





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
  import torch


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


UpperCAmelCase_     							= logging.get_logger(__name__)
UpperCAmelCase_     							= {"""vocab_file""": """spiece.model"""}

UpperCAmelCase_     							= {
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

UpperCAmelCase_     							= {
    """AI-Sweden/gpt-sw3-126m""": 2048,
    """AI-Sweden/gpt-sw3-350m""": 2048,
    """AI-Sweden/gpt-sw3-1.6b""": 2048,
    """AI-Sweden/gpt-sw3-6.7b""": 2048,
    """AI-Sweden/gpt-sw3-20b""": 2048,
}






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							str   		=		VOCAB_FILES_NAMES
   a						:							int   		=		PRETRAINED_VOCAB_FILES_MAP
   a						:							int   		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a						:							str   		=		["input_ids", "attention_mask"]




   def __init__(    self,		__magic_name__,		__magic_name__=False,		__magic_name__=False,		__magic_name__=False,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__ = None,		**__magic_name__,		) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      {} if sp_model_kwargs is None else sp_model_kwargs

       UpperCamelCase__							:				Optional[int]          =      kwargs.get('''name_or_path'''							)
       if name_or_path is None:
           logger.warning(
               '''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
               ''' you are testing the model, this can safely be ignored'''							)
           UpperCamelCase__							:				Tuple          =      '''None'''

       # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
       UpperCamelCase__							:				Any          =      '''<|endoftext|>''' if eos_token is None else eos_token
       UpperCamelCase__							:				Dict          =      '''<unk>''' if unk_token is None else unk_token
       if "gpt-sw3-7b" in name_or_path:
           UpperCamelCase__							:				Union[str, Any]          =      unk_token if pad_token is None else pad_token
           UpperCamelCase__							:				List[str]          =      eos_token if bos_token is None else bos_token
       else:
           UpperCamelCase__							:				Tuple          =      '''<pad>''' if pad_token is None else pad_token
           UpperCamelCase__							:				int          =      '''<s>''' if bos_token is None else bos_token

       super().__init__(
           do_lower_case=__magic_name__,		remove_space=__magic_name__,		keep_accents=__magic_name__,		bos_token=__magic_name__,		eos_token=__magic_name__,		unk_token=__magic_name__,		pad_token=__magic_name__,		sp_model_kwargs=self.sp_model_kwargs,		**__magic_name__,		)

       UpperCamelCase__							:				int          =      do_lower_case
       UpperCamelCase__							:				Tuple          =      remove_space
       UpperCamelCase__							:				List[Any]          =      keep_accents
       UpperCamelCase__							:				Tuple          =      vocab_file

       UpperCamelCase__							:				List[str]          =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
       self.sp_model.Load(__magic_name__							)

       # Used for whitespace normalization in input texts
       # fmt : off
       UpperCamelCase__							:				Tuple          =      {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
       # fmt : on

       # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
       UpperCamelCase__							:				Dict          =      re.compile(
           f"[{''.join(map(__magic_name__,		list(range(0,		9							)							) + list(range(11,		32							)							) + list(range(127,		160							)							) + [160, 173, 8203]							)							)}]"							)




   def __getstate__(    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      self.__dict__.copy()
       UpperCamelCase__							:				List[str]          =      None
       return state




   def __setstate__(    self,		__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      d

       # for backward compatibility
       if not hasattr(self,		'''sp_model_kwargs'''							):
           UpperCamelCase__							:				Union[str, Any]          =      {}

       UpperCamelCase__							:				Optional[int]          =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
       self.sp_model.Load(self.vocab_file							)




   @property
   # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       return len(self.sp_model							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      self.non_printing_characters_re.sub('''''',		__magic_name__							)

       # Normalize whitespaces
       UpperCamelCase__							:				Dict          =      ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]							)

       # NFC Unicode normalization
       UpperCamelCase__							:				Any          =      unicodedata.normalize('''NFC''',		__magic_name__							)
       return text




   def       UpperCamelCase__  (    self,		__magic_name__,		**__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      self.preprocess_text(__magic_name__							)
       return self.sp_model.encode(__magic_name__,		out_type=__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      List[Any]:






       """simple docstring"""
       return self.sp_model.PieceToId(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Dict:






       """simple docstring"""
       return self.sp_model.IdToPiece(__magic_name__							)




   @staticmethod
   def       UpperCamelCase__  (    __magic_name__							) ->      Optional[int]:






       """simple docstring"""
       return out_string




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      []
       UpperCamelCase__							:				List[Any]          =      ''''''
       UpperCamelCase__							:				Optional[int]          =      False
       for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
               # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
               if not prev_is_special:
                   out_string += " "

               out_string += self.sp_model.decode(__magic_name__							) + token
               UpperCamelCase__							:				Any          =      True
               UpperCamelCase__							:				Optional[Any]          =      []
           else:
               current_sub_tokens.append(__magic_name__							)
               UpperCamelCase__							:				Any          =      False
       out_string += self.sp_model.decode(__magic_name__							)

       return out_string




   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      {self.convert_ids_to_tokens(__magic_name__							): i for i in range(self.vocab_size							)}
       vocab.update(self.added_tokens_encoder							)
       return vocab




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None							) ->      List[str]:






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

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

       return (out_vocab_file,)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = False							) ->      Optional[Any]:






       """simple docstring"""

       if isinstance(__magic_name__,		__magic_name__							):
           UpperCamelCase__							:				Optional[Any]          =      self.preprocess_text(__magic_name__							)
           UpperCamelCase__							:				Optional[Any]          =      self.sp_model.encode(__magic_name__							)
       else:
           UpperCamelCase__							:				str          =      [self.preprocess_text(__magic_name__							) for t in text]
           UpperCamelCase__							:				str          =      self.sp_model.encode(__magic_name__							)

       if return_tensors is True or return_tensors == "pt":
           UpperCamelCase__							:				Optional[Any]          =      torch.tensor(__magic_name__							)

       return token_ids




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       return self.sp_model.decode(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      [f"User: {text}" if is_user else f"Bot: {text}" for is_user, text in conversation.iter_texts()]
       UpperCamelCase__							:				Optional[Any]          =      (
           f"{self.eos_token}{self.bos_token}" + f"{self.bos_token}".join(__magic_name__							) + f"{self.bos_token}Bot:"
       )
       return self.encode(text=__magic_name__							)





'''simple docstring'''

import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


a_  :				str						= """\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

a_  :				int						= """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

a_  :				Tuple						= """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""





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





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   return datasets.MetricInfo(
       description=_DESCRIPTION			,  citation=_CITATION			,  inputs_description=_KWARGS_DESCRIPTION			,  features=datasets.Features(
           {
               "predictions": datasets.Value("string"			,  id="sequence"     ),
               "references": datasets.Value("string"			,  id="sequence"     ),
           }     )			,  codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"]			,  reference_urls=[
           "https://en.wikipedia.org/wiki/ROUGE_(metric)",
           "https://github.com/google-research/google-research/tree/master/rouge",
       ]			,  )







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  UpperCamelCase=True			,  UpperCamelCase=False     ):







   """simple docstring"""
   if rouge_types is None:
    lowerCamelCase_      =  ["rouge1", "rouge2", "rougeL", "rougeLsum"]

   lowerCamelCase_      =  rouge_scorer.RougeScorer(rouge_types=UpperCamelCase			,  use_stemmer=UpperCamelCase     )
   if use_aggregator:
    lowerCamelCase_      =  scoring.BootstrapAggregator()
   else:
    lowerCamelCase_      =  []

   for ref, pred in zip(UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  scorer.score(UpperCamelCase			,  UpperCamelCase     )
    if use_aggregator:
     aggregator.add_scores(UpperCamelCase     )
    else:
     scores.append(UpperCamelCase     )

   if use_aggregator:
    lowerCamelCase_      =  aggregator.aggregate()
   else:
    lowerCamelCase_      =  {}
    for key in scores[0]:
     lowerCamelCase_      =  [score[key] for score in scores]

   return result



