"""simple docstring"""


import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


__UpperCamelCase								=    logging.get_logger(__name__)

__UpperCamelCase								=    {
    '''source_spm''': '''source.spm''',
    '''target_spm''': '''target.spm''',
    '''vocab''': '''vocab.json''',
    '''target_vocab_file''': '''target_vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__UpperCamelCase								=    {
    '''source_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'''
    },
    '''target_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'''
    },
    '''vocab''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'''
    },
    '''tokenizer_config_file''': {
        '''Helsinki-NLP/opus-mt-en-de''': (
            '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'''
        )
    },
}

__UpperCamelCase								=    {'''Helsinki-NLP/opus-mt-en-de''': 512}
__UpperCamelCase								=    {}




class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_				=   PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_				=   PRETRAINED_INIT_CONFIGURATION
       SCREAMING_SNAKE_CASE_				=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_				=   ["input_ids", "attention_mask"]
       SCREAMING_SNAKE_CASE_				=   re.compile(">>.+<<"     )  # type: re.Pattern







       def __init__(				self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__="<unk>",							lowerCAmelCase__="</s>",							lowerCAmelCase__="<pad>",							lowerCAmelCase__=512,							lowerCAmelCase__ = None,							lowerCAmelCase__=False,							**lowerCAmelCase__,							)   ->   None:
            snake_case_       =      {} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
                source_lang=lowerCAmelCase__,							target_lang=lowerCAmelCase__,							unk_token=lowerCAmelCase__,							eos_token=lowerCAmelCase__,							pad_token=lowerCAmelCase__,							model_max_length=lowerCAmelCase__,							sp_model_kwargs=self.sp_model_kwargs,							target_vocab_file=lowerCAmelCase__,							separate_vocabs=lowerCAmelCase__,							**lowerCAmelCase__,							)
            assert Path(lowerCAmelCase__).exists(), f'cannot find spm source {source_spm}'

            snake_case_       =      separate_vocabs
            snake_case_       =      load_json(lowerCAmelCase__)
            if self.unk_token not in self.encoder:
                 raise KeyError('<unk> token must be in vocab')
            assert self.pad_token in self.encoder

            if separate_vocabs:
                 snake_case_       =      load_json(lowerCAmelCase__)
                 snake_case_       =      {v: k for k, v in self.target_encoder.items()}
                 snake_case_       =      []
            else:
                 snake_case_       =      {v: k for k, v in self.encoder.items()}
                 snake_case_       =      [k for k in self.encoder if k.startswith('>>') and k.endswith('<<')]

            snake_case_       =      source_lang
            snake_case_       =      target_lang
            snake_case_       =      [source_spm, target_spm]

            # load SentencePiece model for pre-processing
            snake_case_       =      load_spm(lowerCAmelCase__,							self.sp_model_kwargs)
            snake_case_       =      load_spm(lowerCAmelCase__,							self.sp_model_kwargs)
            snake_case_       =      self.spm_source
            snake_case_       =      self.encoder

            # Multilingual target side: default to using first supported language code.

            self._setup_normalizer()







       def 	a_		(				self)   ->   Dict:
            try:
                 from sacremoses import MosesPunctNormalizer

                 snake_case_       =      MosesPunctNormalizer(self.source_lang).normalize
            except (ImportError, FileNotFoundError):
                 warnings.warn('Recommended: pip install sacremoses.')
                 snake_case_       =      lambda lowerCAmelCase__: x







       def 	a_		(				self,							lowerCAmelCase__)   ->   str:
            return self.punc_normalizer(lowerCAmelCase__) if x else ""







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







       def 	a_		(				self,							lowerCAmelCase__)   ->   Union[str, Any]:
            snake_case_       =      self.language_code_re.match(lowerCAmelCase__)
            snake_case_       =      [match.group(0)] if match else []
            return code, self.language_code_re.sub('',							lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__)   ->   List[str]:
            snake_case_ ,			snake_case_       =      self.remove_language_code(lowerCAmelCase__)
            snake_case_       =      self.current_spm.encode(lowerCAmelCase__,							out_type=lowerCAmelCase__)
            return code + pieces







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







       def 	a_		(				self,							lowerCAmelCase__,							**lowerCAmelCase__)   ->   int:
            return super().batch_decode(lowerCAmelCase__,							**lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__,							**lowerCAmelCase__)   ->   str:
            return super().decode(lowerCAmelCase__,							**lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__)   ->   str:
            snake_case_       =      self.spm_source if self._decode_use_source_tokenizer else self.spm_target
            snake_case_       =      []
            snake_case_       =      ''
            for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                      out_string += sp_model.decode_pieces(lowerCAmelCase__) + token + " "
                      snake_case_       =      []
                 else:
                      current_sub_tokens.append(lowerCAmelCase__)
            out_string += sp_model.decode_pieces(lowerCAmelCase__)
            return out_string.strip()







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__=None)   ->   List[int]:
            if token_ids_a is None:
                 return token_ids_a + [self.eos_token_id]
            # We don't expect to process pairs, but leave the pair logic for API consistency
            return token_ids_a + token_ids_a + [self.eos_token_id]







       def 	a_		(				self)   ->   Union[str, Any]:
            snake_case_       =      self.spm_source
            snake_case_       =      self.encoder







       def 	a_		(				self)   ->   Any:
            snake_case_       =      self.spm_target
            if self.separate_vocabs:
                 snake_case_       =      self.target_encoder







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







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

            if self.separate_vocabs:
                 snake_case_       =      os.path.join(
                     lowerCAmelCase__,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab'],							)
                 snake_case_       =      os.path.join(
                     lowerCAmelCase__,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file'],							)
                 save_json(self.encoder,							lowerCAmelCase__)
                 save_json(self.target_encoder,							lowerCAmelCase__)
                 saved_files.append(lowerCAmelCase__)
                 saved_files.append(lowerCAmelCase__)
            else:
                 snake_case_       =      os.path.join(
                     lowerCAmelCase__,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab'])
                 save_json(self.encoder,							lowerCAmelCase__)
                 saved_files.append(lowerCAmelCase__)

            for spm_save_filename, spm_orig_path, spm_model in zip(
                [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']],							self.spm_files,							[self.spm_source, self.spm_target],							):
                 snake_case_       =      os.path.join(
                     lowerCAmelCase__,							(filename_prefix + '-' if filename_prefix else '') + spm_save_filename)
                 if os.path.abspath(lowerCAmelCase__) != os.path.abspath(lowerCAmelCase__) and os.path.isfile(lowerCAmelCase__):
                      copyfile(lowerCAmelCase__,							lowerCAmelCase__)
                      saved_files.append(lowerCAmelCase__)
                 elif not os.path.isfile(lowerCAmelCase__):
                      with open(lowerCAmelCase__,							'wb') as fi:
                           snake_case_       =      spm_model.serialized_model_proto()
                           fi.write(lowerCAmelCase__)
                      saved_files.append(lowerCAmelCase__)

            return tuple(lowerCAmelCase__)







       def 	a_		(				self)   ->   Dict:
            return self.get_src_vocab()







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







       def 	a_		(				self)   ->   Dict:
            return dict(self.target_encoder,							**self.added_tokens_decoder)







       def __getstate__(				self)   ->   Dict:
            snake_case_       =      self.__dict__.copy()
            state.update(
                {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']})
            return state







       def __setstate__(				self,							lowerCAmelCase__)   ->   None:
            snake_case_       =      d

            # for backward compatibility
            if not hasattr(self,							'sp_model_kwargs'):
                 snake_case_       =      {}

            snake_case_ ,			snake_case_       =      (load_spm(lowerCAmelCase__,							self.sp_model_kwargs) for f in self.spm_files)
            snake_case_       =      self.spm_source
            self._setup_normalizer()







       def 	a_		(				self,							*lowerCAmelCase__,							**lowerCAmelCase__)   ->   Any:
            return 1







       def 	a_		(				self,							lowerCAmelCase__)   ->   Tuple:
            snake_case_       =      set(self.all_special_ids)  # call it once instead of inside list comp
            all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special
            return [1 if x in all_special_ids else 0 for x in seq]







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__ = None,							lowerCAmelCase__ = False)   ->   List[int]:
            if already_has_special_tokens:
                 return self._special_token_mask(lowerCAmelCase__)
            elif token_ids_a is None:
                 return self._special_token_mask(lowerCAmelCase__) + [1]
            else:
                 return self._special_token_mask(token_ids_a + token_ids_a) + [1]







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   sentencepiece.SentencePieceProcessor:
     snake_case_       =      sentencepiece.SentencePieceProcessor(**UpperCAmelCase      )
     spm.Load(UpperCAmelCase      )
     return spm







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   None:
     with open(UpperCAmelCase		,      'w'      ) as f:
          json.dump(UpperCAmelCase		,      UpperCAmelCase		,      indent=2      )







def       UpperCAmelCase					(	UpperCAmelCase      )       ->   Union[Dict, List]:
     with open(UpperCAmelCase		,      'r'      ) as f:
          return json.load(UpperCAmelCase      )


"""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__)

