
"""simple docstring"""




import os
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 logging


A__      :	List[str]          =    logging.get_logger(__name__)

A__      :	str          =    """▁"""

A__      :	Union[str, Any]          =    {"""vocab_file""": """sentencepiece.bpe.model"""}

A__      :	Optional[Any]          =    {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

A__      :	Dict          =    {
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							VOCAB_FILES_NAMES
     _A								=							PRETRAINED_VOCAB_FILES_MAP
     _A								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A								=							['input_ids', 'attention_mask']



     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase="<s>" ,			__UpperCamelCase="</s>" ,			__UpperCamelCase="</s>" ,			__UpperCamelCase="<s>" ,			__UpperCamelCase="<unk>" ,			__UpperCamelCase="<pad>" ,			__UpperCamelCase="<mask>" ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					None:
          # Mask token behave like a normal word, i.e. include the space before it
          UpperCAmelCase__							: Any 							=						AddedToken(__UpperCamelCase ,			lstrip=__UpperCamelCase ,			rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase ,			__UpperCamelCase  ) else mask_token

          UpperCAmelCase__							: Union[str, Any] 							=						{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=__UpperCamelCase ,			eos_token=__UpperCamelCase ,			unk_token=__UpperCamelCase ,			sep_token=__UpperCamelCase ,			cls_token=__UpperCamelCase ,			pad_token=__UpperCamelCase ,			mask_token=__UpperCamelCase ,			sp_model_kwargs=self.sp_model_kwargs ,			**__UpperCamelCase ,			)

          UpperCAmelCase__							: int 							=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
          self.sp_model.Load(str(__UpperCamelCase  )  )
          UpperCAmelCase__							: Tuple 							=						vocab_file

          # Original fairseq vocab and spm vocab must be "aligned":
          # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
          # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
          # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
          # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

          # Mimic fairseq token-to-id alignment for the first 4 token
          UpperCAmelCase__							: int 							=						{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          UpperCAmelCase__							: Tuple 							=						1

          UpperCAmelCase__							: Optional[int] 							=						len(self.sp_model  ) + self.fairseq_offset
          UpperCAmelCase__							: str 							=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}



     def __getstate__(      self  )->					Any:
          UpperCAmelCase__							: Union[str, Any] 							=						self.__dict__.copy()
          UpperCAmelCase__							: List[str] 							=						None
          UpperCAmelCase__							: Union[str, Any] 							=						self.sp_model.serialized_model_proto()
          return state



     def __setstate__(      self ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: List[str] 							=						d

          # for backward compatibility
          if not hasattr(self ,			"sp_model_kwargs"  ):
               UpperCAmelCase__							: List[Any] 							=						{}

          UpperCAmelCase__							: Dict 							=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					List[int]:

          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          UpperCAmelCase__							: Optional[int] 							=						[self.cls_token_id]
          UpperCAmelCase__							: Any 							=						[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = False  )->					List[int]:

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

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					List[int]:
          UpperCAmelCase__							: List[Any] 							=						[self.sep_token_id]
          UpperCAmelCase__							: Dict 							=						[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 + sep + token_ids_a + sep  ) * [0]



     @property
     def  lowerCAmelCase__       (      self  )->					Dict:
          return len(self.sp_model  ) + self.fairseq_offset + 1  # Add the <mask> token



     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: List[str] 							=						{self.convert_ids_to_tokens(__UpperCamelCase  ): i for i in range(self.vocab_size  )}
          vocab.update(self.added_tokens_encoder  )
          return vocab



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[str]:
          return self.sp_model.encode(__UpperCamelCase ,			out_type=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Tuple:
          if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
          UpperCAmelCase__							: str 							=						self.sp_model.PieceToId(__UpperCamelCase  )

          # Need to return unknown token if the SP model returned 0
          return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Tuple:
          if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(index - self.fairseq_offset  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					str:
          UpperCAmelCase__							: List[Any] 							=						"".join(__UpperCamelCase  ).replace(__UpperCamelCase ,			" "  ).strip()
          return out_string



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					Tuple[str]:
          if not os.path.isdir(__UpperCamelCase  ):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
               return
          UpperCAmelCase__							: List[str] 							=						os.path.join(
              __UpperCamelCase ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

          if os.path.abspath(self.vocab_file  ) != os.path.abspath(__UpperCamelCase  ) and os.path.isfile(self.vocab_file  ):
               copyfile(self.vocab_file ,			__UpperCamelCase  )
          elif not os.path.isfile(self.vocab_file  ):
               with open(__UpperCamelCase ,			"wb"  ) as fi:
                    UpperCAmelCase__							: str 							=						self.sp_model.serialized_model_proto()
                    fi.write(__UpperCamelCase  )

          return (out_vocab_file,)





"""simple docstring"""




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,
)


A__      :	int          =    getLogger(__name__)



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






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						str(lowerCAmelCase		)
     assert local_rank is not None
     torch.distributed.init_process_group(backend="nccl"       ,    rank=lowerCAmelCase		)

     UpperCAmelCase__							: List[str] 							=						Path(lowerCAmelCase		)
     UpperCAmelCase__							: str 							=						save_dir.joinpath(F"rank_{local_rank}_output.json"		)
     torch.cuda.set_device(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[Any] 							=						AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase		).cuda()
     if fpaa:
          UpperCAmelCase__							: List[Any] 							=						model.half()
     # determine if we need to increase num_beams
     use_task_specific_params(lowerCAmelCase       ,    lowerCAmelCase		)  # update config with task specific params
     UpperCAmelCase__							: List[Any] 							=						generate_kwargs.pop("num_beams"       ,    model.config.num_beams		)  # AttributeError risk?
     if num_return_sequences > num_beams:
          UpperCAmelCase__							: Any 							=						num_return_sequences

     UpperCAmelCase__							: List[Any] 							=						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__							: int 							=						tokenizer.model_max_length
     if prefix is None:
          UpperCAmelCase__							: Union[str, Any] 							=						prefix or getattr(model.config       ,    "prefix"       ,    ""		) or ""
     UpperCAmelCase__							: str 							=						SeqaSeqDataset(
         lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    max_target_length=1024       ,    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__							: Union[str, Any] 							=						ds.make_sortish_sampler(lowerCAmelCase       ,    distributed=lowerCAmelCase       ,    add_extra_examples=lowerCAmelCase       ,    shuffle=lowerCAmelCase		)
     UpperCAmelCase__							: Optional[int] 							=						DataLoader(lowerCAmelCase       ,    sampler=lowerCAmelCase       ,    batch_size=lowerCAmelCase       ,    collate_fn=ds.collate_fn		)
     UpperCAmelCase__							: str 							=						[]
     for batch in tqdm(lowerCAmelCase		):
          UpperCAmelCase__							: Dict 							=						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__							: int 							=						tokenizer.batch_decode(lowerCAmelCase       ,    skip_special_tokens=lowerCAmelCase       ,    clean_up_tokenization_spaces=lowerCAmelCase		)
          UpperCAmelCase__							: int 							=						batch["ids"]
          if num_return_sequences > 1:
               UpperCAmelCase__							: str 							=						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 							a__ (				):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						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__							: Optional[int] 							=						time.time()
     UpperCAmelCase__							,	UpperCAmelCase__							: List[Any] 							=						parser.parse_known_args()
     UpperCAmelCase__							: int 							=						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__							: Dict 							=						Path(args.save_dir + "_tmp"		)
     Path(lowerCAmelCase		).mkdir(exist_ok=lowerCAmelCase		)  # this handles locking.
     UpperCAmelCase__							: List[str] 							=						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__							: List[str] 							=						{}
     if args.src_lang is not None:
          UpperCAmelCase__							: str 							=						args.src_lang
     if args.tgt_lang is not None:
          UpperCAmelCase__							: List[str] 							=						args.tgt_lang

     Path(args.save_dir		).mkdir(exist_ok=lowerCAmelCase		)
     UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						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__							: str 							=						Path(args.save_dir		)
          save_dir.mkdir(exist_ok=lowerCAmelCase		)
          UpperCAmelCase__							: Tuple 							=						gather_results_from_each_node(lowerCAmelCase       ,    lowerCAmelCase       ,    args.sync_timeout		)
          UpperCAmelCase__							: Union[str, Any] 							=						combine_partial_results(lowerCAmelCase		)
          if args.num_return_sequences > 1:
               UpperCAmelCase__							: int 							=						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__							: Optional[Any] 							=						Path(args.data_dir		).joinpath(args.type_path + ".target"		)
          with open(lowerCAmelCase		) as f:
               UpperCAmelCase__							: Optional[int] 							=						[x.rstrip() for x in f.readlines()][: len(lowerCAmelCase		)]

          # Calculate metrics, save metrics,  and save _generations.txt
          UpperCAmelCase__							: List[Any] 							=						"translation" in args.task
          UpperCAmelCase__							: Optional[Any] 							=						calculate_bleu if calc_bleu else calculate_rouge
          UpperCAmelCase__							: Optional[Any] 							=						"bleu" if calc_bleu else "rouge"
          UpperCAmelCase__							: Dict 							=						score_fn(lowerCAmelCase       ,    lowerCAmelCase		)
          UpperCAmelCase__							: List[Any] 							=						len(lowerCAmelCase		)
          UpperCAmelCase__							: Union[str, Any] 							=						time.time() - start_time
          UpperCAmelCase__							: Optional[int] 							=						round(runtime / metrics["n_obs"]       ,    4		)
          UpperCAmelCase__							: Tuple 							=						num_replicas
          # TODO(@stas00): add whatever metadata to metrics
          UpperCAmelCase__							: Any 							=						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 							a__ (				lowerCAmelCase	:       Tuple		):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						[]
     for partial_result in partial_results:
          records.extend(lowerCAmelCase		)
     UpperCAmelCase__							: Dict 							=						sorted(lowerCAmelCase       ,    key=lambda lowerCAmelCase		: x["id"]		)
     UpperCAmelCase__							: List[str] 							=						[x["pred"] for x in records]
     return preds



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     # WAIT FOR lots of .json files
     UpperCAmelCase__							: int 							=						time.time()
     logger.info("waiting for all nodes to finish"		)
     UpperCAmelCase__							: Dict 							=						None
     while (time.time() - start_wait) < timeout:
          UpperCAmelCase__							: str 							=						list(save_dir.glob("rank_*.json"		)		)
          if len(lowerCAmelCase		) < num_replicas:
               continue
          try:
               # make sure all json files are fully saved
               UpperCAmelCase__							: Union[str, Any] 							=						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()



