






import os

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

import datasets


__UpperCAmelCase							:   Tuple							=  datasets.logging.get_logger(__name__)


__UpperCAmelCase							:   Optional[Any]							=  '''\
@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}
}
'''

__UpperCAmelCase							:   Dict							=  '''\
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.
'''

__UpperCAmelCase							:   Union[str, 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]
'''

__UpperCAmelCase							:   Dict							=  {
    '''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 					_snake_case						(			datasets.Metric ):






 def    lowerCAmelCase_							(		self       )						->    Optional[int]:
     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    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    Union[str, Any]:





     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\')."       )
         snake_case__      :Optional[int]         =   'bleurt-base-128'

     if self.config_name.lower() in CHECKPOINT_URLS:
         snake_case__      :int         =   self.config_name.lower()

     elif self.config_name.upper() in CHECKPOINT_URLS:
         snake_case__      :Tuple         =   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
     snake_case__      :Optional[Any]         =   dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]       )
     snake_case__      :str         =   score.BleurtScorer(os.path.join(UpperCamelCase_					,UpperCamelCase_       )       )






 def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    Optional[int]:
     snake_case__      :List[str]         =   self.scorer.score(references=UpperCamelCase_					,candidates=UpperCamelCase_       )
     return {"scores": scores}







from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__UpperCAmelCase							:   str							=  logging.get_logger(__name__)

__UpperCAmelCase							:   List[Any]							=  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase							:   List[Any]							=  {
    "vocab_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
    },
    "merges_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
    },
}

__UpperCAmelCase							:   str							=  {"allegro/herbert-base-cased": 5_1_4}
__UpperCAmelCase							:   List[str]							=  {}


class 					_snake_case						(			_A ):
	_A          =   VOCAB_FILES_NAMES
	_A          =   PRETRAINED_VOCAB_FILES_MAP
	_A          =   PRETRAINED_INIT_CONFIGURATION
	_A          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A          =   HerbertTokenizer






	def __init__(		self					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase="<s>"					,UpperCamelCase="<unk>"					,UpperCamelCase="<pad>"					,UpperCamelCase="<mask>"					,UpperCamelCase="</s>"					,**UpperCamelCase					,)						->    Dict:
					super().__init__(
					    UpperCamelCase					,UpperCamelCase					,tokenizer_file=UpperCamelCase					,cls_token=UpperCamelCase					,unk_token=UpperCamelCase					,pad_token=UpperCamelCase					,mask_token=UpperCamelCase					,sep_token=UpperCamelCase					,**UpperCamelCase					,)






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None       )						->    List[int]:
					snake_case__      :Optional[int]         =   [self.cls_token_id]
					snake_case__      :Any         =   [self.sep_token_id]
					if token_ids_a is None:
									return cls + token_ids_a + sep

					return cls + token_ids_a + sep + token_ids_a + sep






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = False       )						->    List[int]:
					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=UpperCamelCase					,token_ids_a=UpperCamelCase					,already_has_special_tokens=UpperCamelCase       )

					if token_ids_a is None:
									return [1] + ([0] * len(UpperCamelCase       )) + [1]
					return [1] + ([0] * len(UpperCamelCase       )) + [1] + ([0] * len(UpperCamelCase       )) + [1]






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None       )						->    List[int]:
					snake_case__      :Any         =   [self.sep_token_id]
					snake_case__      :Union[str, Any]         =   [self.cls_token_id]

					if token_ids_a is None:
									return len(cls + token_ids_a + sep       ) * [0]
					return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None       )						->    Tuple[str]:
					snake_case__      :List[str]         =   self._tokenizer.model.save(UpperCamelCase					,name=UpperCamelCase       )
					return tuple(UpperCamelCase       )