"""simple docstring"""






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			   = getLogger(__name__)



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase = 8       ,    __UpperCAmelCase = 1_0_2_4       ,    __UpperCAmelCase="val"       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=False       ,    __UpperCAmelCase="summarization"       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=1       ,    __UpperCAmelCase = None       ,    __UpperCAmelCase=""       ,    **__UpperCAmelCase       ,    )  ->  Dict:
   lowercase__:   Optional[int]					=      str(__UpperCAmelCase )
   assert local_rank is not None
   torch.distributed.init_process_group(backend='''nccl'''       ,    rank=__UpperCAmelCase )

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

   lowercase__:   Tuple					=      AutoTokenizer.from_pretrained(__UpperCAmelCase )
   logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}""" )  # if this is wrong, check config.model_type.

   if max_source_length is None:
      lowercase__:   Dict					=      tokenizer.model_max_length
   if prefix is None:
      lowercase__:   Optional[Any]					=      prefix or getattr(model.config       ,    '''prefix'''       ,    '''''' ) or ''''''
   lowercase__:   Union[str, Any]					=      SeqaSeqDataset(
       __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    max_target_length=1_0_2_4       ,    type_path=__UpperCAmelCase       ,    n_obs=__UpperCAmelCase       ,    prefix=__UpperCAmelCase       ,    **__UpperCAmelCase       ,    )
   # 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.
   lowercase__:   List[Any]					=      ds.make_sortish_sampler(__UpperCAmelCase       ,    distributed=__UpperCAmelCase       ,    add_extra_examples=__UpperCAmelCase       ,    shuffle=__UpperCAmelCase )
   lowercase__:   List[Any]					=      DataLoader(__UpperCAmelCase       ,    sampler=__UpperCAmelCase       ,    batch_size=__UpperCAmelCase       ,    collate_fn=ds.collate_fn )
   lowercase__:   str					=      []
   for batch in tqdm(__UpperCAmelCase ):
      lowercase__:   Optional[int]					=      model.generate(
          input_ids=batch['''input_ids'''].to(model.device )       ,    attention_mask=batch['''attention_mask'''].to(model.device )       ,    num_return_sequences=__UpperCAmelCase       ,    num_beams=__UpperCAmelCase       ,    **__UpperCAmelCase       ,    )
      lowercase__:   Any					=      tokenizer.batch_decode(__UpperCAmelCase       ,    skip_special_tokens=__UpperCAmelCase       ,    clean_up_tokenization_spaces=__UpperCAmelCase )
      lowercase__:   Optional[int]					=      batch['''ids''']
      if num_return_sequences > 1:
         lowercase__:   Any					=      chunks(__UpperCAmelCase       ,    __UpperCAmelCase )  # batch size chunks, each of size num_return_seq
      for i, pred in enumerate(__UpperCAmelCase ):
         results.append({'''pred''': pred, '''id''': ids[i].item()} )
   save_json(__UpperCAmelCase       ,    __UpperCAmelCase )
   return results, sampler.num_replicas



def SCREAMING_SNAKE_CASE__			(   )  ->  Optional[Any]:
   lowercase__:   Union[str, 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=__UpperCAmelCase       ,    help='''like cnn_dm/test.source''' )
   parser.add_argument(
       '''--model_name'''       ,    type=__UpperCAmelCase       ,    help='''like facebook/bart-large-cnn,t5-base, etc.'''       ,    default='''sshleifer/distilbart-xsum-12-3'''       ,    )
   parser.add_argument('''--save_dir'''       ,    type=__UpperCAmelCase       ,    help='''where to save'''       ,    default='''tmp_gen''' )
   parser.add_argument('''--max_source_length'''       ,    type=__UpperCAmelCase       ,    default=__UpperCAmelCase )
   parser.add_argument(
       '''--type_path'''       ,    type=__UpperCAmelCase       ,    default='''test'''       ,    help='''which subset to evaluate typically train/val/test''' )
   parser.add_argument('''--task'''       ,    type=__UpperCAmelCase       ,    default='''summarization'''       ,    help='''used for task_specific_params + metrics''' )
   parser.add_argument('''--bs'''       ,    type=__UpperCAmelCase       ,    default=8       ,    required=__UpperCAmelCase       ,    help='''batch size''' )
   parser.add_argument(
       '''--local_rank'''       ,    type=__UpperCAmelCase       ,    default=-1       ,    required=__UpperCAmelCase       ,    help='''should be passed by distributed.launch''' )

   parser.add_argument(
       '''--n_obs'''       ,    type=__UpperCAmelCase       ,    default=__UpperCAmelCase       ,    required=__UpperCAmelCase       ,    help='''How many observations. Defaults to all.''' )
   parser.add_argument(
       '''--num_return_sequences'''       ,    type=__UpperCAmelCase       ,    default=1       ,    required=__UpperCAmelCase       ,    help='''How many sequences to return''' )
   parser.add_argument(
       '''--sync_timeout'''       ,    type=__UpperCAmelCase       ,    default=6_0_0       ,    required=__UpperCAmelCase       ,    help='''How long should master process wait for other processes to finish.'''       ,    )
   parser.add_argument('''--src_lang'''       ,    type=__UpperCAmelCase       ,    default=__UpperCAmelCase       ,    required=__UpperCAmelCase )
   parser.add_argument('''--tgt_lang'''       ,    type=__UpperCAmelCase       ,    default=__UpperCAmelCase       ,    required=__UpperCAmelCase )
   parser.add_argument(
       '''--prefix'''       ,    type=__UpperCAmelCase       ,    required=__UpperCAmelCase       ,    default=__UpperCAmelCase       ,    help='''will be added to the begininng of src examples''' )
   parser.add_argument('''--fp16'''       ,    action='''store_true''' )
   parser.add_argument('''--debug'''       ,    action='''store_true''' )
   lowercase__:   List[str]					=      time.time()
   lowercase__, lowercase__:   List[str]					=      parser.parse_known_args()
   lowercase__:   Union[str, Any]					=      parse_numeric_n_bool_cl_kwargs(__UpperCAmelCase )
   if generate_kwargs and args.local_rank <= 0:
      print(F"""parsed the following generate kwargs: {generate_kwargs}""" )
   lowercase__:   List[Any]					=      Path(args.save_dir + '''_tmp''' )
   Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )  # this handles locking.
   lowercase__:   str					=      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.
   lowercase__:   List[str]					=      {}
   if args.src_lang is not None:
      lowercase__:   int					=      args.src_lang
   if args.tgt_lang is not None:
      lowercase__:   int					=      args.tgt_lang

   Path(args.save_dir ).mkdir(exist_ok=__UpperCAmelCase )
   lowercase__, lowercase__:   Optional[Any]					=      eval_data_dir(
       args.data_dir       ,    __UpperCAmelCase       ,    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=__UpperCAmelCase       ,    **__UpperCAmelCase       ,    )

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

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



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  List:
   lowercase__:   Dict					=      []
   for partial_result in partial_results:
      records.extend(__UpperCAmelCase )
   lowercase__:   Dict					=      sorted(__UpperCAmelCase       ,    key=lambda __UpperCAmelCase : x["id"] )
   lowercase__:   List[str]					=      [x['''pred'''] for x in records]
   return preds



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[Dict[str, List]]:
   # WAIT FOR lots of .json files
   lowercase__:   Optional[Any]					=      time.time()
   logger.info('''waiting for all nodes to finish''' )
   lowercase__:   Optional[Any]					=      None
   while (time.time() - start_wait) < timeout:
      lowercase__:   Tuple					=      list(save_dir.glob('''rank_*.json''' ) )
      if len(__UpperCAmelCase ) < num_replicas:
         continue
      try:
         # make sure all json files are fully saved
         lowercase__:   List[Any]					=      lmap(__UpperCAmelCase       ,    __UpperCAmelCase )
         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()

"""simple docstring"""






from jiwer import compute_measures

import datasets


__A			   = "\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

__A			   = "\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

__A			   = "\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,_KWARGS_DESCRIPTION  )
class     UpperCAmelCase						(datasets.Metric  ):



    """simple docstring"""

    def _snake_case			(   self							):
       return datasets.MetricInfo(
           description=_DESCRIPTION    ,			citation=_CITATION    ,			inputs_description=_KWARGS_DESCRIPTION    ,			features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''string'''    ,			id='''sequence'''							),
                   '''references''': datasets.Value('''string'''    ,			id='''sequence'''							),
               }							)    ,			codebase_urls=['''https://github.com/jitsi/jiwer/''']    ,			reference_urls=[
               '''https://en.wikipedia.org/wiki/Word_error_rate''',
           ]    ,			)






    def _snake_case			(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=False							):
       if concatenate_texts:
          return compute_measures(_UpperCAmelCase    ,			_UpperCAmelCase							)["wer"]
       else:
          lowercase__:   Dict					=      0
          lowercase__:   Union[str, Any]					=      0
          for prediction, reference in zip(_UpperCAmelCase    ,			_UpperCAmelCase							):
             lowercase__:   Tuple					=      compute_measures(_UpperCAmelCase    ,			_UpperCAmelCase							)
             incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
             total += measures["substitutions"] + measures["deletions"] + measures["hits"]
          return incorrect / total
