




'''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


UpperCAmelCase_		:  Union[str, Any]   			=			logging.getLogger(__name__)





def UpperCAmelCase						(					UpperCAmelCase_	):
    _a							:       Dict       					=			p * torch.log(UpperCAmelCase_	)
    _a							:       Dict       					=			0
    return -plogp.sum(dim=-1	)





def UpperCAmelCase						(					UpperCAmelCase_	):
    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 UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_=True      ,  UpperCAmelCase_=True      ,  UpperCAmelCase_=None      ,  UpperCAmelCase_=False	):
    _a							:       str       					=			model.config.num_hidden_layers, model.config.num_attention_heads
    _a							:       List[str]       					=			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							:       str       					=			None

    _a							:       Union[str, Any]       					=			None
    _a							:       Any       					=			None
    _a							:       Tuple       					=			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							:       Optional[int]       					=			model(**UpperCAmelCase_      ,  head_mask=UpperCAmelCase_	)
        _a							:       Optional[Any]       					=			(
            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							:       Optional[int]       					=			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							:       Any       					=			logits.detach().cpu().numpy()
            _a							:       int       					=			inputs["labels"].detach().cpu().numpy()
        else:
            _a							:       Dict       					=			np.append(UpperCAmelCase_      ,  logits.detach().cpu().numpy()      ,  axis=0	)
            _a							:       List[str]       					=			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							:       List[Any]       					=			2
        _a							:       List[str]       					=			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							:       Any       					=			torch.zeros(head_importance.numel()      ,  dtype=torch.long      ,  device=args.device	)
    _a							:       Optional[int]       					=			torch.arange(
        head_importance.numel()      ,  device=args.device	)
    _a							:       Optional[int]       					=			head_ranks.view_as(UpperCAmelCase_	)
    print_ad_tensor(UpperCAmelCase_	)

    return attn_entropy, head_importance, preds, labels





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       Optional[int]       					=			compute_heads_importance(UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  compute_entropy=UpperCAmelCase_	)
    _a							:       Union[str, Any]       					=			np.argmax(UpperCAmelCase_      ,  axis=1	) if args.output_mode == "classification" else np.squeeze(UpperCAmelCase_	)
    _a							:       Optional[int]       					=			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							:       Tuple       					=			torch.ones_like(UpperCAmelCase_	)
    _a							:       str       					=			max(1      ,  int(new_head_mask.numel() * args.masking_amount	)	)

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

        if len(UpperCAmelCase_	) <= num_to_mask:
            break

        # mask heads
        _a							:       Optional[int]       					=			current_heads_to_mask[:num_to_mask]
        logger.info("""Heads to mask: %s"""      ,  str(current_heads_to_mask.tolist()	)	)
        _a							:       Optional[int]       					=			new_head_mask.view(-1	)
        _a							:       Union[str, Any]       					=			0.0
        _a							:       Tuple       					=			new_head_mask.view_as(UpperCAmelCase_	)
        _a							:       List[Any]       					=			new_head_mask.clone().detach()
        print_ad_tensor(UpperCAmelCase_	)

        # Compute metric and head importance again
        _a							:       str       					=			compute_heads_importance(
            UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  compute_entropy=UpperCAmelCase_      ,  head_mask=UpperCAmelCase_	)
        _a							:       Optional[Any]       					=			np.argmax(UpperCAmelCase_      ,  axis=1	) if args.output_mode == "classification" else np.squeeze(UpperCAmelCase_	)
        _a							:       Tuple       					=			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 UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       Union[str, Any]       					=			datetime.now()
    _a							:       List[Any]       					=			compute_heads_importance(
        UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  compute_entropy=UpperCAmelCase_      ,  compute_importance=UpperCAmelCase_      ,  head_mask=UpperCAmelCase_	)
    _a							:       List[Any]       					=			np.argmax(UpperCAmelCase_      ,  axis=1	) if args.output_mode == "classification" else np.squeeze(UpperCAmelCase_	)
    _a							:       List[str]       					=			glue_compute_metrics(args.task_name      ,  UpperCAmelCase_      ,  UpperCAmelCase_	)[args.metric_name]
    _a							:       Union[str, Any]       					=			datetime.now() - before_time

    _a							:       Optional[int]       					=			sum(p.numel() for p in model.parameters()	)
    _a							:       Any       					=			{
        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							:       Optional[int]       					=			datetime.now()
    _a							:       List[Any]       					=			compute_heads_importance(
        UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  compute_entropy=UpperCAmelCase_      ,  compute_importance=UpperCAmelCase_      ,  head_mask=UpperCAmelCase_      ,  actually_pruned=UpperCAmelCase_      ,  )
    _a							:       Dict       					=			np.argmax(UpperCAmelCase_      ,  axis=1	) if args.output_mode == "classification" else np.squeeze(UpperCAmelCase_	)
    _a							:       Tuple       					=			glue_compute_metrics(args.task_name      ,  UpperCAmelCase_      ,  UpperCAmelCase_	)[args.metric_name]
    _a							:       str       					=			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 UpperCAmelCase						(					):
    _a							:       List[Any]       					=			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							:       Tuple       					=			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							:       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							:       Union[str, Any]       					=			torch.device("""cuda"""      ,  args.local_rank	)
        _a							:       Optional[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							:       Tuple       					=			args.task_name.lower()
    if args.task_name not in glue_processors:
        raise ValueError("""Task not found: %s""" % (args.task_name)	)
    _a							:       int       					=			glue_processors[args.task_name]()
    _a							:       Union[str, Any]       					=			glue_output_modes[args.task_name]
    _a							:       Dict       					=			processor.get_labels()
    _a							:       str       					=			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							:       Union[str, 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							:       Dict       					=			AutoTokenizer.from_pretrained(
        args.tokenizer_name if args.tokenizer_name else args.model_name_or_path      ,  cache_dir=args.cache_dir      ,  )
    _a							:       Optional[int]       					=			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							:       Optional[int]       					=			nn.parallel.DistributedDataParallel(
            UpperCAmelCase_      ,  device_ids=[args.local_rank]      ,  output_device=args.local_rank      ,  find_unused_parameters=UpperCAmelCase_	)
    elif args.n_gpu > 1:
        _a							:       Union[str, 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							:       Tuple       					=			GlueDataset(UpperCAmelCase_      ,  tokenizer=UpperCAmelCase_      ,  mode="""dev"""	)
    if args.data_subset > 0:
        _a							:       str       					=			Subset(UpperCAmelCase_      ,  list(range(min(args.data_subset      ,  len(UpperCAmelCase_	)	)	)	)	)
    _a							:       Optional[Any]       					=			SequentialSampler(UpperCAmelCase_	) if args.local_rank == -1 else DistributedSampler(UpperCAmelCase_	)
    _a							:       Dict       					=			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							:       List[str]       					=			mask_heads(UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	)
        prune_heads(UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	)


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     UpperCamelCase					(  snake_case__					):
  a    :torch.FloatTensor
  a    :Optional[torch.FloatTensor]      					=				None






def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_=0.999      ,  UpperCAmelCase_="cosine"      ,  ):
    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

    _a							:       Union[str, Any]       					=			[]
    for i in range(UpperCAmelCase_	):
        _a							:       Optional[int]       					=			i / num_diffusion_timesteps
        _a							:       Optional[Any]       					=			(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     UpperCamelCase					(  snake_case__			,		snake_case__					):
  a    :Any      					=				[e.name for e in KarrasDiffusionSchedulers]
  a    :Tuple      					=				1



  @register_to_config
  def __init__( self		,    __lowerCAmelCase = 1000		,    __lowerCAmelCase = 0.0001		,    __lowerCAmelCase = 0.02		,    __lowerCAmelCase = "linear"		,    __lowerCAmelCase = None		,    __lowerCAmelCase = False		,    __lowerCAmelCase = "epsilon"		,    __lowerCAmelCase = "linspace"		,    __lowerCAmelCase = 0		,    ):







      '''simple docstring'''


      if trained_betas is not None:
          _a							:       str       					=			torch.tensor(__lowerCAmelCase		,    dtype=torch.floataa   )
      elif beta_schedule == "linear":
          _a							:       int       					=			torch.linspace(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    dtype=torch.floataa   )
      elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          _a							:       Any       					=			(
              torch.linspace(beta_start**0.5		,    beta_end**0.5		,    __lowerCAmelCase		,    dtype=torch.floataa   ) ** 2
          )
      elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          _a							:       Optional[Any]       					=			betas_for_alpha_bar(__lowerCAmelCase   )
      else:
          raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''   )

      _a							:       Dict       					=			1.0 - self.betas
      _a							:       Any       					=			torch.cumprod(self.alphas		,    dim=0   )

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

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



  @property
  def 						a_       ( self   ):







      '''simple docstring'''


      if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

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



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase   ):







      '''simple docstring'''


      if isinstance(__lowerCAmelCase		,    torch.Tensor   ):
          _a							:       int       					=			timestep.to(self.timesteps.device   )
      _a							:       Optional[Any]       					=			(self.timesteps == timestep).nonzero().item()
      _a							:       int       					=			self.sigmas[step_index]
      _a							:       Optional[int]       					=			sample / ((sigma**2 + 1) ** 0.5)
      _a							:       str       					=			True
      return sample



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   ):







      '''simple docstring'''


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

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

      return integrated_coeff



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = None   ):







      '''simple docstring'''


      _a							:       Optional[int]       					=			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":
          _a							:       str       					=			np.linspace(0		,    self.config.num_train_timesteps - 1		,    __lowerCAmelCase		,    dtype=__lowerCAmelCase   )[
              ::-1
          ].copy()
      elif self.config.timestep_spacing == "leading":
          _a							:       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
          _a							:       List[str]       					=			(np.arange(0		,    __lowerCAmelCase   ) * step_ratio).round()[::-1].copy().astype(__lowerCAmelCase   )
          timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
          _a							:       Optional[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
          _a							:       List[Any]       					=			(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\'.'''   )

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

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

      _a							:       List[Any]       					=			np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )

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

      _a							:       Any       					=			[]



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Any       					=			np.log(__lowerCAmelCase   )

      # get distribution
      _a							:       Optional[Any]       					=			log_sigma - log_sigmas[:, np.newaxis]

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

      _a							:       Union[str, Any]       					=			log_sigmas[low_idx]
      _a							:       str       					=			log_sigmas[high_idx]

      # interpolate sigmas
      _a							:       Union[str, Any]       					=			(low - log_sigma) / (low - high)
      _a							:       Tuple       					=			np.clip(__lowerCAmelCase		,    0		,    1   )

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



  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       float       					=			in_sigmas[-1].item()
      _a							:       float       					=			in_sigmas[0].item()

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



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = 4		,    __lowerCAmelCase = True		,    ):







      '''simple docstring'''


      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(__lowerCAmelCase		,    torch.Tensor   ):
          _a							:       Tuple       					=			timestep.to(self.timesteps.device   )
      _a							:       List[str]       					=			(self.timesteps == timestep).nonzero().item()
      _a							:       str       					=			self.sigmas[step_index]

      # 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							:       Any       					=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
      elif self.config.prediction_type == "sample":
          _a							:       List[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
      _a							:       Any       					=			(sample - pred_original_sample) / sigma
      self.derivatives.append(__lowerCAmelCase   )
      if len(self.derivatives   ) > order:
          self.derivatives.pop(0   )

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

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

      if not return_dict:
          return (prev_sample,)

      return LMSDiscreteSchedulerOutput(prev_sample=__lowerCAmelCase		,    pred_original_sample=__lowerCAmelCase   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    ):







      '''simple docstring'''


      _a							:       Optional[Any]       					=			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
          _a							:       Any       					=			self.timesteps.to(original_samples.device		,    dtype=torch.floataa   )
          _a							:       Optional[Any]       					=			timesteps.to(original_samples.device		,    dtype=torch.floataa   )
      else:
          _a							:       int       					=			self.timesteps.to(original_samples.device   )
          _a							:       Optional[Any]       					=			timesteps.to(original_samples.device   )

      _a							:       Any       					=			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

      _a							:       List[str]       					=			sigmas[step_indices].flatten()
      while len(sigma.shape   ) < len(original_samples.shape   ):
          _a							:       int       					=			sigma.unsqueeze(-1   )

      _a							:       List[str]       					=			original_samples + noise * sigma
      return noisy_samples



  def __len__( self   ):







      '''simple docstring'''


      return self.config.num_train_timesteps


