



'''simple docstring'''





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							    =						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 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase=0	)				->				Optional[Any]:
  __lowerCamelCase	:	Tuple   	=			(size, size) if isinstance(_A	,_A	) else size
  __lowerCamelCase	,__lowerCamelCase	:	List[Any]   	=			img.size
  __lowerCamelCase	:	Optional[int]   	=			size[1] - original_height if original_height < size[1] else 0
  __lowerCamelCase	:	Dict   	=			size[0] - original_width if original_width < size[0] else 0
  __lowerCamelCase	:	Dict   	=			functional.pad(_A	,(0, 0, pad_width, pad_height)	,fill=_A	)
  return img






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     int      ,     _a				:     List[Any]       ) -> List[str]:
    __lowerCamelCase	:	Union[str, Any]   	=			transforms




  def __call__(			self				:     Any      ,     _a				:     Any      ,     _a				:     Tuple       ) -> Optional[int]:






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






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Tuple       ) -> Tuple:
    pass




  def __call__(			self				:     Union[str, Any]      ,     _a				:     int      ,     _a				:     str       ) -> List[Any]:
    return image, target






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Dict      ,     _a				:     List[str]       ) -> List[str]:
    __lowerCamelCase	:	List[Any]   	=			size




  def __call__(			self				:     Any      ,     _a				:     Dict      ,     _a				:     List[Any]       ) -> Optional[int]:
    __lowerCamelCase	:	List[str]   	=			functional.resize(lowercase_      ,     self.size       )
    __lowerCamelCase	:	Union[str, Any]   	=			functional.resize(lowercase_      ,     self.size      ,     interpolation=transforms.InterpolationMode.NEAREST       )
    return image, target






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     str      ,     _a				:     Union[str, Any]      ,     _a				:     Any=None       ) -> List[str]:
    __lowerCamelCase	:	Optional[int]   	=			min_size
    if max_size is None:
      __lowerCamelCase	:	str   	=			min_size
    __lowerCamelCase	:	List[str]   	=			max_size




  def __call__(			self				:     List[Any]      ,     _a				:     str      ,     _a				:     Any       ) -> Any:
    __lowerCamelCase	:	Dict   	=			random.randint(self.min_size      ,     self.max_size       )
    __lowerCamelCase	:	Optional[int]   	=			functional.resize(lowercase_      ,     lowercase_       )
    __lowerCamelCase	:	Optional[int]   	=			functional.resize(lowercase_      ,     lowercase_      ,     interpolation=transforms.InterpolationMode.NEAREST       )
    return image, target






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Union[str, Any]      ,     _a				:     Any       ) -> List[str]:
    __lowerCamelCase	:	Optional[int]   	=			size if isinstance(lowercase_      ,     lowercase_       ) else (size, size)




  def __call__(			self				:     str      ,     _a				:     Optional[int]      ,     _a				:     Optional[Any]       ) -> Optional[int]:
    __lowerCamelCase	:	Dict   	=			pad_if_smaller(lowercase_      ,     self.size       )
    __lowerCamelCase	:	Union[str, Any]   	=			pad_if_smaller(lowercase_      ,     self.size      ,     fill=255       )
    __lowerCamelCase	:	Optional[int]   	=			transforms.RandomCrop.get_params(lowercase_      ,     self.size       )
    __lowerCamelCase	:	Union[str, Any]   	=			functional.crop(lowercase_      ,     *lowercase_       )
    __lowerCamelCase	:	Dict   	=			functional.crop(lowercase_      ,     *lowercase_       )
    return image, target






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     int      ,     _a				:     Dict       ) -> Any:
    __lowerCamelCase	:	List[Any]   	=			flip_prob




  def __call__(			self				:     Optional[int]      ,     _a				:     Tuple      ,     _a				:     Union[str, Any]       ) -> Dict:






    if random.random() < self.flip_prob:
      __lowerCamelCase	:	Any   	=			functional.hflip(lowercase_       )
      __lowerCamelCase	:	Union[str, Any]   	=			functional.hflip(lowercase_       )
    return image, target






class  lowerCamelCase_   :
  """simple docstring"""




  def __call__(			self				:     Union[str, Any]      ,     _a				:     Dict      ,     _a				:     Tuple       ) -> Tuple:
    __lowerCamelCase	:	List[Any]   	=			functional.pil_to_tensor(lowercase_       )
    __lowerCamelCase	:	Optional[Any]   	=			torch.as_tensor(np.array(lowercase_       )      ,     dtype=torch.intaa       )
    return image, target






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     str      ,     _a				:     Optional[Any]       ) -> Union[str, Any]:
    __lowerCamelCase	:	Tuple   	=			dtype




  def __call__(			self				:     Dict      ,     _a				:     Optional[Any]      ,     _a				:     Tuple       ) -> Dict:
    __lowerCamelCase	:	List[Any]   	=			functional.convert_image_dtype(lowercase_      ,     self.dtype       )
    return image, target






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Any      ,     _a				:     List[Any]      ,     _a				:     Dict       ) -> Optional[int]:
    __lowerCamelCase	:	Optional[int]   	=			mean
    __lowerCamelCase	:	Tuple   	=			std




  def __call__(			self				:     Optional[Any]      ,     _a				:     int      ,     _a				:     List[str]       ) -> Optional[Any]:
    __lowerCamelCase	:	List[str]   	=			functional.normalize(lowercase_      ,     mean=self.mean      ,     std=self.std       )
    return image, target






class  lowerCamelCase_   :
  """simple docstring"""




  def __call__(			self				:     List[Any]      ,     _a				:     str      ,     _a				:     Optional[int]       ) -> Optional[int]:






    if not isinstance(lowercase_      ,     np.ndarray       ):
      __lowerCamelCase	:	List[Any]   	=			np.array(lowercase_       ).astype(np.uinta       )
    # avoid using underflow conversion
    __lowerCamelCase	:	List[str]   	=			255
    __lowerCamelCase	:	Any   	=			target - 1
    __lowerCamelCase	:	List[Any]   	=			255

    __lowerCamelCase	:	Optional[Any]   	=			Image.fromarray(lowercase_       )
    return image, target






@dataclass
class  lowerCamelCase_   :
  """simple docstring"""




  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=lowerCamelCase__       ,       metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}					)
  a_							 =field(
      default=0.15       ,       metadata={"""help""": """Percent to split off of train for validation."""}					)
  a_							 =field(
      default=lowerCamelCase__       ,       metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of training examples to this """
              """value if set."""
          )
      }       ,       )
  a_							 =field(
      default=lowerCamelCase__       ,       metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
              """value if set."""
          )
      }       ,       )
  a_							 =field(
      default=lowerCamelCase__       ,       metadata={"""help""": """Whether or not to reduce all labels by 1 and replace background by 255."""}       ,       )




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> Optional[int]:






    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  lowerCamelCase_   :
  """simple docstring"""




  a_							 =field(
      default="""nvidia/mit-b0"""       ,       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}       ,       )
  a_							 =field(
      default=lowerCamelCase__       ,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}					)
  a_							 =field(
      default=lowerCamelCase__       ,       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=lowerCamelCase__       ,       metadata={"""help""": """Name or path of preprocessor config."""}					)
  a_							 =field(
      default=lowerCamelCase__       ,       metadata={
          """help""": (
              """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
              """with private models)."""
          )
      }       ,       )




def 	a_					(							)				->				Optional[int]:
  __lowerCamelCase	:	Union[str, Any]   	=			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	:	Optional[Any]   	=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
  else:
    __lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	:	Tuple   	=			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	:	Optional[Any]   	=			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	:	List[Any]   	=			None
  if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
    __lowerCamelCase	:	Any   	=			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	:	int   	=			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	:	Optional[Any]   	=			dataset.rename_columns({'pixel_values': 'image'}	)
  if "annotation" in dataset["train"].column_names:
    __lowerCamelCase	:	Any   	=			dataset.rename_columns({'annotation': 'label'}	)

  # If we don't have a validation split, split off a percentage of train as validation.
  __lowerCamelCase	:	int   	=			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	:	List[str]   	=			dataset['train'].train_test_split(data_args.train_val_split	)
    __lowerCamelCase	:	Tuple   	=			split['train']
    __lowerCamelCase	:	int   	=			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	:	Any   	=			'huggingface/label-files'
    __lowerCamelCase	:	Optional[Any]   	=			'ade20k-id2label.json'
  else:
    __lowerCamelCase	:	List[Any]   	=			data_args.dataset_name
    __lowerCamelCase	:	Tuple   	=			'id2label.json'
  __lowerCamelCase	:	Union[str, Any]   	=			json.load(open(hf_hub_download(_A	,_A	,repo_type='dataset'	)	,'r'	)	)
  __lowerCamelCase	:	int   	=			{int(_A	): v for k, v in idalabel.items()}
  __lowerCamelCase	:	Optional[int]   	=			{v: str(_A	) for k, v in idalabel.items()}

  # Load the mean IoU metric from the datasets package
  __lowerCamelCase	:	List[str]   	=			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(_lowerCAmelCase	):
    __lowerCamelCase	,__lowerCamelCase	:	Union[str, Any]   	=			eval_pred
    __lowerCamelCase	:	Tuple   	=			torch.from_numpy(_A	)
    # scale the logits to the size of the label
    __lowerCamelCase	:	Tuple   	=			nn.functional.interpolate(
        _A	,size=labels.shape[-2:]	,mode='bilinear'	,align_corners=_A	,).argmax(dim=1	)

    __lowerCamelCase	:	Dict   	=			logits_tensor.detach().cpu().numpy()
    __lowerCamelCase	:	Union[str, Any]   	=			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	:	Union[str, Any]   	=			metrics.pop('per_category_accuracy'	).tolist()
    __lowerCamelCase	:	List[Any]   	=			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	:	str   	=			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	:	Tuple   	=			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	:	List[Any]   	=			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	:	List[Any]   	=			(image_processor.size['shortest_edge'], image_processor.size['shortest_edge'])
  else:
    __lowerCamelCase	:	Tuple   	=			(image_processor.size['height'], image_processor.size['width'])
  __lowerCamelCase	:	int   	=			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	:	int   	=			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(_lowerCAmelCase	):
    __lowerCamelCase	:	Dict   	=			[]
    __lowerCamelCase	:	List[Any]   	=			[]
    for image, target in zip(example_batch['image']	,example_batch['label']	):
      __lowerCamelCase	,__lowerCamelCase	:	List[str]   	=			train_transforms(image.convert('RGB'	)	,_A	)
      pixel_values.append(_A	)
      labels.append(_A	)

    __lowerCamelCase	:	Optional[Any]   	=			{}
    __lowerCamelCase	:	List[str]   	=			torch.stack(_A	)
    __lowerCamelCase	:	str   	=			torch.stack(_A	)

    return encoding

  def preprocess_val(_lowerCAmelCase	):
    __lowerCamelCase	:	str   	=			[]
    __lowerCamelCase	:	Dict   	=			[]
    for image, target in zip(example_batch['image']	,example_batch['label']	):
      __lowerCamelCase	,__lowerCamelCase	:	Tuple   	=			val_transforms(image.convert('RGB'	)	,_A	)
      pixel_values.append(_A	)
      labels.append(_A	)

    __lowerCamelCase	:	Union[str, Any]   	=			{}
    __lowerCamelCase	:	Any   	=			torch.stack(_A	)
    __lowerCamelCase	:	Tuple   	=			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	:	Tuple   	=			(
          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	:	str   	=			(
          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	:	List[Any]   	=			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	:	List[Any]   	=			None
    if training_args.resume_from_checkpoint is not None:
      __lowerCamelCase	:	List[str]   	=			training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      __lowerCamelCase	:	Optional[int]   	=			last_checkpoint
    __lowerCamelCase	:	Optional[Any]   	=			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	:	Optional[Any]   	=			trainer.evaluate()
    trainer.log_metrics('eval'	,_A	)
    trainer.save_metrics('eval'	,_A	)

  # Write model card and (optionally) push to hub
  __lowerCamelCase	:	Any   	=			{
      '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()











'''simple docstring'''





import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  a_							 =42
  a_							 =None




def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase=0.999	,_lowerCAmelCase="cosine"	,)				->				Union[str, Any]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(_lowerCAmelCase	):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(_lowerCAmelCase	):
      return math.exp(t * -12.0	)

  else:
    raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'	)

  __lowerCamelCase	:	Union[str, Any]   	=			[]
  for i in range(_lowerCAmelCase	):
    __lowerCamelCase	:	Dict   	=			i / num_diffusion_timesteps
    __lowerCamelCase	:	Optional[int]   	=			(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(_lowerCAmelCase	) / alpha_bar_fn(_lowerCAmelCase	)	,_lowerCAmelCase	)	)
  return torch.tensor(_lowerCAmelCase	,dtype=torch.floataa	)






class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  a_							 =[e.name for e in KarrasDiffusionSchedulers]
  a_							 =1




  @register_to_config
  def __init__(			self				:     List[Any]      ,     _a				:     int = 1000      ,     _a				:     float = 0.0001      ,     _a				:     float = 0.02      ,     _a				:     str = "linear"      ,     _a				:     Optional[Union[np.ndarray, List[float]]] = None      ,     _a				:     Optional[bool] = False      ,     _a				:     str = "epsilon"      ,     _a				:     str = "linspace"      ,     _a				:     int = 0      ,     ) -> Tuple:
    if trained_betas is not None:
      __lowerCamelCase	:	Optional[int]   	=			torch.tensor(_a      ,     dtype=torch.floataa       )
    elif beta_schedule == "linear":
      __lowerCamelCase	:	List[str]   	=			torch.linspace(_a      ,     _a      ,     _a      ,     dtype=torch.floataa       )
    elif beta_schedule == "scaled_linear":
      # this schedule is very specific to the latent diffusion model.
      __lowerCamelCase	:	Optional[int]   	=			(
          torch.linspace(beta_start**0.5      ,     beta_end**0.5      ,     _a      ,     dtype=torch.floataa       ) ** 2
      )
    elif beta_schedule == "squaredcos_cap_v2":
      # Glide cosine schedule
      __lowerCamelCase	:	Any   	=			betas_for_alpha_bar(_a       )
    else:
      raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'       )

    __lowerCamelCase	:	Optional[Any]   	=			1.0 - self.betas
    __lowerCamelCase	:	Optional[int]   	=			torch.cumprod(self.alphas      ,     dim=0       )

    __lowerCamelCase	:	str   	=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
    __lowerCamelCase	:	Optional[Any]   	=			np.concatenate([sigmas[::-1], [0.0]]       ).astype(np.floataa       )
    __lowerCamelCase	:	Dict   	=			torch.from_numpy(_a       )

    # setable values
    __lowerCamelCase	:	List[str]   	=			None
    __lowerCamelCase	:	int   	=			use_karras_sigmas
    self.set_timesteps(_a      ,     _a       )
    __lowerCamelCase	:	int   	=			[]
    __lowerCamelCase	:	Dict   	=			False




  @property
  def 						_lowercase					(			self				:     Optional[Any]       ) -> Any:
    # standard deviation of the initial noise distribution
    if self.config.timestep_spacing in ["linspace", "trailing"]:
      return self.sigmas.max()

    return (self.sigmas.max() ** 2 + 1) ** 0.5




  def 						_lowercase					(			self				:     List[Any]      ,     _a				:     torch.FloatTensor      ,     _a				:     Union[float, torch.FloatTensor]       ) -> torch.FloatTensor:
    if isinstance(_a      ,     torch.Tensor       ):
      __lowerCamelCase	:	List[Any]   	=			timestep.to(self.timesteps.device       )
    __lowerCamelCase	:	int   	=			(self.timesteps == timestep).nonzero().item()
    __lowerCamelCase	:	Any   	=			self.sigmas[step_index]
    __lowerCamelCase	:	List[str]   	=			sample / ((sigma**2 + 1) ** 0.5)
    __lowerCamelCase	:	Any   	=			True
    return sample




  def 						_lowercase					(			self				:     Optional[int]      ,     _a				:     str      ,     _a				:     List[str]      ,     _a				:     Union[str, Any]       ) -> Optional[Any]:

    def lms_derivative(_a				:     List[str]       ):
      __lowerCamelCase	:	Any   	=			1.0
      for k in range(_a       ):
        if current_order == k:
          continue
        prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
      return prod

    __lowerCamelCase	:	Any   	=			integrate.quad(_a      ,     self.sigmas[t]      ,     self.sigmas[t + 1]      ,     epsrel=1e-4       )[0]

    return integrated_coeff




  def 						_lowercase					(			self				:     int      ,     _a				:     int      ,     _a				:     Union[str, torch.device] = None       ) -> str:
    __lowerCamelCase	:	Optional[Any]   	=			num_inference_steps

    # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
    if self.config.timestep_spacing == "linspace":
      __lowerCamelCase	:	Any   	=			np.linspace(0      ,     self.config.num_train_timesteps - 1      ,     _a      ,     dtype=_a       )[
          ::-1
      ].copy()
    elif self.config.timestep_spacing == "leading":
      __lowerCamelCase	:	Union[str, Any]   	=			self.config.num_train_timesteps // self.num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # casting to int to avoid issues when num_inference_step is power of 3
      __lowerCamelCase	:	Union[str, Any]   	=			(np.arange(0      ,     _a       ) * step_ratio).round()[::-1].copy().astype(_a       )
      timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
      __lowerCamelCase	:	int   	=			self.config.num_train_timesteps / self.num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # casting to int to avoid issues when num_inference_step is power of 3
      __lowerCamelCase	:	str   	=			(np.arange(self.config.num_train_timesteps      ,     0      ,     -step_ratio       )).round().copy().astype(_a       )
      timesteps -= 1
    else:
      raise ValueError(
          f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'       )

    __lowerCamelCase	:	Optional[Any]   	=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
    __lowerCamelCase	:	List[Any]   	=			np.log(_a       )
    __lowerCamelCase	:	Optional[int]   	=			np.interp(_a      ,     np.arange(0      ,     len(_a       )       )      ,     _a       )

    if self.use_karras_sigmas:
      __lowerCamelCase	:	int   	=			self._convert_to_karras(in_sigmas=_a       )
      __lowerCamelCase	:	str   	=			np.array([self._sigma_to_t(_a      ,     _a       ) for sigma in sigmas]       )

    __lowerCamelCase	:	Optional[Any]   	=			np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )

    __lowerCamelCase	:	Dict   	=			torch.from_numpy(_a       ).to(device=_a       )
    if str(_a       ).startswith('mps'       ):
      # mps does not support float64
      __lowerCamelCase	:	List[str]   	=			torch.from_numpy(_a       ).to(_a      ,     dtype=torch.floataa       )
    else:
      __lowerCamelCase	:	str   	=			torch.from_numpy(_a       ).to(device=_a       )

    __lowerCamelCase	:	List[Any]   	=			[]




  def 						_lowercase					(			self				:     List[Any]      ,     _a				:     List[Any]      ,     _a				:     int       ) -> Tuple:
    # get log sigma
    __lowerCamelCase	:	Optional[int]   	=			np.log(_a       )

    # get distribution
    __lowerCamelCase	:	int   	=			log_sigma - log_sigmas[:, np.newaxis]

    # get sigmas range
    __lowerCamelCase	:	Dict   	=			np.cumsum((dists >= 0)      ,     axis=0       ).argmax(axis=0       ).clip(max=log_sigmas.shape[0] - 2       )
    __lowerCamelCase	:	List[Any]   	=			low_idx + 1

    __lowerCamelCase	:	Optional[Any]   	=			log_sigmas[low_idx]
    __lowerCamelCase	:	str   	=			log_sigmas[high_idx]

    # interpolate sigmas
    __lowerCamelCase	:	List[str]   	=			(low - log_sigma) / (low - high)
    __lowerCamelCase	:	List[str]   	=			np.clip(_a      ,     0      ,     1       )

    # transform interpolation to time range
    __lowerCamelCase	:	Dict   	=			(1 - w) * low_idx + w * high_idx
    __lowerCamelCase	:	List[str]   	=			t.reshape(sigma.shape       )
    return t




  def 						_lowercase					(			self				:     int      ,     _a				:     torch.FloatTensor       ) -> torch.FloatTensor:
    __lowerCamelCase	:	float   	=			in_sigmas[-1].item()
    __lowerCamelCase	:	float   	=			in_sigmas[0].item()

    __lowerCamelCase	:	Optional[Any]   	=			7.0  # 7.0 is the value used in the paper
    __lowerCamelCase	:	Any   	=			np.linspace(0      ,     1      ,     self.num_inference_steps       )
    __lowerCamelCase	:	Optional[Any]   	=			sigma_min ** (1 / rho)
    __lowerCamelCase	:	Tuple   	=			sigma_max ** (1 / rho)
    __lowerCamelCase	:	str   	=			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas




  def 						_lowercase					(			self				:     Dict      ,     _a				:     torch.FloatTensor      ,     _a				:     Union[float, torch.FloatTensor]      ,     _a				:     torch.FloatTensor      ,     _a				:     int = 4      ,     _a				:     bool = True      ,     ) -> Union[LMSDiscreteSchedulerOutput, Tuple]:
    if not self.is_scale_input_called:
      warnings.warn(
          'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
          'See `StableDiffusionPipeline` for a usage example.'       )

    if isinstance(_a      ,     torch.Tensor       ):
      __lowerCamelCase	:	Optional[int]   	=			timestep.to(self.timesteps.device       )
    __lowerCamelCase	:	List[str]   	=			(self.timesteps == timestep).nonzero().item()
    __lowerCamelCase	:	Optional[int]   	=			self.sigmas[step_index]

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
      __lowerCamelCase	:	Optional[Any]   	=			sample - sigma * model_output
    elif self.config.prediction_type == "v_prediction":
      # * c_out + input * c_skip
      __lowerCamelCase	:	List[str]   	=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
    elif self.config.prediction_type == "sample":
      __lowerCamelCase	:	Union[str, Any]   	=			model_output
    else:
      raise ValueError(
          f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'       )

    # 2. Convert to an ODE derivative
    __lowerCamelCase	:	str   	=			(sample - pred_original_sample) / sigma
    self.derivatives.append(_a       )
    if len(self.derivatives       ) > order:
      self.derivatives.pop(0       )

    # 3. Compute linear multistep coefficients
    __lowerCamelCase	:	List[str]   	=			min(step_index + 1      ,     _a       )
    __lowerCamelCase	:	int   	=			[self.get_lms_coefficient(_a      ,     _a      ,     _a       ) for curr_order in range(_a       )]

    # 4. Compute previous sample based on the derivatives path
    __lowerCamelCase	:	Optional[Any]   	=			sample + sum(
        coeff * derivative for coeff, derivative in zip(_a      ,     reversed(self.derivatives       )       )       )

    if not return_dict:
      return (prev_sample,)

    return LMSDiscreteSchedulerOutput(prev_sample=_a      ,     pred_original_sample=_a       )




  def 						_lowercase					(			self				:     List[str]      ,     _a				:     torch.FloatTensor      ,     _a				:     torch.FloatTensor      ,     _a				:     torch.FloatTensor      ,     ) -> torch.FloatTensor:
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    __lowerCamelCase	:	Any   	=			self.sigmas.to(device=original_samples.device      ,     dtype=original_samples.dtype       )
    if original_samples.device.type == "mps" and torch.is_floating_point(_a       ):
      # mps does not support float64
      __lowerCamelCase	:	Optional[int]   	=			self.timesteps.to(original_samples.device      ,     dtype=torch.floataa       )
      __lowerCamelCase	:	Optional[int]   	=			timesteps.to(original_samples.device      ,     dtype=torch.floataa       )
    else:
      __lowerCamelCase	:	int   	=			self.timesteps.to(original_samples.device       )
      __lowerCamelCase	:	Optional[Any]   	=			timesteps.to(original_samples.device       )

    __lowerCamelCase	:	List[str]   	=			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

    __lowerCamelCase	:	Union[str, Any]   	=			sigmas[step_indices].flatten()
    while len(sigma.shape       ) < len(original_samples.shape       ):
      __lowerCamelCase	:	List[Any]   	=			sigma.unsqueeze(-1       )

    __lowerCamelCase	:	Optional[Any]   	=			original_samples + noise * sigma
    return noisy_samples




  def __len__(			self				:     Union[str, Any]       ) -> int:
    return self.config.num_train_timesteps






