




'''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,
)


UpperCamelCase_						:      List[Any]       =						getLogger(__name__)





def      __a     (       _UpperCamelCase:   List[Any] ,						_UpperCamelCase:   str ,						_UpperCamelCase:   str ,						_UpperCamelCase:   int = 8 ,						_UpperCamelCase:   int = 1_024 ,						_UpperCamelCase:   Optional[Any]="val" ,						_UpperCamelCase:   Optional[Any]=None ,						_UpperCamelCase:   Union[str, Any]=False ,						_UpperCamelCase:   Any="summarization" ,						_UpperCamelCase:   int=None ,						_UpperCamelCase:   str=1 ,						_UpperCamelCase:   Dict = None ,						_UpperCamelCase:   Optional[int]="" ,						**_UpperCamelCase:   Optional[int] ,						)						->			Dict:

     """simple docstring"""




     _snake_case							=				str(_UpperCamelCase   )
     assert local_rank is not None
     torch.distributed.init_process_group(backend="nccl" ,						rank=_UpperCamelCase   )

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

     _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:
          _snake_case							=				tokenizer.model_max_length
     if prefix is None:
          _snake_case							=				prefix or getattr(model.config ,						"prefix" ,						""   ) or ""
     _snake_case							=				SeqaSeqDataset(
         _UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						max_target_length=1_024 ,						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.
     _snake_case							=				ds.make_sortish_sampler(_UpperCamelCase ,						distributed=_UpperCamelCase ,						add_extra_examples=_UpperCamelCase ,						shuffle=_UpperCamelCase   )
     _snake_case							=				DataLoader(_UpperCamelCase ,						sampler=_UpperCamelCase ,						batch_size=_UpperCamelCase ,						collate_fn=ds.collate_fn   )
     _snake_case							=				[]
     for batch in tqdm(_UpperCamelCase   ):
          _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 ,						)
          _snake_case							=				tokenizer.batch_decode(_UpperCamelCase ,						skip_special_tokens=_UpperCamelCase ,						clean_up_tokenization_spaces=_UpperCamelCase   )
          _snake_case							=				batch["ids"]
          if num_return_sequences > 1:
               _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      __a     (       )						->			Tuple:

     """simple docstring"""




     _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=600 ,						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"   )
     _snake_case							=				time.time()
     _snake_case							, _snake_case							=				parser.parse_known_args()
     _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}"""   )
     _snake_case							=				Path(args.save_dir + "_tmp"   )
     Path(_UpperCamelCase   ).mkdir(exist_ok=_UpperCamelCase   )  # this handles locking.
     _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.
     _snake_case							=				{}
     if args.src_lang is not None:
          _snake_case							=				args.src_lang
     if args.tgt_lang is not None:
          _snake_case							=				args.tgt_lang

     Path(args.save_dir   ).mkdir(exist_ok=_UpperCamelCase   )
     _snake_case							, _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:
          _snake_case							=				Path(args.save_dir   )
          save_dir.mkdir(exist_ok=_UpperCamelCase   )
          _snake_case							=				gather_results_from_each_node(_UpperCamelCase ,						_UpperCamelCase ,						args.sync_timeout   )
          _snake_case							=				combine_partial_results(_UpperCamelCase   )
          if args.num_return_sequences > 1:
               _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
          _snake_case							=				Path(args.data_dir   ).joinpath(args.type_path + ".target"   )
          with open(_UpperCamelCase   ) as f:
               _snake_case							=				[x.rstrip() for x in f.readlines()][: len(_UpperCamelCase   )]

          # Calculate metrics, save metrics,  and save _generations.txt
          _snake_case							=				"translation" in args.task
          _snake_case							=				calculate_bleu if calc_bleu else calculate_rouge
          _snake_case							=				"bleu" if calc_bleu else "rouge"
          _snake_case							=				score_fn(_UpperCamelCase ,						_UpperCamelCase   )
          _snake_case							=				len(_UpperCamelCase   )
          _snake_case							=				time.time() - start_time
          _snake_case							=				round(runtime / metrics["n_obs"] ,						4   )
          _snake_case							=				num_replicas
          # TODO(@stas00): add whatever metadata to metrics
          _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      __a     (       _UpperCamelCase:   Dict   )						->			List:

     """simple docstring"""




     _snake_case							=				[]
     for partial_result in partial_results:
          records.extend(_UpperCamelCase   )
     _snake_case							=				sorted(_UpperCamelCase ,						key=lambda _UpperCamelCase   : x["id"]   )
     _snake_case							=				[x["pred"] for x in records]
     return preds





def      __a     (       _UpperCamelCase:   List[Any] ,						_UpperCamelCase:   str ,						_UpperCamelCase:   int   )						->			List[Dict[str, List]]:

     """simple docstring"""




     _snake_case							=				time.time()
     logger.info("waiting for all nodes to finish"   )
     _snake_case							=				None
     while (time.time() - start_wait) < timeout:
          _snake_case							=				list(save_dir.glob("rank_*.json"   )   )
          if len(_UpperCamelCase   ) < num_replicas:
               continue
          try:
               # make sure all json files are fully saved
               _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()








'''simple docstring'''





from manim import *

class        _a    (						__lowerCAmelCase							):






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				Rectangle(height=0.5 ,width=0.5   )
          _snake_case							=				Rectangle(height=0.4_6 ,width=0.4_6   ).set_stroke(width=0   )

          _snake_case							=				[mem.copy() for i in range(6   )]
          _snake_case							=				[mem.copy() for i in range(6   )]
          _snake_case							=				VGroup(*_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )
          _snake_case							=				VGroup(*_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )
          _snake_case							=				VGroup(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )
          _snake_case							=				Text("CPU" ,font_size=24   )
          _snake_case							=				Group(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0.5 ,aligned_edge=_SCREAMING_SNAKE_CASE   )
          cpu.move_to([-2.5, -0.5, 0]   )
          self.add(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				[mem.copy() for i in range(4   )]
          _snake_case							=				VGroup(*_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )
          _snake_case							=				Text("GPU" ,font_size=24   )
          _snake_case							=				Group(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0.5 ,aligned_edge=_SCREAMING_SNAKE_CASE   )
          gpu.move_to([-1, -1, 0]   )
          self.add(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				[mem.copy() for i in range(6   )]
          _snake_case							=				VGroup(*_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )

          _snake_case							=				Text("Model" ,font_size=24   )
          _snake_case							=				Group(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0.5 ,aligned_edge=_SCREAMING_SNAKE_CASE   )
          model.move_to([3, -1.0, 0]   )
          self.add(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				[]
          for i, rect in enumerate(_SCREAMING_SNAKE_CASE   ):
               rect.set_stroke(_SCREAMING_SNAKE_CASE   )
               # target = fill.copy().set_fill(YELLOW, opacity=0.7)
               # target.move_to(rect)
               # self.add(target)

               _snake_case							=				Rectangle(height=0.4_6 / 4 ,width=0.4_6 / 3   ).set_stroke(width=0.0   ).set_fill(_SCREAMING_SNAKE_CASE ,opacity=0.7   )

               if i == 0:
                    cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT   ) ,buff=0.0_2 ,direction=_SCREAMING_SNAKE_CASE   )
                    cpu_target.set_x(cpu_target.get_x() + 0.1   )
               elif i == 3:
                    cpu_target.next_to(cpu_targs[0] ,direction=_SCREAMING_SNAKE_CASE ,buff=0.0   )
               else:
                    cpu_target.next_to(cpu_targs[i - 1] ,direction=_SCREAMING_SNAKE_CASE ,buff=0.0   )
               self.add(_SCREAMING_SNAKE_CASE   )
               cpu_targs.append(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				[mem.copy() for i in range(6   )]
          _snake_case							=				VGroup(*_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )

          _snake_case							=				Text("Loaded Checkpoint" ,font_size=24   )
          _snake_case							=				Group(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,aligned_edge=_SCREAMING_SNAKE_CASE ,buff=0.4   )
          checkpoint.move_to([3, 0.5, 0]   )

          _snake_case							=				Square(side_length=2.2   )
          key.move_to([-5, 2, 0]   )

          _snake_case							=				MarkupText(
              f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" ,font_size=18 ,)

          key_text.move_to([-5, 2.4, 0]   )

          self.add(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          _snake_case							=				MarkupText(
              f"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" ,font_size=18 ,)

          blue_text.next_to(_SCREAMING_SNAKE_CASE ,DOWN * 2.4 ,aligned_edge=key_text.get_left()   )

          _snake_case							=				MarkupText(
              f"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>.""" ,font_size=24 ,)
          step_a.move_to([2, 2, 0]   )
          self.play(Write(_SCREAMING_SNAKE_CASE   ) ,Write(_SCREAMING_SNAKE_CASE   )   )

          self.play(Write(_SCREAMING_SNAKE_CASE ,run_time=1   ) ,Create(_SCREAMING_SNAKE_CASE ,run_time=1   )   )

          _snake_case							=				[]
          _snake_case							=				[]
          for i, rect in enumerate(_SCREAMING_SNAKE_CASE   ):
               _snake_case							=				fill.copy().set_fill(_SCREAMING_SNAKE_CASE ,opacity=0.7   )
               target.move_to(_SCREAMING_SNAKE_CASE   )
               first_animations.append(GrowFromCenter(_SCREAMING_SNAKE_CASE ,run_time=1   )   )

               _snake_case							=				target.copy()
               cpu_target.generate_target()
               if i < 5:
                    cpu_target.target.move_to(cpu_left_col_base[i + 1]   )
               else:
                    cpu_target.target.move_to(cpu_right_col_base[i - 5]   )
               second_animations.append(MoveToTarget(_SCREAMING_SNAKE_CASE ,run_time=1.5   )   )

          self.play(*_SCREAMING_SNAKE_CASE   )
          self.play(*_SCREAMING_SNAKE_CASE   )
          self.wait()


