'''simple docstring'''




import argparse
import logging
import os
from datetime import datetime

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

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


__SCREAMING_SNAKE_CASE	:					List[Any]					=  logging.getLogger(__name__)

def      UpperCamelCase_							( _UpperCAmelCase  :				Tuple   )   ->   Any:


       """simple docstring"""







       _UpperCAmelCase					:  Dict											=   p * torch.log(_UpperCAmelCase   )
       _UpperCAmelCase					:  List[str]											=   0
       return -plogp.sum(dim=-1   )

def      UpperCamelCase_							( _UpperCAmelCase  :				List[str]   )   ->   Union[str, Any]:


       """simple docstring"""







       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  :				str      ,     _UpperCAmelCase  :				Tuple      ,     _UpperCAmelCase  :				str      ,     _UpperCAmelCase  :				Optional[Any]=True      ,     _UpperCAmelCase  :				Optional[int]=True      ,     _UpperCAmelCase  :				Tuple=None      ,     _UpperCAmelCase  :				List[str]=False   )   ->   Any:


       """simple docstring"""







       _UpperCAmelCase				,  _UpperCAmelCase					:  Any											=   model.config.num_hidden_layers, model.config.num_attention_heads
       _UpperCAmelCase					:  Dict											=   torch.zeros(_UpperCAmelCase      ,     _UpperCAmelCase   ).to(args.device   )
       _UpperCAmelCase					:  Dict											=   torch.zeros(_UpperCAmelCase      ,     _UpperCAmelCase   ).to(args.device   )

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

       _UpperCAmelCase					:  List[Any]											=   None
       _UpperCAmelCase					:  List[str]											=   None
       _UpperCAmelCase					:  List[str]											=   0.0

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

              # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
              _UpperCAmelCase					:  List[Any]											=   model(**_UpperCAmelCase      ,     head_mask=_UpperCAmelCase   )
              _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(_UpperCAmelCase   ):
                            _UpperCAmelCase					:  Tuple											=   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					:  Optional[int]											=   logits.detach().cpu().numpy()
                     _UpperCAmelCase					:  Dict											=   inputs["labels"].detach().cpu().numpy()
              else:
                     _UpperCAmelCase					:  Optional[int]											=   np.append(_UpperCAmelCase      ,     logits.detach().cpu().numpy()      ,     axis=0   )
                     _UpperCAmelCase					:  Union[str, Any]											=   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:
              _UpperCAmelCase					:  Optional[int]											=   2
              _UpperCAmelCase					:  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:
              _UpperCAmelCase					:  Any											=   (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"   )
       _UpperCAmelCase					:  Union[str, Any]											=   torch.zeros(head_importance.numel()      ,     dtype=torch.long      ,     device=args.device   )
       _UpperCAmelCase					:  int											=   torch.arange(
           head_importance.numel()      ,     device=args.device   )
       _UpperCAmelCase					:  Any											=   head_ranks.view_as(_UpperCAmelCase   )
       print_ad_tensor(_UpperCAmelCase   )

       return attn_entropy, head_importance, preds, labels

def      UpperCamelCase_							( _UpperCAmelCase  :				Any      ,     _UpperCAmelCase  :				str      ,     _UpperCAmelCase  :				Dict   )   ->   int:


       """simple docstring"""







       _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					:  Dict											=   compute_heads_importance(_UpperCAmelCase      ,     _UpperCAmelCase      ,     _UpperCAmelCase      ,     compute_entropy=_UpperCAmelCase   )
       _UpperCAmelCase					:  Tuple											=   np.argmax(_UpperCAmelCase      ,     axis=1   ) if args.output_mode == "classification" else np.squeeze(_UpperCAmelCase   )
       _UpperCAmelCase					:  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   )

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

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

              if len(_UpperCAmelCase   ) <= num_to_mask:
                     break

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

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

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

       return head_mask

def      UpperCamelCase_							( _UpperCAmelCase  :				Dict      ,     _UpperCAmelCase  :				List[str]      ,     _UpperCAmelCase  :				Dict      ,     _UpperCAmelCase  :				Optional[Any]   )   ->   Optional[Any]:


       """simple docstring"""







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

       _UpperCAmelCase					:  Any											=   sum(p.numel() for p in model.parameters()   )
       _UpperCAmelCase					:  Optional[int]											=   {
           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   )
       _UpperCAmelCase					:  Any											=   sum(p.numel() for p in model.parameters()   )

       _UpperCAmelCase					:  Tuple											=   datetime.now()
       _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					:  int											=   compute_heads_importance(
           _UpperCAmelCase      ,     _UpperCAmelCase      ,     _UpperCAmelCase      ,     compute_entropy=_UpperCAmelCase      ,     compute_importance=_UpperCAmelCase      ,     head_mask=_UpperCAmelCase      ,     actually_pruned=_UpperCAmelCase      ,     )
       _UpperCAmelCase					:  str											=   np.argmax(_UpperCAmelCase      ,     axis=1   ) if args.output_mode == "classification" else np.squeeze(_UpperCAmelCase   )
       _UpperCAmelCase					:  Optional[int]											=   glue_compute_metrics(args.task_name      ,     _UpperCAmelCase      ,     _UpperCAmelCase   )[args.metric_name]
       _UpperCAmelCase					:  List[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_							( )   ->   Optional[int]:


       """simple docstring"""







       _UpperCAmelCase					:  Union[str, 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."   )
       _UpperCAmelCase					:  List[str]											=   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:
              _UpperCAmelCase					:  Optional[Any]											=   torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"   )
              _UpperCAmelCase					:  Optional[Any]											=   0 if args.no_cuda else torch.cuda.device_count()
       else:
              torch.cuda.set_device(args.local_rank   )
              _UpperCAmelCase					:  Any											=   torch.device("cuda"      ,     args.local_rank   )
              _UpperCAmelCase					:  Optional[int]											=   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					:  Any											=   args.task_name.lower()
       if args.task_name not in glue_processors:
              raise ValueError("Task not found: %s" % (args.task_name)   )
       _UpperCAmelCase					:  List[str]											=   glue_processors[args.task_name]()
       _UpperCAmelCase					:  Optional[Any]											=   glue_output_modes[args.task_name]
       _UpperCAmelCase					:  List[Any]											=   processor.get_labels()
       _UpperCAmelCase					:  List[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.

       _UpperCAmelCase					:  List[Any]											=   AutoConfig.from_pretrained(
           args.config_name if args.config_name else args.model_name_or_path      ,     num_labels=_UpperCAmelCase      ,     finetuning_task=args.task_name      ,     output_attentions=_UpperCAmelCase      ,     cache_dir=args.cache_dir      ,     )
       _UpperCAmelCase					:  Any											=   AutoTokenizer.from_pretrained(
           args.tokenizer_name if args.tokenizer_name else args.model_name_or_path      ,     cache_dir=args.cache_dir      ,     )
       _UpperCAmelCase					:  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:
              _UpperCAmelCase					:  List[Any]											=   nn.parallel.DistributedDataParallel(
                  _UpperCAmelCase      ,     device_ids=[args.local_rank]      ,     output_device=args.local_rank      ,     find_unused_parameters=_UpperCAmelCase   )
       elif args.n_gpu > 1:
              _UpperCAmelCase					:  int											=   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
       _UpperCAmelCase					:  List[str]											=   GlueDataset(_UpperCAmelCase      ,     tokenizer=_UpperCAmelCase      ,     mode="dev"   )
       if args.data_subset > 0:
              _UpperCAmelCase					:  List[str]											=   Subset(_UpperCAmelCase      ,     list(range(min(args.data_subset      ,     len(_UpperCAmelCase   )   )   )   )   )
       _UpperCAmelCase					:  int											=   SequentialSampler(_UpperCAmelCase   ) if args.local_rank == -1 else DistributedSampler(_UpperCAmelCase   )
       _UpperCAmelCase					:  Any											=   DataLoader(
           _UpperCAmelCase      ,     sampler=_UpperCAmelCase      ,     batch_size=args.batch_size      ,     collate_fn=_UpperCAmelCase   )

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

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


if __name__ == "__main__":
  main()


'''simple docstring'''




from sklearn.metrics import accuracy_score

import datasets


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


__SCREAMING_SNAKE_CASE	:					Any					=  """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,			_KWARGS_DESCRIPTION   )
class      lowerCamelCase_     (datasets.Metric   ):
      '''simple docstring'''

      def 					_A     (	self						:    int	):
             return datasets.MetricInfo(
                 description=_DESCRIPTION						, citation=_CITATION						, inputs_description=_KWARGS_DESCRIPTION						, features=datasets.Features(
                     {
                         "predictions": datasets.Sequence(datasets.Value("int32"	)	),
                         "references": datasets.Sequence(datasets.Value("int32"	)	),
                     }
                     if self.config_name == "multilabel"
                     else {
                         "predictions": datasets.Value("int32"	),
                         "references": datasets.Value("int32"	),
                     }	)						, reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"]						, )





      def 					_A     (	self						:    str						, A						:    List[str]						, A						:    int						, A						:    List[Any]=True						, A						:    List[str]=None	):
             return {
                 "accuracy": float(
                     accuracy_score(A						, A						, normalize=A						, sample_weight=A	)	)
             }

