





import numpy as np

from transformers import Pipeline





def      _UpperCamelCase  (a__ :Tuple							):






							"""simple docstring"""





							UpperCamelCase__					  =      np.max(a__ ,      axis=-1 ,      keepdims=a__							)
							UpperCamelCase__					  =      np.exp(outputs - maxes							)
							return shifted_exp / shifted_exp.sum(axis=-1 ,      keepdims=a__							)

class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def 					_lowerCamelCase		(    self	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      {}
														if "second_text" in kwargs:
																					UpperCamelCase__					  =      kwargs["""second_text"""]
														return preprocess_kwargs, {}, {}






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=None	):
														return self.tokenizer(__lowerCAmelCase	,			text_pair=__lowerCAmelCase	,			return_tensors=self.framework	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														return self.model(**__lowerCAmelCase	)







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      model_outputs.logits[0].numpy()
														UpperCamelCase__					  =      softmax(__lowerCAmelCase	)

														UpperCamelCase__					  =      np.argmax(__lowerCAmelCase	)
														UpperCamelCase__					  =      self.model.config.idalabel[best_class]
														UpperCamelCase__					  =      probabilities[best_class].item()
														UpperCamelCase__					  =      logits.tolist()
														return {"label": label, "score": score, "logits": logits}











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__           =			getLogger(__name__)





def      _UpperCamelCase  (a__ :Union[str, Any] ,      a__ :str ,      a__ :str ,      a__ :int = 8 ,      a__ :int = 1024 ,      a__ :Dict="val" ,      a__ :Optional[int]=None ,      a__ :List[str]=False ,      a__ :Union[str, Any]="summarization" ,      a__ :str=None ,      a__ :Optional[Any]=1 ,      a__ :Dict = None ,      a__ :List[Any]="" ,      **a__ :List[str] ,      ):






							"""simple docstring"""





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

							UpperCamelCase__					  =      Path(a__							)
							UpperCamelCase__					  =      save_dir.joinpath(f"""rank_{local_rank}_output.json"""							)
							torch.cuda.set_device(a__							)
							UpperCamelCase__					  =      AutoModelForSeqaSeqLM.from_pretrained(a__							).cuda()
							if fpaa:
														UpperCamelCase__					  =      model.half()
							# determine if we need to increase num_beams
							use_task_specific_params(a__ ,      a__							)  # 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(a__							)
							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(
							    a__ ,      a__ ,      a__ ,      max_target_length=1024 ,      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.
							UpperCamelCase__					  =      ds.make_sortish_sampler(a__ ,      distributed=a__ ,      add_extra_examples=a__ ,      shuffle=a__							)
							UpperCamelCase__					  =      DataLoader(a__ ,      sampler=a__ ,      batch_size=a__ ,      collate_fn=ds.collate_fn							)
							UpperCamelCase__					  =      []
							for batch in tqdm(a__							):
														UpperCamelCase__					  =      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__ ,      )
														UpperCamelCase__					  =      tokenizer.batch_decode(a__ ,      skip_special_tokens=a__ ,      clean_up_tokenization_spaces=a__							)
														UpperCamelCase__					  =      batch["""ids"""]
														if num_return_sequences > 1:
																					UpperCamelCase__					  =      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  ():






							"""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=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"""							)
							UpperCamelCase__					  =      time.time()
							UpperCamelCase__  ,  UpperCamelCase__					  =      parser.parse_known_args()
							UpperCamelCase__					  =      parse_numeric_n_bool_cl_kwargs(a__							)
							if generate_kwargs and args.local_rank <= 0:
														print(f"""parsed the following generate kwargs: {generate_kwargs}"""							)
							UpperCamelCase__					  =      Path(args.save_dir + """_tmp"""							)
							Path(a__							).mkdir(exist_ok=a__							)  # 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=a__							)
							UpperCamelCase__  ,  UpperCamelCase__					  =      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:
														UpperCamelCase__					  =      Path(args.save_dir							)
														save_dir.mkdir(exist_ok=a__							)
														UpperCamelCase__					  =      gather_results_from_each_node(a__ ,      a__ ,      args.sync_timeout							)
														UpperCamelCase__					  =      combine_partial_results(a__							)
														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(a__ ,      a__							)
																					return
														UpperCamelCase__					  =      Path(args.data_dir							).joinpath(args.type_path + """.target"""							)
														with open(a__							) as f:
																					UpperCamelCase__					  =      [x.rstrip() for x in f.readlines()][: len(a__							)]

														# 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(a__ ,      a__							)
														UpperCamelCase__					  =      len(a__							)
														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(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__ :Dict							):






							"""simple docstring"""





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





def      _UpperCamelCase  (a__ :List[str] ,      a__ :str ,      a__ :List[Any]							):






							"""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(a__							) < num_replicas:
																					continue
														try:
																					# make sure all json files are fully saved
																					UpperCamelCase__					  =      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()




