



"""simple docstring"""




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_UpperCamelCase:				Dict       							=  logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_UpperCamelCase:				Any       							=  {
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_UpperCamelCase:				Dict       							=  {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_UpperCamelCase:				Dict       							=  sorted(arg_to_scheduler.keys())
_UpperCamelCase:				List[str]       							=  '{' + ', '.join(arg_to_scheduler_choices) + '}'


class   a__		(						pl.LightningModule     ):


   def __init__(							self      :							int,     lowerCAmelCase      :							argparse.Namespace,     lowerCAmelCase      :							str=None,     lowerCAmelCase      :							str="base",     lowerCAmelCase      :							Optional[Any]=None,     lowerCAmelCase      :							Dict=None,     lowerCAmelCase      :							Any=None,     **lowerCAmelCase      :							Dict,     )    ->       List[str]:
         super().__init__()
         # TODO: move to self.save_hyperparameters()
         # self.save_hyperparameters()
         # can also expand arguments into trainer signature for easier reading

         self.save_hyperparameters(UpperCamelCase__			)
         lowercase  :      Union[str, Any]					=   0
         lowercase  :      List[str]					=   Path(self.hparams.output_dir			)
         lowercase  :      Union[str, Any]					=   self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
               lowercase  :      Any					=   AutoConfig.from_pretrained(
                   self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,     **({'num_labels': num_labels} if num_labels is not None else {}),     cache_dir=UpperCamelCase__,     **UpperCamelCase__,     )
         else:
               lowercase  :      Optional[Any]					=   config

         lowercase  :      Tuple					=   ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
         for p in extra_model_params:
               if getattr(self.hparams,     UpperCamelCase__,     UpperCamelCase__			):
                     assert hasattr(self.config,     UpperCamelCase__			), f'''model config doesn\'t have a `{p}` attribute'''
                     setattr(self.config,     UpperCamelCase__,     getattr(self.hparams,     UpperCamelCase__			)			)

         if tokenizer is None:
               lowercase  :      Union[str, Any]					=   AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,     cache_dir=UpperCamelCase__,     )
         else:
               lowercase  :      List[Any]					=   tokenizer
         lowercase  :      Tuple					=   MODEL_MODES[mode]
         if model is None:
               lowercase  :      List[str]					=   self.model_type.from_pretrained(
                   self.hparams.model_name_or_path,     from_tf=bool('.ckpt' in self.hparams.model_name_or_path			),     config=self.config,     cache_dir=UpperCamelCase__,     )
         else:
               lowercase  :      List[Any]					=   model


   def        lowercase				(							self      :							Optional[Any],     *lowerCAmelCase      :							Any,     **lowerCAmelCase      :							List[str]			)    ->       Dict:
         lowercase  :      Optional[Any]					=   self.model_type.from_pretrained(*UpperCamelCase__,     **UpperCamelCase__			)


   def        lowercase				(							self      :							Any			)    ->       List[Any]:
         lowercase  :      Union[str, Any]					=   arg_to_scheduler[self.hparams.lr_scheduler]
         lowercase  :      Optional[Any]					=   get_schedule_func(
             self.opt,     num_warmup_steps=self.hparams.warmup_steps,     num_training_steps=self.total_steps()			)
         lowercase  :      Dict					=   {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
         return scheduler


   def        lowercase				(							self      :							List[str]			)    ->       Dict:
         lowercase  :      str					=   self.model
         lowercase  :      Any					=   ['bias', 'LayerNorm.weight']
         lowercase  :      Any					=   [
             {
                 'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay			)],
                 'weight_decay': self.hparams.weight_decay,
             },
             {
                 'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay			)],
                 'weight_decay': 0.0,
             },
         ]
         if self.hparams.adafactor:
               lowercase  :      Tuple					=   Adafactor(
                   UpperCamelCase__,     lr=self.hparams.learning_rate,     scale_parameter=UpperCamelCase__,     relative_step=UpperCamelCase__			)

         else:
               lowercase  :      int					=   AdamW(
                   UpperCamelCase__,     lr=self.hparams.learning_rate,     eps=self.hparams.adam_epsilon			)
         lowercase  :      int					=   optimizer

         lowercase  :      Optional[int]					=   self.get_lr_scheduler()

         return [optimizer], [scheduler]


   def        lowercase				(							self      :							Any,     lowerCAmelCase      :							Any,     lowerCAmelCase      :							Optional[int]			)    ->       int:
         return self.validation_step(UpperCamelCase__,     UpperCamelCase__			)


   def        lowercase				(							self      :							Tuple,     lowerCAmelCase      :							Optional[int]			)    ->       int:
         return self.validation_end(UpperCamelCase__			)


   def        lowercase				(							self      :							int			)    ->       Union[str, Any]:
         lowercase  :      List[str]					=   max(1,     self.hparams.gpus			)  # TODO: consider num_tpu_cores
         lowercase  :      Optional[Any]					=   self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


   def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							Dict			)    ->       List[str]:
         if stage == "test":
               lowercase  :      int					=   len(self.test_dataloader().dataset			)
         else:
               lowercase  :      List[str]					=   self.get_dataloader('train',     self.hparams.train_batch_size,     shuffle=UpperCamelCase__			)
               lowercase  :      Union[str, Any]					=   len(self.train_dataloader().dataset			)


   def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							str,     lowerCAmelCase      :							int,     lowerCAmelCase      :							bool = False			)    ->       List[str]:
         raise NotImplementedError('You must implement this for your task'			)


   def        lowercase				(							self      :							List[Any]			)    ->       Union[str, Any]:
         return self.train_loader


   def        lowercase				(							self      :							Union[str, Any]			)    ->       Dict:
         return self.get_dataloader('dev',     self.hparams.eval_batch_size,     shuffle=UpperCamelCase__			)


   def        lowercase				(							self      :							Union[str, Any]			)    ->       Optional[int]:
         return self.get_dataloader('test',     self.hparams.eval_batch_size,     shuffle=UpperCamelCase__			)


   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							Any			)    ->       Any:
         return os.path.join(
             self.hparams.data_dir,     'cached_{}_{}_{}'.format(
                 UpperCamelCase__,     list(filter(UpperCamelCase__,     self.hparams.model_name_or_path.split('/'			)			)			).pop(),     str(self.hparams.max_seq_length			),     ),     )


   @pl.utilities.rank_zero_only
   def        lowercase				(							self      :							Any,     lowerCAmelCase      :							Dict[str, Any]			)    ->       str:
         lowercase  :      int					=   self.output_dir.joinpath('best_tfmr'			)
         lowercase  :      List[Any]					=   self.step_count
         self.model.save_pretrained(UpperCamelCase__			)
         self.tokenizer.save_pretrained(UpperCamelCase__			)






   @staticmethod
   def        lowercase				(							lowerCAmelCase      :							int,     lowerCAmelCase      :							Union[str, Any]			)    ->       Union[str, Any]:
         parser.add_argument(
             '--model_name_or_path',     default=UpperCamelCase__,     type=UpperCamelCase__,     required=UpperCamelCase__,     help='Path to pretrained model or model identifier from huggingface.co/models',     )
         parser.add_argument(
             '--config_name',     default='',     type=UpperCamelCase__,     help='Pretrained config name or path if not the same as model_name'			)
         parser.add_argument(
             '--tokenizer_name',     default=UpperCamelCase__,     type=UpperCamelCase__,     help='Pretrained tokenizer name or path if not the same as model_name',     )
         parser.add_argument(
             '--cache_dir',     default='',     type=UpperCamelCase__,     help='Where do you want to store the pre-trained models downloaded from huggingface.co',     )
         parser.add_argument(
             '--encoder_layerdrop',     type=UpperCamelCase__,     help='Encoder layer dropout probability (Optional). Goes into model.config',     )
         parser.add_argument(
             '--decoder_layerdrop',     type=UpperCamelCase__,     help='Decoder layer dropout probability (Optional). Goes into model.config',     )
         parser.add_argument(
             '--dropout',     type=UpperCamelCase__,     help='Dropout probability (Optional). Goes into model.config',     )
         parser.add_argument(
             '--attention_dropout',     type=UpperCamelCase__,     help='Attention dropout probability (Optional). Goes into model.config',     )
         parser.add_argument('--learning_rate',     default=5e-5,     type=UpperCamelCase__,     help='The initial learning rate for Adam.'			)
         parser.add_argument(
             '--lr_scheduler',     default='linear',     choices=UpperCamelCase__,     metavar=UpperCamelCase__,     type=UpperCamelCase__,     help='Learning rate scheduler',     )
         parser.add_argument('--weight_decay',     default=0.0,     type=UpperCamelCase__,     help='Weight decay if we apply some.'			)
         parser.add_argument('--adam_epsilon',     default=1e-8,     type=UpperCamelCase__,     help='Epsilon for Adam optimizer.'			)
         parser.add_argument('--warmup_steps',     default=0,     type=UpperCamelCase__,     help='Linear warmup over warmup_steps.'			)
         parser.add_argument('--num_workers',     default=4,     type=UpperCamelCase__,     help='kwarg passed to DataLoader'			)
         parser.add_argument('--num_train_epochs',     dest='max_epochs',     default=3,     type=UpperCamelCase__			)
         parser.add_argument('--train_batch_size',     default=32,     type=UpperCamelCase__			)
         parser.add_argument('--eval_batch_size',     default=32,     type=UpperCamelCase__			)
         parser.add_argument('--adafactor',     action='store_true'			)




class   a__		(						pl.Callback     ):


   def        lowercase				(							self      :							Dict,     lowerCAmelCase      :							int,     lowerCAmelCase      :							str			)    ->       Optional[Any]:
         if (
             trainer.is_global_zero and trainer.global_rank == 0
         ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
               pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.




class   a__		(						pl.Callback     ):


   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							Any,     lowerCAmelCase      :							Union[str, Any]			)    ->       str:
         lowercase  :      str					=   trainer.lr_schedulers[0]['scheduler']
         lowercase  :      List[Any]					=   {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()			)}
         pl_module.logger.log_metrics(UpperCamelCase__			)


   def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							pl.Trainer,     lowerCAmelCase      :							pl.LightningModule			)    ->       int:
         rank_zero_info('***** Validation results *****'			)
         lowercase  :      List[Any]					=   trainer.callback_metrics
         # Log results
         for key in sorted(UpperCamelCase__			):
               if key not in ["log", "progress_bar"]:
                     rank_zero_info('{} = {}\n'.format(UpperCamelCase__,     str(metrics[key]			)			)			)






   def        lowercase				(							self      :							int,     lowerCAmelCase      :							pl.Trainer,     lowerCAmelCase      :							pl.LightningModule			)    ->       List[Any]:
         rank_zero_info('***** Test results *****'			)
         lowercase  :      Any					=   trainer.callback_metrics
         # Log and save results to file
         lowercase  :      List[str]					=   os.path.join(pl_module.hparams.output_dir,     'test_results.txt'			)
         with open(UpperCamelCase__,     'w'			) as writer:
               for key in sorted(UpperCamelCase__			):
                     if key not in ["log", "progress_bar"]:
                           rank_zero_info('{} = {}\n'.format(UpperCamelCase__,     str(metrics[key]			)			)			)
                           writer.write('{} = {}\n'.format(UpperCamelCase__,     str(metrics[key]			)			)			)




def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase				)     ->			Optional[int]:






      '''simple docstring'''


      parser.add_argument(
          '--output_dir'	,      default=_UpperCAmelCase	,      type=_UpperCAmelCase	,      required=_UpperCAmelCase	,      help='The output directory where the model predictions and checkpoints will be written.'	,      )
      parser.add_argument(
          '--fp16'	,      action='store_true'	,      help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'	,      )

      parser.add_argument(
          '--fp16_opt_level'	,      type=_UpperCAmelCase	,      default='O2'	,      help=(
              'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
              'See details at https://nvidia.github.io/apex/amp.html'
          )	,      )
      parser.add_argument('--n_tpu_cores'	,      dest='tpu_cores'	,      type=_UpperCAmelCase				)
      parser.add_argument('--max_grad_norm'	,      dest='gradient_clip_val'	,      default=1.0	,      type=_UpperCAmelCase	,      help='Max gradient norm'				)
      parser.add_argument('--do_train'	,      action='store_true'	,      help='Whether to run training.'				)
      parser.add_argument('--do_predict'	,      action='store_true'	,      help='Whether to run predictions on the test set.'				)
      parser.add_argument(
          '--gradient_accumulation_steps'	,      dest='accumulate_grad_batches'	,      type=_UpperCAmelCase	,      default=1	,      help='Number of updates steps to accumulate before performing a backward/update pass.'	,      )
      parser.add_argument('--seed'	,      type=_UpperCAmelCase	,      default=42	,      help='random seed for initialization'				)
      parser.add_argument(
          '--data_dir'	,      default=_UpperCAmelCase	,      type=_UpperCAmelCase	,      required=_UpperCAmelCase	,      help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'	,      )





def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase=None	,      _UpperCAmelCase=True	,      _UpperCAmelCase=None	,      _UpperCAmelCase=[]	,      _UpperCAmelCase=None	,      _UpperCAmelCase=None	,      **_UpperCAmelCase	,      )     ->			Any:






      '''simple docstring'''


      pl.seed_everything(args.seed				)

      # init model
      lowercase  :      List[str]					=   Path(model.hparams.output_dir				)
      odir.mkdir(exist_ok=_UpperCAmelCase				)

      # add custom checkpoints
      if checkpoint_callback is None:
            lowercase  :      Tuple					=   pl.callbacks.ModelCheckpoint(
                filepath=args.output_dir	,      prefix='checkpoint'	,      monitor='val_loss'	,      mode='min'	,      save_top_k=1				)
      if early_stopping_callback:
            extra_callbacks.append(_UpperCAmelCase				)
      if logging_callback is None:
            lowercase  :      Tuple					=   LoggingCallback()

      lowercase  :      List[Any]					=   {}

      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
            lowercase  :      Optional[Any]					=   16
            # train_params["amp_level"] = args.fp16_opt_level

      if args.gpus > 1:
            lowercase  :      Union[str, Any]					=   'auto'  # "ddp"
            lowercase  :      Optional[Any]					=   'ddp'

      lowercase  :      str					=   args.accumulate_grad_batches
      lowercase  :      Any					=   None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
      lowercase  :      Tuple					=   'auto'

      lowercase  :      Optional[int]					=   pl.Trainer.from_argparse_args(
          _UpperCAmelCase	,      weights_summary=_UpperCAmelCase	,      callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]	,      logger=_UpperCAmelCase	,      **_UpperCAmelCase	,      )

      if args.do_train:
            trainer.fit(_UpperCAmelCase				)

      return trainer





"""simple docstring"""




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

import numpy as np
import torch

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


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


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				42
   _lowerCamelCase												=				None




def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase=0.9_9_9	,      _UpperCAmelCase="cosine"	,      )     ->			Optional[Any]:






      '''simple docstring'''


      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_UpperCAmelCase				):
                  return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(_UpperCAmelCase				):
                  return math.exp(t * -1_2.0				)

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

      lowercase  :      Any					=   []
      for i in range(_UpperCAmelCase				):
            lowercase  :      List[str]					=   i / num_diffusion_timesteps
            lowercase  :      str					=   (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase				) / alpha_bar_fn(_UpperCAmelCase				)	,      _UpperCAmelCase				)				)
      return torch.tensor(_UpperCAmelCase	,      dtype=torch.floataa				)





class   a__		(						SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				[e.name for e in KarrasDiffusionSchedulers]
   _lowerCamelCase												=				1


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

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

         lowercase  :      Optional[Any]					=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
         lowercase  :      Optional[int]					=   np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
         lowercase  :      int					=   torch.from_numpy(lowerCAmelCase			)

         # setable values
         lowercase  :      List[str]					=   None
         lowercase  :      Tuple					=   np.linspace(0,     num_train_timesteps - 1,     lowerCAmelCase,     dtype=lowerCAmelCase			)[::-1].copy()
         lowercase  :      Dict					=   torch.from_numpy(lowerCAmelCase			)
         lowercase  :      str					=   False


   @property
   def        lowercase				(							self      :							Tuple			)    ->       Tuple:
         # 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      :							Any,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Union[float, torch.FloatTensor]			)    ->       torch.FloatTensor:
         if isinstance(lowerCAmelCase,     torch.Tensor			):
               lowercase  :      Any					=   timestep.to(self.timesteps.device			)
         lowercase  :      List[Any]					=   (self.timesteps == timestep).nonzero().item()
         lowercase  :      Union[str, Any]					=   self.sigmas[step_index]
         lowercase  :      Dict					=   sample / ((sigma**2 + 1) ** 0.5)
         lowercase  :      Union[str, Any]					=   True
         return sample


   def        lowercase				(							self      :							Tuple,     lowerCAmelCase      :							int,     lowerCAmelCase      :							Union[str, torch.device] = None			)    ->       List[Any]:
         lowercase  :      Union[str, 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":
               lowercase  :      int					=   np.linspace(0,     self.config.num_train_timesteps - 1,     lowerCAmelCase,     dtype=lowerCAmelCase			)[
                   ::-1
               ].copy()
         elif self.config.timestep_spacing == "leading":
               lowercase  :      List[str]					=   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
               lowercase  :      Any					=   (np.arange(0,     lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase			)
               timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
               lowercase  :      Optional[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
               lowercase  :      Optional[int]					=   (np.arange(self.config.num_train_timesteps,     0,     -step_ratio			)).round().copy().astype(lowerCAmelCase			)
               timesteps -= 1
         else:
               raise ValueError(
                   f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''			)

         lowercase  :      int					=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
         lowercase  :      List[str]					=   np.interp(lowerCAmelCase,     np.arange(0,     len(lowerCAmelCase			)			),     lowerCAmelCase			)
         lowercase  :      Any					=   np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
         lowercase  :      Optional[Any]					=   torch.from_numpy(lowerCAmelCase			).to(device=lowerCAmelCase			)
         if str(lowerCAmelCase			).startswith('mps'			):
               # mps does not support float64
               lowercase  :      List[Any]					=   torch.from_numpy(lowerCAmelCase			).to(lowerCAmelCase,     dtype=torch.floataa			)
         else:
               lowercase  :      List[str]					=   torch.from_numpy(lowerCAmelCase			).to(device=lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Union[float, torch.FloatTensor],     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Optional[torch.Generator] = None,     lowerCAmelCase      :							bool = True,     )    ->       Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

         if (
             isinstance(lowerCAmelCase,     lowerCAmelCase			)
             or isinstance(lowerCAmelCase,     torch.IntTensor			)
             or isinstance(lowerCAmelCase,     torch.LongTensor			)
         ):
               raise ValueError(
                   (
                       'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                       ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
                       ' one of the `scheduler.timesteps` as a timestep.'
                   ),     )

         if not self.is_scale_input_called:
               logger.warning(
                   'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                   'See `StableDiffusionPipeline` for a usage example.'			)

         if isinstance(lowerCAmelCase,     torch.Tensor			):
               lowercase  :      List[str]					=   timestep.to(self.timesteps.device			)

         lowercase  :      List[str]					=   (self.timesteps == timestep).nonzero().item()
         lowercase  :      Optional[int]					=   self.sigmas[step_index]

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
               lowercase  :      Optional[Any]					=   sample - sigma * model_output
         elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               lowercase  :      int					=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         elif self.config.prediction_type == "sample":
               raise NotImplementedError('prediction_type not implemented yet: sample'			)
         else:
               raise ValueError(
                   f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''			)

         lowercase  :      List[Any]					=   self.sigmas[step_index]
         lowercase  :      str					=   self.sigmas[step_index + 1]
         lowercase  :      Optional[int]					=   (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
         lowercase  :      Optional[int]					=   (sigma_to**2 - sigma_up**2) ** 0.5

         # 2. Convert to an ODE derivative
         lowercase  :      Tuple					=   (sample - pred_original_sample) / sigma

         lowercase  :      str					=   sigma_down - sigma

         lowercase  :      int					=   sample + derivative * dt

         lowercase  :      Dict					=   model_output.device
         lowercase  :      List[Any]					=   randn_tensor(model_output.shape,     dtype=model_output.dtype,     device=lowerCAmelCase,     generator=lowerCAmelCase			)

         lowercase  :      List[str]					=   prev_sample + noise * sigma_up

         if not return_dict:
               return (prev_sample,)

         return EulerAncestralDiscreteSchedulerOutput(
             prev_sample=lowerCAmelCase,     pred_original_sample=lowerCAmelCase			)


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

         lowercase  :      Union[str, Any]					=   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

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






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