List[str]				=      model.transformer.wte
  UpperCamelCase_      :		List[Any]				=      copy.deepcopy(model.transformer.wte.weight    )
  return secondary_learner




from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

a_					      = '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'





def      __lowercase  (		lowerCamelCase      :  str						,						lowerCamelCase      :  List[str]						,						lowerCamelCase      :  str=8			):
 UpperCamelCase_      :		Union[str, Any]				=      height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 UpperCamelCase_      :		List[str]				=      width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor





def      __lowercase  (		lowerCamelCase      :  Optional[Any]						,						lowerCamelCase      :  Optional[int]=512						,						lowerCamelCase      :  str=512			):
 UpperCamelCase_      :		Any				=      pil_image.resize((w, h)						,						resample=Image.BICUBIC						,						reducing_gap=1			)
 UpperCamelCase_      :		Tuple				=      np.array(pil_image.convert('RGB'			)			)
 UpperCamelCase_      :		Optional[int]				=      arr.astype(np.floataa			) / 1_2_7.5 - 1
 UpperCamelCase_      :		int				=      np.transpose(lowerCamelCase						,						[2, 0, 1]			)
 UpperCamelCase_      :		List[Any]				=      torch.from_numpy(lowerCamelCase			).unsqueeze(0			)
 return image






class 	_lowercase		(      snake_case_ ):

 def __init__(				self			:							Union[str, Any]     ,   snake_case			:							UNetaDConditionModel     ,   snake_case			:							DDPMScheduler     ,   snake_case			:							VQModel     ,   )     ->		str:

  """simple docstring"""





  super().__init__()

  self.register_modules(
      unet=snake_case     ,   scheduler=snake_case     ,   movq=snake_case     ,   )
  UpperCamelCase_      :		Optional[Any]				=      2 ** (len(self.movq.config.block_out_channels    ) - 1)

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							str     ,   snake_case			:							List[Any]     ,   snake_case			:							int    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      min(int(num_inference_steps * strength    )     ,   snake_case    )

  UpperCamelCase_      :		Optional[Any]				=      max(num_inference_steps - init_timestep     ,   0    )
  UpperCamelCase_      :		Union[str, Any]				=      self.scheduler.timesteps[t_start:]

  return timesteps, num_inference_steps - t_start

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							int     ,   snake_case			:							Union[str, Any]     ,   snake_case			:							List[str]     ,   snake_case			:							str     ,   snake_case			:							Optional[int]     ,   snake_case			:							Dict     ,   snake_case			:							List[Any]=None    )     ->		Union[str, Any]:

  """simple docstring"""





  if not isinstance(snake_case     ,   (torch.Tensor, PIL.Image.Image, list)    ):
   raise ValueError(
       f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(snake_case    )}"    )

  UpperCamelCase_      :		Union[str, Any]				=      image.to(device=snake_case     ,   dtype=snake_case    )

  UpperCamelCase_      :		Any				=      batch_size * num_images_per_prompt

  if image.shape[1] == 4:
   UpperCamelCase_      :		List[Any]				=      image

  else:
   if isinstance(snake_case     ,   snake_case    ) and len(snake_case    ) != batch_size:
    raise ValueError(
        f"You have passed a list of generators of length {len(snake_case    )}, but requested an effective batch"
        f" size of {batch_size}. Make sure the batch size matches the length of the generators."    )

   elif isinstance(snake_case     ,   snake_case    ):
    UpperCamelCase_      :		Any				=      [
        self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(snake_case    )
    ]
    UpperCamelCase_      :		Union[str, Any]				=      torch.cat(snake_case     ,   dim=0    )
   else:
    UpperCamelCase_      :		Optional[Any]				=      self.movq.encode(snake_case    ).latent_dist.sample(snake_case    )

   UpperCamelCase_      :		List[Any]				=      self.movq.config.scaling_factor * init_latents

  UpperCamelCase_      :		List[str]				=      torch.cat([init_latents]     ,   dim=0    )

  UpperCamelCase_      :		Optional[Any]				=      init_latents.shape
  UpperCamelCase_      :		Optional[int]				=      randn_tensor(snake_case     ,   generator=snake_case     ,   device=snake_case     ,   dtype=snake_case    )

  # get latents
  UpperCamelCase_      :		Union[str, Any]				=      self.scheduler.add_noise(snake_case     ,   snake_case     ,   snake_case    )

  UpperCamelCase_      :		Optional[Any]				=      init_latents

  return latents

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]     ,   snake_case			:							Dict=0    )     ->		Optional[Any]:

  """simple docstring"""





  if is_accelerate_available():
   from accelerate import cpu_offload
  else:
   raise ImportError('Please install accelerate via `pip install accelerate`'    )

  UpperCamelCase_      :		str				=      torch.device(f"cuda:{gpu_id}"    )

  UpperCamelCase_      :		Dict				=      [
      self.unet,
      self.movq,
  ]
  for cpu_offloaded_model in models:
   if cpu_offloaded_model is not None:
    cpu_offload(snake_case     ,   snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							Tuple=0    )     ->		Optional[Any]:

  """simple docstring"""





  if is_accelerate_available() and is_accelerate_version('>='     ,   '0.17.0.dev0'    ):
   from accelerate import cpu_offload_with_hook
  else:
   raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'    )

  UpperCamelCase_      :		Any				=      torch.device(f"cuda:{gpu_id}"    )

  if self.device.type != "cpu":
   self.to('cpu'     ,   silence_dtype_warnings=snake_case    )
   torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

  UpperCamelCase_      :		Any				=      None
  for cpu_offloaded_model in [self.unet, self.movq]:
   UpperCamelCase_,				UpperCamelCase_      :		Any				=      cpu_offload_with_hook(snake_case     ,   snake_case     ,   prev_module_hook=snake_case    )

  # We'll offload the last model manually.
  UpperCamelCase_      :		Optional[int]				=      hook

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		Optional[Any]:

  """simple docstring"""





  if not hasattr(self.unet     ,   '_hf_hook'    ):
   return self.device
  for module in self.unet.modules():
   if (
       hasattr(snake_case     ,   '_hf_hook'    )
       and hasattr(module._hf_hook     ,   'execution_device'    )
       and module._hf_hook.execution_device is not None
   ):
    return torch.device(module._hf_hook.execution_device    )
  return self.device


 @torch.no_grad()
 @replace_example_docstring(snake_case    )
 def __call__(				self			:							Optional[int]     ,   snake_case			:							Union[torch.FloatTensor, List[torch.FloatTensor]]     ,   snake_case			:							Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]     ,   snake_case			:							Union[torch.FloatTensor, List[torch.FloatTensor]]     ,   snake_case			:							torch.FloatTensor     ,   snake_case			:							int = 5_1_2     ,   snake_case			:							int = 5_1_2     ,   snake_case			:							int = 1_0_0     ,   snake_case			:							float = 4.0     ,   snake_case			:							float = 0.3     ,   snake_case			:							int = 1     ,   snake_case			:							Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,   snake_case			:							Optional[str] = "pil"     ,   snake_case			:							bool = True     ,   )     ->		Any:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      self._execution_device

  UpperCamelCase_      :		str				=      guidance_scale > 1.0

  if isinstance(snake_case     ,   snake_case    ):
   UpperCamelCase_      :		Dict				=      torch.cat(snake_case     ,   dim=0    )
  if isinstance(snake_case     ,   snake_case    ):
   UpperCamelCase_      :		Optional[int]				=      torch.cat(snake_case     ,   dim=0    )
  if isinstance(snake_case     ,   snake_case    ):
   UpperCamelCase_      :		Dict				=      torch.cat(snake_case     ,   dim=0    )

  UpperCamelCase_      :		int				=      image_embeds.shape[0]

  if do_classifier_free_guidance:
   UpperCamelCase_      :		Optional[Any]				=      image_embeds.repeat_interleave(snake_case     ,   dim=0    )
   UpperCamelCase_      :		Any				=      negative_image_embeds.repeat_interleave(snake_case     ,   dim=0    )
   UpperCamelCase_      :		Tuple				=      hint.repeat_interleave(snake_case     ,   dim=0    )

  UpperCamelCase_      :		int				=      torch.cat([negative_image_embeds, image_embeds]     ,   dim=0    ).to(dtype=self.unet.dtype     ,   device=snake_case    )
  UpperCamelCase_      :		Union[str, Any]				=      torch.cat([hint, hint]     ,   dim=0    ).to(dtype=self.unet.dtype     ,   device=snake_case    )

  if not isinstance(snake_case     ,   snake_case    ):
   UpperCamelCase_      :		Optional[Any]				=      [image]
  if not all(isinstance(snake_case     ,   (PIL.Image.Image, torch.Tensor)    ) for i in image    ):
   raise ValueError(
       f"Input is in incorrect format: {[type(snake_case    ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"    )

  UpperCamelCase_      :		Union[str, Any]				=      torch.cat([prepare_image(snake_case     ,   snake_case     ,   snake_case    ) for i in image]     ,   dim=0    )
  UpperCamelCase_      :		Dict				=      image.to(dtype=image_embeds.dtype     ,   device=snake_case    )

  UpperCamelCase_      :		List[Any]				=      self.movq.encode(snake_case    )['latents']
  UpperCamelCase_      :		str				=      latents.repeat_interleave(snake_case     ,   dim=0    )
  self.scheduler.set_timesteps(snake_case     ,   device=snake_case    )
  UpperCamelCase_,				UpperCamelCase_      :		int				=      self.get_timesteps(snake_case     ,   snake_case     ,   snake_case    )
  UpperCamelCase_      :		Any				=      timesteps[:1].repeat(batch_size * num_images_per_prompt    )
  UpperCamelCase_,				UpperCamelCase_      :		Optional[Any]				=      downscale_height_and_width(snake_case     ,   snake_case     ,   self.movq_scale_factor    )
  UpperCamelCase_      :		List[Any]				=      self.prepare_latents(
      snake_case     ,   snake_case     ,   snake_case     ,   snake_case     ,   image_embeds.dtype     ,   snake_case     ,   snake_case    )
  for i, t in enumerate(self.progress_bar(snake_case    )    ):
   # expand the latents if we are doing classifier free guidance
   UpperCamelCase_      :		int				=      torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

   UpperCamelCase_      :		Optional[Any]				=      {'image_embeds': image_embeds, 'hint': hint}
   UpperCamelCase_      :		Optional[Any]				=      self.unet(
       sample=snake_case     ,   timestep=snake_case     ,   encoder_hidden_states=snake_case     ,   added_cond_kwargs=snake_case     ,   return_dict=snake_case     ,   )[0]

   if do_classifier_free_guidance:
    UpperCamelCase_,				UpperCamelCase_      :		Dict				=      noise_pred.split(latents.shape[1]     ,   dim=1    )
    UpperCamelCase_,				UpperCamelCase_      :		List[Any]				=      noise_pred.chunk(2    )
    UpperCamelCase_,				UpperCamelCase_      :		Union[str, Any]				=      variance_pred.chunk(2    )
    UpperCamelCase_      :		Union[str, Any]				=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
    UpperCamelCase_      :		Tuple				=      torch.cat([noise_pred, variance_pred_text]     ,   dim=1    )

   if not (
       hasattr(self.scheduler.config     ,   'variance_type'    )
       and self.scheduler.config.variance_type in ["learned", "learned_range"]
   ):
    UpperCamelCase_,				UpperCamelCase_      :		int				=      noise_pred.split(latents.shape[1]     ,   dim=1    )

   # compute the previous noisy sample x_t -> x_t-1

   UpperCamelCase_      :		List[Any]				=      self.scheduler.step(
       snake_case     ,   snake_case     ,   snake_case     ,   generator=snake_case     ,   )[0]

  # post-processing
  UpperCamelCase_      :		Any				=      self.movq.decode(snake_case     ,   force_not_quantize=snake_case    )['sample']

  if output_type not in ["pt", "np", "pil"]:
   raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"    )

  if output_type in ["np", "pil"]:
   UpperCamelCase_      :		List[Any]				=      image * 0.5 + 0.5
   UpperCamelCase_      :		Optional[int]				=      image.clamp(0     ,   1    )
   UpperCamelCase_      :		Dict				=      image.cpu().permute(0     ,   2     ,   3     ,   1    ).float().numpy()

  if output_type == "pil":
   UpperCamelCase_      :		Optional[Any]				=      self.numpy_to_pil(snake_case    )

  if not return_dict:
   return (image,)

  return ImagePipelineOutput(images=snake_case    )



