






import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


UpperCamelCase												=      logging.getLogger(__name__)



@dataclass
class  __UpperCAmelCase						:
			__snake_case							: Any							=				field(
			    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}       )
			__snake_case							: List[str]							=				field(
			    default=SCREAMING_SNAKE_CASE__			,metadata={"help": "Pretrained config name or path if not the same as model_name"}       )
			__snake_case							: Optional[int]							=				field(
			    default="NER"			,metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}       )
			__snake_case							: str							=				field(
			    default=SCREAMING_SNAKE_CASE__			,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}       )
			__snake_case							: List[Any]							=				field(default=SCREAMING_SNAKE_CASE__			,metadata={"help": "Set this flag to use fast tokenization."}       )
			# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
			# or just modify its tokenizer_config.json.
			__snake_case							: int							=				field(
			    default=SCREAMING_SNAKE_CASE__			,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,)



@dataclass
class  __UpperCAmelCase						:
			__snake_case							: Dict							=				field(
			    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}       )
			__snake_case							: Any							=				field(
			    metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}       )
			__snake_case							: Union[str, Any]							=				field(
			    default=128			,metadata={
			        "help": (
			            "The maximum total input sequence length after tokenization. Sequences longer "
			            "than this will be truncated, sequences shorter will be padded."
			        )
			    }			,)
			__snake_case							: Optional[int]							=				field(
			    default=SCREAMING_SNAKE_CASE__			,metadata={"help": "Overwrite the cached training and evaluation sets"}       )

def 			__lowerCamelCase     (						)				->    int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
						_SCREAMING_SNAKE_CASE       =    parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir						)
						    and os.listdir(training_args.output_dir						)
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
												    """ --overwrite_output_dir to overcome."""						)

						_SCREAMING_SNAKE_CASE       =    import_module("""tasks"""						)

						try:
												_SCREAMING_SNAKE_CASE       =    getattr(__lowerCAmelCase			,model_args.task_type						)
												_SCREAMING_SNAKE_CASE       =    token_classification_task_clazz()
						except AttributeError:
												raise ValueError(
												    F'Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
												    F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'						)

						# Setup logging
						logging.basicConfig(
						    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""			,datefmt="""%m/%d/%Y %H:%M:%S"""			,level=logging.INFO			,)
						logger.info(
						    """n_replicas: %s, distributed training: %s, 16-bits training: %s"""			,training_args.n_replicas			,bool(training_args.n_replicas > 1						)			,training_args.fpaa			,)
						logger.info("""Training/evaluation parameters %s"""			,__lowerCAmelCase						)

						# Prepare Token Classification task
						_SCREAMING_SNAKE_CASE       =    token_classification_task.get_labels(data_args.labels						)
						_SCREAMING_SNAKE_CASE       =    dict(enumerate(__lowerCAmelCase						)						)
						_SCREAMING_SNAKE_CASE       =    len(__lowerCAmelCase						)

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						_SCREAMING_SNAKE_CASE       =    AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,num_labels=__lowerCAmelCase			,idalabel=__lowerCAmelCase			,labelaid={label: i for i, label in enumerate(__lowerCAmelCase						)}			,cache_dir=model_args.cache_dir			,)
						_SCREAMING_SNAKE_CASE       =    AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,cache_dir=model_args.cache_dir			,use_fast=model_args.use_fast			,)

						with training_args.strategy.scope():
												_SCREAMING_SNAKE_CASE       =    TFAutoModelForTokenClassification.from_pretrained(
												    model_args.model_name_or_path			,from_pt=bool(""".bin""" in model_args.model_name_or_path						)			,config=__lowerCAmelCase			,cache_dir=model_args.cache_dir			,)

						# Get datasets
						_SCREAMING_SNAKE_CASE       =    (
						    TFTokenClassificationDataset(
						        token_classification_task=__lowerCAmelCase			,data_dir=data_args.data_dir			,tokenizer=__lowerCAmelCase			,labels=__lowerCAmelCase			,model_type=config.model_type			,max_seq_length=data_args.max_seq_length			,overwrite_cache=data_args.overwrite_cache			,mode=Split.train			,)
						    if training_args.do_train
						    else None
						)
						_SCREAMING_SNAKE_CASE       =    (
						    TFTokenClassificationDataset(
						        token_classification_task=__lowerCAmelCase			,data_dir=data_args.data_dir			,tokenizer=__lowerCAmelCase			,labels=__lowerCAmelCase			,model_type=config.model_type			,max_seq_length=data_args.max_seq_length			,overwrite_cache=data_args.overwrite_cache			,mode=Split.dev			,)
						    if training_args.do_eval
						    else None
						)

						def align_predictions(snake_case__			,snake_case__						) -> Tuple[List[int], List[int]]:
												_SCREAMING_SNAKE_CASE       =    np.argmax(__lowerCAmelCase			,axis=2						)
												_SCREAMING_SNAKE_CASE       =    preds.shape
												_SCREAMING_SNAKE_CASE       =    [[] for _ in range(__lowerCAmelCase						)]
												_SCREAMING_SNAKE_CASE       =    [[] for _ in range(__lowerCAmelCase						)]

												for i in range(__lowerCAmelCase						):
																		for j in range(__lowerCAmelCase						):
																								if label_ids[i, j] != -1_00:
																														out_label_list[i].append(label_map[label_ids[i][j]]						)
																														preds_list[i].append(label_map[preds[i][j]]						)

												return preds_list, out_label_list

						def compute_metrics(snake_case__						) -> Dict:
												_SCREAMING_SNAKE_CASE       =    align_predictions(p.predictions			,p.label_ids						)

												return {
												    "precision": precision_score(__lowerCAmelCase			,__lowerCAmelCase						),
												    "recall": recall_score(__lowerCAmelCase			,__lowerCAmelCase						),
												    "f1": fa_score(__lowerCAmelCase			,__lowerCAmelCase						),
												}

						# Initialize our Trainer
						_SCREAMING_SNAKE_CASE       =    TFTrainer(
						    model=__lowerCAmelCase			,args=__lowerCAmelCase			,train_dataset=train_dataset.get_dataset() if train_dataset else None			,eval_dataset=eval_dataset.get_dataset() if eval_dataset else None			,compute_metrics=__lowerCAmelCase			,)

						# Training
						if training_args.do_train:
												trainer.train()
												trainer.save_model()
												tokenizer.save_pretrained(training_args.output_dir						)

						# Evaluation
						_SCREAMING_SNAKE_CASE       =    {}
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""						)

												_SCREAMING_SNAKE_CASE       =    trainer.evaluate()
												_SCREAMING_SNAKE_CASE       =    os.path.join(training_args.output_dir			,"""eval_results.txt"""						)

												with open(__lowerCAmelCase			,"""w"""						) as writer:
																		logger.info("""***** Eval results *****"""						)

																		for key, value in result.items():
																								logger.info("""  %s = %s"""			,__lowerCAmelCase			,__lowerCAmelCase						)
																								writer.write("""%s = %s\n""" % (key, value)						)

																		results.update(__lowerCAmelCase						)

																		 # Predict
						if training_args.do_predict:
												_SCREAMING_SNAKE_CASE       =    TFTokenClassificationDataset(
												    token_classification_task=__lowerCAmelCase			,data_dir=data_args.data_dir			,tokenizer=__lowerCAmelCase			,labels=__lowerCAmelCase			,model_type=config.model_type			,max_seq_length=data_args.max_seq_length			,overwrite_cache=data_args.overwrite_cache			,mode=Split.test			,)

												_SCREAMING_SNAKE_CASE       =    trainer.predict(test_dataset.get_dataset()						)
												_SCREAMING_SNAKE_CASE       =    align_predictions(__lowerCAmelCase			,__lowerCAmelCase						)
												_SCREAMING_SNAKE_CASE       =    classification_report(__lowerCAmelCase			,__lowerCAmelCase						)

												logger.info("""\n%s"""			,__lowerCAmelCase						)

												_SCREAMING_SNAKE_CASE       =    os.path.join(training_args.output_dir			,"""test_results.txt"""						)

												with open(__lowerCAmelCase			,"""w"""						) as writer:
																		writer.write("""%s\n""" % report						)

												# Save predictions
												_SCREAMING_SNAKE_CASE       =    os.path.join(training_args.output_dir			,"""test_predictions.txt"""						)

												with open(__lowerCAmelCase			,"""w"""						) as writer:
																		with open(os.path.join(data_args.data_dir			,"""test.txt"""						)			,"""r"""						) as f:
																								_SCREAMING_SNAKE_CASE       =    0

																								for line in f:
																														if line.startswith("""-DOCSTART-"""						) or line == "" or line == "\n":
																																				writer.write(__lowerCAmelCase						)

																																				if not preds_list[example_id]:
																																										example_id += 1
																														elif preds_list[example_id]:
																																				_SCREAMING_SNAKE_CASE       =    line.split()[0] + " " + preds_list[example_id].pop(0						) + "\n"

																																				writer.write(__lowerCAmelCase						)
																														else:
																																				logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""			,line.split()[0]						)

						return results


if __name__ == "__main__":
		main()







"""simple docstring"""
import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

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







class  _SCREAMING_SNAKE_CASE       (     nn.Module				):



 '''simple docstring'''





 def __init__(self					,     lowerCAmelCase__					,     lowerCAmelCase__=1.0   ):

  '''simple docstring'''







  super().__init__()

  _UpperCamelCase      :	Optional[Any]					      =  cut_size
  _UpperCamelCase      :	List[str]					      =  cut_power






 def lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__   ):

  '''simple docstring'''







  _UpperCamelCase      ,	_UpperCamelCase      :	int					      =  pixel_values.shape[2:4]
  _UpperCamelCase      :	Any					      =  min(lowerCAmelCase__					,     lowerCAmelCase__   )
  _UpperCamelCase      :	str					      =  min(lowerCAmelCase__					,     lowerCAmelCase__					,     self.cut_size   )
  _UpperCamelCase      :	Union[str, Any]					      =  []
  for _ in range(lowerCAmelCase__   ):
   _UpperCamelCase      :	Tuple					      =  int(torch.rand([]   ) ** self.cut_power * (max_size - min_size) + min_size   )
   _UpperCamelCase      :	Union[str, Any]					      =  torch.randint(0					,     sideX - size + 1					,     ()   )
   _UpperCamelCase      :	Any					      =  torch.randint(0					,     sideY - size + 1					,     ()   )
   _UpperCamelCase      :	List[Any]					      =  pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
   cutouts.append(F.adaptive_avg_poolad(lowerCAmelCase__					,     self.cut_size   )   )
  return torch.cat(lowerCAmelCase__   )






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		str  ,	__lowerCAmelCase			:		Optional[int]						)    ->       Union[str, Any]:
 _UpperCamelCase      :	List[str]					      =  F.normalize(__lowerCAmelCase  ,	dim=-1						)
 _UpperCamelCase      :	Optional[Any]					      =  F.normalize(__lowerCAmelCase  ,	dim=-1						)
 return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		str						)    ->       List[str]:
 for param in model.parameters():
  _UpperCamelCase      :	Any					      =  value







class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__				):



 '''simple docstring'''





 def __init__(self					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     ):

  '''simple docstring'''







  super().__init__()
  self.register_modules(
      vae=lowerCAmelCase__					,     text_encoder=lowerCAmelCase__					,     clip_model=lowerCAmelCase__					,     tokenizer=lowerCAmelCase__					,     unet=lowerCAmelCase__					,     scheduler=lowerCAmelCase__					,     feature_extractor=lowerCAmelCase__					,     )

  _UpperCamelCase      :	Union[str, Any]					      =  transforms.Normalize(mean=feature_extractor.image_mean					,     std=feature_extractor.image_std   )
  _UpperCamelCase      :	Optional[int]					      =  (
      feature_extractor.size
      if isinstance(feature_extractor.size					,     lowerCAmelCase__   )
      else feature_extractor.size["shortest_edge"]
  )
  _UpperCamelCase      :	Dict					      =  MakeCutouts(self.cut_out_size   )

  set_requires_grad(self.text_encoder					,     lowerCAmelCase__   )
  set_requires_grad(self.clip_model					,     lowerCAmelCase__   )






 def lowercase_      (self					,     lowerCAmelCase__ = "auto"   ):

  '''simple docstring'''







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






 def lowercase_      (self   ):

  '''simple docstring'''







  self.enable_attention_slicing(lowerCAmelCase__   )






 def lowercase_      (self   ):

  '''simple docstring'''







  set_requires_grad(self.vae					,     lowerCAmelCase__   )






 def lowercase_      (self   ):

  '''simple docstring'''







  set_requires_grad(self.vae					,     lowerCAmelCase__   )






 def lowercase_      (self   ):

  '''simple docstring'''







  set_requires_grad(self.unet					,     lowerCAmelCase__   )






 def lowercase_      (self   ):

  '''simple docstring'''







  set_requires_grad(self.unet					,     lowerCAmelCase__   )






 @torch.enable_grad()
 def lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=True					,     ):

  '''simple docstring'''







  _UpperCamelCase      :	Dict					      =  latents.detach().requires_grad_()

  _UpperCamelCase      :	Optional[Any]					      =  self.scheduler.scale_model_input(lowerCAmelCase__					,     lowerCAmelCase__   )

  # predict the noise residual
  _UpperCamelCase      :	Dict					      =  self.unet(lowerCAmelCase__					,     lowerCAmelCase__					,     encoder_hidden_states=lowerCAmelCase__   ).sample

  if isinstance(self.scheduler					,     (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
   _UpperCamelCase      :	Optional[Any]					      =  self.scheduler.alphas_cumprod[timestep]
   _UpperCamelCase      :	Optional[int]					      =  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
   _UpperCamelCase      :	Tuple					      =  (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

   _UpperCamelCase      :	List[str]					      =  torch.sqrt(lowerCAmelCase__   )
   _UpperCamelCase      :	Optional[Any]					      =  pred_original_sample * (fac) + latents * (1 - fac)
  elif isinstance(self.scheduler					,     lowerCAmelCase__   ):
   _UpperCamelCase      :	Optional[int]					      =  self.scheduler.sigmas[index]
   _UpperCamelCase      :	Optional[Any]					      =  latents - sigma * noise_pred
  else:
   raise ValueError(F"scheduler type {type(self.scheduler   )} not supported"   )

  _UpperCamelCase      :	List[Any]					      =  1 / self.vae.config.scaling_factor * sample
  _UpperCamelCase      :	Dict					      =  self.vae.decode(lowerCAmelCase__   ).sample
  _UpperCamelCase      :	Union[str, Any]					      =  (image / 2 + 0.5).clamp(0					,     1   )

  if use_cutouts:
   _UpperCamelCase      :	Dict					      =  self.make_cutouts(lowerCAmelCase__					,     lowerCAmelCase__   )
  else:
   _UpperCamelCase      :	List[str]					      =  transforms.Resize(self.cut_out_size   )(lowerCAmelCase__   )
  _UpperCamelCase      :	str					      =  self.normalize(lowerCAmelCase__   ).to(latents.dtype   )

  _UpperCamelCase      :	List[Any]					      =  self.clip_model.get_image_features(lowerCAmelCase__   )
  _UpperCamelCase      :	Dict					      =  image_embeddings_clip / image_embeddings_clip.norm(p=2					,     dim=-1					,     keepdim=lowerCAmelCase__   )

  if use_cutouts:
   _UpperCamelCase      :	str					      =  spherical_dist_loss(lowerCAmelCase__					,     lowerCAmelCase__   )
   _UpperCamelCase      :	str					      =  dists.view([num_cutouts, sample.shape[0], -1]   )
   _UpperCamelCase      :	Optional[Any]					      =  dists.sum(2   ).mean(0   ).sum() * clip_guidance_scale
  else:
   _UpperCamelCase      :	Any					      =  spherical_dist_loss(lowerCAmelCase__					,     lowerCAmelCase__   ).mean() * clip_guidance_scale

  _UpperCamelCase      :	List[str]					      =  -torch.autograd.grad(lowerCAmelCase__					,     lowerCAmelCase__   )[0]

  if isinstance(self.scheduler					,     lowerCAmelCase__   ):
   _UpperCamelCase      :	Tuple					      =  latents.detach() + grads * (sigma**2)
   _UpperCamelCase      :	int					      =  noise_pred_original
  else:
   _UpperCamelCase      :	Union[str, Any]					      =  noise_pred_original - torch.sqrt(lowerCAmelCase__   ) * grads
  return noise_pred, latents






 @torch.no_grad()
 def __call__(self					,     lowerCAmelCase__					,     lowerCAmelCase__ = 5_12					,     lowerCAmelCase__ = 5_12					,     lowerCAmelCase__ = 50					,     lowerCAmelCase__ = 7.5					,     lowerCAmelCase__ = 1					,     lowerCAmelCase__ = 0.0					,     lowerCAmelCase__ = 1_00					,     lowerCAmelCase__ = None					,     lowerCAmelCase__ = 4					,     lowerCAmelCase__ = True					,     lowerCAmelCase__ = None					,     lowerCAmelCase__ = None					,     lowerCAmelCase__ = "pil"					,     lowerCAmelCase__ = True					,     ):

  '''simple docstring'''







  if isinstance(lowerCAmelCase__					,     lowerCAmelCase__   ):
   _UpperCamelCase      :	str					      =  1
  elif isinstance(lowerCAmelCase__					,     lowerCAmelCase__   ):
   _UpperCamelCase      :	Dict					      =  len(lowerCAmelCase__   )
  else:
   raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__   )}"   )

  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}."   )

  # get prompt text embeddings
  _UpperCamelCase      :	Dict					      =  self.tokenizer(
      lowerCAmelCase__					,     padding="max_length"					,     max_length=self.tokenizer.model_max_length					,     truncation=lowerCAmelCase__					,     return_tensors="pt"					,     )
  _UpperCamelCase      :	List[str]					      =  self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
  # duplicate text embeddings for each generation per prompt
  _UpperCamelCase      :	str					      =  text_embeddings.repeat_interleave(lowerCAmelCase__					,     dim=0   )

  if clip_guidance_scale > 0:
   if clip_prompt is not None:
    _UpperCamelCase      :	List[Any]					      =  self.tokenizer(
        lowerCAmelCase__					,     padding="max_length"					,     max_length=self.tokenizer.model_max_length					,     truncation=lowerCAmelCase__					,     return_tensors="pt"					,     ).input_ids.to(self.device   )
   else:
    _UpperCamelCase      :	str					      =  text_input.input_ids.to(self.device   )
   _UpperCamelCase      :	Dict					      =  self.clip_model.get_text_features(lowerCAmelCase__   )
   _UpperCamelCase      :	Optional[int]					      =  text_embeddings_clip / text_embeddings_clip.norm(p=2					,     dim=-1					,     keepdim=lowerCAmelCase__   )
   # duplicate text embeddings clip for each generation per prompt
   _UpperCamelCase      :	int					      =  text_embeddings_clip.repeat_interleave(lowerCAmelCase__					,     dim=0   )

  # 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.
  _UpperCamelCase      :	Optional[Any]					      =  guidance_scale > 1.0
  # get unconditional embeddings for classifier free guidance
  if do_classifier_free_guidance:
   _UpperCamelCase      :	Optional[int]					      =  text_input.input_ids.shape[-1]
   _UpperCamelCase      :	List[str]					      =  self.tokenizer([""]					,     padding="max_length"					,     max_length=lowerCAmelCase__					,     return_tensors="pt"   )
   _UpperCamelCase      :	List[str]					      =  self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
   # duplicate unconditional embeddings for each generation per prompt
   _UpperCamelCase      :	Dict					      =  uncond_embeddings.repeat_interleave(lowerCAmelCase__					,     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
   _UpperCamelCase      :	Tuple					      =  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`.
  _UpperCamelCase      :	Optional[Any]					      =  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
  _UpperCamelCase      :	Optional[Any]					      =  text_embeddings.dtype
  if latents is None:
   if self.device.type == "mps":
    # randn does not work reproducibly on mps
    _UpperCamelCase      :	Optional[Any]					      =  torch.randn(lowerCAmelCase__					,     generator=lowerCAmelCase__					,     device="cpu"					,     dtype=lowerCAmelCase__   ).to(
        self.device   )
   else:
    _UpperCamelCase      :	Union[str, Any]					      =  torch.randn(lowerCAmelCase__					,     generator=lowerCAmelCase__					,     device=self.device					,     dtype=lowerCAmelCase__   )
  else:
   if latents.shape != latents_shape:
    raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"   )
   _UpperCamelCase      :	Optional[int]					      =  latents.to(self.device   )

  # set timesteps
  _UpperCamelCase      :	List[str]					      =  "offset" in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
  _UpperCamelCase      :	str					      =  {}
  if accepts_offset:
   _UpperCamelCase      :	Tuple					      =  1

  self.scheduler.set_timesteps(lowerCAmelCase__					,     **lowerCAmelCase__   )

  # Some schedulers like PNDM have timesteps as arrays
  # It's more optimized to move all timesteps to correct device beforehand
  _UpperCamelCase      :	str					      =  self.scheduler.timesteps.to(self.device   )

  # scale the initial noise by the standard deviation required by the scheduler
  _UpperCamelCase      :	Tuple					      =  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]
  _UpperCamelCase      :	str					      =  "eta" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
  _UpperCamelCase      :	Optional[int]					      =  {}
  if accepts_eta:
   _UpperCamelCase      :	Union[str, Any]					      =  eta

  # check if the scheduler accepts generator
  _UpperCamelCase      :	Optional[int]					      =  "generator" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
  if accepts_generator:
   _UpperCamelCase      :	List[str]					      =  generator

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

   # predict the noise residual
   _UpperCamelCase      :	List[str]					      =  self.unet(lowerCAmelCase__					,     lowerCAmelCase__					,     encoder_hidden_states=lowerCAmelCase__   ).sample

   # perform classifier free guidance
   if do_classifier_free_guidance:
    _UpperCamelCase      ,	_UpperCamelCase      :	List[Any]					      =  noise_pred.chunk(2   )
    _UpperCamelCase      :	List[Any]					      =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

   # perform clip guidance
   if clip_guidance_scale > 0:
    _UpperCamelCase      :	List[str]					      =  (
        text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
    )
    _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  self.cond_fn(
        lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     )

   # compute the previous noisy sample x_t -> x_t-1
   _UpperCamelCase      :	Tuple					      =  self.scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__   ).prev_sample

  # scale and decode the image latents with vae
  _UpperCamelCase      :	Tuple					      =  1 / self.vae.config.scaling_factor * latents
  _UpperCamelCase      :	Union[str, Any]					      =  self.vae.decode(lowerCAmelCase__   ).sample

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

  if output_type == "pil":
   _UpperCamelCase      :	Dict					      =  self.numpy_to_pil(lowerCAmelCase__   )

  if not return_dict:
   return (image, None)

  return StableDiffusionPipelineOutput(images=lowerCAmelCase__					,     nsfw_content_detected=lowerCAmelCase__   )



