


import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


_A		:				List[str]									=       getLogger(__name__)



def     __lowerCAmelCase						(							snake_case	:    Tuple						,      snake_case	:    str						,      snake_case	:    str						,      snake_case	:    int = 8						,      snake_case	:    int = 1024						,      snake_case	:    Union[str, Any]="val"						,      snake_case	:    Union[str, Any]=None						,      snake_case	:    str=False						,      snake_case	:    int="summarization"						,      snake_case	:    List[Any]=None						,      snake_case	:    List[Any]=1						,      snake_case	:    Dict = None						,      snake_case	:    Any=""						,      **snake_case	:    Optional[int]						,      )       ->							Dict:
   __lowerCamelCase:				Optional[Any]     =	str(snake_case )
   assert local_rank is not None
   torch.distributed.init_process_group(backend="""nccl"""						,      rank=snake_case )

   __lowerCamelCase:				Optional[int]     =	Path(snake_case )
   __lowerCamelCase:				List[Any]     =	save_dir.joinpath(f'rank_{local_rank}_output.json' )
   torch.cuda.set_device(snake_case )
   __lowerCamelCase:				Dict     =	AutoModelForSeqaSeqLM.from_pretrained(snake_case ).cuda()
   if fpaa:
      __lowerCamelCase:				Optional[int]     =	model.half()
   # determine if we need to increase num_beams
   use_task_specific_params(snake_case						,      snake_case )  # update config with task specific params
   __lowerCamelCase:				str     =	generate_kwargs.pop("""num_beams"""						,      model.config.num_beams )  # AttributeError risk?
   if num_return_sequences > num_beams:
      __lowerCamelCase:				List[Any]     =	num_return_sequences

   __lowerCamelCase:				Optional[Any]     =	AutoTokenizer.from_pretrained(snake_case )
   logger.info(f'Inferred tokenizer type: {tokenizer.__class__}' )  # if this is wrong, check config.model_type.

   if max_source_length is None:
      __lowerCamelCase:				Optional[Any]     =	tokenizer.model_max_length
   if prefix is None:
      __lowerCamelCase:				Any     =	prefix or getattr(model.config						,      """prefix"""						,      """""" ) or """"""
   __lowerCamelCase:				int     =	SeqaSeqDataset(
       snake_case						,      snake_case						,      snake_case						,      max_target_length=1024						,      type_path=snake_case						,      n_obs=snake_case						,      prefix=snake_case						,      **snake_case						,      )
   # I set shuffle=True for a more accurate progress bar.
   # If all the longest samples are first, the prog bar estimate is too high at the beginning.
   __lowerCamelCase:				Dict     =	ds.make_sortish_sampler(snake_case						,      distributed=snake_case						,      add_extra_examples=snake_case						,      shuffle=snake_case )
   __lowerCamelCase:				int     =	DataLoader(snake_case						,      sampler=snake_case						,      batch_size=snake_case						,      collate_fn=ds.collate_fn )
   __lowerCamelCase:				List[str]     =	[]
   for batch in tqdm(snake_case ):
      __lowerCamelCase:				List[Any]     =	model.generate(
          input_ids=batch["""input_ids"""].to(model.device )						,      attention_mask=batch["""attention_mask"""].to(model.device )						,      num_return_sequences=snake_case						,      num_beams=snake_case						,      **snake_case						,      )
      __lowerCamelCase:				Any     =	tokenizer.batch_decode(snake_case						,      skip_special_tokens=snake_case						,      clean_up_tokenization_spaces=snake_case )
      __lowerCamelCase:				Optional[int]     =	batch["""ids"""]
      if num_return_sequences > 1:
         __lowerCamelCase:				str     =	chunks(snake_case						,      snake_case )  # batch size chunks, each of size num_return_seq
      for i, pred in enumerate(snake_case ):
         results.append({"""pred""": pred, """id""": ids[i].item()} )
   save_json(snake_case						,      snake_case )
   return results, sampler.num_replicas



def     __lowerCAmelCase						(							)       ->							Any:
   __lowerCamelCase:				List[Any]     =	argparse.ArgumentParser(
       epilog="""Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate""" )
   parser.add_argument("""--data_dir"""						,      type=snake_case						,      help="""like cnn_dm/test.source""" )
   parser.add_argument(
       """--model_name"""						,      type=snake_case						,      help="""like facebook/bart-large-cnn,t5-base, etc."""						,      default="""sshleifer/distilbart-xsum-12-3"""						,      )
   parser.add_argument("""--save_dir"""						,      type=snake_case						,      help="""where to save"""						,      default="""tmp_gen""" )
   parser.add_argument("""--max_source_length"""						,      type=snake_case						,      default=snake_case )
   parser.add_argument(
       """--type_path"""						,      type=snake_case						,      default="""test"""						,      help="""which subset to evaluate typically train/val/test""" )
   parser.add_argument("""--task"""						,      type=snake_case						,      default="""summarization"""						,      help="""used for task_specific_params + metrics""" )
   parser.add_argument("""--bs"""						,      type=snake_case						,      default=8						,      required=snake_case						,      help="""batch size""" )
   parser.add_argument(
       """--local_rank"""						,      type=snake_case						,      default=-1						,      required=snake_case						,      help="""should be passed by distributed.launch""" )

   parser.add_argument(
       """--n_obs"""						,      type=snake_case						,      default=snake_case						,      required=snake_case						,      help="""How many observations. Defaults to all.""" )
   parser.add_argument(
       """--num_return_sequences"""						,      type=snake_case						,      default=1						,      required=snake_case						,      help="""How many sequences to return""" )
   parser.add_argument(
       """--sync_timeout"""						,      type=snake_case						,      default=600						,      required=snake_case						,      help="""How long should master process wait for other processes to finish."""						,      )
   parser.add_argument("""--src_lang"""						,      type=snake_case						,      default=snake_case						,      required=snake_case )
   parser.add_argument("""--tgt_lang"""						,      type=snake_case						,      default=snake_case						,      required=snake_case )
   parser.add_argument(
       """--prefix"""						,      type=snake_case						,      required=snake_case						,      default=snake_case						,      help="""will be added to the begininng of src examples""" )
   parser.add_argument("""--fp16"""						,      action="""store_true""" )
   parser.add_argument("""--debug"""						,      action="""store_true""" )
   __lowerCamelCase:				Optional[int]     =	time.time()
   __lowerCamelCase:				List[str]     =	parser.parse_known_args()
   __lowerCamelCase:				Tuple     =	parse_numeric_n_bool_cl_kwargs(snake_case )
   if generate_kwargs and args.local_rank <= 0:
      print(f'parsed the following generate kwargs: {generate_kwargs}' )
   __lowerCamelCase:				List[Any]     =	Path(args.save_dir + """_tmp""" )
   Path(snake_case ).mkdir(exist_ok=snake_case )  # this handles locking.
   __lowerCamelCase:				int     =	list(json_save_dir.glob("""rank_*.json""" ) )
   if intermediate_files:
      raise ValueError(f'Found files at {json_save_dir} please move or remove them.' )
      # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
   __lowerCamelCase:				Any     =	{}
   if args.src_lang is not None:
      __lowerCamelCase:				Tuple     =	args.src_lang
   if args.tgt_lang is not None:
      __lowerCamelCase:				Optional[Any]     =	args.tgt_lang

   Path(args.save_dir ).mkdir(exist_ok=snake_case )
   __lowerCamelCase:				Any     =	eval_data_dir(
       args.data_dir						,      snake_case						,      args.model_name						,      type_path=args.type_path						,      bs=args.bs						,      fpaa=args.fpaa						,      task=args.task						,      local_rank=args.local_rank						,      n_obs=args.n_obs						,      max_source_length=args.max_source_length						,      num_return_sequences=args.num_return_sequences						,      prefix=args.prefix						,      dataset_kwargs=snake_case						,      **snake_case						,      )

   if args.local_rank <= 0:
      __lowerCamelCase:				Optional[Any]     =	Path(args.save_dir )
      save_dir.mkdir(exist_ok=snake_case )
      __lowerCamelCase:				Tuple     =	gather_results_from_each_node(snake_case						,      snake_case						,      args.sync_timeout )
      __lowerCamelCase:				Tuple     =	combine_partial_results(snake_case )
      if args.num_return_sequences > 1:
         __lowerCamelCase:				Tuple     =	save_dir.joinpath("""pseudolabel_results.json""" )
         print(f'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/' )
         save_json(snake_case						,      snake_case )
         return
      __lowerCamelCase:				Any     =	Path(args.data_dir ).joinpath(args.type_path + """.target""" )
      with open(snake_case ) as f:
         __lowerCamelCase:				Union[str, Any]     =	[x.rstrip() for x in f.readlines()][: len(snake_case )]

      # Calculate metrics, save metrics,  and save _generations.txt
      __lowerCamelCase:				Union[str, Any]     =	"""translation""" in args.task
      __lowerCamelCase:				List[Any]     =	calculate_bleu if calc_bleu else calculate_rouge
      __lowerCamelCase:				Dict     =	"""bleu""" if calc_bleu else """rouge"""
      __lowerCamelCase:				Dict     =	score_fn(snake_case						,      snake_case )
      __lowerCamelCase:				Optional[int]     =	len(snake_case )
      __lowerCamelCase:				Optional[int]     =	time.time() - start_time
      __lowerCamelCase:				int     =	round(runtime / metrics["""n_obs"""]						,      4 )
      __lowerCamelCase:				Union[str, Any]     =	num_replicas
      # TODO(@stas00): add whatever metadata to metrics
      __lowerCamelCase:				List[Any]     =	save_dir.joinpath(f'{args.type_path}_{metric_name}.json' )
      save_json(snake_case						,      snake_case						,      indent=snake_case )
      print(snake_case )
      write_txt_file(snake_case						,      save_dir.joinpath(f'{args.type_path}_generations.txt' ) )
      if args.debug:
         write_txt_file(snake_case						,      save_dir.joinpath(f'{args.type_path}.target' ) )
      else:
         shutil.rmtree(snake_case )



def     __lowerCAmelCase						(							snake_case	:    Optional[Any] )       ->							List:
   __lowerCamelCase:				int     =	[]
   for partial_result in partial_results:
      records.extend(snake_case )
   __lowerCamelCase:				List[str]     =	sorted(snake_case						,      key=lambda snake_case : x["id"] )
   __lowerCamelCase:				Dict     =	[x["""pred"""] for x in records]
   return preds



def     __lowerCAmelCase						(							snake_case	:    Dict						,      snake_case	:    int						,      snake_case	:    int )       ->							List[Dict[str, List]]:
   # WAIT FOR lots of .json files
   __lowerCamelCase:				Optional[int]     =	time.time()
   logger.info("""waiting for all nodes to finish""" )
   __lowerCamelCase:				int     =	None
   while (time.time() - start_wait) < timeout:
      __lowerCamelCase:				int     =	list(save_dir.glob("""rank_*.json""" ) )
      if len(snake_case ) < num_replicas:
         continue
      try:
         # make sure all json files are fully saved
         __lowerCamelCase:				Any     =	lmap(snake_case						,      snake_case )
         return json_data
      except JSONDecodeError:
         continue
   else:
      raise TimeoutError("""Rank 0 gave up on waiting for other processes""" )
   # Unreachable


if __name__ == "__main__":
   # Usage for MT:
   run_generate()






from sklearn.metrics import fa_score

import datasets


_A		:				Any									=       '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


_A		:				Dict									=       '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


_A		:				Union[str, Any]									=       '''
@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        a					(			datasets.Metric   ):





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        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.f1_score.html"""]    ,	)







     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				str=None    ,	SCREAMING_SNAKE_CASE_					:				str=1    ,	SCREAMING_SNAKE_CASE_					:				List[str]="binary"    ,	SCREAMING_SNAKE_CASE_					:				List[str]=None							):
        __lowerCamelCase:				str     =	fa_score(
            SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_    ,	pos_label=SCREAMING_SNAKE_CASE_    ,	average=SCREAMING_SNAKE_CASE_    ,	sample_weight=SCREAMING_SNAKE_CASE_							)
        return {"f1": float(SCREAMING_SNAKE_CASE_							) if score.size == 1 else score}


