


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					:       Union[str, Any]			    =     getLogger(__name__)



def UpperCamelCase_				(				__a			,				__a			,				__a			,				__a = 8			,				__a = 1_024			,				__a="val"			,				__a=None			,				__a=False			,				__a="summarization"			,				__a=None			,				__a=1			,				__a = None			,				__a=""			,				**__a			,				)	->   Dict:
      a__	:  List[str]     =   str(__a      )
      assert local_rank is not None
      torch.distributed.init_process_group(backend="nccl"			,				rank=__a      )

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

      a__	:  int     =   AutoTokenizer.from_pretrained(__a      )
      logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''      )  # if this is wrong, check config.model_type.

      if max_source_length is None:
            a__	:  Optional[int]     =   tokenizer.model_max_length
      if prefix is None:
            a__	:  List[str]     =   prefix or getattr(model.config			,				"prefix"			,				""      ) or ""
      a__	:  Tuple     =   SeqaSeqDataset(
          __a			,				__a			,				__a			,				max_target_length=1_024			,				type_path=__a			,				n_obs=__a			,				prefix=__a			,				**__a			,				)
      # 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.
      a__	:  Any     =   ds.make_sortish_sampler(__a			,				distributed=__a			,				add_extra_examples=__a			,				shuffle=__a      )
      a__	:  List[str]     =   DataLoader(__a			,				sampler=__a			,				batch_size=__a			,				collate_fn=ds.collate_fn      )
      a__	:  List[str]     =   []
      for batch in tqdm(__a      ):
            a__	:  List[str]     =   model.generate(
                input_ids=batch["input_ids"].to(model.device      )			,				attention_mask=batch["attention_mask"].to(model.device      )			,				num_return_sequences=__a			,				num_beams=__a			,				**__a			,				)
            a__	:  int     =   tokenizer.batch_decode(__a			,				skip_special_tokens=__a			,				clean_up_tokenization_spaces=__a      )
            a__	:  Optional[int]     =   batch["ids"]
            if num_return_sequences > 1:
                  a__	:  Any     =   chunks(__a			,				__a      )  # batch size chunks, each of size num_return_seq
            for i, pred in enumerate(__a      ):
                  results.append({"pred": pred, "id": ids[i].item()}      )
      save_json(__a			,				__a      )
      return results, sampler.num_replicas



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

      parser.add_argument(
          "--n_obs"			,				type=__a			,				default=__a			,				required=__a			,				help="How many observations. Defaults to all."      )
      parser.add_argument(
          "--num_return_sequences"			,				type=__a			,				default=1			,				required=__a			,				help="How many sequences to return"      )
      parser.add_argument(
          "--sync_timeout"			,				type=__a			,				default=600			,				required=__a			,				help="How long should master process wait for other processes to finish."			,				)
      parser.add_argument("--src_lang"			,				type=__a			,				default=__a			,				required=__a      )
      parser.add_argument("--tgt_lang"			,				type=__a			,				default=__a			,				required=__a      )
      parser.add_argument(
          "--prefix"			,				type=__a			,				required=__a			,				default=__a			,				help="will be added to the begininng of src examples"      )
      parser.add_argument("--fp16"			,				action="store_true"      )
      parser.add_argument("--debug"			,				action="store_true"      )
      a__	:  Tuple     =   time.time()
      a__,	a__	:  str     =   parser.parse_known_args()
      a__	:  Any     =   parse_numeric_n_bool_cl_kwargs(__a      )
      if generate_kwargs and args.local_rank <= 0:
            print(f'''parsed the following generate kwargs: {generate_kwargs}'''      )
      a__	:  int     =   Path(args.save_dir + "_tmp"      )
      Path(__a      ).mkdir(exist_ok=__a      )  # this handles locking.
      a__	:  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.
      a__	:  Dict     =   {}
      if args.src_lang is not None:
            a__	:  Any     =   args.src_lang
      if args.tgt_lang is not None:
            a__	:  Tuple     =   args.tgt_lang

      Path(args.save_dir      ).mkdir(exist_ok=__a      )
      a__,	a__	:  int     =   eval_data_dir(
          args.data_dir			,				__a			,				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=__a			,				**__a			,				)

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

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



def UpperCamelCase_				(				__a      )	->   List:
      a__	:  List[str]     =   []
      for partial_result in partial_results:
            records.extend(__a      )
      a__	:  List[str]     =   sorted(__a			,				key=lambda __a      : x["id"]      )
      a__	:  Union[str, Any]     =   [x["pred"] for x in records]
      return preds



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

import operator as op


def 						__magic_name__       (      __UpperCAmelCase      )						->   Dict:

					'''simple docstring'''

					snake_case_		     =							[]
					snake_case_		     =							lambda __UpperCAmelCase, __UpperCAmelCase      : int(x / y      )  # noqa: E731 integer division operation
					snake_case_		     =							{
					    '''^''': op.pow,
					    '''*''': op.mul,
					    '''/''': div,
					    '''+''': op.add,
					    '''-''': op.sub,
					}  # operators & their respective operation

					# print table header
					print('''Symbol'''.center(8      ), '''Action'''.center(12      ), '''Stack''', sep=''' | '''      )
					print('''-''' * (30 + len(__UpperCAmelCase      ))      )

					for x in post_fix:
										if x.isdigit():  # if x in digit
															stack.append(__UpperCAmelCase      )  # append x to stack
															# output in tabular format
															print(x.rjust(8      ), ('''push(''' + x + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )
										else:
															snake_case_		     =							stack.pop()  # pop stack
															# output in tabular format
															print(''''''.rjust(8      ), ('''pop(''' + b + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )

															snake_case_		     =							stack.pop()  # pop stack
															# output in tabular format
															print(''''''.rjust(8      ), ('''pop(''' + a + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )

															stack.append(
															    str(opr[x](int(__UpperCAmelCase      ), int(__UpperCAmelCase      )      )      )      )  # evaluate the 2 values popped from stack & push result to stack
															# output in tabular format
															print(
															    x.rjust(8      ), ('''push(''' + a + x + b + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | ''', )

					return int(stack[0]      )


if __name__ == "__main__":
							a    :							Any										=    input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
							print('\n\tResult = ', solve(Postfix))

