'''simple docstring'''

import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


lowerCAmelCase_ : Tuple						  =      "\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

lowerCAmelCase_ : List[Any]						  =      "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

lowerCAmelCase_ : List[str]						  =      "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"





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


   def __UpperCAmelCase       (   self				:	List[Any])      ->					str:
         if version.parse(scb.__version__) < version.parse("1.4.12"):
               raise ImportWarning(
                   "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
                   "You can install it with `pip install \"sacrebleu>=1.4.12\"`.")
         return datasets.MetricInfo(
             description=_DESCRIPTION   ,		citation=_CITATION   ,		homepage="https://github.com/mjpost/sacreBLEU#chrf--chrf"   ,		inputs_description=_KWARGS_DESCRIPTION   ,		features=datasets.Features(
                 {
                     "predictions": datasets.Value("string"   ,		id="sequence"),
                     "references": datasets.Sequence(datasets.Value("string"   ,		id="sequence")   ,		id="references"),
                 })   ,		codebase_urls=["https://github.com/mjpost/sacreBLEU#chrf--chrf"]   ,		reference_urls=[
                 "https://github.com/m-popovic/chrF",
             ]   ,		)


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	int = CHRF.CHAR_ORDER   ,		__lowerCAmelCase				:	int = CHRF.WORD_ORDER   ,		__lowerCAmelCase				:	int = CHRF.BETA   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	bool = False   ,		)      ->					Any:
         lowercase_     =      len(references[0])
         if any(len(__lowerCAmelCase) != references_per_prediction for refs in references):
               raise ValueError("Sacrebleu requires the same number of references for each prediction")
         lowercase_     =      [[refs[i] for refs in references] for i in range(__lowerCAmelCase)]

         lowercase_     =      CHRF(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase)
         lowercase_     =      sb_chrf.corpus_score(__lowerCAmelCase   ,		__lowerCAmelCase)

         return {
             "score": output.score,
             "char_order": output.char_order,
             "word_order": output.word_order,
             "beta": output.beta,
         }







'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


lowerCAmelCase_ : Optional[Any]						  =      logging.get_logger(__name__)

lowerCAmelCase_ : Any						  =      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_ : Tuple						  =      {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase_ : Dict						  =      {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=['input_ids', 'attention_mask']
   lowerCamelCase_    					=GPTaTokenizer


   def __init__(   self				:	Dict   ,		__lowerCAmelCase				:	List[Any]=None   ,		__lowerCAmelCase				:	Optional[Any]=None   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	Any="<|endoftext|>"   ,		__lowerCAmelCase				:	Union[str, Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	List[Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	Optional[Any]=False   ,		**__lowerCAmelCase				:	Dict   ,		)      ->					int:
         super().__init__(
             __lowerCAmelCase   ,		__lowerCAmelCase   ,		tokenizer_file=__lowerCAmelCase   ,		unk_token=__lowerCAmelCase   ,		bos_token=__lowerCAmelCase   ,		eos_token=__lowerCAmelCase   ,		add_prefix_space=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)

         lowercase_     =      kwargs.pop("add_bos_token"   ,		__lowerCAmelCase)

         lowercase_     =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get("add_prefix_space"   ,		__lowerCAmelCase) != add_prefix_space:
               lowercase_     =      getattr(__lowerCAmelCase   ,		pre_tok_state.pop("type"))
               lowercase_     =      add_prefix_space
               lowercase_     =      pre_tok_class(**__lowerCAmelCase)

         lowercase_     =      add_prefix_space


   def __UpperCAmelCase       (   self				:	List[str]   ,		*__lowerCAmelCase				:	List[str]   ,		**__lowerCAmelCase				:	str)      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)
         assert self.add_prefix_space or not is_split_into_words, (
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		*__lowerCAmelCase				:	Union[str, Any]   ,		**__lowerCAmelCase				:	Union[str, Any])      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)

         assert self.add_prefix_space or not is_split_into_words, (
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[str] = None)      ->					Tuple[str]:
         lowercase_     =      self._tokenizer.model.save(__lowerCAmelCase   ,		name=__lowerCAmelCase)
         return tuple(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	"Conversation")      ->					List[int]:
         lowercase_     =      []
         for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase) + [self.eos_token_id])

         if len(__lowerCAmelCase) > self.model_max_length:
               lowercase_     =      input_ids[-self.model_max_length :]
         return input_ids






