"""simple docstring"""


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

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


__UpperCamelCase								=    logging.get_logger(__name__)

__UpperCamelCase								=    {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__UpperCamelCase								=    {
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

__UpperCamelCase								=    {
    'microsoft/biogpt': 1024,
}







def       UpperCAmelCase					(	UpperCAmelCase      )       ->   str:
     snake_case_       =      set()
     snake_case_       =      word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)      )
          snake_case_       =      char
     return pairs




class 				UpperCamelCase      (       UpperCAmelCase_     ):
       SCREAMING_SNAKE_CASE_				=   VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_				=   PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_				=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_				=   ["input_ids", "attention_mask"]







       def __init__(				self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__="<unk>",							lowerCAmelCase__="<s>",							lowerCAmelCase__="</s>",							lowerCAmelCase__="</s>",							lowerCAmelCase__="<pad>",							**lowerCAmelCase__,							)   ->   Any:
            super().__init__(
                bos_token=__UpperCamelCase,							eos_token=__UpperCamelCase,							sep_token=__UpperCamelCase,							unk_token=__UpperCamelCase,							pad_token=__UpperCamelCase,							**__UpperCamelCase,							)

            try:
                 import sacremoses
            except ImportError:
                 raise ImportError(
                     'You need to install sacremoses to use BioGptTokenizer. '
                     'See https://pypi.org/project/sacremoses/ for installation.')

            snake_case_       =      'en'
            snake_case_       =      sacremoses
            # cache of sm.MosesTokenizer instance
            snake_case_       =      {}
            snake_case_       =      {}
            with open(__UpperCamelCase,							encoding='utf-8') as vocab_handle:
                 snake_case_       =      json.load(__UpperCamelCase)
            snake_case_       =      {v: k for k, v in self.encoder.items()}
            with open(__UpperCamelCase,							encoding='utf-8') as merges_handle:
                 snake_case_       =      merges_handle.read().split('\n')[:-1]
            snake_case_       =      [tuple(merge.split()[:2]) for merge in merges]
            snake_case_       =      dict(zip(__UpperCamelCase,							range(len(__UpperCamelCase))))
            snake_case_       =      {}







       @property
       def 	a_		(				self)   ->   Any:
            return len(self.encoder)







       def 	a_		(				self)   ->   Tuple:
            return dict(self.encoder,							**self.added_tokens_encoder)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__)   ->   Dict:
            if lang not in self.cache_moses_tokenizer:
                 snake_case_       =      self.sm.MosesTokenizer(lang=__UpperCamelCase)
                 snake_case_       =      moses_tokenizer
            return self.cache_moses_tokenizer[lang].tokenize(
                __UpperCamelCase,							aggressive_dash_splits=__UpperCamelCase,							return_str=__UpperCamelCase,							escape=__UpperCamelCase)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__)   ->   List[Any]:
            if lang not in self.cache_moses_detokenizer:
                 snake_case_       =      self.sm.MosesDetokenizer(lang=__UpperCamelCase)
                 snake_case_       =      moses_detokenizer
            return self.cache_moses_detokenizer[lang].detokenize(__UpperCamelCase)







       def 	a_		(				self,							lowerCAmelCase__)   ->   str:
            snake_case_       =      tuple(token[:-1]) + (token[-1] + '</w>',)
            if token in self.cache:
                 return self.cache[token]
            snake_case_       =      get_pairs(__UpperCamelCase)

            if not pairs:
                 return token + "</w>"

            while True:
                 snake_case_       =      min(__UpperCamelCase,							key=lambda lowerCAmelCase__: self.bpe_ranks.get(__UpperCamelCase,							float('inf')))
                 if bigram not in self.bpe_ranks:
                      break
                 snake_case_ ,			snake_case_       =      bigram
                 snake_case_       =      []
                 snake_case_       =      0
                 while i < len(__UpperCamelCase):
                      try:
                           snake_case_       =      word.index(__UpperCamelCase,							__UpperCamelCase)
                      except ValueError:
                           new_word.extend(word[i:])
                           break
                      else:
                           new_word.extend(word[i:j])
                           snake_case_       =      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
                 snake_case_       =      tuple(__UpperCamelCase)
                 snake_case_       =      new_word
                 if len(__UpperCamelCase) == 1:
                      break
                 else:
                      snake_case_       =      get_pairs(__UpperCamelCase)
            snake_case_       =      ' '.join(__UpperCamelCase)
            if word == "\n  </w>":
                 snake_case_       =      '\n</w>'
            snake_case_       =      word
            return word







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__=False)   ->   Optional[int]:
            if bypass_tokenizer:
                 snake_case_       =      text.split()
            else:
                 snake_case_       =      self.moses_tokenize(__UpperCamelCase,							self.lang)

            snake_case_       =      []
            for token in text:
                 if token:
                      split_tokens.extend(list(self.bpe(__UpperCamelCase).split(' ')))

            return split_tokens







       def 	a_		(				self,							lowerCAmelCase__)   ->   List[Any]:
            return self.encoder.get(__UpperCamelCase,							self.encoder.get(self.unk_token))







       def 	a_		(				self,							lowerCAmelCase__)   ->   str:
            return self.decoder.get(__UpperCamelCase,							self.unk_token)







       def 	a_		(				self,							lowerCAmelCase__)   ->   Dict:
            snake_case_       =      [t.replace(' ',							'').replace('</w>',							' ') for t in tokens]
            snake_case_       =      ''.join(__UpperCamelCase).split()
            # detokenize
            snake_case_       =      self.moses_detokenize(__UpperCamelCase,							self.lang)
            return text







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__ = None)   ->   Dict:
            if token_ids_a is None:
                 return [self.sep_token_id] + token_ids_a
            snake_case_       =      [self.sep_token_id]
            return sep + token_ids_a + sep + token_ids_a







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__ = None,							lowerCAmelCase__ = False)   ->   List[Any]:
            if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=__UpperCamelCase,							token_ids_a=__UpperCamelCase,							already_has_special_tokens=__UpperCamelCase)
            # no bos used in fairseq
            if token_ids_a is not None:
                 return [1] + ([0] * len(__UpperCamelCase)) + [1] + ([0] * len(__UpperCamelCase))
            return [1] + ([0] * len(__UpperCamelCase))







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__ = None)   ->   str:
            snake_case_       =      [self.sep_token_id]

            # no bos used in fairseq
            if token_ids_a is None:
                 return len(token_ids_a + sep) * [0]
            return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]







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

            snake_case_       =      0
            with open(__UpperCamelCase,							'w',							encoding='utf-8') as writer:
                 for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),							key=lambda lowerCAmelCase__: 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!')
                           snake_case_       =      token_index
                      writer.write(' '.join(__UpperCamelCase) + '\n')
                      index += 1

            return vocab_file, merge_file







       def __getstate__(				self)   ->   List[str]:
            snake_case_       =      self.__dict__.copy()
            snake_case_       =      None
            return state







       def __setstate__(				self,							lowerCAmelCase__)   ->   str:
            snake_case_       =      d

            try:
                 import sacremoses
            except ImportError:
                 raise ImportError(
                     'You need to install sacremoses to use XLMTokenizer. '
                     'See https://pypi.org/project/sacremoses/ for installation.')

            snake_case_       =      sacremoses


"""simple docstring"""


import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets







@contextmanager
def       UpperCAmelCase					(	)       ->   str:
     def filter_log(UpperCAmelCase      ):
          return False if "This IS expected if you are initializing" in record.msg else True

     snake_case_       =      datasets.utils.logging.get_logger('transformers.modeling_utils'      )
     logger.addFilter(UpperCAmelCase      )
     try:
          yield
     finally:
          logger.removeFilter(UpperCAmelCase      )


__UpperCamelCase								=    '''\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
'''

__UpperCamelCase								=    '''\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.
'''

__UpperCamelCase								=    '''
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. \'en\').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bertscore = datasets.load_metric("bertscore")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")
    >>> print([round(v, 2) for v in results["f1"]])
    [1.0, 1.0]
'''




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







       def 	a_		(				self)   ->   Dict:
            return datasets.MetricInfo(
                description=_DESCRIPTION,							citation=_CITATION,							homepage='https://github.com/Tiiiger/bert_score',							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/Tiiiger/bert_score'],							reference_urls=[
                    'https://github.com/Tiiiger/bert_score',
                    'https://arxiv.org/abs/1904.09675',
                ],							)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=False,							lowerCAmelCase__=False,							lowerCAmelCase__=None,							lowerCAmelCase__=64,							lowerCAmelCase__=4,							lowerCAmelCase__=False,							lowerCAmelCase__=False,							lowerCAmelCase__=None,							lowerCAmelCase__=False,							)   ->   Union[str, Any]:
            snake_case_       =      bert_score.utils.get_hash
            snake_case_       =      bert_score.BERTScorer

            if version.parse(bert_score.__version__) >= version.parse('0.3.10'):
                 snake_case_       =      functools.partial(lowerCAmelCase__,							use_fast_tokenizer=lowerCAmelCase__)
                 snake_case_       =      functools.partial(lowerCAmelCase__,							use_fast_tokenizer=lowerCAmelCase__)
            elif use_fast_tokenizer:
                 raise ImportWarning(
                     'To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'
                     'You can install it with `pip install "bert-score>=0.3.10"`.')

            if model_type is None:
                 assert lang is not None, "either lang or model_type should be specified"
                 snake_case_       =      bert_score.utils.langamodel[lang.lower()]

            if num_layers is None:
                 snake_case_       =      bert_score.utils.modelalayers[model_type]

            snake_case_       =      get_hash(
                model=lowerCAmelCase__,							num_layers=lowerCAmelCase__,							idf=lowerCAmelCase__,							rescale_with_baseline=lowerCAmelCase__,							use_custom_baseline=baseline_path is not None,							)

            with filter_logging_context():
                 if not hasattr(self,							'cached_bertscorer') or self.cached_bertscorer.hash != hashcode:
                      snake_case_       =      scorer(
                          model_type=lowerCAmelCase__,							num_layers=lowerCAmelCase__,							batch_size=lowerCAmelCase__,							nthreads=lowerCAmelCase__,							all_layers=lowerCAmelCase__,							idf=lowerCAmelCase__,							device=lowerCAmelCase__,							lang=lowerCAmelCase__,							rescale_with_baseline=lowerCAmelCase__,							baseline_path=lowerCAmelCase__,							)

            ((snake_case_) ,			(snake_case_) ,			(snake_case_))       =      self.cached_bertscorer.score(
                cands=lowerCAmelCase__,							refs=lowerCAmelCase__,							verbose=lowerCAmelCase__,							batch_size=lowerCAmelCase__,							)
            snake_case_       =      {
                'precision': P.tolist(),
                'recall': R.tolist(),
                'f1': F.tolist(),
                'hashcode': hashcode,
            }
            return output_dict







       def 	a_		(				self,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							**lowerCAmelCase__)   ->   List[str]:
            # References can be strings or lists of strings
            # Let's change strings to lists of strings with one element
            if references is not None:
                 snake_case_       =      [[ref] if isinstance(lowerCAmelCase__,							lowerCAmelCase__) else ref for ref in references]
            super().add_batch(predictions=lowerCAmelCase__,							references=lowerCAmelCase__,							**lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							**lowerCAmelCase__)   ->   List[str]:
            # References can be strings or lists of strings
            # Let's change strings to lists of strings with one element
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      [reference]
            super().add(prediction=lowerCAmelCase__,							reference=lowerCAmelCase__,							**lowerCAmelCase__)

