





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    : Dict    	=  getLogger(__name__)


def     SCREAMING_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    ,       ):
       _UpperCamelCase              =						str(lowerCAmelCase					)
       assert local_rank is not None
       torch.distributed.init_process_group(backend='''nccl'''    ,       rank=lowerCAmelCase					)

       _UpperCamelCase              =						Path(lowerCAmelCase					)
       _UpperCamelCase              =						save_dir.joinpath(f'''rank_{local_rank}_output.json'''					)
       torch.cuda.set_device(lowerCAmelCase					)
       _UpperCamelCase              =						AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase					).cuda()
       if fpaa:
              _UpperCamelCase              =						model.half()
       # determine if we need to increase num_beams
       use_task_specific_params(lowerCAmelCase    ,       lowerCAmelCase					)  # 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(lowerCAmelCase					)
       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(
           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.
       _UpperCamelCase              =						ds.make_sortish_sampler(lowerCAmelCase    ,       distributed=lowerCAmelCase    ,       add_extra_examples=lowerCAmelCase    ,       shuffle=lowerCAmelCase					)
       _UpperCamelCase              =						DataLoader(lowerCAmelCase    ,       sampler=lowerCAmelCase    ,       batch_size=lowerCAmelCase    ,       collate_fn=ds.collate_fn					)
       _UpperCamelCase              =						[]
       for batch in tqdm(lowerCAmelCase					):
              _UpperCamelCase              =						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    ,       )
              _UpperCamelCase              =						tokenizer.batch_decode(lowerCAmelCase    ,       skip_special_tokens=lowerCAmelCase    ,       clean_up_tokenization_spaces=lowerCAmelCase					)
              _UpperCamelCase              =						batch['''ids''']
              if num_return_sequences > 1:
                     _UpperCamelCase              =						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     SCREAMING_SNAKE_CASE			():
       _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=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'''					)
       _UpperCamelCase              =						time.time()
       _UpperCamelCase       ,		_UpperCamelCase              =						parser.parse_known_args()
       _UpperCamelCase              =						parse_numeric_n_bool_cl_kwargs(lowerCAmelCase					)
       if generate_kwargs and args.local_rank <= 0:
              print(f'''parsed the following generate kwargs: {generate_kwargs}'''					)
       _UpperCamelCase              =						Path(args.save_dir + '''_tmp'''					)
       Path(lowerCAmelCase					).mkdir(exist_ok=lowerCAmelCase					)  # 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=lowerCAmelCase					)
       _UpperCamelCase       ,		_UpperCamelCase              =						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:
              _UpperCamelCase              =						Path(args.save_dir					)
              save_dir.mkdir(exist_ok=lowerCAmelCase					)
              _UpperCamelCase              =						gather_results_from_each_node(lowerCAmelCase    ,       lowerCAmelCase    ,       args.sync_timeout					)
              _UpperCamelCase              =						combine_partial_results(lowerCAmelCase					)
              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(lowerCAmelCase    ,       lowerCAmelCase					)
                     return
              _UpperCamelCase              =						Path(args.data_dir					).joinpath(args.type_path + '''.target'''					)
              with open(lowerCAmelCase					) as f:
                     _UpperCamelCase              =						[x.rstrip() for x in f.readlines()][: len(lowerCAmelCase					)]

              # 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(lowerCAmelCase    ,       lowerCAmelCase					)
              _UpperCamelCase              =						len(lowerCAmelCase					)
              _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(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     SCREAMING_SNAKE_CASE			(lowerCAmelCase					):
       _UpperCamelCase              =						[]
       for partial_result in partial_results:
              records.extend(lowerCAmelCase					)
       _UpperCamelCase              =						sorted(lowerCAmelCase    ,       key=lambda lowerCAmelCase					: x["id"]					)
       _UpperCamelCase              =						[x['''pred'''] for x in records]
       return preds


def     SCREAMING_SNAKE_CASE			(lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
       # WAIT FOR lots of .json files
       _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(lowerCAmelCase					) < num_replicas:
                     continue
              try:
                     # make sure all json files are fully saved
                     _UpperCamelCase              =						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()













import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_big_bird import BigBirdTokenizer
else:
       lowercase    : Union[str, Any]    	=  None

lowercase    : Union[str, Any]    	=  logging.get_logger(__name__)
lowercase    : Tuple    	=  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase    : Any    	=  {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

lowercase    : Tuple    	=  {
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}


lowercase    : List[str]    	=  """▁"""

class  __A(						__UpperCAmelCase ):
     __A	  =       VOCAB_FILES_NAMES
     __A	  =       PRETRAINED_VOCAB_FILES_MAP
     __A	  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	  =       BigBirdTokenizer
     __A	  =       ["input_ids", "attention_mask"]
     __A	  =       []

     def __init__(      self,       A=None,       A=None,       A="<unk>",       A="<s>",       A="</s>",       A="<pad>",       A="[SEP]",       A="[MASK]",       A="[CLS]",       **A,       ):





            """simple docstring"""

            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else bos_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else eos_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else unk_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else pad_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else cls_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else sep_token

            # Mask token behave like a normal word, i.e. include the space before it
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else mask_token

            super().__init__(
                A,       tokenizer_file=A,       bos_token=A,       eos_token=A,       unk_token=A,       sep_token=A,       pad_token=A,       cls_token=A,       mask_token=A,       **A,       )

            _UpperCamelCase              =						vocab_file
            _UpperCamelCase              =						False if not self.vocab_file else True

     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            _UpperCamelCase              =						[self.sep_token_id]
            _UpperCamelCase              =						[self.cls_token_id]
            if token_ids_a is None:
                   return cls + token_ids_a + sep
            return cls + token_ids_a + sep + token_ids_a + sep

     def 	_UpperCamelCase     (      self,       A,       A = None,       A = False      ):





            """simple docstring"""

            if already_has_special_tokens:
                   if token_ids_a is not None:
                          raise ValueError(
                              '''You should not supply a second sequence if the provided sequence of '''
                              '''ids is already formatted with special tokens for the model.'''      )
                   return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

            if token_ids_a is None:
                   return [1] + ([0] * len(A      )) + [1]
            return [1] + ([0] * len(A      )) + [1] + ([0] * len(A      )) + [1]

     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            _UpperCamelCase              =						[self.sep_token_id]
            _UpperCamelCase              =						[self.cls_token_id]

            if token_ids_a is None:
                   return len(cls + token_ids_a + sep      ) * [0]
            return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]



     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                       '''tokenizer.'''      )

            if not os.path.isdir(A      ):
                   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
                   return
            _UpperCamelCase              =						os.path.join(
                A,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

            if os.path.abspath(self.vocab_file      ) != os.path.abspath(A      ):
                   copyfile(self.vocab_file,       A      )

            return (out_vocab_file,)






