



'''simple docstring'''





import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


lowercase_									=    logging.getLogger(__name__)




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     if os.path.exists(__A):
          if os.path.exists(os.path.join(__A    ,      '''config.json''')) and os.path.isfile(
              os.path.join(__A    ,      '''config.json''')):
               os.remove(os.path.join(__A    ,      '''config.json'''))
          if os.path.exists(os.path.join(__A    ,      '''pytorch_model.bin''')) and os.path.isfile(
              os.path.join(__A    ,      '''pytorch_model.bin''')):
               os.remove(os.path.join(__A    ,      '''pytorch_model.bin'''))
     else:
          os.makedirs(__A)
     model.save_pretrained(__A)




def lowerCAmelCase    (__A    ,      __A=False):



     """simple docstring"""



     _a           =    2
     if unlogit:
          _a           =    torch.pow(__A    ,      __A)
     _a           =    p * torch.log(__A)
     _a           =    0
     return -plogp.sum(dim=-1)




def lowerCAmelCase    (__A):



     """simple docstring"""



     logger.info('''lv, h >\t''' + '''\t'''.join(F'''{x + 1}''' for x in range(len(__A))))
     for row in range(len(__A)):
          if tensor.dtype != torch.long:
               logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:.5f}''' for x in tensor[row].cpu().data))
          else:
               logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:d}''' for x in tensor[row].cpu().data))




def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A=True    ,      __A=True    ,      __A=None    ,      __A=False):



     """simple docstring"""



     _a    ,       _a           =    model.config.num_hidden_layers, model.config.num_attention_heads
     _a           =    torch.zeros(__A    ,      __A).to(args.device)
     _a           =    torch.zeros(__A    ,      __A).to(args.device)

     if head_mask is None:
          _a           =    torch.ones(__A    ,      __A).to(args.device)

     head_mask.requires_grad_(requires_grad=__A)
     # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
     if actually_pruned:
          _a           =    None

     _a           =    0.0
     _a           =    0.0
     for step, inputs in enumerate(tqdm(__A    ,      desc='''Iteration'''    ,      disable=args.local_rank not in [-1, 0])):
          _a           =    tuple(t.to(args.device) for t in inputs)
          ((_a)    ,       )           =    inputs

          # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
          _a           =    model(__A    ,      labels=__A    ,      head_mask=__A)
          #  (loss), lm_logits, presents, (all hidden_states), (attentions)
          _a    ,       _a    ,       _a           =    (
              outputs[0],
              outputs[1],
              outputs[-1],
          )  # Loss and logits are the first, attention the last
          loss.backward()  # Backpropagate to populate the gradients in the head mask
          total_loss += loss.detach().cpu().numpy()
          if compute_entropy:
               for layer, attn in enumerate(__A):
                    _a           =    entropy(attn.detach()    ,      __A)
                    attn_entropy[layer] += masked_entropy.sum(-1).sum(0).sum(0).detach()

          if compute_importance:
               head_importance += head_mask.grad.abs().detach()
          tot_tokens += torch.ones_like(__A).float().detach().sum().data

     # Normalize
     attn_entropy /= tot_tokens
     head_importance /= tot_tokens
     # Layerwise importance normalization
     if not args.dont_normalize_importance_by_layer:
          _a           =    2
          _a           =    torch.pow(torch.pow(__A    ,      __A).sum(-1)    ,      1 / exponent)
          head_importance /= norm_by_layer.unsqueeze(-1) + 1e-20

     if not args.dont_normalize_global_importance:
          _a           =    (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

     # Print matrices
     if compute_entropy:
          logger.info('''Attention entropies''')
          print_ad_tensor(__A)
     if compute_importance:
          logger.info('''Head importance scores''')
          print_ad_tensor(__A)
     logger.info('''Head ranked by importance scores''')
     _a           =    torch.zeros(head_importance.numel()    ,      dtype=torch.long    ,      device=args.device)
     _a           =    torch.arange(
         head_importance.numel()    ,      device=args.device)
     _a           =    head_ranks.view_as(__A)
     print_ad_tensor(__A)
     return attn_entropy, head_importance, total_loss




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a    ,       _a    ,       _a           =    compute_heads_importance(__A    ,      __A    ,      __A    ,      compute_entropy=__A)
     _a           =    1 / loss  # instead of downsteam score use the LM loss
     logger.info('''Pruning: original score: %f, threshold: %f'''    ,      __A    ,      original_score * args.masking_threshold)

     _a           =    torch.ones_like(__A)
     _a           =    max(1    ,      int(new_head_mask.numel() * args.masking_amount))

     _a           =    original_score
     while current_score >= original_score * args.masking_threshold:
          _a           =    new_head_mask.clone().detach()  # save current head mask
          # heads from least important to most - keep only not-masked heads
          _a           =    float('''Inf''')
          _a           =    head_importance.view(-1).sort()[1]

          if len(__A) <= num_to_mask:
               print('''BREAK BY num_to_mask''')
               break

          # mask heads
          _a           =    current_heads_to_mask[:num_to_mask]
          logger.info('''Heads to mask: %s'''    ,      str(current_heads_to_mask.tolist()))
          _a           =    new_head_mask.view(-1)
          _a           =    0.0
          _a           =    new_head_mask.view_as(__A)
          _a           =    new_head_mask.clone().detach()
          print_ad_tensor(__A)

          # Compute metric and head importance again
          _a    ,       _a    ,       _a           =    compute_heads_importance(
              __A    ,      __A    ,      __A    ,      compute_entropy=__A    ,      head_mask=__A)
          _a           =    1 / loss
          logger.info(
              '''Masking: current score: %f, remaining heads %d (%.1f percents)'''    ,      __A    ,      new_head_mask.sum()    ,      new_head_mask.sum() / new_head_mask.numel() * 100    ,      )

     logger.info('''Final head mask''')
     print_ad_tensor(__A)
     np.save(os.path.join(args.output_dir    ,      '''head_mask.npy''')    ,      head_mask.detach().cpu().numpy())

     return head_mask




def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    datetime.now()
     _a    ,       _a    ,       _a           =    compute_heads_importance(
         __A    ,      __A    ,      __A    ,      compute_entropy=__A    ,      compute_importance=__A    ,      head_mask=__A)
     _a           =    1 / loss
     _a           =    datetime.now() - before_time

     _a           =    sum(p.numel() for p in model.parameters())
     _a           =    {
         layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__A))
     }

     for k, v in heads_to_prune.items():
          if isinstance(__A    ,      __A):
               _a           =    [
                   v,
               ]

     assert sum(len(__A) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
     model.prune_heads(__A)
     _a           =    sum(p.numel() for p in model.parameters())

     _a           =    datetime.now()
     _a    ,       _a    ,       _a           =    compute_heads_importance(
         __A    ,      __A    ,      __A    ,      compute_entropy=__A    ,      compute_importance=__A    ,      head_mask=__A    ,      actually_pruned=__A    ,      )

     _a           =    1 / loss
     _a           =    datetime.now() - before_time

     logger.info(
         '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''    ,      __A    ,      __A    ,      pruned_num_params / original_num_params * 100    ,      )
     logger.info('''Pruning: score with masking: %f score with pruning: %f'''    ,      __A    ,      __A)
     logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''    ,      original_time / new_time * 100)
     save_model(__A    ,      args.output_dir)




def lowerCAmelCase    ():



     """simple docstring"""



     _a           =    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--data_dir'''    ,      default=__A    ,      type=__A    ,      required=__A    ,      help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''    ,      )
     parser.add_argument(
         '''--model_name_or_path'''    ,      default=__A    ,      type=__A    ,      required=__A    ,      help='''Path to pretrained model or model identifier from huggingface.co/models'''    ,      )
     parser.add_argument(
         '''--output_dir'''    ,      default=__A    ,      type=__A    ,      required=__A    ,      help='''The output directory where the model predictions and checkpoints will be written.'''    ,      )

     # Other parameters
     parser.add_argument(
         '''--config_name'''    ,      default=''''''    ,      type=__A    ,      help='''Pretrained config name or path if not the same as model_name_or_path'''    ,      )
     parser.add_argument(
         '''--tokenizer_name'''    ,      default=''''''    ,      type=__A    ,      help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''    ,      )
     parser.add_argument(
         '''--cache_dir'''    ,      default=__A    ,      type=__A    ,      help='''Where do you want to store the pre-trained models downloaded from s3'''    ,      )
     parser.add_argument(
         '''--data_subset'''    ,      type=__A    ,      default=-1    ,      help='''If > 0: limit the data to a subset of data_subset instances.''')
     parser.add_argument(
         '''--overwrite_output_dir'''    ,      action='''store_true'''    ,      help='''Whether to overwrite data in output directory''')
     parser.add_argument(
         '''--overwrite_cache'''    ,      action='''store_true'''    ,      help='''Overwrite the cached training and evaluation sets''')

     parser.add_argument(
         '''--dont_normalize_importance_by_layer'''    ,      action='''store_true'''    ,      help='''Don\'t normalize importance score by layers''')
     parser.add_argument(
         '''--dont_normalize_global_importance'''    ,      action='''store_true'''    ,      help='''Don\'t normalize all importance scores between 0 and 1'''    ,      )

     parser.add_argument(
         '''--try_masking'''    ,      action='''store_true'''    ,      help='''Whether to try to mask head until a threshold of accuracy.''')
     parser.add_argument(
         '''--masking_threshold'''    ,      default=0.9    ,      type=__A    ,      help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''    ,      )
     parser.add_argument(
         '''--masking_amount'''    ,      default=0.1    ,      type=__A    ,      help='''Amount to heads to masking at each masking step.''')
     parser.add_argument('''--metric_name'''    ,      default='''acc'''    ,      type=__A    ,      help='''Metric to use for head masking.''')

     parser.add_argument(
         '''--max_seq_length'''    ,      default=128    ,      type=__A    ,      help=(
             '''The maximum total input sequence length after WordPiece tokenization. \n'''
             '''Sequences longer than this will be truncated, sequences shorter padded.'''
         )    ,      )
     parser.add_argument('''--batch_size'''    ,      default=1    ,      type=__A    ,      help='''Batch size.''')

     parser.add_argument('''--seed'''    ,      type=__A    ,      default=42)
     parser.add_argument('''--local_rank'''    ,      type=__A    ,      default=-1    ,      help='''local_rank for distributed training on gpus''')
     parser.add_argument('''--no_cuda'''    ,      action='''store_true'''    ,      help='''Whether not to use CUDA when available''')
     parser.add_argument('''--server_ip'''    ,      type=__A    ,      default=''''''    ,      help='''Can be used for distant debugging.''')
     parser.add_argument('''--server_port'''    ,      type=__A    ,      default=''''''    ,      help='''Can be used for distant debugging.''')
     _a           =    parser.parse_args()

     if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd

          print('''Waiting for debugger attach''')
          ptvsd.enable_attach(address=(args.server_ip, args.server_port)    ,      redirect_output=__A)
          ptvsd.wait_for_attach()

     # Setup devices and distributed training
     if args.local_rank == -1 or args.no_cuda:
          _a           =    torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
          _a           =    0 if args.no_cuda else torch.cuda.device_count()
     else:
          torch.cuda.set_device(args.local_rank)
          _a           =    torch.device('''cuda'''    ,      args.local_rank)
          _a           =    1
          torch.distributed.init_process_group(backend='''nccl''')  # Initializes the distributed backend

     # Setup logging
     logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
     logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device    ,      args.n_gpu    ,      bool(args.local_rank != -1)))

     _a           =    GPTaLMHeadModel.from_pretrained(args.model_name_or_path)

     # Distributed and parallel training
     model.to(args.device)
     if args.local_rank != -1:
          _a           =    nn.parallel.DistributedDataParallel(
              __A    ,      device_ids=[args.local_rank]    ,      output_device=args.local_rank    ,      find_unused_parameters=__A)
     elif args.n_gpu > 1:
          _a           =    nn.DataParallel(__A)

     # Print/save training arguments
     os.makedirs(args.output_dir    ,      exist_ok=__A)
     torch.save(__A    ,      os.path.join(args.output_dir    ,      '''run_args.bin'''))
     logger.info('''Training/evaluation parameters %s'''    ,      __A)

     # Prepare dataset
     _a           =    np.concatenate(
         [
             np.loadtxt(args.data_dir    ,      dtype=np.intaa),
         ])
     _a           =    (torch.from_numpy(__A),)
     _a           =    TensorDataset(*__A)
     _a           =    RandomSampler(__A)
     _a           =    DataLoader(__A    ,      sampler=__A    ,      batch_size=args.batch_size)

     # Compute head entropy and importance score
     compute_heads_importance(__A    ,      __A    ,      __A)

     # Try head masking (set heads to zero until the score goes under a threshole)
     # and head pruning (remove masked heads and see the effect on the network)
     if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
          _a           =    mask_heads(__A    ,      __A    ,      __A)
          prune_heads(__A    ,      __A    ,      __A    ,      __A)


if __name__ == "__main__":
  main()








'''simple docstring'''





import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     if isinstance(__A    ,      torch.Tensor):
          return image
     elif isinstance(__A    ,      PIL.Image.Image):
          _a           =    [image]

     if isinstance(image[0]    ,      PIL.Image.Image):
          _a           =    [np.array(i.resize((w, h)    ,      resample=PIL_INTERPOLATION['''lanczos''']))[None, :] for i in image]
          _a           =    np.concatenate(__A    ,      axis=0)
          _a           =    np.array(__A).astype(np.floataa) / 2_55.0
          _a           =    image.transpose(0    ,      3    ,      1    ,      2)
          _a           =    2.0 * image - 1.0
          _a           =    torch.from_numpy(__A)
     elif isinstance(image[0]    ,      torch.Tensor):
          _a           =    torch.cat(__A    ,      dim=0)
     return image




def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A=0.99_95):



     """simple docstring"""



     if not isinstance(__A    ,      np.ndarray):
          _a           =    True
          _a           =    va.device
          _a           =    va.cpu().numpy()
          _a           =    va.cpu().numpy()

     _a           =    np.sum(va * va / (np.linalg.norm(__A) * np.linalg.norm(__A)))
     if np.abs(__A) > DOT_THRESHOLD:
          _a           =    (1 - t) * va + t * va
     else:
          _a           =    np.arccos(__A)
          _a           =    np.sin(__A)
          _a           =    theta_a * t
          _a           =    np.sin(__A)
          _a           =    np.sin(theta_a - theta_t) / sin_theta_a
          _a           =    sin_theta_t / sin_theta_a
          _a           =    sa * va + sa * va

     if inputs_are_torch:
          _a           =    torch.from_numpy(__A).to(__A)

     return va




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     _a           =    F.normalize(__A    ,      dim=-1)
     _a           =    F.normalize(__A    ,      dim=-1)
     return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     for param in model.parameters():
          _a           =    value






class __A     (    A		):



 '''simple docstring'''







 def __init__(self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A=None					,  A=None					,  A=None					,  )       ->	str:


      """simple docstring"""



      super().__init__()
      self.register_modules(
          vae=A					,  text_encoder=A					,  clip_model=A					,  tokenizer=A					,  unet=A					,  scheduler=A					,  feature_extractor=A					,  coca_model=A					,  coca_tokenizer=A					,  coca_transform=A					,  )
      _a           =    (
          feature_extractor.size
          if isinstance(feature_extractor.size					,  A			)
          else feature_extractor.size['''shortest_edge''']
      )
      _a           =    transforms.Normalize(mean=feature_extractor.image_mean					,  std=feature_extractor.image_std			)
      set_requires_grad(self.text_encoder					,  A			)
      set_requires_grad(self.clip_model					,  A			)



 def    a__    (self					,  A = "auto"			)       ->	Union[str, Any]:


      """simple docstring"""



      if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           _a           =    self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(A			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      self.enable_attention_slicing(A			)



 def    a__    (self			)       ->	int:


      """simple docstring"""



      set_requires_grad(self.vae					,  A			)



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      set_requires_grad(self.vae					,  A			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      set_requires_grad(self.unet					,  A			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      set_requires_grad(self.unet					,  A			)



 def    a__    (self					,  A					,  A					,  A			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    min(int(num_inference_steps * strength			)					,  A			)

      _a           =    max(num_inference_steps - init_timestep					,  0			)
      _a           =    self.scheduler.timesteps[t_start:]

      return timesteps, num_inference_steps - t_start



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A=None			)       ->	List[str]:


      """simple docstring"""



      if not isinstance(A					,  torch.Tensor			):
           raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(A			)}'''			)

      _a           =    image.to(device=A					,  dtype=A			)

      if isinstance(A					,  A			):
           _a           =    [
               self.vae.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(A			)
           ]
           _a           =    torch.cat(A					,  dim=0			)
      else:
           _a           =    self.vae.encode(A			).latent_dist.sample(A			)

      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      _a           =    0.18215 * init_latents
      _a           =    init_latents.repeat_interleave(A					,  dim=0			)

      _a           =    randn_tensor(init_latents.shape					,  generator=A					,  device=A					,  dtype=A			)

      # get latents
      _a           =    self.scheduler.add_noise(A					,  A					,  A			)
      _a           =    init_latents

      return latents



 def    a__    (self					,  A			)       ->	Tuple:


      """simple docstring"""



      _a           =    self.coca_transform(A			).unsqueeze(0			)
      with torch.no_grad(), torch.cuda.amp.autocast():
           _a           =    self.coca_model.generate(transformed_image.to(device=self.device					,  dtype=self.coca_model.dtype			)			)
      _a           =    self.coca_tokenizer.decode(generated[0].cpu().numpy()			)
      return generated.split('''<end_of_text>'''			)[0].replace('''<start_of_text>'''					,  ''''''			).rstrip(''' .,'''			)



 def    a__    (self					,  A					,  A			)       ->	List[Any]:


      """simple docstring"""



      _a           =    self.feature_extractor.preprocess(A			)
      _a           =    torch.from_numpy(clip_image_input['''pixel_values'''][0]			).unsqueeze(0			).to(self.device			).half()
      _a           =    self.clip_model.get_image_features(A			)
      _a           =    image_embeddings_clip / image_embeddings_clip.norm(p=2					,  dim=-1					,  keepdim=A			)
      _a           =    image_embeddings_clip.repeat_interleave(A					,  dim=0			)
      return image_embeddings_clip



 @torch.enable_grad()
 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    latents.detach().requires_grad_()

      _a           =    self.scheduler.scale_model_input(A					,  A			)

      # predict the noise residual
      _a           =    self.unet(A					,  A					,  encoder_hidden_states=A			).sample

      if isinstance(self.scheduler					,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)			):
           _a           =    self.scheduler.alphas_cumprod[timestep]
           _a           =    1 - alpha_prod_t
           # compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           _a           =    (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

           _a           =    torch.sqrt(A			)
           _a           =    pred_original_sample * (fac) + latents * (1 - fac)
      elif isinstance(self.scheduler					,  A			):
           _a           =    self.scheduler.sigmas[index]
           _a           =    latents - sigma * noise_pred
      else:
           raise ValueError(f'''scheduler type {type(self.scheduler			)} not supported'''			)

      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      _a           =    1 / 0.18215 * sample
      _a           =    self.vae.decode(A			).sample
      _a           =    (image / 2 + 0.5).clamp(0					,  1			)

      _a           =    transforms.Resize(self.feature_extractor_size			)(A			)
      _a           =    self.normalize(A			).to(latents.dtype			)

      _a           =    self.clip_model.get_image_features(A			)
      _a           =    image_embeddings_clip / image_embeddings_clip.norm(p=2					,  dim=-1					,  keepdim=A			)

      _a           =    spherical_dist_loss(A					,  A			).mean() * clip_guidance_scale

      _a           =    -torch.autograd.grad(A					,  A			)[0]

      if isinstance(self.scheduler					,  A			):
           _a           =    latents.detach() + grads * (sigma**2)
           _a           =    noise_pred_original
      else:
           _a           =    noise_pred_original - torch.sqrt(A			) * grads
      return noise_pred, latents



 @torch.no_grad()
 def __call__(self					,  A					,  A					,  A = None					,  A = None					,  A = 512					,  A = 512					,  A = 0.6					,  A = 50					,  A = 7.5					,  A = 1					,  A = 0.0					,  A = 100					,  A = None					,  A = "pil"					,  A = True					,  A = 0.8					,  A = 0.1					,  A = 0.1					,  )       ->	str:


      """simple docstring"""



      if isinstance(A					,  A			) and len(A			) != batch_size:
           raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(A			)} generators.'''			)

      if height % 8 != 0 or width % 8 != 0:
           raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''			)

      if isinstance(A					,  torch.Generator			) and batch_size > 1:
           _a           =    [generator] + [None] * (batch_size - 1)

      _a           =    [
          ('''model''', self.coca_model is None),
          ('''tokenizer''', self.coca_tokenizer is None),
          ('''transform''', self.coca_transform is None),
      ]
      _a           =    [x[0] for x in coca_is_none if x[1]]
      _a           =    ''', '''.join(A			)
      # generate prompts with coca model if prompt is None
      if content_prompt is None:
           if len(A			):
                raise ValueError(
                    f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
                    f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''			)
           _a           =    self.get_image_description(A			)
      if style_prompt is None:
           if len(A			):
                raise ValueError(
                    f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
                    f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''			)
           _a           =    self.get_image_description(A			)

      # get prompt text embeddings for content and style
      _a           =    self.tokenizer(
          A					,  padding='''max_length'''					,  max_length=self.tokenizer.model_max_length					,  truncation=A					,  return_tensors='''pt'''					,  )
      _a           =    self.text_encoder(content_text_input.input_ids.to(self.device			)			)[0]

      _a           =    self.tokenizer(
          A					,  padding='''max_length'''					,  max_length=self.tokenizer.model_max_length					,  truncation=A					,  return_tensors='''pt'''					,  )
      _a           =    self.text_encoder(style_text_input.input_ids.to(self.device			)			)[0]

      _a           =    slerp(A					,  A					,  A			)

      # duplicate text embeddings for each generation per prompt
      _a           =    text_embeddings.repeat_interleave(A					,  dim=0			)

      # set timesteps
      _a           =    '''offset''' in set(inspect.signature(self.scheduler.set_timesteps			).parameters.keys()			)
      _a           =    {}
      if accepts_offset:
           _a           =    1

      self.scheduler.set_timesteps(A					,  **A			)
      # Some schedulers like PNDM have timesteps as arrays
      # It's more optimized to move all timesteps to correct device beforehand
      self.scheduler.timesteps.to(self.device			)

      _a    ,       _a           =    self.get_timesteps(A					,  A					,  self.device			)
      _a           =    timesteps[:1].repeat(A			)

      # Preprocess image
      _a           =    preprocess(A					,  A					,  A			)
      _a           =    self.prepare_latents(
          A					,  A					,  A					,  text_embeddings.dtype					,  self.device					,  A			)

      _a           =    preprocess(A					,  A					,  A			)
      _a           =    self.prepare_latents(
          A					,  A					,  A					,  text_embeddings.dtype					,  self.device					,  A			)

      _a           =    slerp(A					,  A					,  A			)

      if clip_guidance_scale > 0:
           _a           =    self.get_clip_image_embeddings(A					,  A			)
           _a           =    self.get_clip_image_embeddings(A					,  A			)
           _a           =    slerp(
               A					,  A					,  A			)

      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      _a           =    guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
           _a           =    content_text_input.input_ids.shape[-1]
           _a           =    self.tokenizer(['''''']					,  padding='''max_length'''					,  max_length=A					,  return_tensors='''pt'''			)
           _a           =    self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]
           # duplicate unconditional embeddings for each generation per prompt
           _a           =    uncond_embeddings.repeat_interleave(A					,  dim=0			)

           # 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([uncond_embeddings, text_embeddings]			)

      # get the initial random noise unless the user supplied it

      # Unlike in other pipelines, latents need to be generated in the target device
      # for 1-to-1 results reproducibility with the CompVis implementation.
      # However this currently doesn't work in `mps`.
      _a           =    (batch_size, self.unet.config.in_channels, height // 8, width // 8)
      _a           =    text_embeddings.dtype
      if latents is None:
           if self.device.type == "mps":
                # randn does not work reproducibly on mps
                _a           =    torch.randn(A					,  generator=A					,  device='''cpu'''					,  dtype=A			).to(
                    self.device			)
           else:
                _a           =    torch.randn(A					,  generator=A					,  device=self.device					,  dtype=A			)
      else:
           if latents.shape != latents_shape:
                raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''			)
           _a           =    latents.to(self.device			)

      # scale the initial noise by the standard deviation required by the scheduler
      _a           =    latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      _a           =    '''eta''' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
      _a           =    {}
      if accepts_eta:
           _a           =    eta

      # check if the scheduler accepts generator
      _a           =    '''generator''' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
      if accepts_generator:
           _a           =    generator

      with self.progress_bar(total=A			):
           for i, t in enumerate(A			):
                # expand the latents if we are doing classifier free guidance
                _a           =    torch.cat([latents] * 2			) if do_classifier_free_guidance else latents
                _a           =    self.scheduler.scale_model_input(A					,  A			)

                # predict the noise residual
                _a           =    self.unet(A					,  A					,  encoder_hidden_states=A			).sample

                # perform classifier free guidance
                if do_classifier_free_guidance:
                     _a    ,       _a           =    noise_pred.chunk(2			)
                     _a           =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # perform clip guidance
                if clip_guidance_scale > 0:
                     _a           =    (
                         text_embeddings.chunk(2			)[1] if do_classifier_free_guidance else text_embeddings
                     )
                     _a    ,       _a           =    self.cond_fn(
                         A					,  A					,  A					,  A					,  A					,  A					,  A					,  )

                # compute the previous noisy sample x_t -> x_t-1
                _a           =    self.scheduler.step(A					,  A					,  A					,  **A			).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      _a           =    1 / 0.18215 * latents
      _a           =    self.vae.decode(A			).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(A			)

      if not return_dict:
           return (image, None)

      return StableDiffusionPipelineOutput(images=A					,  nsfw_content_detected=A			)



