



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	       =	getLogger(__name__)
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]	,					snake_case__	:   str	,					snake_case__	:   str	,					snake_case__	:   int = 8	,					snake_case__	:   int = 1024	,					snake_case__	:   Any="val"	,					snake_case__	:   int=None	,					snake_case__	:   Optional[Any]=False	,					snake_case__	:   Any="summarization"	,					snake_case__	:   Dict=None	,					snake_case__	:   List[Any]=1	,					snake_case__	:   Dict = None	,					snake_case__	:   Optional[Any]=""	,					**snake_case__	:   List[str]	,					)     ->					Dict:
   UpperCamelCase   :  Optional[Any]						     =     str(snake_case__     )
   assert local_rank is not None
   torch.distributed.init_process_group(backend='nccl'	,					rank=snake_case__     )

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

   UpperCamelCase   :  List[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:
      UpperCamelCase   :  Any						     =     tokenizer.model_max_length
   if prefix is None:
      UpperCamelCase   :  Optional[int]						     =     prefix or getattr(model.config	,					'prefix'	,					''     ) or ''
   UpperCamelCase   :  Dict						     =     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.
   UpperCamelCase   :  str						     =     ds.make_sortish_sampler(snake_case__	,					distributed=snake_case__	,					add_extra_examples=snake_case__	,					shuffle=snake_case__     )
   UpperCamelCase   :  Optional[Any]						     =     DataLoader(snake_case__	,					sampler=snake_case__	,					batch_size=snake_case__	,					collate_fn=ds.collate_fn     )
   UpperCamelCase   :  Optional[Any]						     =     []
   for batch in tqdm(snake_case__     ):
      UpperCamelCase   :  Tuple						     =     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__	,					)
      UpperCamelCase   :  Tuple						     =     tokenizer.batch_decode(snake_case__	,					skip_special_tokens=snake_case__	,					clean_up_tokenization_spaces=snake_case__     )
      UpperCamelCase   :  List[str]						     =     batch['ids']
      if num_return_sequences > 1:
         UpperCamelCase   :  Any						     =     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 		UpperCamelCase     (       )     ->					Optional[Any]:
   UpperCamelCase   :  List[str]						     =     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'     )
   UpperCamelCase   :  str						     =     time.time()
   UpperCamelCase     ,					UpperCamelCase   :  str						     =     parser.parse_known_args()
   UpperCamelCase   :  str						     =     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}"""     )
   UpperCamelCase   :  List[Any]						     =     Path(args.save_dir + '_tmp'     )
   Path(snake_case__     ).mkdir(exist_ok=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   :  Union[str, Any]						     =     {}
   if args.src_lang is not None:
      UpperCamelCase   :  Any						     =     args.src_lang
   if args.tgt_lang is not None:
      UpperCamelCase   :  str						     =     args.tgt_lang

   Path(args.save_dir     ).mkdir(exist_ok=snake_case__     )
   UpperCamelCase     ,					UpperCamelCase   :  List[str]						     =     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:
      UpperCamelCase   :  Tuple						     =     Path(args.save_dir     )
      save_dir.mkdir(exist_ok=snake_case__     )
      UpperCamelCase   :  Tuple						     =     gather_results_from_each_node(snake_case__	,					snake_case__	,					args.sync_timeout     )
      UpperCamelCase   :  Tuple						     =     combine_partial_results(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(snake_case__	,					snake_case__     )
         return
      UpperCamelCase   :  Tuple						     =     Path(args.data_dir     ).joinpath(args.type_path + '.target'     )
      with open(snake_case__     ) as f:
         UpperCamelCase   :  Any						     =     [x.rstrip() for x in f.readlines()][: len(snake_case__     )]

      # Calculate metrics, save metrics,  and save _generations.txt
      UpperCamelCase   :  Tuple						     =     'translation' in args.task
      UpperCamelCase   :  Any						     =     calculate_bleu if calc_bleu else calculate_rouge
      UpperCamelCase   :  Tuple						     =     'bleu' if calc_bleu else 'rouge'
      UpperCamelCase   :  Dict						     =     score_fn(snake_case__	,					snake_case__     )
      UpperCamelCase   :  Optional[Any]						     =     len(snake_case__     )
      UpperCamelCase   :  str						     =     time.time() - start_time
      UpperCamelCase   :  Union[str, Any]						     =     round(runtime / metrics['n_obs']	,					4     )
      UpperCamelCase   :  Any						     =     num_replicas
      # TODO(@stas00): add whatever metadata to metrics
      UpperCamelCase   :  Tuple						     =     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 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					List:
   UpperCamelCase   :  Optional[int]						     =     []
   for partial_result in partial_results:
      records.extend(snake_case__     )
   UpperCamelCase   :  List[str]						     =     sorted(snake_case__	,					key=lambda snake_case__     : x["id"]     )
   UpperCamelCase   :  Dict						     =     [x['pred'] for x in records]
   return preds
def 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   int	,					snake_case__	:   Union[str, Any]     )     ->					List[Dict[str, List]]:
   # WAIT FOR lots of .json files
   UpperCamelCase   :  List[Any]						     =     time.time()
   logger.info('waiting for all nodes to finish'     )
   UpperCamelCase   :  Dict						     =     None
   while (time.time() - start_wait) < timeout:
      UpperCamelCase   :  str						     =     list(save_dir.glob('rank_*.json'     )     )
      if len(snake_case__     ) < num_replicas:
         continue
      try:
         # make sure all json files are fully saved
         UpperCamelCase   :  Optional[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 abc import ABC, abstractmethod
from argparse import ArgumentParser



class     lowerCAmelCase_		(  a__							):
       @staticmethod
       @abstractmethod
       def  snake_case_	(      SCREAMING_SNAKE_CASE_	)					->		int:
          raise NotImplementedError()






       @abstractmethod
       def  snake_case_	(      self	)					->		str:
          raise NotImplementedError()



