





import json
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image
from torch import nn
from torchvision import transforms
from torchvision.transforms import functional

import transformers
from transformers import (
    AutoConfig,
    AutoImageProcessor,
    AutoModelForSemanticSegmentation,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCAmelCase__  :	str     =logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=2.0.0''', '''To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt''')





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase=0 )  ->     List[str]:
				lowerCamelCase		       =(size, size) if isinstance(a_	,					a_ ) else size
				lowerCamelCase       ,     lowerCamelCase		       =img.size
				lowerCamelCase		       =size[1] - original_height if original_height < size[1] else 0
				lowerCamelCase		       =size[0] - original_width if original_width < size[0] else 0
				lowerCamelCase		       =functional.pad(a_	,					(0, 0, pad_width, pad_height)	,					fill=a_ )
				return img

class 					__A   :


				def __init__(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =transforms


				def __call__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):

								for t in self.transforms:
												lowerCamelCase       ,     lowerCamelCase		       =t(lowercase_      ,       lowercase_							)
								return image, target




class 					__A   :


				def __init__(						self							):
								pass


				def __call__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								return image, target




class 					__A   :


				def __init__(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =size


				def __call__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =functional.resize(lowercase_      ,       self.size							)
								lowerCamelCase		       =functional.resize(lowercase_      ,       self.size      ,       interpolation=transforms.InterpolationMode.NEAREST							)
								return image, target




class 					__A   :


				def __init__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_=None							):
								lowerCamelCase		       =min_size
								if max_size is None:
												lowerCamelCase		       =min_size
								lowerCamelCase		       =max_size


				def __call__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =random.randint(self.min_size      ,       self.max_size							)
								lowerCamelCase		       =functional.resize(lowercase_      ,       lowercase_							)
								lowerCamelCase		       =functional.resize(lowercase_      ,       lowercase_      ,       interpolation=transforms.InterpolationMode.NEAREST							)
								return image, target




class 					__A   :


				def __init__(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =size if isinstance(lowercase_      ,       lowercase_							) else (size, size)


				def __call__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =pad_if_smaller(lowercase_      ,       self.size							)
								lowerCamelCase		       =pad_if_smaller(lowercase_      ,       self.size      ,       fill=255							)
								lowerCamelCase		       =transforms.RandomCrop.get_params(lowercase_      ,       self.size							)
								lowerCamelCase		       =functional.crop(lowercase_      ,       *lowercase_							)
								lowerCamelCase		       =functional.crop(lowercase_      ,       *lowercase_							)
								return image, target




class 					__A   :


				def __init__(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =flip_prob


				def __call__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):

								if random.random() < self.flip_prob:
												lowerCamelCase		       =functional.hflip(lowercase_							)
												lowerCamelCase		       =functional.hflip(lowercase_							)
								return image, target




class 					__A   :


				def __call__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =functional.pil_to_tensor(lowercase_							)
								lowerCamelCase		       =torch.as_tensor(np.array(lowercase_							)      ,       dtype=torch.intaa							)
								return image, target




class 					__A   :


				def __init__(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =dtype


				def __call__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =functional.convert_image_dtype(lowercase_      ,       self.dtype							)
								return image, target




class 					__A   :


				def __init__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =mean
								lowerCamelCase		       =std


				def __call__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =functional.normalize(lowercase_      ,       mean=self.mean      ,       std=self.std							)
								return image, target




class 					__A   :


				def __call__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):

								if not isinstance(lowercase_      ,       np.ndarray							):
												lowerCamelCase		       =np.array(lowercase_							).astype(np.uinta							)
								# avoid using underflow conversion
								lowerCamelCase		       =255
								lowerCamelCase		       =target - 1
								lowerCamelCase		       =255

								lowerCamelCase		       =Image.fromarray(lowercase_							)
								return image, target




@dataclass
class 					__A   :
				__A            = field(
				    default="""segments/sidewalk-semantic"""			,    metadata={
				        """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
				    }			,    )
				__A            = field(
				    default=a_			,    metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}			)
				__A            = field(
				    default=0.1_5			,    metadata={"""help""": """Percent to split off of train for validation."""}			)
				__A            = field(
				    default=a_			,    metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of training examples to this """
				            """value if set."""
				        )
				    }			,    )
				__A            = field(
				    default=a_			,    metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
				            """value if set."""
				        )
				    }			,    )
				__A            = field(
				    default=a_			,    metadata={"""help""": """Whether or not to reduce all labels by 1 and replace background by 255."""}			,    )


				def _snake_case				(						self							):

								if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
												raise ValueError(
												    """You must specify either a dataset name from the hub or a train and/or validation directory."""							)




@dataclass
class 					__A   :
				__A            = field(
				    default="""nvidia/mit-b0"""			,    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}			,    )
				__A            = field(
				    default=a_			,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}			)
				__A            = field(
				    default=a_			,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}			)
				__A            = field(
				    default="""main"""			,    metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}			,    )
				__A            = field(default=a_			,    metadata={"""help""": """Name or path of preprocessor config."""}			)
				__A            = field(
				    default=a_			,    metadata={
				        """help""": (
				            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
				            """with private models)."""
				        )
				    }			,    )





def    _lowercase    (  )  ->     int:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				lowerCamelCase		       =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								lowerCamelCase       ,     lowerCamelCase       ,     lowerCamelCase		       =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
				else:
								lowerCamelCase       ,     lowerCamelCase       ,     lowerCamelCase		       =parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry("""run_semantic_segmentation"""	,					a_	,					a_ )

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""	,					datefmt="""%m/%d/%Y %H:%M:%S"""	,					handlers=[logging.StreamHandler(sys.stdout )]	,					)

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				lowerCamelCase		       =training_args.get_process_log_level()
				logger.setLevel(a_ )
				transformers.utils.logging.set_verbosity(a_ )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
				    + F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
				logger.info(F"""Training/evaluation parameters {training_args}""" )

				# Detecting last checkpoint.
				lowerCamelCase		       =None
				if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
								lowerCamelCase		       =get_last_checkpoint(training_args.output_dir )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
												raise ValueError(
												    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
												    """Use --overwrite_output_dir to overcome.""" )
								elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
												logger.info(
												    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
												    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )

    # Load dataset
    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
    # download the dataset.
    # TODO support datasets from local folders
				lowerCamelCase		       =load_dataset(data_args.dataset_name	,					cache_dir=model_args.cache_dir )

				# Rename column names to standardized names (only "image" and "label" need to be present)
				if "pixel_values" in dataset["train"].column_names:
								lowerCamelCase		       =dataset.rename_columns({"""pixel_values""": """image"""} )
				if "annotation" in dataset["train"].column_names:
								lowerCamelCase		       =dataset.rename_columns({"""annotation""": """label"""} )

				# If we don't have a validation split, split off a percentage of train as validation.
				lowerCamelCase		       =None if """validation""" in dataset.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split	,					a_ ) and data_args.train_val_split > 0.0:
								lowerCamelCase		       =dataset["""train"""].train_test_split(data_args.train_val_split )
								lowerCamelCase		       =split["""train"""]
								lowerCamelCase		       =split["""test"""]

				# Prepare label mappings.
				# We'll include these in the model's config to get human readable labels in the Inference API.
				if data_args.dataset_name == "scene_parse_150":
								lowerCamelCase		       ="""huggingface/label-files"""
								lowerCamelCase		       ="""ade20k-id2label.json"""
				else:
								lowerCamelCase		       =data_args.dataset_name
								lowerCamelCase		       ="""id2label.json"""
				lowerCamelCase		       =json.load(open(hf_hub_download(a_	,					a_	,					repo_type="""dataset""" )	,					"""r""" ) )
				lowerCamelCase		       ={int(a_ ): v for k, v in idalabel.items()}
				lowerCamelCase		       ={v: str(a_ ) for k, v in idalabel.items()}

				# Load the mean IoU metric from the datasets package
				lowerCamelCase		       =evaluate.load("""mean_iou""" )

				# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
				# predictions and label_ids field) and has to return a dictionary string to float.
				@torch.no_grad()
				def compute_metrics(_UpperCAmelCase ):
								lowerCamelCase       ,     lowerCamelCase		       =eval_pred
								lowerCamelCase		       =torch.from_numpy(a_ )
								# scale the logits to the size of the label
								lowerCamelCase		       =nn.functional.interpolate(
								    a_	,					size=labels.shape[-2:]	,					mode="""bilinear"""	,					align_corners=a_	,					).argmax(dim=1 )

								lowerCamelCase		       =logits_tensor.detach().cpu().numpy()
								lowerCamelCase		       =metric.compute(
								    predictions=a_	,					references=a_	,					num_labels=len(a_ )	,					ignore_index=0	,					reduce_labels=image_processor.do_reduce_labels	,					)
								# add per category metrics as individual key-value pairs
								lowerCamelCase		       =metrics.pop("""per_category_accuracy""" ).tolist()
								lowerCamelCase		       =metrics.pop("""per_category_iou""" ).tolist()

								metrics.update({F"""accuracy_{idalabel[i]}""": v for i, v in enumerate(a_ )} )
								metrics.update({F"""iou_{idalabel[i]}""": v for i, v in enumerate(a_ )} )

								return metrics

				lowerCamelCase		       =AutoConfig.from_pretrained(
				    model_args.config_name or model_args.model_name_or_path	,					labelaid=a_	,					idalabel=a_	,					cache_dir=model_args.cache_dir	,					revision=model_args.model_revision	,					use_auth_token=True if model_args.use_auth_token else None	,					)
				lowerCamelCase		       =AutoModelForSemanticSegmentation.from_pretrained(
				    model_args.model_name_or_path	,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path )	,					config=a_	,					cache_dir=model_args.cache_dir	,					revision=model_args.model_revision	,					use_auth_token=True if model_args.use_auth_token else None	,					)
				lowerCamelCase		       =AutoImageProcessor.from_pretrained(
				    model_args.image_processor_name or model_args.model_name_or_path	,					cache_dir=model_args.cache_dir	,					revision=model_args.model_revision	,					use_auth_token=True if model_args.use_auth_token else None	,					)

				# Define torchvision transforms to be applied to each image + target.
				# Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
				# Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
				if "shortest_edge" in image_processor.size:
								# We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
								lowerCamelCase		       =(image_processor.size["""shortest_edge"""], image_processor.size["""shortest_edge"""])
				else:
								lowerCamelCase		       =(image_processor.size["""height"""], image_processor.size["""width"""])
				lowerCamelCase		       =Compose(
				    [
				        ReduceLabels() if data_args.reduce_labels else Identity(),
				        RandomCrop(size=a_ ),
				        RandomHorizontalFlip(flip_prob=0.5 ),
				        PILToTensor(),
				        ConvertImageDtype(torch.float ),
				        Normalize(mean=image_processor.image_mean	,					std=image_processor.image_std ),
				    ] )
				# Define torchvision transform to be applied to each image.
				# jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
				lowerCamelCase		       =Compose(
				    [
				        ReduceLabels() if data_args.reduce_labels else Identity(),
				        Resize(size=a_ ),
				        PILToTensor(),
				        ConvertImageDtype(torch.float ),
				        Normalize(mean=image_processor.image_mean	,					std=image_processor.image_std ),
				    ] )

				def preprocess_train(_UpperCAmelCase ):
								lowerCamelCase		       =[]
								lowerCamelCase		       =[]
								for image, target in zip(example_batch["""image"""]	,					example_batch["""label"""] ):
												lowerCamelCase       ,     lowerCamelCase		       =train_transforms(image.convert("""RGB""" )	,					a_ )
												pixel_values.append(a_ )
												labels.append(a_ )

								lowerCamelCase		       ={}
								lowerCamelCase		       =torch.stack(a_ )
								lowerCamelCase		       =torch.stack(a_ )

								return encoding

				def preprocess_val(_UpperCAmelCase ):
								lowerCamelCase		       =[]
								lowerCamelCase		       =[]
								for image, target in zip(example_batch["""image"""]	,					example_batch["""label"""] ):
												lowerCamelCase       ,     lowerCamelCase		       =val_transforms(image.convert("""RGB""" )	,					a_ )
												pixel_values.append(a_ )
												labels.append(a_ )

								lowerCamelCase		       ={}
								lowerCamelCase		       =torch.stack(a_ )
								lowerCamelCase		       =torch.stack(a_ )

								return encoding

				if training_args.do_train:
								if "train" not in dataset:
												raise ValueError("""--do_train requires a train dataset""" )
								if data_args.max_train_samples is not None:
												lowerCamelCase		       =(
												    dataset["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
												)
								# Set the training transforms
								dataset["train"].set_transform(a_ )

				if training_args.do_eval:
								if "validation" not in dataset:
												raise ValueError("""--do_eval requires a validation dataset""" )
								if data_args.max_eval_samples is not None:
												lowerCamelCase		       =(
												    dataset["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
												)
								# Set the validation transforms
								dataset["validation"].set_transform(a_ )

				# Initalize our trainer
				lowerCamelCase		       =Trainer(
				    model=a_	,					args=a_	,					train_dataset=dataset["""train"""] if training_args.do_train else None	,					eval_dataset=dataset["""validation"""] if training_args.do_eval else None	,					compute_metrics=a_	,					tokenizer=a_	,					data_collator=a_	,					)

				# Training
				if training_args.do_train:
								lowerCamelCase		       =None
								if training_args.resume_from_checkpoint is not None:
												lowerCamelCase		       =training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												lowerCamelCase		       =last_checkpoint
								lowerCamelCase		       =trainer.train(resume_from_checkpoint=a_ )
								trainer.save_model()
								trainer.log_metrics("""train"""	,					train_result.metrics )
								trainer.save_metrics("""train"""	,					train_result.metrics )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								lowerCamelCase		       =trainer.evaluate()
								trainer.log_metrics("""eval"""	,					a_ )
								trainer.save_metrics("""eval"""	,					a_ )

				# Write model card and (optionally) push to hub
				lowerCamelCase		       ={
				    """finetuned_from""": model_args.model_name_or_path,
				    """dataset""": data_args.dataset_name,
				    """tags""": ["""image-segmentation""", """vision"""],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**a_ )
				else:
								trainer.create_model_card(**a_ )


if __name__ == "__main__":
							main()













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 							a_ (						nn.Module		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_			,			lowercase_=1.0   )					->					Any:
														'''simple docstring'''

														super().__init__()

														lowerCAmelCase_										=     cut_size
														lowerCAmelCase_										=     cut_power






							def      _lowercase       (		self			,			lowercase_			,			lowercase_   )					->					int:
														'''simple docstring'''

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


def      lowerCamelCase					(						a_ ,						a_	)	->			Optional[int]:
							lowerCAmelCase_										=     F.normalize(a_ ,						dim=-1	)
							lowerCAmelCase_										=     F.normalize(a_ ,						dim=-1	)
							return (x - y).norm(dim=-1	).div(2	).arcsin().pow(2	).mul(2	)
def      lowerCamelCase					(						a_ ,						a_	)	->			int:
							for param in model.parameters():
														lowerCAmelCase_										=     value




class 							a_ (						a_		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			)					->					str:
														'''simple docstring'''

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

														lowerCAmelCase_										=     transforms.Normalize(mean=feature_extractor.image_mean			,			std=feature_extractor.image_std   )
														lowerCAmelCase_										=     (
														    feature_extractor.size
														    if isinstance(feature_extractor.size			,			lowercase_   )
														    else feature_extractor.size['shortest_edge']
														)
														lowerCAmelCase_										=     MakeCutouts(self.cut_out_size   )

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

							def      _lowercase       (		self			,			lowercase_ = "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
																					lowerCAmelCase_										=     self.unet.config.attention_head_dim // 2
														self.unet.set_attention_slice(lowercase_   )

							def      _lowercase       (		self   )					->					List[Any]:
														'''simple docstring'''

														self.enable_attention_slicing(lowercase_   )

							def      _lowercase       (		self   )					->					str:
														'''simple docstring'''

														set_requires_grad(self.vae			,			lowercase_   )

							def      _lowercase       (		self   )					->					List[str]:
														'''simple docstring'''

														set_requires_grad(self.vae			,			lowercase_   )

							def      _lowercase       (		self   )					->					List[str]:
														'''simple docstring'''

														set_requires_grad(self.unet			,			lowercase_   )

							def      _lowercase       (		self   )					->					Tuple:
														'''simple docstring'''

														set_requires_grad(self.unet			,			lowercase_   )

							@torch.enable_grad()
							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_=True			,			)					->					Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     latents.detach().requires_grad_()

														lowerCAmelCase_										=     self.scheduler.scale_model_input(lowercase_			,			lowercase_   )

														# predict the noise residual
														lowerCAmelCase_										=     self.unet(lowercase_			,			lowercase_			,			encoder_hidden_states=lowercase_   ).sample

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

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

														lowerCAmelCase_										=     1 / self.vae.config.scaling_factor * sample
														lowerCAmelCase_										=     self.vae.decode(lowercase_   ).sample
														lowerCAmelCase_										=     (image / 2 + 0.5).clamp(0			,			1   )

														if use_cutouts:
																					lowerCAmelCase_										=     self.make_cutouts(lowercase_			,			lowercase_   )
														else:
																					lowerCAmelCase_										=     transforms.Resize(self.cut_out_size   )(lowercase_   )
														lowerCAmelCase_										=     self.normalize(lowercase_   ).to(latents.dtype   )

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

														if use_cutouts:
																					lowerCAmelCase_										=     spherical_dist_loss(lowercase_			,			lowercase_   )
																					lowerCAmelCase_										=     dists.view([num_cutouts, sample.shape[0], -1]   )
																					lowerCAmelCase_										=     dists.sum(2   ).mean(0   ).sum() * clip_guidance_scale
														else:
																					lowerCAmelCase_										=     spherical_dist_loss(lowercase_			,			lowercase_   ).mean() * clip_guidance_scale

														lowerCAmelCase_										=     -torch.autograd.grad(lowercase_			,			lowercase_   )[0]

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






							@torch.no_grad()
							def __call__(		self			,			lowercase_			,			lowercase_ = 5_1_2			,			lowercase_ = 5_1_2			,			lowercase_ = 5_0			,			lowercase_ = 7.5			,			lowercase_ = 1			,			lowercase_ = 0.0			,			lowercase_ = 1_0_0			,			lowercase_ = None			,			lowercase_ = 4			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = "pil"			,			lowercase_ = True			,			)					->					str:
														'''simple docstring'''

														if isinstance(lowercase_			,			lowercase_   ):
																					lowerCAmelCase_										=     1
														elif isinstance(lowercase_			,			lowercase_   ):
																					lowerCAmelCase_										=     len(lowercase_   )
														else:
																					raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowercase_   )}'''   )

														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
														lowerCAmelCase_										=     self.tokenizer(
														    lowercase_			,			padding='max_length'			,			max_length=self.tokenizer.model_max_length			,			truncation=lowercase_			,			return_tensors='pt'			,			)
														lowerCAmelCase_										=     self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
														# duplicate text embeddings for each generation per prompt
														lowerCAmelCase_										=     text_embeddings.repeat_interleave(lowercase_			,			dim=0   )

														if clip_guidance_scale > 0:
																					if clip_prompt is not None:
																												lowerCAmelCase_										=     self.tokenizer(
																												    lowercase_			,			padding='max_length'			,			max_length=self.tokenizer.model_max_length			,			truncation=lowercase_			,			return_tensors='pt'			,			).input_ids.to(self.device   )
																					else:
																												lowerCAmelCase_										=     text_input.input_ids.to(self.device   )
																					lowerCAmelCase_										=     self.clip_model.get_text_features(lowercase_   )
																					lowerCAmelCase_										=     text_embeddings_clip / text_embeddings_clip.norm(p=2			,			dim=-1			,			keepdim=lowercase_   )
																					# duplicate text embeddings clip for each generation per prompt
																					lowerCAmelCase_										=     text_embeddings_clip.repeat_interleave(lowercase_			,			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.
														lowerCAmelCase_										=     guidance_scale > 1.0
														# get unconditional embeddings for classifier free guidance
														if do_classifier_free_guidance:
																					lowerCAmelCase_										=     text_input.input_ids.shape[-1]
																					lowerCAmelCase_										=     self.tokenizer(['']			,			padding='max_length'			,			max_length=lowercase_			,			return_tensors='pt'   )
																					lowerCAmelCase_										=     self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
																					# duplicate unconditional embeddings for each generation per prompt
																					lowerCAmelCase_										=     uncond_embeddings.repeat_interleave(lowercase_			,			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
																					lowerCAmelCase_										=     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`.
														lowerCAmelCase_										=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
														lowerCAmelCase_										=     text_embeddings.dtype
														if latents is None:
																					if self.device.type == "mps":
																												# randn does not work reproducibly on mps
																												lowerCAmelCase_										=     torch.randn(lowercase_			,			generator=lowercase_			,			device='cpu'			,			dtype=lowercase_   ).to(
																												    self.device   )
																					else:
																												lowerCAmelCase_										=     torch.randn(lowercase_			,			generator=lowercase_			,			device=self.device			,			dtype=lowercase_   )
														else:
																					if latents.shape != latents_shape:
																												raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
																					lowerCAmelCase_										=     latents.to(self.device   )

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

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

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

														# scale the initial noise by the standard deviation required by the scheduler
														lowerCAmelCase_										=     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]
														lowerCAmelCase_										=     'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
														lowerCAmelCase_										=     {}
														if accepts_eta:
																					lowerCAmelCase_										=     eta

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

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

																					# predict the noise residual
																					lowerCAmelCase_										=     self.unet(lowercase_			,			lowercase_			,			encoder_hidden_states=lowercase_   ).sample

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

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

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

														# scale and decode the image latents with vae
														lowerCAmelCase_										=     1 / self.vae.config.scaling_factor * latents
														lowerCAmelCase_										=     self.vae.decode(lowercase_   ).sample

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

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

														if not return_dict:
																					return (image, None)

														return StableDiffusionPipelineOutput(images=lowercase_			,			nsfw_content_detected=lowercase_   )



