

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,
)


_SCREAMING_SNAKE_CASE  :						int         =    getLogger(__name__)
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A = 8   ,	_A = 10_24   ,	_A="val"   ,	_A=None   ,	_A=False   ,	_A="summarization"   ,	_A=None   ,	_A=1   ,	_A = None   ,	_A=""   ,	**_A   ,	):





     '''simple docstring'''




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

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

     SCREAMING_SNAKE_CASE__					=					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:
          SCREAMING_SNAKE_CASE__					=					tokenizer.model_max_length
     if prefix is None:
          SCREAMING_SNAKE_CASE__					=					prefix or getattr(model.config   ,	'''prefix'''   ,	''''''       ) or ''''''
     SCREAMING_SNAKE_CASE__					=					SeqaSeqDataset(
         _A   ,	_A   ,	_A   ,	max_target_length=10_24   ,	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.
     SCREAMING_SNAKE_CASE__					=					ds.make_sortish_sampler(_A   ,	distributed=_A   ,	add_extra_examples=_A   ,	shuffle=_A       )
     SCREAMING_SNAKE_CASE__					=					DataLoader(_A   ,	sampler=_A   ,	batch_size=_A   ,	collate_fn=ds.collate_fn       )
     SCREAMING_SNAKE_CASE__					=					[]
     for batch in tqdm(_A       ):
          SCREAMING_SNAKE_CASE__					=					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   ,	)
          SCREAMING_SNAKE_CASE__					=					tokenizer.batch_decode(_A   ,	skip_special_tokens=_A   ,	clean_up_tokenization_spaces=_A       )
          SCREAMING_SNAKE_CASE__					=					batch['''ids''']
          if num_return_sequences > 1:
               SCREAMING_SNAKE_CASE__					=					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'''




     SCREAMING_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=_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=6_00   ,	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'''       )
     SCREAMING_SNAKE_CASE__					=					time.time()
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					parser.parse_known_args()
     SCREAMING_SNAKE_CASE__					=					parse_numeric_n_bool_cl_kwargs(_A       )
     if generate_kwargs and args.local_rank <= 0:
          print(F'''parsed the following generate kwargs: {generate_kwargs}'''       )
     SCREAMING_SNAKE_CASE__					=					Path(args.save_dir + '''_tmp'''       )
     Path(_A       ).mkdir(exist_ok=_A       )  # this handles locking.
     SCREAMING_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.
     SCREAMING_SNAKE_CASE__					=					{}
     if args.src_lang is not None:
          SCREAMING_SNAKE_CASE__					=					args.src_lang
     if args.tgt_lang is not None:
          SCREAMING_SNAKE_CASE__					=					args.tgt_lang

     Path(args.save_dir       ).mkdir(exist_ok=_A       )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					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:
          SCREAMING_SNAKE_CASE__					=					Path(args.save_dir       )
          save_dir.mkdir(exist_ok=_A       )
          SCREAMING_SNAKE_CASE__					=					gather_results_from_each_node(_A   ,	_A   ,	args.sync_timeout       )
          SCREAMING_SNAKE_CASE__					=					combine_partial_results(_A       )
          if args.num_return_sequences > 1:
               SCREAMING_SNAKE_CASE__					=					save_dir.joinpath('''pseudolabel_results.json'''       )
               print(F'''Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'''       )
               save_json(_A   ,	_A       )
               return
          SCREAMING_SNAKE_CASE__					=					Path(args.data_dir       ).joinpath(args.type_path + '''.target'''       )
          with open(_A       ) as f:
               SCREAMING_SNAKE_CASE__					=					[x.rstrip() for x in f.readlines()][: len(_A       )]

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





     '''simple docstring'''




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





     '''simple docstring'''




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





import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


_SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						int         =    {'''vocab_file''': '''spiece.model'''}

_SCREAMING_SNAKE_CASE  :						int         =    {
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    }
}

_SCREAMING_SNAKE_CASE  :						List[Any]         =    {
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

# Segments (not really needed)
_SCREAMING_SNAKE_CASE  :						Optional[int]         =    0
_SCREAMING_SNAKE_CASE  :						Optional[Any]         =    1
_SCREAMING_SNAKE_CASE  :						int         =    2
_SCREAMING_SNAKE_CASE  :						str         =    3
_SCREAMING_SNAKE_CASE  :						str         =    4






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      VOCAB_FILES_NAMES
  a     =      PRETRAINED_VOCAB_FILES_MAP
  a     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a     =      "left"






  def __init__(							self		:	Optional[int]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	List[str]=False			,     __lowerCamelCase		:	Optional[Any]=True			,     __lowerCamelCase		:	Optional[Any]=False			,     __lowerCamelCase		:	List[str]="<s>"			,     __lowerCamelCase		:	Any="</s>"			,     __lowerCamelCase		:	Tuple="<unk>"			,     __lowerCamelCase		:	List[str]="<sep>"			,     __lowerCamelCase		:	List[str]="<pad>"			,     __lowerCamelCase		:	int="<cls>"			,     __lowerCamelCase		:	Any="<mask>"			,     __lowerCamelCase		:	Dict=["<eop>", "<eod>"]			,     __lowerCamelCase		:	Optional[Dict[str, Any]] = None			,     **__lowerCamelCase		:	int			,     )	->							None:
       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE__					=					AddedToken(__lowerCamelCase			,     lstrip=__lowerCamelCase			,     rstrip=__lowerCamelCase      ) if isinstance(__lowerCamelCase			,     __lowerCamelCase      ) else mask_token

       SCREAMING_SNAKE_CASE__					=					{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           do_lower_case=__lowerCamelCase			,     remove_space=__lowerCamelCase			,     keep_accents=__lowerCamelCase			,     bos_token=__lowerCamelCase			,     eos_token=__lowerCamelCase			,     unk_token=__lowerCamelCase			,     sep_token=__lowerCamelCase			,     pad_token=__lowerCamelCase			,     cls_token=__lowerCamelCase			,     mask_token=__lowerCamelCase			,     additional_special_tokens=__lowerCamelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **__lowerCamelCase			,     )

       SCREAMING_SNAKE_CASE__					=					3

       SCREAMING_SNAKE_CASE__					=					do_lower_case
       SCREAMING_SNAKE_CASE__					=					remove_space
       SCREAMING_SNAKE_CASE__					=					keep_accents
       SCREAMING_SNAKE_CASE__					=					vocab_file

       SCREAMING_SNAKE_CASE__					=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(__lowerCamelCase      )






  @property
  def    lowercase_	(							self		:	Optional[Any]      )	->							Union[str, Any]:
       return len(self.sp_model      )






  def    lowercase_	(							self		:	Tuple      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					{self.convert_ids_to_tokens(__lowerCamelCase      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab






  def __getstate__(							self		:	List[Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.__dict__.copy()
       SCREAMING_SNAKE_CASE__					=					None
       return state






  def __setstate__(							self		:	Any			,     __lowerCamelCase		:	Tuple      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					d

       # for backward compatibility
       if not hasattr(self			,     '''sp_model_kwargs'''      ):
            SCREAMING_SNAKE_CASE__					=					{}

       SCREAMING_SNAKE_CASE__					=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(self.vocab_file      )






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Union[str, Any]      )	->							List[str]:
       if self.remove_space:
            SCREAMING_SNAKE_CASE__					=					''' '''.join(inputs.strip().split()      )
       else:
            SCREAMING_SNAKE_CASE__					=					inputs
       SCREAMING_SNAKE_CASE__					=					outputs.replace('''``'''			,     '''"'''      ).replace('''\'\''''			,     '''"'''      )

       if not self.keep_accents:
            SCREAMING_SNAKE_CASE__					=					unicodedata.normalize('''NFKD'''			,     __lowerCamelCase      )
            SCREAMING_SNAKE_CASE__					=					''''''.join([c for c in outputs if not unicodedata.combining(__lowerCamelCase      )]      )
       if self.do_lower_case:
            SCREAMING_SNAKE_CASE__					=					outputs.lower()

       return outputs






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	str      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					self.preprocess_text(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					self.sp_model.encode(__lowerCamelCase			,     out_type=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					[]
       for piece in pieces:
            if len(__lowerCamelCase      ) > 1 and piece[-1] == str(''','''      ) and piece[-2].isdigit():
                 SCREAMING_SNAKE_CASE__					=					self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCamelCase			,     ''''''      )      )
                 if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                      if len(cur_pieces[0]      ) == 1:
                           SCREAMING_SNAKE_CASE__					=					cur_pieces[1:]
                      else:
                           SCREAMING_SNAKE_CASE__					=					cur_pieces[0][1:]
                 cur_pieces.append(piece[-1]      )
                 new_pieces.extend(__lowerCamelCase      )
            else:
                 new_pieces.append(__lowerCamelCase      )

       return new_pieces






  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	Optional[int]      )	->							Optional[Any]:
       return self.sp_model.PieceToId(__lowerCamelCase      )






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	Tuple      )	->							List[Any]:
       return self.sp_model.IdToPiece(__lowerCamelCase      )






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	Tuple      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					''''''.join(__lowerCamelCase      ).replace(__lowerCamelCase			,     ''' '''      ).strip()
       return out_string






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	bool = True			,     **__lowerCamelCase		:	int			,     )	->							str:
       SCREAMING_SNAKE_CASE__					=					kwargs.pop('''use_source_tokenizer'''			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.convert_ids_to_tokens(__lowerCamelCase			,     skip_special_tokens=__lowerCamelCase      )

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       SCREAMING_SNAKE_CASE__					=					[]
       SCREAMING_SNAKE_CASE__					=					[]
       for token in filtered_tokens:
            if skip_special_tokens and token in self.all_special_ids:
                 continue
            if token in self.added_tokens_encoder:
                 if current_sub_text:
                      sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase      )      )
                      SCREAMING_SNAKE_CASE__					=					[]
                 sub_texts.append(__lowerCamelCase      )
            else:
                 current_sub_text.append(__lowerCamelCase      )
       if current_sub_text:
            sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase      )      )

       # Mimic the behavior of the Rust tokenizer:
       # By default, there are no spaces between special tokens
       SCREAMING_SNAKE_CASE__					=					''''''.join(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
            SCREAMING_SNAKE_CASE__					=					self.clean_up_tokenization(__lowerCamelCase      )
            return clean_text
       else:
            return text






  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None      )	->							List[int]:
       SCREAMING_SNAKE_CASE__					=					[self.sep_token_id]
       SCREAMING_SNAKE_CASE__					=					[self.cls_token_id]
       if token_ids_a is None:
            return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls






  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None			,     __lowerCamelCase		:	bool = False      )	->							List[int]:

       if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__lowerCamelCase			,     token_ids_a=__lowerCamelCase			,     already_has_special_tokens=__lowerCamelCase      )

       if token_ids_a is not None:
            return ([0] * len(__lowerCamelCase      )) + [1] + ([0] * len(__lowerCamelCase      )) + [1, 1]
       return ([0] * len(__lowerCamelCase      )) + [1, 1]






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None      )	->							List[int]:
       SCREAMING_SNAKE_CASE__					=					[self.sep_token_id]
       SCREAMING_SNAKE_CASE__					=					[2]

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







  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[str] = None      )	->							Tuple[str]:
       if not os.path.isdir(__lowerCamelCase      ):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
            return
       SCREAMING_SNAKE_CASE__					=					os.path.join(
           __lowerCamelCase			,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

       if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowerCamelCase      ) and os.path.isfile(self.vocab_file      ):
            copyfile(self.vocab_file			,     __lowerCamelCase      )
       elif not os.path.isfile(self.vocab_file      ):
            with open(__lowerCamelCase			,     '''wb'''      ) as fi:
                 SCREAMING_SNAKE_CASE__					=					self.sp_model.serialized_model_proto()
                 fi.write(__lowerCamelCase      )

       return (out_vocab_file,)


