



'''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 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = 8	,  _lowerCAmelCase = 1_024	,  _lowerCAmelCase="val"	,  _lowerCAmelCase=None	,  _lowerCAmelCase=False	,  _lowerCAmelCase="summarization"	,  _lowerCAmelCase=None	,  _lowerCAmelCase=1	,  _lowerCAmelCase = None	,  _lowerCAmelCase=""	,  **_lowerCAmelCase	,  )						->					Dict:
      snake_case__    :       Tuple       		= str(_lowerCAmelCase						)
      assert local_rank is not None
      torch.distributed.init_process_group(backend="""nccl"""	,  rank=_lowerCAmelCase						)

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

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

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





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

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

      Path(args.save_dir						).mkdir(exist_ok=_lowerCAmelCase						)
      snake_case__		,							snake_case__    :       Any       		= eval_data_dir(
          args.data_dir	,  _lowerCAmelCase	,  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=_lowerCAmelCase	,  **_lowerCAmelCase	,  )

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

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





def 		__snake_case(							_lowerCAmelCase						)						->					List:
      snake_case__    :       Optional[Any]       		= []
      for partial_result in partial_results:
            records.extend(_lowerCAmelCase						)
      snake_case__    :       Optional[int]       		= sorted(_lowerCAmelCase	,  key=lambda _lowerCAmelCase						: x["id"]						)
      snake_case__    :       Tuple       		= [x["""pred"""] for x in records]
      return preds





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					List[Dict[str, List]]:
      # WAIT FOR lots of .json files
      snake_case__    :       str       		= time.time()
      logger.info("""waiting for all nodes to finish"""						)
      snake_case__    :       Optional[Any]       		= None
      while (time.time() - start_wait) < timeout:
            snake_case__    :       List[Any]       		= list(save_dir.glob("""rank_*.json"""						)						)
            if len(_lowerCAmelCase						) < num_replicas:
                  continue
            try:
                  # make sure all json files are fully saved
                  snake_case__    :       str       		= lmap(_lowerCAmelCase	,  _lowerCAmelCase						)
                  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 baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Tuple					,	snake_case_   :	List[str]		):
          if isinstance(snake_case_					,	snake_case_		):
                # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
                # modified it, it will not be accepted here again, since `auto` values would have been overridden
                snake_case__    :       Optional[Any]       		= deepcopy(snake_case_		)
          elif os.path.exists(snake_case_		):
                with io.open(snake_case_					,	"""r"""					,	encoding="""utf-8"""		) as f:
                      snake_case__    :       Union[str, Any]       		= json.load(snake_case_		)
          else:
                try:
                      snake_case__    :       List[Any]       		= baseaa.urlsafe_baadecode(snake_case_		).decode("""utf-8"""		)
                      snake_case__    :       Optional[int]       		= json.loads(snake_case_		)
                except (UnicodeDecodeError, AttributeError, ValueError):
                      raise ValueError(
                          f"Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"		)

          snake_case__    :       str       		= config

          self.set_stage_and_offload()




    def lowerCamelCase			(       self   :	int		):
          # zero stage - this is done as early as possible, before model is created, to allow
          # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
          # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
          snake_case__    :       Union[str, Any]       		= self.get_value("""zero_optimization.stage"""					,	-1		)

          # offload
          snake_case__    :       Optional[Any]       		= False
          if self.is_zeroa() or self.is_zeroa():
                snake_case__    :       int       		= set(["""cpu""", """nvme"""]		)
                snake_case__    :       Any       		= set(
                    [
                        self.get_value("""zero_optimization.offload_optimizer.device"""		),
                        self.get_value("""zero_optimization.offload_param.device"""		),
                    ]		)
                if len(offload_devices & offload_devices_valid		) > 0:
                      snake_case__    :       List[Any]       		= True




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	Optional[Any]		):
          snake_case__    :       Optional[Any]       		= self.config

          # find the config node of interest if it exists
          snake_case__    :       List[Any]       		= ds_key_long.split("""."""		)
          snake_case__    :       Tuple       		= nodes.pop()
          for node in nodes:
                snake_case__    :       Optional[Any]       		= config.get(snake_case_		)
                if config is None:
                      return None, ds_key

          return config, ds_key




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	int					,	snake_case_   :	List[str]=None		):
          snake_case__		,							snake_case__    :       Dict       		= self.find_config_node(snake_case_		)
          if config is None:
                return default
          return config.get(snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Dict					,	snake_case_   :	Optional[int]					,	snake_case_   :	int=False		):
          snake_case__    :       Dict       		= self.config

          # find the config node of interest if it exists
          snake_case__    :       str       		= ds_key_long.split("""."""		)
          for node in nodes:
                snake_case__    :       Union[str, Any]       		= config
                snake_case__    :       List[Any]       		= config.get(snake_case_		)
                if config is None:
                      if must_exist:
                            raise ValueError(f"Can't find {ds_key_long} entry in the config: {self.config}"		)
                      else:
                            return

        # if found remove it
          if parent_config is not None:
                parent_config.pop(snake_case_		)




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	Optional[Any]		):
          snake_case__    :       Dict       		= self.get_value(snake_case_		)
          return False if value is None else bool(snake_case_		)




    def lowerCamelCase			(       self   :	List[str]					,	snake_case_   :	Union[str, Any]		):
          snake_case__    :       Optional[int]       		= self.get_value(snake_case_		)
          return False if value is None else not bool(snake_case_		)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          return self._stage == 2




    def lowerCamelCase			(       self   :	Dict		):
          return self._stage == 3




    def lowerCamelCase			(       self   :	List[Any]		):
          return self._offload







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Any					,	snake_case_   :	List[Any]		):
          snake_case__    :       Tuple       		= engine




    def lowerCamelCase			(       self   :	Dict					,	snake_case_   :	Optional[Any]					,	**snake_case_   :	int		):
          # runs backpropagation and handles mixed precision
          self.engine.backward(snake_case_					,	**snake_case_		)

          # Deepspeed's `engine.step` performs the following operations:
          # - gradient accumulation check
          # - gradient clipping
          # - optimizer step
          # - zero grad
          # - checking overflow
          # - lr_scheduler step (only if engine.lr_scheduler is not None)
          self.engine.step()
          # and this plugin overrides the above calls with no-ops when Accelerate runs under
          # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
          # training loop that works transparently under many training regimes.







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    def __init__(       self   :	Tuple					,	snake_case_   :	Any		):
          super().__init__(snake_case_					,	device_placement=snake_case_					,	scaler=snake_case_		)
          snake_case__    :       Optional[int]       		= hasattr(self.optimizer					,	"""overflow"""		)




    def lowerCamelCase			(       self   :	int					,	snake_case_   :	Optional[Any]=None		):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




    def lowerCamelCase			(       self   :	List[Any]		):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




    @property
    def lowerCamelCase			(       self   :	List[str]		):
          if self.__has_overflow__:
                return self.optimizer.overflow
          return False







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    def __init__(       self   :	Tuple					,	snake_case_   :	Optional[Any]					,	snake_case_   :	Tuple		):
          super().__init__(snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Optional[int]		):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Optional[int]					,	snake_case_   :	Dict					,	snake_case_   :	int=0.001					,	snake_case_   :	Optional[Any]=0					,	**snake_case_   :	List[Any]		):
          snake_case__    :       int       		= params
          snake_case__    :       Optional[Any]       		= lr
          snake_case__    :       Any       		= weight_decay
          snake_case__    :       Optional[Any]       		= kwargs







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Dict					,	snake_case_   :	str					,	snake_case_   :	Optional[int]=None					,	snake_case_   :	Any=0					,	**snake_case_   :	List[str]		):
          snake_case__    :       List[Any]       		= optimizer
          snake_case__    :       List[Any]       		= total_num_steps
          snake_case__    :       List[Any]       		= warmup_num_steps
          snake_case__    :       Optional[Any]       		= kwargs
