

import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


_lowerCamelCase      :					Any					    =      datasets.logging.get_logger(__name__)


_lowerCamelCase      :					List[str]					    =      '''\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
'''

_lowerCamelCase      :					Tuple					    =      '''\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''

_lowerCamelCase      :					Any					    =      '''
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    \'scores\': List of scores.
Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bleurt = datasets.load_metric("bleurt")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results["scores"]])
    [1.03, 1.04]
'''

_lowerCamelCase      :					List[Any]					    =      {
    '''bleurt-tiny-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip''',
    '''bleurt-tiny-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip''',
    '''bleurt-base-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip''',
    '''bleurt-base-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip''',
    '''bleurt-large-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip''',
    '''bleurt-large-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip''',
    '''BLEURT-20-D3''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip''',
    '''BLEURT-20-D6''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip''',
    '''BLEURT-20-D12''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip''',
    '''BLEURT-20''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip''',
}





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



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return datasets.MetricInfo(
                 description=_DESCRIPTION ,   citation=_CITATION ,   homepage='''https://github.com/google-research/bleurt''' ,   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/bleurt'''] ,   reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696'''] ,   )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if self.config_name == "default":
                   logger.warning(
                       '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
                       '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''     )
                   __A    ='''bleurt-base-128'''

             if self.config_name.lower() in CHECKPOINT_URLS:
                   __A    =self.config_name.lower()

             elif self.config_name.upper() in CHECKPOINT_URLS:
                   __A    =self.config_name.upper()

             else:
                   raise KeyError(
                       f'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'''     )

             # download the model checkpoint specified by self.config_name and set up the scorer
             __A    =dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]     )
             __A    =score.BleurtScorer(os.path.join(lowercase__ ,   lowercase__     )     )


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =self.scorer.score(references=lowercase__ ,   candidates=lowercase__     )
             return {"scores": scores}





import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_lowerCamelCase      :					List[Any]					    =      logging.getLogger(__name__)





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def __init__(							self ,   lowercase__=-1     ):







             '''simple docstring'''



             __A    =label_idx

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             if isinstance(lowercase__ ,   lowercase__     ):
                   __A    =mode.value
             __A    =os.path.join(lowercase__ ,   f'''{mode}.txt'''     )
             __A    =1
             __A    =[]
             with open(lowercase__ ,   encoding='''utf-8'''     ) as f:
                   __A    =[]
                   __A    =[]
                   for line in f:
                         if line.startswith('''-DOCSTART-'''     ) or line == "" or line == "\n":
                               if words:
                                     examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,   words=lowercase__ ,   labels=lowercase__     )     )
                                     guid_index += 1
                                     __A    =[]
                                     __A    =[]
                         else:
                               __A    =line.split(''' '''     )
                               words.append(splits[0]     )
                               if len(lowercase__     ) > 1:
                                     labels.append(splits[self.label_idx].replace('''\n''' ,   ''''''     )     )
                               else:
                                     # Examples could have no label for mode = "test"
                                     labels.append('''O'''     )
                   if words:
                         examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,   words=lowercase__ ,   labels=lowercase__     )     )
             return examples

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =0
             for line in test_input_reader:
                   if line.startswith('''-DOCSTART-'''     ) or line == "" or line == "\n":
                         writer.write(lowercase__     )
                         if not preds_list[example_id]:
                               example_id += 1
                   elif preds_list[example_id]:
                         __A    =line.split()[0] + ''' ''' + preds_list[example_id].pop(0     ) + '''\n'''
                         writer.write(lowercase__     )
                   else:
                         logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''' ,   line.split()[0]     )


       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if path:
                   with open(lowercase__ ,   '''r'''     ) as f:
                         __A    =f.read().splitlines()
                   if "O" not in labels:
                         __A    =['''O'''] + labels
                   return labels
             else:
                   return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def __init__(							self     ):







             '''simple docstring'''



             super().__init__(label_idx=-2     )


       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if path:
                   with open(lowercase__ ,   '''r'''     ) as f:
                         __A    =f.read().splitlines()
                   if "O" not in labels:
                         __A    =['''O'''] + labels
                   return labels
             else:
                   return [
                       "O",
                       "B-ADVP",
                       "B-INTJ",
                       "B-LST",
                       "B-PRT",
                       "B-NP",
                       "B-SBAR",
                       "B-VP",
                       "B-ADJP",
                       "B-CONJP",
                       "B-PP",
                       "I-ADVP",
                       "I-INTJ",
                       "I-LST",
                       "I-PRT",
                       "I-NP",
                       "I-SBAR",
                       "I-VP",
                       "I-ADJP",
                       "I-CONJP",
                       "I-PP",
                   ]





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             if isinstance(lowercase__ ,   lowercase__     ):
                   __A    =mode.value
             __A    =os.path.join(lowercase__ ,   f'''{mode}.txt'''     )
             __A    =1
             __A    =[]

             with open(lowercase__ ,   encoding='''utf-8'''     ) as f:
                   for sentence in parse_incr(lowercase__     ):
                         __A    =[]
                         __A    =[]
                         for token in sentence:
                               words.append(token['''form''']     )
                               labels.append(token['''upos''']     )
                         assert len(lowercase__     ) == len(lowercase__     )
                         if words:
                               examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,   words=lowercase__ ,   labels=lowercase__     )     )
                               guid_index += 1
             return examples

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =0
             for sentence in parse_incr(lowercase__     ):
                   __A    =preds_list[example_id]
                   __A    =''''''
                   for token in sentence:
                         out += f'''{token['form']} ({token['upos']}|{s_p.pop(0     )}) '''
                   out += "\n"
                   writer.write(lowercase__     )
                   example_id += 1


       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if path:
                   with open(lowercase__ ,   '''r'''     ) as f:
                         return f.read().splitlines()
             else:
                   return [
                       "ADJ",
                       "ADP",
                       "ADV",
                       "AUX",
                       "CCONJ",
                       "DET",
                       "INTJ",
                       "NOUN",
                       "NUM",
                       "PART",
                       "PRON",
                       "PROPN",
                       "PUNCT",
                       "SCONJ",
                       "SYM",
                       "VERB",
                       "X",
                   ]


