


"""simple docstring"""






import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


SCREAMING_SNAKE_CASE__				:  Any						= logging.getLogger(__name__)




def        A_					(					UpperCAmelCase__				)     ->     Union[str, Any]:
   a   :	Any						=    p * torch.log(UpperCAmelCase__				)
   a   :	int						=    0
   return -plogp.sum(dim=-1				)




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




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__=True       ,   UpperCAmelCase__=True       ,   UpperCAmelCase__=None       ,   UpperCAmelCase__=False				)     ->     Any:
   a	, a   :	Tuple						=    model.config.num_hidden_layers, model.config.num_attention_heads
   a   :	Dict						=    torch.zeros(UpperCAmelCase__       ,   UpperCAmelCase__				).to(args.device				)
   a   :	Union[str, Any]						=    torch.zeros(UpperCAmelCase__       ,   UpperCAmelCase__				).to(args.device				)

   if head_mask is None:
      a   :	str						=    torch.ones(UpperCAmelCase__       ,   UpperCAmelCase__				).to(args.device				)

   head_mask.requires_grad_(requires_grad=UpperCAmelCase__				)
   # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
   if actually_pruned:
      a   :	Union[str, Any]						=    None

   a   :	Optional[Any]						=    None
   a   :	Any						=    None
   a   :	List[str]						=    0.0

   for step, inputs in enumerate(tqdm(UpperCAmelCase__       ,   desc='Iteration'       ,   disable=args.local_rank not in [-1, 0]				)				):
      for k, v in inputs.items():
         a   :	str						=    v.to(args.device				)

      # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
      a   :	Union[str, Any]						=    model(**UpperCAmelCase__       ,   head_mask=UpperCAmelCase__				)
      a	, a	, a   :	Tuple						=    (
          outputs[0],
          outputs[1],
          outputs[-1],
      )  # Loss and logits are the first, attention the last
      loss.backward()  # Backpropagate to populate the gradients in the head mask

      if compute_entropy:
         for layer, attn in enumerate(UpperCAmelCase__				):
            a   :	Union[str, Any]						=    entropy(attn.detach()				) * inputs['attention_mask'].float().unsqueeze(1				)
            attn_entropy[layer] += masked_entropy.sum(-1				).sum(0				).detach()

      if compute_importance:
         head_importance += head_mask.grad.abs().detach()

      # Also store our logits/labels if we want to compute metrics afterwards
      if preds is None:
         a   :	Dict						=    logits.detach().cpu().numpy()
         a   :	Tuple						=    inputs['labels'].detach().cpu().numpy()
      else:
         a   :	int						=    np.append(UpperCAmelCase__       ,   logits.detach().cpu().numpy()       ,   axis=0				)
         a   :	int						=    np.append(UpperCAmelCase__       ,   inputs['labels'].detach().cpu().numpy()       ,   axis=0				)

      tot_tokens += inputs["attention_mask"].float().detach().sum().data

   # Normalize
   attn_entropy /= tot_tokens
   head_importance /= tot_tokens
   # Layerwise importance normalization
   if not args.dont_normalize_importance_by_layer:
      a   :	Tuple						=    2
      a   :	List[Any]						=    torch.pow(torch.pow(UpperCAmelCase__       ,   UpperCAmelCase__				).sum(-1				)       ,   1 / exponent				)
      head_importance /= norm_by_layer.unsqueeze(-1				) + 1e-20

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

   # Print/save matrices
   np.save(os.path.join(args.output_dir       ,   'attn_entropy.npy'				)       ,   attn_entropy.detach().cpu().numpy()				)
   np.save(os.path.join(args.output_dir       ,   'head_importance.npy'				)       ,   head_importance.detach().cpu().numpy()				)

   logger.info('Attention entropies'				)
   print_ad_tensor(UpperCAmelCase__				)
   logger.info('Head importance scores'				)
   print_ad_tensor(UpperCAmelCase__				)
   logger.info('Head ranked by importance scores'				)
   a   :	Union[str, Any]						=    torch.zeros(head_importance.numel()       ,   dtype=torch.long       ,   device=args.device				)
   a   :	str						=    torch.arange(
       head_importance.numel()       ,   device=args.device				)
   a   :	str						=    head_ranks.view_as(UpperCAmelCase__				)
   print_ad_tensor(UpperCAmelCase__				)

   return attn_entropy, head_importance, preds, labels




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     List[Any]:
   a	, a	, a	, a   :	Dict						=    compute_heads_importance(UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   compute_entropy=UpperCAmelCase__				)
   a   :	Any						=    np.argmax(UpperCAmelCase__       ,   axis=1				) if args.output_mode == 'classification' else np.squeeze(UpperCAmelCase__				)
   a   :	Union[str, Any]						=    glue_compute_metrics(args.task_name       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)[args.metric_name]
   logger.info('Pruning: original score: %f, threshold: %f'       ,   UpperCAmelCase__       ,   original_score * args.masking_threshold				)

   a   :	Dict						=    torch.ones_like(UpperCAmelCase__				)
   a   :	str						=    max(1       ,   int(new_head_mask.numel() * args.masking_amount				)				)

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

      if len(UpperCAmelCase__				) <= num_to_mask:
         break

      # mask heads
      a   :	Dict						=    current_heads_to_mask[:num_to_mask]
      logger.info('Heads to mask: %s'       ,   str(current_heads_to_mask.tolist()				)				)
      a   :	Union[str, Any]						=    new_head_mask.view(-1				)
      a   :	List[Any]						=    0.0
      a   :	Dict						=    new_head_mask.view_as(UpperCAmelCase__				)
      a   :	Dict						=    new_head_mask.clone().detach()
      print_ad_tensor(UpperCAmelCase__				)

      # Compute metric and head importance again
      a	, a	, a	, a   :	str						=    compute_heads_importance(
          UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   compute_entropy=UpperCAmelCase__       ,   head_mask=UpperCAmelCase__				)
      a   :	List[Any]						=    np.argmax(UpperCAmelCase__       ,   axis=1				) if args.output_mode == 'classification' else np.squeeze(UpperCAmelCase__				)
      a   :	Any						=    glue_compute_metrics(args.task_name       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)[args.metric_name]
      logger.info(
          'Masking: current score: %f, remaining heads %d (%.1f percents)'       ,   UpperCAmelCase__       ,   new_head_mask.sum()       ,   new_head_mask.sum() / new_head_mask.numel() * 100       ,   )

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

   return head_mask




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Dict:
   a   :	Any						=    datetime.now()
   a	, a	, a	, a   :	Any						=    compute_heads_importance(
       UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   compute_entropy=UpperCAmelCase__       ,   compute_importance=UpperCAmelCase__       ,   head_mask=UpperCAmelCase__				)
   a   :	Optional[Any]						=    np.argmax(UpperCAmelCase__       ,   axis=1				) if args.output_mode == 'classification' else np.squeeze(UpperCAmelCase__				)
   a   :	List[Any]						=    glue_compute_metrics(args.task_name       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)[args.metric_name]
   a   :	Dict						=    datetime.now() - before_time

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

   assert sum(len(UpperCAmelCase__				) for h in heads_to_prune.values()				) == (1 - head_mask.long()).sum().item()
   model.prune_heads(UpperCAmelCase__				)
   a   :	Union[str, Any]						=    sum(p.numel() for p in model.parameters()				)

   a   :	Dict						=    datetime.now()
   a	, a	, a	, a   :	Dict						=    compute_heads_importance(
       UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   compute_entropy=UpperCAmelCase__       ,   compute_importance=UpperCAmelCase__       ,   head_mask=UpperCAmelCase__       ,   actually_pruned=UpperCAmelCase__       ,   )
   a   :	List[Any]						=    np.argmax(UpperCAmelCase__       ,   axis=1				) if args.output_mode == 'classification' else np.squeeze(UpperCAmelCase__				)
   a   :	Optional[Any]						=    glue_compute_metrics(args.task_name       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)[args.metric_name]
   a   :	Any						=    datetime.now() - before_time

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




def        A_					(					)     ->     Union[str, Any]:
   a   :	Dict						=    argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--data_dir'       ,   default=UpperCAmelCase__       ,   type=UpperCAmelCase__       ,   required=UpperCAmelCase__       ,   help='The input data dir. Should contain the .tsv files (or other data files) for the task.'       ,   )
   parser.add_argument(
       '--model_name_or_path'       ,   default=UpperCAmelCase__       ,   type=UpperCAmelCase__       ,   required=UpperCAmelCase__       ,   help='Path to pretrained model or model identifier from huggingface.co/models'       ,   )
   parser.add_argument(
       '--task_name'       ,   default=UpperCAmelCase__       ,   type=UpperCAmelCase__       ,   required=UpperCAmelCase__       ,   help='The name of the task to train selected in the list: ' + ', '.join(glue_processors.keys()				)       ,   )
   parser.add_argument(
       '--output_dir'       ,   default=UpperCAmelCase__       ,   type=UpperCAmelCase__       ,   required=UpperCAmelCase__       ,   help='The output directory where the model predictions and checkpoints will be written.'       ,   )

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

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

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

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

   parser.add_argument('--seed'       ,   type=UpperCAmelCase__       ,   default=42				)
   parser.add_argument('--local_rank'       ,   type=UpperCAmelCase__       ,   default=-1       ,   help='local_rank for distributed training on gpus'				)
   parser.add_argument('--no_cuda'       ,   action='store_true'       ,   help='Whether not to use CUDA when available'				)
   parser.add_argument('--server_ip'       ,   type=UpperCAmelCase__       ,   default=''       ,   help='Can be used for distant debugging.'				)
   parser.add_argument('--server_port'       ,   type=UpperCAmelCase__       ,   default=''       ,   help='Can be used for distant debugging.'				)
   a   :	Union[str, Any]						=    parser.parse_args()

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

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

   # Setup devices and distributed training
   if args.local_rank == -1 or args.no_cuda:
      a   :	Optional[int]						=    torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'				)
      a   :	List[str]						=    0 if args.no_cuda else torch.cuda.device_count()
   else:
      torch.cuda.set_device(args.local_rank				)
      a   :	Optional[int]						=    torch.device('cuda'       ,   args.local_rank				)
      a   :	Any						=    1
      torch.distributed.init_process_group(backend='nccl'				)  # Initializes the distributed backend

   # Setup logging
   logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN				)
   logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device       ,   args.n_gpu       ,   bool(args.local_rank != -1				)				)				)
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(args.local_rank				):
      transformers.utils.logging.set_verbosity_info()
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()

   # Set seeds
   set_seed(args.seed				)

   # Prepare GLUE task
   a   :	List[Any]						=    args.task_name.lower()
   if args.task_name not in glue_processors:
      raise ValueError('Task not found: %s' % (args.task_name)				)
   a   :	Any						=    glue_processors[args.task_name]()
   a   :	List[Any]						=    glue_output_modes[args.task_name]
   a   :	str						=    processor.get_labels()
   a   :	Optional[Any]						=    len(UpperCAmelCase__				)

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

   a   :	List[Any]						=    AutoConfig.from_pretrained(
       args.config_name if args.config_name else args.model_name_or_path       ,   num_labels=UpperCAmelCase__       ,   finetuning_task=args.task_name       ,   output_attentions=UpperCAmelCase__       ,   cache_dir=args.cache_dir       ,   )
   a   :	str						=    AutoTokenizer.from_pretrained(
       args.tokenizer_name if args.tokenizer_name else args.model_name_or_path       ,   cache_dir=args.cache_dir       ,   )
   a   :	Union[str, Any]						=    AutoModelForSequenceClassification.from_pretrained(
       args.model_name_or_path       ,   from_tf=bool('.ckpt' in args.model_name_or_path				)       ,   config=UpperCAmelCase__       ,   cache_dir=args.cache_dir       ,   )

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

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

   # Prepare dataset for the GLUE task
   a   :	Optional[int]						=    GlueDataset(UpperCAmelCase__       ,   tokenizer=UpperCAmelCase__       ,   mode='dev'				)
   if args.data_subset > 0:
      a   :	Union[str, Any]						=    Subset(UpperCAmelCase__       ,   list(range(min(args.data_subset       ,   len(UpperCAmelCase__				)				)				)				)				)
   a   :	List[Any]						=    SequentialSampler(UpperCAmelCase__				) if args.local_rank == -1 else DistributedSampler(UpperCAmelCase__				)
   a   :	Union[str, Any]						=    DataLoader(
       UpperCAmelCase__       ,   sampler=UpperCAmelCase__       ,   batch_size=args.batch_size       ,   collate_fn=UpperCAmelCase__				)

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

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


if __name__ == "__main__":
 main()









"""simple docstring"""






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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)


@flax.struct.dataclass
class 	A_       :





  """simple docstring"""

  lowercase		:	CommonSchedulerState

  # setable values
  lowercase		:	jnp.ndarray
  lowercase		:	jnp.ndarray
  lowercase		:	jnp.ndarray
  lowercase		:	Optional[int]   =						None

  # running values
  lowercase		:	Optional[jnp.ndarray]   =						None
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Tuple:
     return cls(common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase						)



@dataclass
class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	LMSDiscreteSchedulerState



class 	A_       (     _UpperCAmelCase      ,  _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	int   =						[e.name for e in FlaxKarrasDiffusionSchedulers]

  lowercase		:	jnp.dtype
  @property
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     return True
  @register_to_config
  def __init__(						self  ,					__UpperCAmelCase = 10_00  ,					__UpperCAmelCase = 0.0001  ,					__UpperCAmelCase = 0.02  ,					__UpperCAmelCase = "linear"  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "epsilon"  ,					__UpperCAmelCase = jnp.floataa  ,					)				->					Union[str, Any]:
     a   :	Dict						=    dtype
  def 							lowercase_     (						self  ,					__UpperCAmelCase = None						)				->					LMSDiscreteSchedulerState:
     if common is None:
        a   :	Dict						=    CommonSchedulerState.create(self						)

     a   :	List[Any]						=    jnp.arange(0  ,					self.config.num_train_timesteps						).round()[::-1]
     a   :	Dict						=    ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

     # standard deviation of the initial noise distribution
     a   :	Union[str, Any]						=    sigmas.max()

     return LMSDiscreteSchedulerState.create(
         common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					jnp.ndarray:
     ((a)	, )   :	Optional[int]						=    jnp.where(state.timesteps == timestep  ,					size=1						)
     a   :	Optional[Any]						=    step_index[0]

     a   :	Union[str, Any]						=    state.sigmas[step_index]
     a   :	Optional[int]						=    sample / ((sigma**2 + 1) ** 0.5)
     return sample
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Optional[int]:

     def lms_derivative(__UpperCAmelCase						):
        a   :	Optional[Any]						=    1.0
        for k in range(__UpperCAmelCase						):
           if current_order == k:
              continue
           prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
        return prod

     a   :	Union[str, Any]						=    integrate.quad(__UpperCAmelCase  ,					state.sigmas[t]  ,					state.sigmas[t + 1]  ,					epsrel=1E-4						)[0]

     return integrated_coeff
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = ()						)				->					LMSDiscreteSchedulerState:
     a   :	Optional[int]						=    jnp.linspace(self.config.num_train_timesteps - 1  ,					0  ,					__UpperCAmelCase  ,					dtype=self.dtype						)

     a   :	Dict						=    jnp.floor(__UpperCAmelCase						).astype(jnp.intaa						)
     a   :	str						=    jnp.ceil(__UpperCAmelCase						).astype(jnp.intaa						)

     a   :	List[Any]						=    jnp.mod(__UpperCAmelCase  ,					1.0						)

     a   :	Optional[int]						=    ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
     a   :	int						=    (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
     a   :	Optional[int]						=    jnp.concatenate([sigmas, jnp.array([0.0]  ,					dtype=self.dtype						)]						)

     a   :	Any						=    timesteps.astype(jnp.intaa						)

     # initial running values
     a   :	Optional[int]						=    jnp.zeros((0,) + shape  ,					dtype=self.dtype						)

     return state.replace(
         timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					num_inference_steps=__UpperCAmelCase  ,					derivatives=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = 4  ,					__UpperCAmelCase = True  ,					)				->					Union[FlaxLMSSchedulerOutput, Tuple]:
     if state.num_inference_steps is None:
        raise ValueError(
            'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'						)

     a   :	Dict						=    state.sigmas[timestep]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        a   :	Any						=    sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        a   :	str						=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     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
     a   :	str						=    (sample - pred_original_sample) / sigma
     a   :	int						=    state.replace(derivatives=jnp.append(state.derivatives  ,					__UpperCAmelCase						)						)
     if len(state.derivatives						) > order:
        a   :	Any						=    state.replace(derivatives=jnp.delete(state.derivatives  ,					0						)						)

     # 3. Compute linear multistep coefficients
     a   :	Any						=    min(timestep + 1  ,					__UpperCAmelCase						)
     a   :	Dict						=    [self.get_lms_coefficient(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						) for curr_order in range(__UpperCAmelCase						)]

     # 4. Compute previous sample based on the derivatives path
     a   :	int						=    sample + sum(
         coeff * derivative for coeff, derivative in zip(__UpperCAmelCase  ,					reversed(state.derivatives						)						)						)

     if not return_dict:
        return (prev_sample, state)

     return FlaxLMSSchedulerOutput(prev_sample=__UpperCAmelCase  ,					state=__UpperCAmelCase						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					)				->					jnp.ndarray:
     a   :	Dict						=    state.sigmas[timesteps].flatten()
     a   :	Union[str, Any]						=    broadcast_to_shape_from_left(__UpperCAmelCase  ,					noise.shape						)

     a   :	List[Any]						=    original_samples + noise * sigma

     return noisy_samples






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





