





import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer





def      _UpperCamelCase  (a__ :Optional[Any] ,      a__ :Tuple ,      a__ :Optional[int] ,      a__ :Tuple ,      a__ :List[str]=True ,      a__ :str="pt"							):






							"""simple docstring"""





							UpperCamelCase__					  =      {"""add_prefix_space""": True} if isinstance(a__ ,      a__							) and not line.startswith(""" """							) else {}
							UpperCamelCase__					  =      padding_side
							return tokenizer(
							    [line] ,      max_length=a__ ,      padding="""max_length""" if pad_to_max_length else None ,      truncation=a__ ,      return_tensors=a__ ,      add_special_tokens=a__ ,      **a__ ,      )





def      _UpperCamelCase  (a__ :Tuple ,      a__ :Optional[Any] ,      a__ :str=None ,      ):






							"""simple docstring"""





							UpperCamelCase__					  =      input_ids.ne(a__							).any(dim=0							)
							if attention_mask is None:
														return input_ids[:, keep_column_mask]
							else:
														return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])

class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase="train"	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase=""	,			):
														super().__init__()
														UpperCamelCase__					  =      Path(__lowerCAmelCase	).joinpath(type_path + """.source"""	)
														UpperCamelCase__					  =      Path(__lowerCAmelCase	).joinpath(type_path + """.target"""	)
														UpperCamelCase__					  =      self.get_char_lens(self.src_file	)
														UpperCamelCase__					  =      max_source_length
														UpperCamelCase__					  =      max_target_length
														assert min(self.src_lens	) > 0, f"""found empty line in {self.src_file}"""
														UpperCamelCase__					  =      tokenizer
														UpperCamelCase__					  =      prefix
														if n_obs is not None:
																					UpperCamelCase__					  =      self.src_lens[:n_obs]
														UpperCamelCase__					  =      src_lang
														UpperCamelCase__					  =      tgt_lang






							def __len__(    self	):
														return len(self.src_lens	)






							def __getitem__(    self	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      index + 1  # linecache starts at 1
														UpperCamelCase__					  =      self.prefix + linecache.getline(str(self.src_file	)	,			__lowerCAmelCase	).rstrip("""\n"""	)
														UpperCamelCase__					  =      linecache.getline(str(self.tgt_file	)	,			__lowerCAmelCase	).rstrip("""\n"""	)
														assert source_line, f"""empty source line for index {index}"""
														assert tgt_line, f"""empty tgt line for index {index}"""

														# Need to add eos token manually for T5
														if isinstance(self.tokenizer	,			__lowerCAmelCase	):
																					source_line += self.tokenizer.eos_token
																					tgt_line += self.tokenizer.eos_token

														# Pad source and target to the right
														UpperCamelCase__					  =      (
														    self.tokenizer.question_encoder if isinstance(self.tokenizer	,			__lowerCAmelCase	) else self.tokenizer
														)
														UpperCamelCase__					  =      self.tokenizer.generator if isinstance(self.tokenizer	,			__lowerCAmelCase	) else self.tokenizer

														UpperCamelCase__					  =      encode_line(__lowerCAmelCase	,			__lowerCAmelCase	,			self.max_source_length	,			"""right"""	)
														UpperCamelCase__					  =      encode_line(__lowerCAmelCase	,			__lowerCAmelCase	,			self.max_target_length	,			"""right"""	)

														UpperCamelCase__					  =      source_inputs["""input_ids"""].squeeze()
														UpperCamelCase__					  =      target_inputs["""input_ids"""].squeeze()
														UpperCamelCase__					  =      source_inputs["""attention_mask"""].squeeze()
														return {
														    "input_ids": source_ids,
														    "attention_mask": src_mask,
														    "decoder_input_ids": target_ids,
														}






							@staticmethod
							def 					_lowerCamelCase		(    __lowerCAmelCase	):
														return [len(__lowerCAmelCase	) for x in Path(__lowerCAmelCase	).open().readlines()]







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      torch.stack([x["""input_ids"""] for x in batch]	)
														UpperCamelCase__					  =      torch.stack([x["""attention_mask"""] for x in batch]	)
														UpperCamelCase__					  =      torch.stack([x["""decoder_input_ids"""] for x in batch]	)
														UpperCamelCase__					  =      (
														    self.tokenizer.generator.pad_token_id
														    if isinstance(self.tokenizer	,			__lowerCAmelCase	)
														    else self.tokenizer.pad_token_id
														)
														UpperCamelCase__					  =      (
														    self.tokenizer.question_encoder.pad_token_id
														    if isinstance(self.tokenizer	,			__lowerCAmelCase	)
														    else self.tokenizer.pad_token_id
														)
														UpperCamelCase__					  =      trim_batch(__lowerCAmelCase	,			__lowerCAmelCase	)
														UpperCamelCase__  ,  UpperCamelCase__					  =      trim_batch(__lowerCAmelCase	,			__lowerCAmelCase	,			attention_mask=__lowerCAmelCase	)
														UpperCamelCase__					  =      {
														    """input_ids""": source_ids,
														    """attention_mask""": source_mask,
														    """decoder_input_ids""": y,
														}
														return batch






UpperCamelCase__           =			getLogger(__name__)





def      _UpperCamelCase  (a__ :List[List]							):






							"""simple docstring"""





							return list(itertools.chain.from_iterable(a__							)							)





def      _UpperCamelCase  (a__ :str							):






							"""simple docstring"""





							UpperCamelCase__					  =      get_git_info()
							save_json(a__ ,      os.path.join(a__ ,      """git_log.json"""							)							)





def      _UpperCamelCase  (a__ :List[str] ,      a__ :List[Any] ,      a__ :List[str]=4 ,      **a__ :int							):






							"""simple docstring"""





							with open(a__ ,      """w"""							) as f:
														json.dump(a__ ,      a__ ,      indent=a__ ,      **a__							)





def      _UpperCamelCase  (a__ :List[str]							):






							"""simple docstring"""





							with open(a__							) as f:
														return json.load(a__							)





def      _UpperCamelCase  ():






							"""simple docstring"""





							UpperCamelCase__					  =      git.Repo(search_parent_directories=a__							)
							UpperCamelCase__					  =      {
							    """repo_id""": str(a__							),
							    """repo_sha""": str(repo.head.object.hexsha							),
							    """repo_branch""": str(repo.active_branch							),
							    """hostname""": str(socket.gethostname()							),
							}
							return repo_infos





def      _UpperCamelCase  (a__ :Callable ,      a__ :Iterable							):






							"""simple docstring"""





							return list(map(a__ ,      a__							)							)





def      _UpperCamelCase  (a__ :Tuple ,      a__ :Union[str, Any]							):






							"""simple docstring"""





							with open(a__ ,      """wb"""							) as f:
														return pickle.dump(a__ ,      a__							)





def      _UpperCamelCase  (a__ :Any							):






							"""simple docstring"""





							def remove_articles(a__ :Optional[Any]							):
														return re.sub(r"""\b(a|an|the)\b""" ,      """ """ ,      a__							)

							def white_space_fix(a__ :str							):
														return " ".join(text.split()							)

							def remove_punc(a__ :Dict							):
														UpperCamelCase__					  =      set(string.punctuation							)
														return "".join(ch for ch in text if ch not in exclude							)

							def lower(a__ :List[str]							):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(a__							)							)							)							)





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






							"""simple docstring"""





							UpperCamelCase__					  =      normalize_answer(a__							).split()
							UpperCamelCase__					  =      normalize_answer(a__							).split()
							UpperCamelCase__					  =      Counter(a__							) & Counter(a__							)
							UpperCamelCase__					  =      sum(common.values()							)
							if num_same == 0:
														return 0
							UpperCamelCase__					  =      1.0 * num_same / len(a__							)
							UpperCamelCase__					  =      1.0 * num_same / len(a__							)
							UpperCamelCase__					  =      (2 * precision * recall) / (precision + recall)
							return fa





def      _UpperCamelCase  (a__ :str ,      a__ :str							):






							"""simple docstring"""





							return normalize_answer(a__							) == normalize_answer(a__							)





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






							"""simple docstring"""





							assert len(a__							) == len(a__							)
							UpperCamelCase__					  =      0
							for hypo, pred in zip(a__ ,      a__							):
														em += exact_match_score(a__ ,      a__							)
							if len(a__							) > 0:
														em /= len(a__							)
							return {"em": em}





def      _UpperCamelCase  (a__ :str							):






							"""simple docstring"""





							return model_prefix.startswith("""rag"""							)





def      _UpperCamelCase  (a__ :Any ,      a__ :Tuple ,      a__ :Any							):






							"""simple docstring"""





							UpperCamelCase__					  =      {p: p for p in extra_params}
							# T5 models don't have `dropout` param, they have `dropout_rate` instead
							UpperCamelCase__					  =      """dropout_rate"""
							for p in extra_params:
														if getattr(a__ ,      a__ ,      a__							):
																					if not hasattr(a__ ,      a__							) and not hasattr(a__ ,      equivalent_param[p]							):
																												logger.info("""config doesn't have a `{}` attribute""".format(a__							)							)
																												delattr(a__ ,      a__							)
																												continue
																					UpperCamelCase__					  =      p if hasattr(a__ ,      a__							) else equivalent_param[p]
																					setattr(a__ ,      a__ ,      getattr(a__ ,      a__							)							)
																					delattr(a__ ,      a__							)
							return hparams, config











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()




