
import json
import sys




def 	A_ (							snake_case							:   List[str] ,      snake_case							:   str )      ->    int:



						'''simple docstring'''

						with open(__snake_case ,      encoding='''utf-8''' ) as f:
												__UpperCamelCase       =			json.load(__snake_case )

						__UpperCamelCase       =			['''<details>''', '''<summary>Show updated benchmarks!</summary>''', ''' ''']

						for benchmark_name in sorted(__snake_case ):
												__UpperCamelCase       =			results[benchmark_name]

												__UpperCamelCase       =			benchmark_name.split('''/''' )[-1]
												output_md.append(f"### Benchmark: {benchmark_file_name}" )

												__UpperCamelCase       =			'''| metric |'''
												__UpperCamelCase       =			'''|--------|'''
												__UpperCamelCase       =			'''| new / old (diff) |'''
												for metric_name in sorted(__snake_case ):
																		__UpperCamelCase       =			benchmark_res[metric_name]
																		__UpperCamelCase       =			metric_vals['''new''']
																		__UpperCamelCase       =			metric_vals.get('''old''' ,      __snake_case )
																		__UpperCamelCase       =			metric_vals.get('''diff''' ,      __snake_case )

																		__UpperCamelCase       =			f" {new_val:f}" if isinstance(__snake_case ,      (int, float) ) else '''None'''

																		if old_val is not None:
																								val_str += f" / {old_val:f}" if isinstance(__snake_case ,      (int, float) ) else "None"
																		if dif_val is not None:
																								val_str += f" ({dif_val:f})" if isinstance(__snake_case ,      (int, float) ) else "None"

																		title += " " + metric_name + " |"
																		lines += "---|"
																		value += val_str + " |"

												output_md += [title, lines, value, " "]

						output_md.append('''</details>''' )

						with open(__snake_case ,      '''w''' ,      encoding='''utf-8''' ) as f:
												f.writelines('''\n'''.join(__snake_case ) )


if __name__ == "__main__":
				lowercase__							:							List[str]									=			sys.argv[1]
				lowercase__							:							Optional[int]									=			sys.argv[2]

				format_json_to_md(input_json_file, output_md_file)






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,
)


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




def 	A_ (							snake_case							:   str ,      snake_case							:   str ,      snake_case							:   str ,      snake_case							:   int = 8 ,      snake_case							:   int = 1024 ,      snake_case							:   Dict="val" ,      snake_case							:   Tuple=None ,      snake_case							:   int=False ,      snake_case							:   Union[str, Any]="summarization" ,      snake_case							:   Optional[Any]=None ,      snake_case							:   List[str]=1 ,      snake_case							:   Dict = None ,      snake_case							:   Optional[Any]="" ,      **snake_case							:   Tuple ,      )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase       =			str(snake_case )
						assert local_rank is not None
						torch.distributed.init_process_group(backend='''nccl''' ,      rank=snake_case )

						__UpperCamelCase       =			Path(snake_case )
						__UpperCamelCase       =			save_dir.joinpath(f"rank_{local_rank}_output.json" )
						torch.cuda.set_device(snake_case )
						__UpperCamelCase       =			AutoModelForSeqaSeqLM.from_pretrained(snake_case ).cuda()
						if fpaa:
												__UpperCamelCase       =			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       =			generate_kwargs.pop('''num_beams''' ,      model.config.num_beams )  # AttributeError risk?
						if num_return_sequences > num_beams:
												__UpperCamelCase       =			num_return_sequences

						__UpperCamelCase       =			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       =			tokenizer.model_max_length
						if prefix is None:
												__UpperCamelCase       =			prefix or getattr(model.config ,      '''prefix''' ,      '''''' ) or ''''''
						__UpperCamelCase       =			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       =			ds.make_sortish_sampler(snake_case ,      distributed=snake_case ,      add_extra_examples=snake_case ,      shuffle=snake_case )
						__UpperCamelCase       =			DataLoader(snake_case ,      sampler=snake_case ,      batch_size=snake_case ,      collate_fn=ds.collate_fn )
						__UpperCamelCase       =			[]
						for batch in tqdm(snake_case ):
												__UpperCamelCase       =			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       =			tokenizer.batch_decode(snake_case ,      skip_special_tokens=snake_case ,      clean_up_tokenization_spaces=snake_case )
												__UpperCamelCase       =			batch['''ids''']
												if num_return_sequences > 1:
																		__UpperCamelCase       =			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 	A_ (							)      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			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       =			time.time()
						__UpperCamelCase  ,							__UpperCamelCase       =			parser.parse_known_args()
						__UpperCamelCase       =			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       =			Path(args.save_dir + '''_tmp''' )
						Path(snake_case ).mkdir(exist_ok=snake_case )  # this handles locking.
						__UpperCamelCase       =			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       =			{}
						if args.src_lang is not None:
												__UpperCamelCase       =			args.src_lang
						if args.tgt_lang is not None:
												__UpperCamelCase       =			args.tgt_lang

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

												# Calculate metrics, save metrics,  and save _generations.txt
												__UpperCamelCase       =			'''translation''' in args.task
												__UpperCamelCase       =			calculate_bleu if calc_bleu else calculate_rouge
												__UpperCamelCase       =			'''bleu''' if calc_bleu else '''rouge'''
												__UpperCamelCase       =			score_fn(snake_case ,      snake_case )
												__UpperCamelCase       =			len(snake_case )
												__UpperCamelCase       =			time.time() - start_time
												__UpperCamelCase       =			round(runtime / metrics['''n_obs'''] ,      4 )
												__UpperCamelCase       =			num_replicas
												# TODO(@stas00): add whatever metadata to metrics
												__UpperCamelCase       =			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 	A_ (							snake_case							:   Optional[int] )      ->    List:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   List[str] ,      snake_case							:   int ,      snake_case							:   str )      ->    List[Dict[str, List]]:



						'''simple docstring'''

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




