code: 
  
  
  
  
  
  '''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
  
  
  lowercase_			       =				logging.getLogger(__name__)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str					)		->Union[str, Any]:
      _SCREAMING_SNAKE_CASE             =    p * torch.log(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    0
      return -plogp.sum(dim=-1					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[Any]					)		->Optional[Any]:
      logger.info("""lv, h >\t""" + """\t""".join(F'{x + 1}' for x in range(len(__lowerCamelCase					)					)					)					)
      for row in range(len(__lowerCamelCase					)					):
          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 lowerCamelCase		(  __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					Optional[Any]      , __lowerCamelCase				:					int      , __lowerCamelCase				:					str=True      , __lowerCamelCase				:					List[Any]=True      , __lowerCamelCase				:					Optional[int]=None      , __lowerCamelCase				:					List[str]=False					)		->List[str]:
      _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    model.config.num_hidden_layers, model.config.num_attention_heads
      _SCREAMING_SNAKE_CASE             =    torch.zeros(__lowerCamelCase      , __lowerCamelCase					).to(args.device					)
      _SCREAMING_SNAKE_CASE             =    torch.zeros(__lowerCamelCase      , __lowerCamelCase					).to(args.device					)
  
      if head_mask is None:
          _SCREAMING_SNAKE_CASE             =    torch.ones(__lowerCamelCase      , __lowerCamelCase					).to(args.device					)
  
      head_mask.requires_grad_(requires_grad=__lowerCamelCase					)
      # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
      if actually_pruned:
          _SCREAMING_SNAKE_CASE             =    None
  
      _SCREAMING_SNAKE_CASE             =    None
      _SCREAMING_SNAKE_CASE             =    None
      _SCREAMING_SNAKE_CASE             =    0.0
  
      for step, inputs in enumerate(tqdm(__lowerCamelCase      , desc="""Iteration"""      , disable=args.local_rank not in [-1, 0]					)					):
          for k, v in inputs.items():
              _SCREAMING_SNAKE_CASE             =    v.to(args.device					)
  
          # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
          _SCREAMING_SNAKE_CASE             =    model(**__lowerCamelCase      , head_mask=__lowerCamelCase					)
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    (
              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(__lowerCamelCase					):
                  _SCREAMING_SNAKE_CASE             =    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:
              _SCREAMING_SNAKE_CASE             =    logits.detach().cpu().numpy()
              _SCREAMING_SNAKE_CASE             =    inputs["""labels"""].detach().cpu().numpy()
          else:
              _SCREAMING_SNAKE_CASE             =    np.append(__lowerCamelCase      , logits.detach().cpu().numpy()      , axis=0					)
              _SCREAMING_SNAKE_CASE             =    np.append(__lowerCamelCase      , 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:
          _SCREAMING_SNAKE_CASE             =    2
          _SCREAMING_SNAKE_CASE             =    torch.pow(torch.pow(__lowerCamelCase      , __lowerCamelCase					).sum(-1					)      , 1 / exponent					)
          head_importance /= norm_by_layer.unsqueeze(-1					) + 1e-2_0
  
      if not args.dont_normalize_global_importance:
          _SCREAMING_SNAKE_CASE             =    (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(__lowerCamelCase					)
      logger.info("""Head importance scores"""					)
      print_ad_tensor(__lowerCamelCase					)
      logger.info("""Head ranked by importance scores"""					)
      _SCREAMING_SNAKE_CASE             =    torch.zeros(head_importance.numel()      , dtype=torch.long      , device=args.device					)
      _SCREAMING_SNAKE_CASE             =    torch.arange(
          head_importance.numel()      , device=args.device					)
      _SCREAMING_SNAKE_CASE             =    head_ranks.view_as(__lowerCamelCase					)
      print_ad_tensor(__lowerCamelCase					)
  
      return attn_entropy, head_importance, preds, labels
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Any      , __lowerCamelCase				:					Dict      , __lowerCamelCase				:					Optional[int]					)		->List[Any]:
      _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    compute_heads_importance(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase      , compute_entropy=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    np.argmax(__lowerCamelCase      , axis=1					) if args.output_mode == """classification""" else np.squeeze(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    glue_compute_metrics(args.task_name      , __lowerCamelCase      , __lowerCamelCase					)[args.metric_name]
      logger.info("""Pruning: original score: %f, threshold: %f"""      , __lowerCamelCase      , original_score * args.masking_threshold					)
  
      _SCREAMING_SNAKE_CASE             =    torch.ones_like(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    max(1      , int(new_head_mask.numel() * args.masking_amount					)					)
  
      _SCREAMING_SNAKE_CASE             =    original_score
      while current_score >= original_score * args.masking_threshold:
          _SCREAMING_SNAKE_CASE             =    new_head_mask.clone()  # save current head mask
          # heads from least important to most - keep only not-masked heads
          _SCREAMING_SNAKE_CASE             =    float("""Inf"""					)
          _SCREAMING_SNAKE_CASE             =    head_importance.view(-1					).sort()[1]
  
          if len(__lowerCamelCase					) <= num_to_mask:
              break
  
          # mask heads
          _SCREAMING_SNAKE_CASE             =    current_heads_to_mask[:num_to_mask]
          logger.info("""Heads to mask: %s"""      , str(current_heads_to_mask.tolist()					)					)
          _SCREAMING_SNAKE_CASE             =    new_head_mask.view(-1					)
          _SCREAMING_SNAKE_CASE             =    0.0
          _SCREAMING_SNAKE_CASE             =    new_head_mask.view_as(__lowerCamelCase					)
          _SCREAMING_SNAKE_CASE             =    new_head_mask.clone().detach()
          print_ad_tensor(__lowerCamelCase					)
  
          # Compute metric and head importance again
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    compute_heads_importance(
              __lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase      , compute_entropy=__lowerCamelCase      , head_mask=__lowerCamelCase					)
          _SCREAMING_SNAKE_CASE             =    np.argmax(__lowerCamelCase      , axis=1					) if args.output_mode == """classification""" else np.squeeze(__lowerCamelCase					)
          _SCREAMING_SNAKE_CASE             =    glue_compute_metrics(args.task_name      , __lowerCamelCase      , __lowerCamelCase					)[args.metric_name]
          logger.info(
              """Masking: current score: %f, remaining heads %d (%.1f percents)"""      , __lowerCamelCase      , new_head_mask.sum()      , new_head_mask.sum() / new_head_mask.numel() * 100      , )
  
      logger.info("""Final head mask"""					)
      print_ad_tensor(__lowerCamelCase					)
      np.save(os.path.join(args.output_dir      , """head_mask.npy"""					)      , head_mask.detach().cpu().numpy()					)
  
      return head_mask
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[int]      , __lowerCamelCase				:					Any      , __lowerCamelCase				:					Tuple      , __lowerCamelCase				:					Tuple					)		->str:
      _SCREAMING_SNAKE_CASE             =    datetime.now()
      _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    compute_heads_importance(
          __lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase      , compute_entropy=__lowerCamelCase      , compute_importance=__lowerCamelCase      , head_mask=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    np.argmax(__lowerCamelCase      , axis=1					) if args.output_mode == """classification""" else np.squeeze(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    glue_compute_metrics(args.task_name      , __lowerCamelCase      , __lowerCamelCase					)[args.metric_name]
      _SCREAMING_SNAKE_CASE             =    datetime.now() - before_time
  
      _SCREAMING_SNAKE_CASE             =    sum(p.numel() for p in model.parameters()					)
      _SCREAMING_SNAKE_CASE             =    {
          layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__lowerCamelCase					)					)
      }
  
      assert sum(len(__lowerCamelCase					) for h in heads_to_prune.values()					) == (1 - head_mask.long()).sum().item()
      model.prune_heads(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    sum(p.numel() for p in model.parameters()					)
  
      _SCREAMING_SNAKE_CASE             =    datetime.now()
      _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    compute_heads_importance(
          __lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase      , compute_entropy=__lowerCamelCase      , compute_importance=__lowerCamelCase      , head_mask=__lowerCamelCase      , actually_pruned=__lowerCamelCase      , )
      _SCREAMING_SNAKE_CASE             =    np.argmax(__lowerCamelCase      , axis=1					) if args.output_mode == """classification""" else np.squeeze(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    glue_compute_metrics(args.task_name      , __lowerCamelCase      , __lowerCamelCase					)[args.metric_name]
      _SCREAMING_SNAKE_CASE             =    datetime.now() - before_time
  
      logger.info(
          """Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"""      , __lowerCamelCase      , __lowerCamelCase      , pruned_num_params / original_num_params * 100      , )
      logger.info("""Pruning: score with masking: %f score with pruning: %f"""      , __lowerCamelCase      , __lowerCamelCase					)
      logger.info("""Pruning: speed ratio (new timing / original timing): %f percents"""      , original_time / new_time * 100					)
  
  
  
  
  
  def lowerCamelCase		(  )		->Any:
      _SCREAMING_SNAKE_CASE             =    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--data_dir"""      , default=__lowerCamelCase      , type=__lowerCamelCase      , required=__lowerCamelCase      , 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=__lowerCamelCase      , type=__lowerCamelCase      , required=__lowerCamelCase      , help="""Path to pretrained model or model identifier from huggingface.co/models"""      , )
      parser.add_argument(
          """--task_name"""      , default=__lowerCamelCase      , type=__lowerCamelCase      , required=__lowerCamelCase      , help="""The name of the task to train selected in the list: """ + """, """.join(glue_processors.keys()					)      , )
      parser.add_argument(
          """--output_dir"""      , default=__lowerCamelCase      , type=__lowerCamelCase      , required=__lowerCamelCase      , help="""The output directory where the model predictions and checkpoints will be written."""      , )
  
      # Other parameters
      parser.add_argument(
          """--config_name"""      , default=""""""      , type=__lowerCamelCase      , help="""Pretrained config name or path if not the same as model_name_or_path"""      , )
      parser.add_argument(
          """--tokenizer_name"""      , default=""""""      , type=__lowerCamelCase      , help="""Pretrained tokenizer name or path if not the same as model_name_or_path"""      , )
      parser.add_argument(
          """--cache_dir"""      , default=__lowerCamelCase      , type=__lowerCamelCase      , help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""      , )
      parser.add_argument(
          """--data_subset"""      , type=__lowerCamelCase      , 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=__lowerCamelCase      , help="""masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."""      , )
      parser.add_argument(
          """--masking_amount"""      , default=0.1      , type=__lowerCamelCase      , help="""Amount to heads to masking at each masking step."""					)
      parser.add_argument("""--metric_name"""      , default="""acc"""      , type=__lowerCamelCase      , help="""Metric to use for head masking."""					)
  
      parser.add_argument(
          """--max_seq_length"""      , default=128      , type=__lowerCamelCase      , 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=__lowerCamelCase      , help="""Batch size."""					)
  
      parser.add_argument("""--seed"""      , type=__lowerCamelCase      , default=42					)
      parser.add_argument("""--local_rank"""      , type=__lowerCamelCase      , 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=__lowerCamelCase      , default=""""""      , help="""Can be used for distant debugging."""					)
      parser.add_argument("""--server_port"""      , type=__lowerCamelCase      , default=""""""      , help="""Can be used for distant debugging."""					)
      _SCREAMING_SNAKE_CASE             =    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=__lowerCamelCase					)
          ptvsd.wait_for_attach()
  
      # Setup devices and distributed training
      if args.local_rank == -1 or args.no_cuda:
          _SCREAMING_SNAKE_CASE             =    torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""					)
          _SCREAMING_SNAKE_CASE             =    0 if args.no_cuda else torch.cuda.device_count()
      else:
          torch.cuda.set_device(args.local_rank					)
          _SCREAMING_SNAKE_CASE             =    torch.device("""cuda"""      , args.local_rank					)
          _SCREAMING_SNAKE_CASE             =    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
      _SCREAMING_SNAKE_CASE             =    args.task_name.lower()
      if args.task_name not in glue_processors:
          raise ValueError("""Task not found: %s""" % (args.task_name)					)
      _SCREAMING_SNAKE_CASE             =    glue_processors[args.task_name]()
      _SCREAMING_SNAKE_CASE             =    glue_output_modes[args.task_name]
      _SCREAMING_SNAKE_CASE             =    processor.get_labels()
      _SCREAMING_SNAKE_CASE             =    len(__lowerCamelCase					)
  
      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
  
      _SCREAMING_SNAKE_CASE             =    AutoConfig.from_pretrained(
          args.config_name if args.config_name else args.model_name_or_path      , num_labels=__lowerCamelCase      , finetuning_task=args.task_name      , output_attentions=__lowerCamelCase      , cache_dir=args.cache_dir      , )
      _SCREAMING_SNAKE_CASE             =    AutoTokenizer.from_pretrained(
          args.tokenizer_name if args.tokenizer_name else args.model_name_or_path      , cache_dir=args.cache_dir      , )
      _SCREAMING_SNAKE_CASE             =    AutoModelForSequenceClassification.from_pretrained(
          args.model_name_or_path      , from_tf=bool(""".ckpt""" in args.model_name_or_path					)      , config=__lowerCamelCase      , cache_dir=args.cache_dir      , )
  
      # Distributed and parallel training
      model.to(args.device					)
      if args.local_rank != -1:
          _SCREAMING_SNAKE_CASE             =    nn.parallel.DistributedDataParallel(
              __lowerCamelCase      , device_ids=[args.local_rank]      , output_device=args.local_rank      , find_unused_parameters=__lowerCamelCase					)
      elif args.n_gpu > 1:
          _SCREAMING_SNAKE_CASE             =    nn.DataParallel(__lowerCamelCase					)
  
      # Print/save training arguments
      os.makedirs(args.output_dir      , exist_ok=__lowerCamelCase					)
      torch.save(__lowerCamelCase      , os.path.join(args.output_dir      , """run_args.bin"""					)					)
      logger.info("""Training/evaluation parameters %s"""      , __lowerCamelCase					)
  
      # Prepare dataset for the GLUE task
      _SCREAMING_SNAKE_CASE             =    GlueDataset(__lowerCamelCase      , tokenizer=__lowerCamelCase      , mode="""dev"""					)
      if args.data_subset > 0:
          _SCREAMING_SNAKE_CASE             =    Subset(__lowerCamelCase      , list(range(min(args.data_subset      , len(__lowerCamelCase					)					)					)					)					)
      _SCREAMING_SNAKE_CASE             =    SequentialSampler(__lowerCamelCase					) if args.local_rank == -1 else DistributedSampler(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    DataLoader(
          __lowerCamelCase      , sampler=__lowerCamelCase      , batch_size=args.batch_size      , collate_fn=__lowerCamelCase					)
  
      # Compute head entropy and importance score
      compute_heads_importance(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
  
      # 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:
          _SCREAMING_SNAKE_CASE             =    mask_heads(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
          prune_heads(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
  
  
  if __name__ == "__main__":
        main()
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
  # and https://github.com/hojonathanho/diffusion
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      add_noise_common,
      get_velocity_common,
  )
  
  
  @flax.struct.dataclass
  class       a_  :
  
      '''simple docstring'''
      UpperCamelCase									=				42
      UpperCamelCase									=				42
  
      # setable values
      UpperCamelCase									=				42
      UpperCamelCase									=				42
      UpperCamelCase									=				None
  
  
      @classmethod
      def     snake_case_(	cls				,	A				,	A				,	A				,	A				,	)	->     List[Any]:
          return cls(
              common=A				,	final_alpha_cumprod=A				,	init_noise_sigma=A				,	timesteps=A				,	)
  
  
  
  
  @dataclass
  class       a_  (       snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				42
  
  
  
  
  class       a_  (       snake_case_   ,					snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				[e.name for e in FlaxKarrasDiffusionSchedulers]
  
      UpperCamelCase									=				42
  
  
      @property
      def     snake_case_(	self )	->     List[Any]:
          return True
  
  
      @register_to_config
      def __init__(	self				,	A = 1000				,	A = 0.0001				,	A = 0.02				,	A = "linear"				,	A = None				,	A = True				,	A = 0				,	A = "epsilon"				,	A = jnp.floataa				,	)	->     int:
          _SCREAMING_SNAKE_CASE             =    dtype
  
  
      def     snake_case_(	self				,	A = None )	->     DDIMSchedulerState:
          if common is None:
              _SCREAMING_SNAKE_CASE             =    CommonSchedulerState.create(self )
  
          # At every step in ddim, we are looking into the previous alphas_cumprod
          # For the final step, there is no previous alphas_cumprod because we are already at 0
          # `set_alpha_to_one` decides whether we set this parameter simply to one or
          # whether we use the final alpha of the "non-previous" one.
          _SCREAMING_SNAKE_CASE             =    (
              jnp.array(1.0				,	dtype=self.dtype ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
          )
  
          # standard deviation of the initial noise distribution
          _SCREAMING_SNAKE_CASE             =    jnp.array(1.0				,	dtype=self.dtype )
  
          _SCREAMING_SNAKE_CASE             =    jnp.arange(0				,	self.config.num_train_timesteps ).round()[::-1]
  
          return DDIMSchedulerState.create(
              common=A				,	final_alpha_cumprod=A				,	init_noise_sigma=A				,	timesteps=A				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A = None )	->     jnp.ndarray:
          return sample
  
  
      def     snake_case_(	self				,	A				,	A				,	A = () )	->     DDIMSchedulerState:
          _SCREAMING_SNAKE_CASE             =    self.config.num_train_timesteps // num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # rounding to avoid issues when num_inference_step is power of 3
          _SCREAMING_SNAKE_CASE             =    (jnp.arange(0				,	A ) * step_ratio).round()[::-1] + self.config.steps_offset
  
          return state.replace(
              num_inference_steps=A				,	timesteps=A				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A )	->     int:
          _SCREAMING_SNAKE_CASE             =    state.common.alphas_cumprod[timestep]
          _SCREAMING_SNAKE_CASE             =    jnp.where(
              prev_timestep >= 0				,	state.common.alphas_cumprod[prev_timestep]				,	state.final_alpha_cumprod )
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t_prev
  
          _SCREAMING_SNAKE_CASE             =    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
          return variance
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A = 0.0				,	A = True				,	)	->     Union[FlaxDDIMSchedulerOutput, 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""" )
  
          # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
          # Ideally, read DDIM paper in-detail understanding
  
          # Notation (<variable name> -> <name in paper>
          # - pred_noise_t -> e_theta(x_t, t)
          # - pred_original_sample -> f_theta(x_t, t) or x_0
          # - std_dev_t -> sigma_t
          # - eta -> η
          # - pred_sample_direction -> "direction pointing to x_t"
          # - pred_prev_sample -> "x_t-1"
  
          # 1. get previous step value (=t-1)
          _SCREAMING_SNAKE_CASE             =    timestep - self.config.num_train_timesteps // state.num_inference_steps
  
          _SCREAMING_SNAKE_CASE             =    state.common.alphas_cumprod
          _SCREAMING_SNAKE_CASE             =    state.final_alpha_cumprod
  
          # 2. compute alphas, betas
          _SCREAMING_SNAKE_CASE             =    alphas_cumprod[timestep]
          _SCREAMING_SNAKE_CASE             =    jnp.where(prev_timestep >= 0				,	alphas_cumprod[prev_timestep]				,	A )
  
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t
  
          # 3. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          if self.config.prediction_type == "epsilon":
              _SCREAMING_SNAKE_CASE             =    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
              _SCREAMING_SNAKE_CASE             =    model_output
          elif self.config.prediction_type == "sample":
              _SCREAMING_SNAKE_CASE             =    model_output
              _SCREAMING_SNAKE_CASE             =    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
              _SCREAMING_SNAKE_CASE             =    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
              _SCREAMING_SNAKE_CASE             =    (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
          else:
              raise ValueError(
                  f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                  """ `v_prediction`""" )
  
          # 4. compute variance: "sigma_t(η)" -> see formula (16)
          # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
          _SCREAMING_SNAKE_CASE             =    self._get_variance(A				,	A				,	A )
          _SCREAMING_SNAKE_CASE             =    eta * variance ** 0.5
  
          # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _SCREAMING_SNAKE_CASE             =    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon
  
          # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _SCREAMING_SNAKE_CASE             =    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
          if not return_dict:
              return (prev_sample, state)
  
          return FlaxDDIMSchedulerOutput(prev_sample=A				,	state=A )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	)	->     jnp.ndarray:
          return add_noise_common(state.common				,	A				,	A				,	A )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	)	->     jnp.ndarray:
          return get_velocity_common(state.common				,	A				,	A				,	A )
  
  
  
  
  
      def __len__(	self )	->     Any:
          return self.config.num_train_timesteps
  
  
  
  