
'''simple docstring'''






import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


UpperCAmelCase_							: Tuple =							getLogger(__name__)


UpperCAmelCase_							: Dict =							'cuda' if torch.cuda.is_available() else 'cpu'







def 		A_							(						_lowerCAmelCase :     List[str]					,						_lowerCAmelCase :     str					,						_lowerCAmelCase :     str					,						_lowerCAmelCase :     int = 8					,						_lowerCAmelCase :     str = DEFAULT_DEVICE					,						_lowerCAmelCase :     Dict=False					,						_lowerCAmelCase :     str="summarization"					,						_lowerCAmelCase :     Dict=None					,						**_lowerCAmelCase :     Tuple					,						):



      """simple docstring"""




      _lowerCamelCase					: Optional[Any]		    = Path(_lowerCAmelCase				).open("w"					,						encoding="utf-8"				)
      _lowerCamelCase					: str		    = str(_lowerCAmelCase				)
      _lowerCamelCase					: Dict		    = AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase				).to(_lowerCAmelCase				)
      if fpaa:
            _lowerCamelCase					: int		    = model.half()

      _lowerCamelCase					: Any		    = AutoTokenizer.from_pretrained(_lowerCAmelCase				)
      logger.info(F'Inferred tokenizer type: {tokenizer.__class__}'				)  # if this is wrong, check config.model_type.

      _lowerCamelCase					: Union[str, Any]		    = time.time()
      # update config with task specific params
      use_task_specific_params(_lowerCAmelCase					,						_lowerCAmelCase				)
      if prefix is None:
            _lowerCamelCase					: str		    = prefix or getattr(model.config					,						"prefix"					,						""				) or ""
      for examples_chunk in tqdm(list(chunks(_lowerCAmelCase					,						_lowerCAmelCase				)				)				):
            _lowerCamelCase					: Tuple		    = [prefix + text for text in examples_chunk]
            _lowerCamelCase					: Any		    = tokenizer(_lowerCAmelCase					,						return_tensors="pt"					,						truncation=_lowerCAmelCase					,						padding="longest"				).to(_lowerCAmelCase				)
            _lowerCamelCase					: Union[str, Any]		    = model.generate(
                input_ids=batch.input_ids					,						attention_mask=batch.attention_mask					,						**_lowerCAmelCase					,						)
            _lowerCamelCase					: Tuple		    = tokenizer.batch_decode(_lowerCAmelCase					,						skip_special_tokens=_lowerCAmelCase					,						clean_up_tokenization_spaces=_lowerCAmelCase				)
            for hypothesis in dec:
                  fout.write(hypothesis + "\n"				)
                  fout.flush()
      fout.close()
      _lowerCamelCase					: Any		    = int(time.time() - start_time				)  # seconds
      _lowerCamelCase					: Optional[int]		    = len(_lowerCAmelCase				)
      return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs					,						4				)}







def 		A_							(						):



      """simple docstring"""




      return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"				)







def 		A_							(						_lowerCAmelCase :     Dict=True				):



      """simple docstring"""




      _lowerCamelCase					: Tuple		    = argparse.ArgumentParser()
      parser.add_argument("model_name"					,						type=_lowerCAmelCase					,						help="like facebook/bart-large-cnn,t5-base, etc."				)
      parser.add_argument("input_path"					,						type=_lowerCAmelCase					,						help="like cnn_dm/test.source"				)
      parser.add_argument("save_path"					,						type=_lowerCAmelCase					,						help="where to save summaries"				)
      parser.add_argument("--reference_path"					,						type=_lowerCAmelCase					,						required=_lowerCAmelCase					,						help="like cnn_dm/test.target"				)
      parser.add_argument("--score_path"					,						type=_lowerCAmelCase					,						required=_lowerCAmelCase					,						default="metrics.json"					,						help="where to save metrics"				)
      parser.add_argument("--device"					,						type=_lowerCAmelCase					,						required=_lowerCAmelCase					,						default=_lowerCAmelCase					,						help="cuda, cuda:1, cpu etc."				)
      parser.add_argument(
          "--prefix"					,						type=_lowerCAmelCase					,						required=_lowerCAmelCase					,						default=_lowerCAmelCase					,						help="will be added to the begininng of src examples"				)
      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(
          "--n_obs"					,						type=_lowerCAmelCase					,						default=-1					,						required=_lowerCAmelCase					,						help="How many observations. Defaults to all."				)
      parser.add_argument("--fp16"					,						action="store_true"				)
      parser.add_argument("--dump-args"					,						action="store_true"					,						help="print the custom hparams with the results"				)
      parser.add_argument(
          "--info"					,						nargs="?"					,						type=_lowerCAmelCase					,						const=datetime_now()					,						help=(
              "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
              " lang=en-ru. If no value is passed, the current datetime string will be used."
          )					,						)
      # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
      _lowerCamelCase    ,		_lowerCamelCase					: List[Any]		    = parser.parse_known_args()
      _lowerCamelCase					: List[str]		    = parse_numeric_n_bool_cl_kwargs(_lowerCAmelCase				)
      if parsed_args and verbose:
            print(F'parsed the following generate kwargs: {parsed_args}'				)
      _lowerCamelCase					: Optional[int]		    = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path				).readlines()]
      if args.n_obs > 0:
            _lowerCamelCase					: Optional[Any]		    = examples[: args.n_obs]
      Path(args.save_path				).parent.mkdir(exist_ok=_lowerCAmelCase				)

      if args.reference_path is None and Path(args.score_path				).exists():
            warnings.warn(F'score_path {args.score_path} will be overwritten unless you type ctrl-c.'				)

      if args.device == "cpu" and args.fpaa:
            # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
            raise ValueError("Can't mix --fp16 and --device cpu"				)

      _lowerCamelCase					: int		    = generate_summaries_or_translations(
          _lowerCAmelCase					,						args.save_path					,						args.model_name					,						batch_size=args.bs					,						device=args.device					,						fpaa=args.fpaa					,						task=args.task					,						prefix=args.prefix					,						**_lowerCAmelCase					,						)

      if args.reference_path is None:
            return {}

      # Compute scores
      _lowerCamelCase					: List[str]		    = calculate_bleu if "translation" in args.task else calculate_rouge
      _lowerCamelCase					: Any		    = [x.rstrip() for x in open(args.save_path				).readlines()]
      _lowerCamelCase					: List[Any]		    = [x.rstrip() for x in open(args.reference_path				).readlines()][: len(_lowerCAmelCase				)]
      _lowerCamelCase					: dict		    = score_fn(_lowerCAmelCase					,						_lowerCAmelCase				)
      scores.update(_lowerCAmelCase				)

      if args.dump_args:
            scores.update(_lowerCAmelCase				)
      if args.info:
            _lowerCamelCase					: Union[str, Any]		    = args.info

      if verbose:
            print(_lowerCAmelCase				)

      if args.score_path is not None:
            json.dump(_lowerCAmelCase					,						open(args.score_path					,						"w"				)				)

      return scores


if __name__ == "__main__":
 # Usage for MT:
 # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
 run_generate(verbose=True)

"""simple docstring"""






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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase							=				logging.get_logger(__name__)

_lowercase							=				{
    '''vocab_file''': '''vocab.txt''',
    '''merges_file''': '''bpe.codes''',
}

_lowercase							=				{
    '''vocab_file''': {
        '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt''',
        '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt''',
    },
    '''merges_file''': {
        '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes''',
        '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes''',
    },
}

_lowercase							=				{
    '''vinai/phobert-base''': 256,
    '''vinai/phobert-large''': 256,
}



def      lowerCAmelCase__			(		__magic_name__      )						->Optional[int]:
			__lowercase       =       set()
			__lowercase       =       word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)      )
						__lowercase       =       char

			__lowercase       =       set(__magic_name__      )
			return pairs







class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			int          =   VOCAB_FILES_NAMES
							_lowerCamelCase					:			List[str]          =   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase					:			Optional[Any]          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="<unk>"  ,    _lowerCamelCase="<pad>"  ,    _lowerCamelCase="<mask>"  ,    **_lowerCamelCase  ,    )			->							Tuple:




										'''simple docstring'''
										super().__init__(
										    bos_token=_lowerCamelCase  ,    eos_token=_lowerCamelCase  ,    unk_token=_lowerCamelCase  ,    sep_token=_lowerCamelCase  ,    cls_token=_lowerCamelCase  ,    pad_token=_lowerCamelCase  ,    mask_token=_lowerCamelCase  ,    **_lowerCamelCase  ,    )

										__lowercase       =       vocab_file
										__lowercase       =       merges_file

										__lowercase       =       {}
										__lowercase       =       0
										__lowercase       =       1
										__lowercase       =       2
										__lowercase       =       3

										self.add_from_file(_lowerCamelCase       )

										__lowercase       =       {v: k for k, v in self.encoder.items()}

										with open(_lowerCamelCase  ,    encoding="utf-8"       ) as merges_handle:
													__lowercase       =       merges_handle.read().split("\n"       )[:-1]
										__lowercase       =       [tuple(merge.split()[:-1]       ) for merge in merges]
										__lowercase       =       dict(zip(_lowerCamelCase  ,    range(len(_lowerCamelCase       )       )       )       )
										__lowercase       =       {}
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''

										if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										__lowercase       =       [self.cls_token_id]
										__lowercase       =       [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False       )			->							List[int]:




										'''simple docstring'''

										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 None:
													return [1] + ([0] * len(_lowerCamelCase       )) + [1]
										return [1] + ([0] * len(_lowerCamelCase       )) + [1, 1] + ([0] * len(_lowerCamelCase       )) + [1]
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''
										__lowercase       =       [self.sep_token_id]
										__lowercase       =       [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   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										return len(self.encoder       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										return dict(self.encoder  ,    **self.added_tokens_encoder       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[Any]:




										'''simple docstring'''
										if token in self.cache:
													return self.cache[token]
										__lowercase       =       tuple(_lowerCamelCase       )
										__lowercase       =       tuple(list(word[:-1]       ) + [word[-1] + "</w>"]       )
										__lowercase       =       get_pairs(_lowerCamelCase       )

										if not pairs:
													return token

										while True:
													__lowercase       =       min(_lowerCamelCase  ,    key=lambda _lowerCamelCase       : self.bpe_ranks.get(_lowerCamelCase  ,    float("inf"       )       )       )
													if bigram not in self.bpe_ranks:
																break
													__lowercase   ,					__lowercase       =       bigram
													__lowercase       =       []
													__lowercase       =       0
													while i < len(_lowerCamelCase       ):
																try:
																			__lowercase       =       word.index(_lowerCamelCase  ,    _lowerCamelCase       )
																except ValueError:
																			new_word.extend(word[i:]       )
																			break
																else:
																			new_word.extend(word[i:j]       )
																			__lowercase       =       j

																if word[i] == first and i < len(_lowerCamelCase       ) - 1 and word[i + 1] == second:
																			new_word.append(first + second       )
																			i += 2
																else:
																			new_word.append(word[i]       )
																			i += 1
													__lowercase       =       tuple(_lowerCamelCase       )
													__lowercase       =       new_word
													if len(_lowerCamelCase       ) == 1:
																break
													else:
																__lowercase       =       get_pairs(_lowerCamelCase       )
										__lowercase       =       "@@ ".join(_lowerCamelCase       )
										__lowercase       =       word[:-4]
										__lowercase       =       word
										return word
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										__lowercase       =       []

										__lowercase       =       re.findall(R"\S+\n?"  ,    _lowerCamelCase       )

										for token in words:
													split_tokens.extend(list(self.bpe(_lowerCamelCase       ).split(" "       )       )       )
										return split_tokens
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Optional[Any]:




										'''simple docstring'''
										return self.encoder.get(_lowerCamelCase  ,    self.encoder.get(self.unk_token       )       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Optional[int]:




										'''simple docstring'''
										return self.decoder.get(_lowerCamelCase  ,    self.unk_token       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       " ".join(_lowerCamelCase       ).replace("@@ "  ,    ""       ).strip()
										return out_string
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple[str]:




										'''simple docstring'''
										if not os.path.isdir(_lowerCamelCase       ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
													return
										__lowercase       =       os.path.join(
										    _lowerCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
										__lowercase       =       os.path.join(
										    _lowerCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]       )

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

										if os.path.abspath(self.merges_file       ) != os.path.abspath(_lowerCamelCase       ):
													copyfile(self.merges_file  ,    _lowerCamelCase       )

										return out_vocab_file, out_merge_file





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Dict:




										'''simple docstring'''
										if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ):
													try:
																with open(_lowerCamelCase  ,    "r"  ,    encoding="utf-8"       ) as fd:
																			self.add_from_file(_lowerCamelCase       )
													except FileNotFoundError as fnfe:
																raise fnfe
													except UnicodeError:
																raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset'''       )
													return

										__lowercase       =       f.readlines()
										for lineTmp in lines:
													__lowercase       =       lineTmp.strip()
													__lowercase       =       line.rfind(" "       )
													if idx == -1:
																raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'"       )
													__lowercase       =       line[:idx]
													__lowercase       =       len(self.encoder       )





