


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,
)


__UpperCamelCase       :       Dict						       =				getLogger(__name__)

def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				int = 8    , SCREAMING_SNAKE_CASE	:				int = 1024    , SCREAMING_SNAKE_CASE	:				int="val"    , SCREAMING_SNAKE_CASE	:				Optional[Any]=None    , SCREAMING_SNAKE_CASE	:				List[Any]=False    , SCREAMING_SNAKE_CASE	:				List[Any]="summarization"    , SCREAMING_SNAKE_CASE	:				Union[str, Any]=None    , SCREAMING_SNAKE_CASE	:				List[Any]=1    , SCREAMING_SNAKE_CASE	:				Dict = None    , SCREAMING_SNAKE_CASE	:				List[str]=""    , **SCREAMING_SNAKE_CASE	:				Tuple    , ):







 """simple docstring"""



 UpperCamelCase__					:    List[str]          =   str(SCREAMING_SNAKE_CASE     )
 assert local_rank is not None
 torch.distributed.init_process_group(backend='''nccl'''    , rank=SCREAMING_SNAKE_CASE     )

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

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

 if max_source_length is None:
  UpperCamelCase__					:    Optional[int]          =   tokenizer.model_max_length
 if prefix is None:
  UpperCamelCase__					:    int          =   prefix or getattr(model.config    , '''prefix'''    , ''''''     ) or ''''''
 UpperCamelCase__					:    Union[str, Any]          =   SeqaSeqDataset(
     SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , max_target_length=1024    , type_path=SCREAMING_SNAKE_CASE    , n_obs=SCREAMING_SNAKE_CASE    , prefix=SCREAMING_SNAKE_CASE    , **SCREAMING_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.
 UpperCamelCase__					:    Tuple          =   ds.make_sortish_sampler(SCREAMING_SNAKE_CASE    , distributed=SCREAMING_SNAKE_CASE    , add_extra_examples=SCREAMING_SNAKE_CASE    , shuffle=SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    List[Any]          =   DataLoader(SCREAMING_SNAKE_CASE    , sampler=SCREAMING_SNAKE_CASE    , batch_size=SCREAMING_SNAKE_CASE    , collate_fn=ds.collate_fn     )
 UpperCamelCase__					:    Any          =   []
 for batch in tqdm(SCREAMING_SNAKE_CASE     ):
  UpperCamelCase__					:    str          =   model.generate(
      input_ids=batch['''input_ids'''].to(model.device     )    , attention_mask=batch['''attention_mask'''].to(model.device     )    , num_return_sequences=SCREAMING_SNAKE_CASE    , num_beams=SCREAMING_SNAKE_CASE    , **SCREAMING_SNAKE_CASE    , )
  UpperCamelCase__					:    Tuple          =   tokenizer.batch_decode(SCREAMING_SNAKE_CASE    , skip_special_tokens=SCREAMING_SNAKE_CASE    , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE     )
  UpperCamelCase__					:    Tuple          =   batch['''ids''']
  if num_return_sequences > 1:
   UpperCamelCase__					:    str          =   chunks(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(SCREAMING_SNAKE_CASE     ):
   results.append({'''pred''': pred, '''id''': ids[i].item()}     )
 save_json(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 return results, sampler.num_replicas

def 			_a  (				):







 """simple docstring"""



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

 parser.add_argument(
     '''--n_obs'''    , type=SCREAMING_SNAKE_CASE    , default=SCREAMING_SNAKE_CASE    , required=SCREAMING_SNAKE_CASE    , help='''How many observations. Defaults to all.'''     )
 parser.add_argument(
     '''--num_return_sequences'''    , type=SCREAMING_SNAKE_CASE    , default=1    , required=SCREAMING_SNAKE_CASE    , help='''How many sequences to return'''     )
 parser.add_argument(
     '''--sync_timeout'''    , type=SCREAMING_SNAKE_CASE    , default=600    , required=SCREAMING_SNAKE_CASE    , help='''How long should master process wait for other processes to finish.'''    , )
 parser.add_argument('''--src_lang'''    , type=SCREAMING_SNAKE_CASE    , default=SCREAMING_SNAKE_CASE    , required=SCREAMING_SNAKE_CASE     )
 parser.add_argument('''--tgt_lang'''    , type=SCREAMING_SNAKE_CASE    , default=SCREAMING_SNAKE_CASE    , required=SCREAMING_SNAKE_CASE     )
 parser.add_argument(
     '''--prefix'''    , type=SCREAMING_SNAKE_CASE    , required=SCREAMING_SNAKE_CASE    , default=SCREAMING_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'''     )
 UpperCamelCase__					:    Any          =   time.time()
 UpperCamelCase__       ,				UpperCamelCase__					:    List[Any]          =   parser.parse_known_args()
 UpperCamelCase__					:    Union[str, Any]          =   parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE     )
 if generate_kwargs and args.local_rank <= 0:
  print(F"parsed the following generate kwargs: {generate_kwargs}"     )
 UpperCamelCase__					:    str          =   Path(args.save_dir + '''_tmp'''     )
 Path(SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE     )  # this handles locking.
 UpperCamelCase__					:    List[Any]          =   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.
 UpperCamelCase__					:    Optional[Any]          =   {}
 if args.src_lang is not None:
  UpperCamelCase__					:    Union[str, Any]          =   args.src_lang
 if args.tgt_lang is not None:
  UpperCamelCase__					:    Optional[int]          =   args.tgt_lang

 Path(args.save_dir     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE     )
 UpperCamelCase__       ,				UpperCamelCase__					:    Dict          =   eval_data_dir(
     args.data_dir    , SCREAMING_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=SCREAMING_SNAKE_CASE    , **SCREAMING_SNAKE_CASE    , )

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

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

def 			_a  (				SCREAMING_SNAKE_CASE	:				Dict     ):







 """simple docstring"""



 UpperCamelCase__					:    int          =   []
 for partial_result in partial_results:
  records.extend(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    List[str]          =   sorted(SCREAMING_SNAKE_CASE    , key=lambda SCREAMING_SNAKE_CASE     : x["id"]     )
 UpperCamelCase__					:    Optional[Any]          =   [x['''pred'''] for x in records]
 return preds

def 			_a  (				SCREAMING_SNAKE_CASE	:				Optional[Any]    , SCREAMING_SNAKE_CASE	:				Tuple    , SCREAMING_SNAKE_CASE	:				List[Any]     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   time.time()
 logger.info('''waiting for all nodes to finish'''     )
 UpperCamelCase__					:    str          =   None
 while (time.time() - start_wait) < timeout:
  UpperCamelCase__					:    int          =   list(save_dir.glob('''rank_*.json'''     )     )
  if len(SCREAMING_SNAKE_CASE     ) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   UpperCamelCase__					:    List[Any]          =   lmap(SCREAMING_SNAKE_CASE    , SCREAMING_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()






__UpperCamelCase       :       List[Any]						       =				256
# Modulus to hash a string
__UpperCamelCase       :       Union[str, Any]						       =				100_0003

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   len(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    List[Any]          =   len(SCREAMING_SNAKE_CASE     )
 if p_len > t_len:
  return False

 UpperCamelCase__					:    Any          =   0
 UpperCamelCase__					:    str          =   0
 UpperCamelCase__					:    List[Any]          =   1

 # Calculating the hash of pattern and substring of text
 for i in range(SCREAMING_SNAKE_CASE     ):
  UpperCamelCase__					:    Any          =   (ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
  UpperCamelCase__					:    List[str]          =   (ord(text[i]     ) + text_hash * alphabet_size) % modulus
  if i == p_len - 1:
   continue
  UpperCamelCase__					:    Dict          =   (modulus_power * alphabet_size) % modulus

 for i in range(0    , t_len - p_len + 1     ):
  if text_hash == p_hash and text[i : i + p_len] == pattern:
   return True
  if i == t_len - p_len:
   continue
  # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
  UpperCamelCase__					:    Optional[int]          =   (
      (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
      + ord(text[i + p_len]     )
  ) % modulus
 return False

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Tuple          =   '''abc1abc12'''
 UpperCamelCase__					:    Dict          =   '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 UpperCamelCase__					:    List[str]          =   '''alskfjaldsk23adsfabcabc'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ) and not rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 2)
 UpperCamelCase__					:    Optional[int]          =   '''ABABX'''
 UpperCamelCase__					:    int          =   '''ABABZABABYABABX'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 3)
 UpperCamelCase__					:    int          =   '''AAAB'''
 UpperCamelCase__					:    str          =   '''ABAAAAAB'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 4)
 UpperCamelCase__					:    Union[str, Any]          =   '''abcdabcy'''
 UpperCamelCase__					:    List[str]          =   '''abcxabcdabxabcdabcdabcy'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 5)
 UpperCamelCase__					:    Tuple          =   '''Lü'''
 UpperCamelCase__					:    Any          =   '''Lüsai'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Dict          =   '''Lue'''
 assert not rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 print('''Success.'''     )


if __name__ == "__main__":
       test_rabin_karp()


