

"""simple docstring"""




import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
  if is_torch_available():
    import torch
  if is_tf_available():
    import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


UpperCAmelCase__	:       Tuple			      =      logging.get_logger(__name__)

UpperCAmelCase__	:       Optional[int]			      =      {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

UpperCAmelCase__	:       Union[str, Any]			      =      {
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
}

UpperCAmelCase__	:       str			      =      {
    """Salesforce/codegen-350M-mono""": 2_048,
}






@lru_cache()
def    __lowercase (       )       ->  Dict:
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				(
          list(range(ord("""!"""     )  ,      ord("""~"""     ) + 1     )     ) + list(range(ord("""¡"""     )  ,      ord("""¬"""     ) + 1     )     ) + list(range(ord("""®"""     )  ,      ord("""ÿ"""     ) + 1     )     )
      )
      SCREAMING_SNAKE_CASE	: List[Any]					       =				bs[:]
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				0
      for b in range(2**8     ):
            if b not in bs:
                  bs.append(_A     )
                  cs.append(2**8 + n     )
                  n += 1
      SCREAMING_SNAKE_CASE	: Any					       =				[chr(_A     ) for n in cs]
      return dict(zip(_A  ,      _A     )     )






def    __lowercase (       _A     )       ->  List[str]:
      SCREAMING_SNAKE_CASE	: List[str]					       =				set()
      SCREAMING_SNAKE_CASE	: Any					       =				word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)     )
            SCREAMING_SNAKE_CASE	: Tuple					       =				char
      return pairs







class     a__    (				UpperCAmelCase    ):







  """simple docstring"""




  UpperCAmelCase__ :	Tuple         =VOCAB_FILES_NAMES
  UpperCAmelCase__ :	Tuple         =PRETRAINED_VOCAB_FILES_MAP
  UpperCAmelCase__ :	int         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  UpperCAmelCase__ :	Optional[int]         =["""input_ids""", """attention_mask"""]


  def __init__(     self      :	Optional[Any]			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	List[Any]			,			UpperCAmelCase__      :	List[str]="replace"			,			UpperCAmelCase__      :	Dict="<|endoftext|>"			,			UpperCAmelCase__      :	Optional[Any]="<|endoftext|>"			,			UpperCAmelCase__      :	str="<|endoftext|>"			,			UpperCAmelCase__      :	int=None			,			UpperCAmelCase__      :	Union[str, Any]=False			,			UpperCAmelCase__      :	Union[str, Any]=False			,			**UpperCAmelCase__      :	Any			,			)   ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Dict					       =				AddedToken(UpperCAmelCase__			,			lstrip=UpperCAmelCase__			,			rstrip=UpperCAmelCase__						) if isinstance(UpperCAmelCase__			,			UpperCAmelCase__						) else bos_token
        SCREAMING_SNAKE_CASE	: Tuple					       =				AddedToken(UpperCAmelCase__			,			lstrip=UpperCAmelCase__			,			rstrip=UpperCAmelCase__						) if isinstance(UpperCAmelCase__			,			UpperCAmelCase__						) else eos_token
        SCREAMING_SNAKE_CASE	: str					       =				AddedToken(UpperCAmelCase__			,			lstrip=UpperCAmelCase__			,			rstrip=UpperCAmelCase__						) if isinstance(UpperCAmelCase__			,			UpperCAmelCase__						) else unk_token
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				AddedToken(UpperCAmelCase__			,			lstrip=UpperCAmelCase__			,			rstrip=UpperCAmelCase__						) if isinstance(UpperCAmelCase__			,			UpperCAmelCase__						) else pad_token
        super().__init__(
            errors=UpperCAmelCase__			,			unk_token=UpperCAmelCase__			,			bos_token=UpperCAmelCase__			,			eos_token=UpperCAmelCase__			,			pad_token=UpperCAmelCase__			,			add_prefix_space=UpperCAmelCase__			,			add_bos_token=UpperCAmelCase__			,			**UpperCAmelCase__			,			)
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				add_bos_token

        with open(UpperCAmelCase__			,			encoding="""utf-8"""						) as vocab_handle:
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				json.load(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				{v: k for k, v in self.encoder.items()}
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				errors  # how to handle errors in decoding
        SCREAMING_SNAKE_CASE	: Tuple					       =				bytes_to_unicode()
        SCREAMING_SNAKE_CASE	: Any					       =				{v: k for k, v in self.byte_encoder.items()}
        with open(UpperCAmelCase__			,			encoding="""utf-8"""						) as merges_handle:
              SCREAMING_SNAKE_CASE	: str					       =				merges_handle.read().split("""\n"""						)[1:-1]
        SCREAMING_SNAKE_CASE	: Dict					       =				[tuple(merge.split()						) for merge in bpe_merges]
        SCREAMING_SNAKE_CASE	: List[Any]					       =				dict(zip(UpperCAmelCase__			,			range(len(UpperCAmelCase__						)						)						)						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				{}
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        SCREAMING_SNAKE_CASE	: Dict					       =				re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""						)


  @property
  def 	_lowercase (     self      :	str						)   ->Optional[int]:
        """simple docstring"""







        return len(self.encoder						)


  def 	_lowercase (     self      :	Union[str, Any]						)   ->Dict:
        """simple docstring"""







        return dict(self.encoder			,			**self.added_tokens_encoder						)


  def 	_lowercase (     self      :	Dict			,			UpperCAmelCase__      :	Optional[Any]						)   ->Optional[int]:
        """simple docstring"""







        if token in self.cache:
              return self.cache[token]
        SCREAMING_SNAKE_CASE	: str					       =				tuple(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Dict					       =				get_pairs(UpperCAmelCase__						)

        if not pairs:
              return token

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

                    if word[i] == first and i < len(UpperCAmelCase__						) - 1 and word[i + 1] == second:
                          new_word.append(first + second						)
                          i += 2
                    else:
                          new_word.append(word[i]						)
                          i += 1
              SCREAMING_SNAKE_CASE	: Tuple					       =				tuple(UpperCAmelCase__						)
              SCREAMING_SNAKE_CASE	: List[Any]					       =				new_word
              if len(UpperCAmelCase__						) == 1:
                    break
              else:
                    SCREAMING_SNAKE_CASE	: List[Any]					       =				get_pairs(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				""" """.join(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				word
        return word


  def 	_lowercase (     self      :	Optional[Any]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	str=None						)   ->List[str]:
        """simple docstring"""







        if self.add_bos_token:
              SCREAMING_SNAKE_CASE	: Tuple					       =				[self.bos_token_id]
        else:
              SCREAMING_SNAKE_CASE	: int					       =				[]

        SCREAMING_SNAKE_CASE	: Dict					       =				bos_token_ids + token_ids_a

        if token_ids_a is None:
              return output

        return output + bos_token_ids + token_ids_a


  def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	Any						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: str					       =				[]
        for token in re.findall(self.pat			,			UpperCAmelCase__						):
              SCREAMING_SNAKE_CASE	: int					       =				"""""".join(
                  self.byte_encoder[b] for b in token.encode("""utf-8"""						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
              bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCAmelCase__						).split(""" """						)						)
        return bpe_tokens


  def 	_lowercase (     self      :	Optional[int]			,			UpperCAmelCase__      :	Optional[int]						)   ->Union[str, Any]:
        """simple docstring"""







        return self.encoder.get(UpperCAmelCase__			,			self.encoder.get(self.unk_token						)						)


  def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	int						)   ->Tuple:
        """simple docstring"""







        return self.decoder.get(UpperCAmelCase__						)


  def 	_lowercase (     self      :	int			,			UpperCAmelCase__      :	str						)   ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				"""""".join(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Any					       =				bytearray([self.byte_decoder[c] for c in text]						).decode("""utf-8"""			,			errors=self.errors						)
        return text


  def 	_lowercase (     self      :	List[str]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Optional[str] = None						)   ->Tuple[str]:
        """simple docstring"""







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

        with open(UpperCAmelCase__			,			"""w"""			,			encoding="""utf-8"""						) as f:
              f.write(json.dumps(self.encoder			,			indent=2			,			sort_keys=UpperCAmelCase__			,			ensure_ascii=UpperCAmelCase__						) + """\n"""						)

        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				0
        with open(UpperCAmelCase__			,			"""w"""			,			encoding="""utf-8"""						) as writer:
              writer.write("""#version: 0.2\n"""						)
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,			key=lambda UpperCAmelCase__						: kv[1]						):
                    if index != token_index:
                          logger.warning(
                              f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                              """ Please check that the tokenizer is not corrupted!"""						)
                          SCREAMING_SNAKE_CASE	: Any					       =				token_index
                    writer.write(""" """.join(UpperCAmelCase__						) + """\n"""						)
                    index += 1

        return vocab_file, merge_file


  def 	_lowercase (     self      :	Optional[Any]			,			UpperCAmelCase__      :	List[str]			,			UpperCAmelCase__      :	Tuple=False			,			**UpperCAmelCase__      :	int						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				kwargs.pop("""add_prefix_space"""			,			self.add_prefix_space						)
        if is_split_into_words or add_prefix_space:
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				""" """ + text
        return (text, kwargs)


  def 	_lowercase (     self      :	str			,			UpperCAmelCase__      :	Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]			,			UpperCAmelCase__      :	bool = False			,			UpperCAmelCase__      :	bool = None			,			UpperCAmelCase__      :	Optional[List[str]] = None			,			**UpperCAmelCase__      :	List[str]			,			)   ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				super()._decode(
            token_ids=UpperCAmelCase__			,			skip_special_tokens=UpperCAmelCase__			,			clean_up_tokenization_spaces=UpperCAmelCase__			,			**UpperCAmelCase__			,			)

        if truncate_before_pattern is not None and len(UpperCAmelCase__						) > 0:
              SCREAMING_SNAKE_CASE	: str					       =				self.truncate(UpperCAmelCase__			,			UpperCAmelCase__						)

        return decoded_text




  def 	_lowercase (     self      :	Any			,			UpperCAmelCase__      :	Optional[int]			,			UpperCAmelCase__      :	List[str]						)   ->Union[str, Any]:
        """simple docstring"""







        def find_re(UpperCAmelCase__      :	Union[str, Any]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Dict						):
              SCREAMING_SNAKE_CASE	: str					       =				pattern.search(UpperCAmelCase__			,			UpperCAmelCase__						)
              return m.start() if m else -1

        SCREAMING_SNAKE_CASE	: Optional[int]					       =				[re.compile(UpperCAmelCase__			,			re.MULTILINE						) for pattern in truncate_before_pattern]

        SCREAMING_SNAKE_CASE	: List[str]					       =				list(re.finditer("""^print"""			,			UpperCAmelCase__			,			re.MULTILINE						)						)

        if len(UpperCAmelCase__						) > 1:
              SCREAMING_SNAKE_CASE	: Tuple					       =				completion[: prints[1].start()]

        SCREAMING_SNAKE_CASE	: List[Any]					       =				list(re.finditer("""^def"""			,			UpperCAmelCase__			,			re.MULTILINE						)						)

        if len(UpperCAmelCase__						) > 1:
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				completion[: defs[1].start()]

        SCREAMING_SNAKE_CASE	: str					       =				0

        SCREAMING_SNAKE_CASE	: List[Any]					       =				[
            pos for pos in [find_re(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						) for terminal in terminals] if pos != -1
        ]

        if len(UpperCAmelCase__						) > 0:
              return completion[: min(UpperCAmelCase__						)]
        else:
              return completion







import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


UpperCAmelCase__	:       Tuple			      =      """\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
"""

UpperCAmelCase__	:       Union[str, Any]			      =      """\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
"""


UpperCAmelCase__	:       str			      =      """
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric(\"frugalscore\")
    >>> results = frugalscore.compute(predictions=['hello there', 'huggingface'], references=['hello world', 'hugging face'])
    >>> print([round(s, 3) for s in results[\"scores\"]])
    [0.631, 0.645]
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,     _KWARGS_DESCRIPTION    )
class     a__    (				datasets.Metric    ):







  """simple docstring"""




  def 	_lowercase (     self      :	Any						)   ->Optional[int]:
        """simple docstring"""







        return datasets.MetricInfo(
            description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
                {
                    """predictions""": datasets.Value("""string"""						),
                    """references""": datasets.Value("""string"""						),
                }						)			,			homepage="""https://github.com/moussaKam/FrugalScore"""			,			)


  def 	_lowercase (     self      :	Dict			,			UpperCAmelCase__      :	Tuple						)   ->Any:
        """simple docstring"""







        if self.config_name == "default":
              SCREAMING_SNAKE_CASE	: int					       =				"""moussaKam/frugalscore_tiny_bert-base_bert-score"""
        else:
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.config_name
        SCREAMING_SNAKE_CASE	: Tuple					       =				AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				AutoTokenizer.from_pretrained(UpperCAmelCase__						)




  def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Optional[Any]			,			UpperCAmelCase__      :	str=3_2			,			UpperCAmelCase__      :	Optional[int]=1_2_8			,			UpperCAmelCase__      :	Optional[int]=None			,			)   ->Any:
        """simple docstring"""







        assert len(UpperCAmelCase__						) == len(
            UpperCAmelCase__						), "predictions and references should have the same number of sentences."
        if device is not None:
              assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
        else:
              SCREAMING_SNAKE_CASE	: List[str]					       =				"""gpu""" if torch.cuda.is_available() else """cpu"""
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				TrainingArguments(
            """trainer"""			,			fpaa=(device == """gpu""")			,			per_device_eval_batch_size=UpperCAmelCase__			,			report_to="""all"""			,			no_cuda=(device == """cpu""")			,			log_level="""warning"""			,			)
        SCREAMING_SNAKE_CASE	: int					       =				{"""sentence1""": predictions, """sentence2""": references}
        SCREAMING_SNAKE_CASE	: List[str]					       =				datasets.Dataset.from_dict(UpperCAmelCase__						)

        def tokenize_function(UpperCAmelCase__      :	Any						):
              return self.tokenizer(
                  data["""sentence1"""]			,			data["""sentence2"""]			,			max_length=UpperCAmelCase__			,			truncation=UpperCAmelCase__			,			padding=UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: Dict					       =				raw_datasets.map(UpperCAmelCase__			,			batched=UpperCAmelCase__						)
        tokenized_datasets.remove_columns(["""sentence1""", """sentence2"""]						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				Trainer(self.model			,			UpperCAmelCase__			,			tokenizer=self.tokenizer						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				trainer.predict(UpperCAmelCase__						)
        return {"scores": list(predictions.predictions.squeeze(-1						)						)}




