




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,
)


lowerCamelCase__					:  Tuple      							=				getLogger(__name__)

def    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[int]     ,     __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      int = 8     ,     __UpperCAmelCase		:      int = 10_24     ,     __UpperCAmelCase		:      Tuple="val"     ,     __UpperCAmelCase		:      List[Any]=None     ,     __UpperCAmelCase		:      Any=False     ,     __UpperCAmelCase		:      List[Any]="summarization"     ,     __UpperCAmelCase		:      Any=None     ,     __UpperCAmelCase		:      Any=1     ,     __UpperCAmelCase		:      Dict = None     ,     __UpperCAmelCase		:      Any=""     ,     **__UpperCAmelCase		:      List[Any]     ,     )   ->					Dict:
      SCREAMING_SNAKE_CASE_      		=     str(__UpperCAmelCase				)
      assert local_rank is not None
      torch.distributed.init_process_group(backend='nccl'     ,     rank=__UpperCAmelCase				)

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

      SCREAMING_SNAKE_CASE_      		=     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:
            SCREAMING_SNAKE_CASE_      		=     tokenizer.model_max_length
      if prefix is None:
            SCREAMING_SNAKE_CASE_      		=     prefix or getattr(model.config     ,     'prefix'     ,     ''				) or ''
      SCREAMING_SNAKE_CASE_      		=     SeqaSeqDataset(
          __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     max_target_length=10_24     ,     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.
      SCREAMING_SNAKE_CASE_      		=     ds.make_sortish_sampler(__UpperCAmelCase     ,     distributed=__UpperCAmelCase     ,     add_extra_examples=__UpperCAmelCase     ,     shuffle=__UpperCAmelCase				)
      SCREAMING_SNAKE_CASE_      		=     DataLoader(__UpperCAmelCase     ,     sampler=__UpperCAmelCase     ,     batch_size=__UpperCAmelCase     ,     collate_fn=ds.collate_fn				)
      SCREAMING_SNAKE_CASE_      		=     []
      for batch in tqdm(__UpperCAmelCase				):
            SCREAMING_SNAKE_CASE_      		=     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     ,     )
            SCREAMING_SNAKE_CASE_      		=     tokenizer.batch_decode(__UpperCAmelCase     ,     skip_special_tokens=__UpperCAmelCase     ,     clean_up_tokenization_spaces=__UpperCAmelCase				)
            SCREAMING_SNAKE_CASE_      		=     batch['ids']
            if num_return_sequences > 1:
                  SCREAMING_SNAKE_CASE_      		=     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    UpperCAmelCase_				( )   ->					Dict:
      SCREAMING_SNAKE_CASE_      		=     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_00     ,     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'				)
      SCREAMING_SNAKE_CASE_      		=     time.time()
      SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     parser.parse_known_args()
      SCREAMING_SNAKE_CASE_      		=     parse_numeric_n_bool_cl_kwargs(__UpperCAmelCase				)
      if generate_kwargs and args.local_rank <= 0:
            print(f"parsed the following generate kwargs: {generate_kwargs}"				)
      SCREAMING_SNAKE_CASE_      		=     Path(args.save_dir + '_tmp'				)
      Path(__UpperCAmelCase				).mkdir(exist_ok=__UpperCAmelCase				)  # this handles locking.
      SCREAMING_SNAKE_CASE_      		=     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.
      SCREAMING_SNAKE_CASE_      		=     {}
      if args.src_lang is not None:
            SCREAMING_SNAKE_CASE_      		=     args.src_lang
      if args.tgt_lang is not None:
            SCREAMING_SNAKE_CASE_      		=     args.tgt_lang

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

            # Calculate metrics, save metrics,  and save _generations.txt
            SCREAMING_SNAKE_CASE_      		=     'translation' in args.task
            SCREAMING_SNAKE_CASE_      		=     calculate_bleu if calc_bleu else calculate_rouge
            SCREAMING_SNAKE_CASE_      		=     'bleu' if calc_bleu else 'rouge'
            SCREAMING_SNAKE_CASE_      		=     score_fn(__UpperCAmelCase     ,     __UpperCAmelCase				)
            SCREAMING_SNAKE_CASE_      		=     len(__UpperCAmelCase				)
            SCREAMING_SNAKE_CASE_      		=     time.time() - start_time
            SCREAMING_SNAKE_CASE_      		=     round(runtime / metrics['n_obs']     ,     4				)
            SCREAMING_SNAKE_CASE_      		=     num_replicas
            # TODO(@stas00): add whatever metadata to metrics
            SCREAMING_SNAKE_CASE_      		=     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    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[int]				)   ->					List:
      SCREAMING_SNAKE_CASE_      		=     []
      for partial_result in partial_results:
            records.extend(__UpperCAmelCase				)
      SCREAMING_SNAKE_CASE_      		=     sorted(__UpperCAmelCase     ,     key=lambda __UpperCAmelCase				: x["id"]				)
      SCREAMING_SNAKE_CASE_      		=     [x['pred'] for x in records]
      return preds

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





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     {}



 def  lowerCAmelCase_    (				self    :						List[str]		):
       print(self.vertex		)
       for i in self.vertex:
             print(_lowerCAmelCase							,							' -> '							,							' -> '.join([str(_lowerCAmelCase		) for j in self.vertex[i]]		)		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						int		):
       # check if vertex is already present,
       if from_vertex in self.vertex:
             self.vertex[from_vertex].append(_lowerCAmelCase		)
       else:
             # else make a new vertex
             SCREAMING_SNAKE_CASE_      		=     [to_vertex]



 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       # visited array for storing already visited nodes
       SCREAMING_SNAKE_CASE_      		=     [False] * len(self.vertex		)

       # call the recursive helper function
       for i in range(len(self.vertex		)		):
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase							,							_lowerCAmelCase		)




 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						list		):
       # mark start vertex as visited
       SCREAMING_SNAKE_CASE_      		=     True

       print(_lowerCAmelCase							,							end=' '		)

       # Recur for all the vertices that are adjacent to this node
       for i in self.vertex:
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase							,							_lowerCAmelCase		)


if __name__ == "__main__":
   lowerCamelCase__					:  List[Any]      							=				Graph()
   g.add_edge(0, 1)
   g.add_edge(0, 2)
   g.add_edge(1, 2)
   g.add_edge(2, 0)
   g.add_edge(2, 3)
   g.add_edge(3, 3)

   g.print_graph()
   print('DFS:')
   g.dfs()

   # OUTPUT:
   # 0  ->  1 -> 2
   # 1  ->  2
   # 2  ->  0 -> 3
   # 3  ->  3
   # DFS:
   #  0 1 2 3