
'''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


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







def __A  (    lowerCAmelCase_       ):
     _UpperCAmelCase					:  int         =    p * torch.log(lowerCAmelCase_       )
     _UpperCAmelCase					:  Any         =    0
     return -plogp.sum(dim=-1       )







def __A  (    lowerCAmelCase_       ):
     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 __A  (    lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_=True ,       lowerCAmelCase_=True ,       lowerCAmelCase_=None ,       lowerCAmelCase_=False       ):
     _UpperCAmelCase  ,     _UpperCAmelCase					:  Optional[Any]         =    model.config.num_hidden_layers, model.config.num_attention_heads
     _UpperCAmelCase					:  Optional[Any]         =    torch.zeros(lowerCAmelCase_ ,       lowerCAmelCase_       ).to(args.device       )
     _UpperCAmelCase					:  Any         =    torch.zeros(lowerCAmelCase_ ,       lowerCAmelCase_       ).to(args.device       )

     if head_mask is None:
          _UpperCAmelCase					:  str         =    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:
          _UpperCAmelCase					:  Tuple         =    None

     _UpperCAmelCase					:  List[str]         =    None
     _UpperCAmelCase					:  str         =    None
     _UpperCAmelCase					:  Union[str, Any]         =    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():
               _UpperCAmelCase					:  Optional[Any]         =    v.to(args.device       )

          # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
          _UpperCAmelCase					:  Optional[int]         =    model(**lowerCAmelCase_ ,       head_mask=lowerCAmelCase_       )
          _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  Dict         =    (
              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_       ):
                    _UpperCAmelCase					:  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:
               _UpperCAmelCase					:  List[str]         =    logits.detach().cpu().numpy()
               _UpperCAmelCase					:  Optional[Any]         =    inputs["""labels"""].detach().cpu().numpy()
          else:
               _UpperCAmelCase					:  Optional[int]         =    np.append(lowerCAmelCase_ ,       logits.detach().cpu().numpy() ,       axis=0       )
               _UpperCAmelCase					:  Dict         =    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:
          _UpperCAmelCase					:  List[Any]         =    2
          _UpperCAmelCase					:  Tuple         =    torch.pow(torch.pow(lowerCAmelCase_ ,       lowerCAmelCase_       ).sum(-1       ) ,       1 / exponent       )
          head_importance /= norm_by_layer.unsqueeze(-1       ) + 1e-20

     if not args.dont_normalize_global_importance:
          _UpperCAmelCase					:  List[str]         =    (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"""       )
     _UpperCAmelCase					:  List[str]         =    torch.zeros(head_importance.numel() ,       dtype=torch.long ,       device=args.device       )
     _UpperCAmelCase					:  Any         =    torch.arange(
         head_importance.numel() ,       device=args.device       )
     _UpperCAmelCase					:  Union[str, Any]         =    head_ranks.view_as(lowerCAmelCase_       )
     print_ad_tensor(lowerCAmelCase_       )

     return attn_entropy, head_importance, preds, labels







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_       ):
     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  int         =    compute_heads_importance(lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       compute_entropy=lowerCAmelCase_       )
     _UpperCAmelCase					:  List[str]         =    np.argmax(lowerCAmelCase_ ,       axis=1       ) if args.output_mode == """classification""" else np.squeeze(lowerCAmelCase_       )
     _UpperCAmelCase					:  Tuple         =    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       )

     _UpperCAmelCase					:  List[Any]         =    torch.ones_like(lowerCAmelCase_       )
     _UpperCAmelCase					:  List[str]         =    max(1 ,       int(new_head_mask.numel() * args.masking_amount       )       )

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

          if len(lowerCAmelCase_       ) <= num_to_mask:
               break

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

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

     _UpperCAmelCase					:  List[Any]         =    sum(p.numel() for p in model.parameters()       )
     _UpperCAmelCase					:  Any         =    {
         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_       )
     _UpperCAmelCase					:  str         =    sum(p.numel() for p in model.parameters()       )

     _UpperCAmelCase					:  Optional[Any]         =    datetime.now()
     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  int         =    compute_heads_importance(
         lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       compute_entropy=lowerCAmelCase_ ,       compute_importance=lowerCAmelCase_ ,       head_mask=lowerCAmelCase_ ,       actually_pruned=lowerCAmelCase_ ,       )
     _UpperCAmelCase					:  List[str]         =    np.argmax(lowerCAmelCase_ ,       axis=1       ) if args.output_mode == """classification""" else np.squeeze(lowerCAmelCase_       )
     _UpperCAmelCase					:  Tuple         =    glue_compute_metrics(args.task_name ,       lowerCAmelCase_ ,       lowerCAmelCase_       )[args.metric_name]
     _UpperCAmelCase					:  Union[str, Any]         =    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 __A  (    ):
     _UpperCAmelCase					:  Any         =    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."""       )
     _UpperCAmelCase					:  Dict         =    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:
          _UpperCAmelCase					:  Optional[int]         =    torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""       )
          _UpperCAmelCase					:  Any         =    0 if args.no_cuda else torch.cuda.device_count()
     else:
          torch.cuda.set_device(args.local_rank       )
          _UpperCAmelCase					:  List[Any]         =    torch.device("""cuda""" ,       args.local_rank       )
          _UpperCAmelCase					:  Dict         =    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
     _UpperCAmelCase					:  List[Any]         =    args.task_name.lower()
     if args.task_name not in glue_processors:
          raise ValueError("""Task not found: %s""" % (args.task_name)       )
     _UpperCAmelCase					:  Any         =    glue_processors[args.task_name]()
     _UpperCAmelCase					:  Optional[int]         =    glue_output_modes[args.task_name]
     _UpperCAmelCase					:  List[Any]         =    processor.get_labels()
     _UpperCAmelCase					:  List[str]         =    len(lowerCAmelCase_       )

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

     _UpperCAmelCase					:  Union[str, Any]         =    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 ,       )
     _UpperCAmelCase					:  Union[str, Any]         =    AutoTokenizer.from_pretrained(
         args.tokenizer_name if args.tokenizer_name else args.model_name_or_path ,       cache_dir=args.cache_dir ,       )
     _UpperCAmelCase					:  str         =    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:
          _UpperCAmelCase					:  int         =    nn.parallel.DistributedDataParallel(
              lowerCAmelCase_ ,       device_ids=[args.local_rank] ,       output_device=args.local_rank ,       find_unused_parameters=lowerCAmelCase_       )
     elif args.n_gpu > 1:
          _UpperCAmelCase					:  List[str]         =    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
     _UpperCAmelCase					:  int         =    GlueDataset(lowerCAmelCase_ ,       tokenizer=lowerCAmelCase_ ,       mode="""dev"""       )
     if args.data_subset > 0:
          _UpperCAmelCase					:  List[str]         =    Subset(lowerCAmelCase_ ,       list(range(min(args.data_subset ,       len(lowerCAmelCase_       )       )       )       )       )
     _UpperCAmelCase					:  Dict         =    SequentialSampler(lowerCAmelCase_       ) if args.local_rank == -1 else DistributedSampler(lowerCAmelCase_       )
     _UpperCAmelCase					:  int         =    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:
          _UpperCAmelCase					:  Tuple         =    mask_heads(lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_       )
          prune_heads(lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_       )


if __name__ == "__main__":
   main()




'''simple docstring'''

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_=0.999 ,       lowerCAmelCase_="cosine" ,       ):
     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

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

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

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







class 	__lowerCAmelCase    (  __a       ,					__a				):
     snake_case   :    Any							      =      [e.name for e in KarrasDiffusionSchedulers]
     snake_case   :    List[Any]							      =      2



     @register_to_config
     def __init__(self						,	lowerCAmelCase__ = 1_0_0_0						,	lowerCAmelCase__ = 0.0_0_0_8_5						,	lowerCAmelCase__ = 0.0_1_2						,	lowerCAmelCase__ = "linear"						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = "epsilon"						,	lowerCAmelCase__ = "linspace"						,	lowerCAmelCase__ = 0						,	):
          if trained_betas is not None:
               _UpperCAmelCase					:  Optional[int]         =    torch.tensor(lowerCAmelCase__						,	dtype=torch.floataa	)
          elif beta_schedule == "linear":
               _UpperCAmelCase					:  Optional[int]         =    torch.linspace(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	dtype=torch.floataa	)
          elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _UpperCAmelCase					:  Tuple         =    (
                   torch.linspace(beta_start**0.5						,	beta_end**0.5						,	lowerCAmelCase__						,	dtype=torch.floataa	) ** 2
               )
          elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _UpperCAmelCase					:  Union[str, Any]         =    betas_for_alpha_bar(lowerCAmelCase__	)
          else:
               raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"	)

          _UpperCAmelCase					:  Union[str, Any]         =    1.0 - self.betas
          _UpperCAmelCase					:  List[Any]         =    torch.cumprod(self.alphas						,	dim=0	)

          #  set all values
          self.set_timesteps(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__=None	):
          if schedule_timesteps is None:
               _UpperCAmelCase					:  Optional[Any]         =    self.timesteps

          _UpperCAmelCase					:  Optional[Any]         =    (schedule_timesteps == timestep).nonzero()

          # The sigma index that is taken for the **very** first `step`
          # is always the second index (or the last index if there is only 1)
          # This way we can ensure we don't accidentally skip a sigma in
          # case we start in the middle of the denoising schedule (e.g. for image-to-image)
          if len(self._index_counter	) == 0:
               _UpperCAmelCase					:  Dict         =    1 if len(lowerCAmelCase__	) > 1 else 0
          else:
               _UpperCAmelCase					:  str         =    timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__	) else timestep
               _UpperCAmelCase					:  Any         =    self._index_counter[timestep_int]

          return indices[pos].item()



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

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



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          _UpperCAmelCase					:  Dict         =    self.index_for_timestep(lowerCAmelCase__	)

          if self.state_in_first_order:
               _UpperCAmelCase					:  Any         =    self.sigmas[step_index]
          else:
               _UpperCAmelCase					:  Tuple         =    self.sigmas_interpol[step_index - 1]

          _UpperCAmelCase					:  Dict         =    sample / ((sigma**2 + 1) ** 0.5)
          return sample



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	):
          _UpperCAmelCase					:  Dict         =    num_inference_steps

          _UpperCAmelCase					:  int         =    num_train_timesteps or self.config.num_train_timesteps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
               _UpperCAmelCase					:  Dict         =    np.linspace(0						,	num_train_timesteps - 1						,	lowerCAmelCase__						,	dtype=lowerCAmelCase__	)[::-1].copy()
          elif self.config.timestep_spacing == "leading":
               _UpperCAmelCase					:  List[str]         =    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
               _UpperCAmelCase					:  Optional[Any]         =    (np.arange(0						,	lowerCAmelCase__	) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__	)
               timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
               _UpperCAmelCase					:  Any         =    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
               _UpperCAmelCase					:  List[Any]         =    (np.arange(lowerCAmelCase__						,	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'."	)

          _UpperCAmelCase					:  List[str]         =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
          _UpperCAmelCase					:  Dict         =    torch.from_numpy(np.log(lowerCAmelCase__	)	).to(lowerCAmelCase__	)

          _UpperCAmelCase					:  Optional[Any]         =    np.interp(lowerCAmelCase__						,	np.arange(0						,	len(lowerCAmelCase__	)	)						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  Dict         =    np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
          _UpperCAmelCase					:  Any         =    torch.from_numpy(lowerCAmelCase__	).to(device=lowerCAmelCase__	)

          # compute up and down sigmas
          _UpperCAmelCase					:  str         =    sigmas.roll(-1	)
          _UpperCAmelCase					:  List[Any]         =    0.0
          _UpperCAmelCase					:  Optional[int]         =    (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
          _UpperCAmelCase					:  Any         =    (sigmas_next**2 - sigmas_up**2) ** 0.5
          _UpperCAmelCase					:  Tuple         =    0.0

          # compute interpolated sigmas
          _UpperCAmelCase					:  Union[str, Any]         =    sigmas.log().lerp(sigmas_down.log()						,	0.5	).exp()
          _UpperCAmelCase					:  int         =    0.0

          # set sigmas
          _UpperCAmelCase					:  List[Any]         =    torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2	), sigmas[-1:]]	)
          _UpperCAmelCase					:  str         =    torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2	), sigmas_interpol[-1:]]	)
          _UpperCAmelCase					:  Optional[int]         =    torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2	), sigmas_up[-1:]]	)
          _UpperCAmelCase					:  List[Any]         =    torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2	), sigmas_down[-1:]]	)

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

          _UpperCAmelCase					:  int         =    self.sigma_to_t(lowerCAmelCase__	).to(lowerCAmelCase__						,	dtype=timesteps.dtype	)
          _UpperCAmelCase					:  Optional[int]         =    torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])						,	dim=-1	).flatten()

          _UpperCAmelCase					:  Any         =    torch.cat([timesteps[:1], interleaved_timesteps]	)

          _UpperCAmelCase					:  Dict         =    None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          _UpperCAmelCase					:  Dict         =    defaultdict(lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__	):
          # get log sigma
          _UpperCAmelCase					:  Union[str, Any]         =    sigma.log()

          # get distribution
          _UpperCAmelCase					:  str         =    log_sigma - self.log_sigmas[:, None]

          # get sigmas range
          _UpperCAmelCase					:  str         =    dists.ge(0	).cumsum(dim=0	).argmax(dim=0	).clamp(max=self.log_sigmas.shape[0] - 2	)
          _UpperCAmelCase					:  List[Any]         =    low_idx + 1

          _UpperCAmelCase					:  Optional[int]         =    self.log_sigmas[low_idx]
          _UpperCAmelCase					:  Dict         =    self.log_sigmas[high_idx]

          # interpolate sigmas
          _UpperCAmelCase					:  Union[str, Any]         =    (low - log_sigma) / (low - high)
          _UpperCAmelCase					:  Union[str, Any]         =    w.clamp(0						,	1	)

          # transform interpolation to time range
          _UpperCAmelCase					:  Union[str, Any]         =    (1 - w) * low_idx + w * high_idx
          _UpperCAmelCase					:  str         =    t.view(sigma.shape	)
          return t



     @property
     def     snake_case_ (self	):
          return self.sample is None



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = True						,	):
          _UpperCAmelCase					:  Union[str, Any]         =    self.index_for_timestep(lowerCAmelCase__	)

          # advance index counter by 1
          _UpperCAmelCase					:  Optional[Any]         =    timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__	) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
               _UpperCAmelCase					:  Tuple         =    self.sigmas[step_index]
               _UpperCAmelCase					:  List[str]         =    self.sigmas_interpol[step_index]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_up[step_index]
               _UpperCAmelCase					:  Optional[int]         =    self.sigmas_down[step_index - 1]
          else:
               # 2nd order / KPDM2's method
               _UpperCAmelCase					:  List[Any]         =    self.sigmas[step_index - 1]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_interpol[step_index - 1]
               _UpperCAmelCase					:  List[Any]         =    self.sigmas_up[step_index - 1]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_down[step_index - 1]

          # currently only gamma=0 is supported. This usually works best anyways.
          # We can support gamma in the future but then need to scale the timestep before
          # passing it to the model which requires a change in API
          _UpperCAmelCase					:  int         =    0
          _UpperCAmelCase					:  str         =    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          _UpperCAmelCase					:  Optional[int]         =    model_output.device
          _UpperCAmelCase					:  Tuple         =    randn_tensor(model_output.shape						,	dtype=model_output.dtype						,	device=lowerCAmelCase__						,	generator=lowerCAmelCase__	)

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
               _UpperCAmelCase					:  int         =    sigma_hat if self.state_in_first_order else sigma_interpol
               _UpperCAmelCase					:  List[Any]         =    sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
               _UpperCAmelCase					:  Tuple         =    sigma_hat if self.state_in_first_order else sigma_interpol
               _UpperCAmelCase					:  Dict         =    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**2 + 1)
               )
          elif self.config.prediction_type == "sample":
               raise NotImplementedError("""prediction_type not implemented yet: sample"""	)
          else:
               raise ValueError(
                   F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"	)

          if self.state_in_first_order:
               # 2. Convert to an ODE derivative for 1st order
               _UpperCAmelCase					:  Any         =    (sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               _UpperCAmelCase					:  Any         =    sigma_interpol - sigma_hat

               # store for 2nd order step
               _UpperCAmelCase					:  List[str]         =    sample
               _UpperCAmelCase					:  Optional[Any]         =    dt
               _UpperCAmelCase					:  Optional[int]         =    sample + derivative * dt
          else:
               # DPM-Solver-2
               # 2. Convert to an ODE derivative for 2nd order
               _UpperCAmelCase					:  Optional[int]         =    (sample - pred_original_sample) / sigma_interpol
               # 3. delta timestep
               _UpperCAmelCase					:  List[Any]         =    sigma_down - sigma_hat

               _UpperCAmelCase					:  int         =    self.sample
               _UpperCAmelCase					:  Union[str, Any]         =    None

               _UpperCAmelCase					:  str         =    sample + derivative * dt
               _UpperCAmelCase					:  List[str]         =    prev_sample + noise * sigma_up

          if not return_dict:
               return (prev_sample,)

          return SchedulerOutput(prev_sample=lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          _UpperCAmelCase					:  List[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
               _UpperCAmelCase					:  Any         =    self.timesteps.to(original_samples.device						,	dtype=torch.floataa	)
               _UpperCAmelCase					:  Dict         =    timesteps.to(original_samples.device						,	dtype=torch.floataa	)
          else:
               _UpperCAmelCase					:  Dict         =    self.timesteps.to(original_samples.device	)
               _UpperCAmelCase					:  Tuple         =    timesteps.to(original_samples.device	)

          _UpperCAmelCase					:  Any         =    [self.index_for_timestep(lowerCAmelCase__						,	lowerCAmelCase__	) for t in timesteps]

          _UpperCAmelCase					:  List[Any]         =    sigmas[step_indices].flatten()
          while len(sigma.shape	) < len(original_samples.shape	):
               _UpperCAmelCase					:  Dict         =    sigma.unsqueeze(-1	)

          _UpperCAmelCase					:  str         =    original_samples + noise * sigma
          return noisy_samples



     def __len__(self	):
          return self.config.num_train_timesteps


