"""simple docstring"""


import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase								=    logging.get_logger(__name__)

__UpperCamelCase								=    {
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}




class 				UpperCamelCase      (       a_     ):
       SCREAMING_SNAKE_CASE_				=   'hubert'







       def __init__(				self,							lowerCAmelCase__=32,							lowerCAmelCase__=768,							lowerCAmelCase__=12,							lowerCAmelCase__=12,							lowerCAmelCase__=3072,							lowerCAmelCase__="gelu",							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=True,							lowerCAmelCase__=0.0,							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.02,							lowerCAmelCase__=1e-5,							lowerCAmelCase__="group",							lowerCAmelCase__="gelu",							lowerCAmelCase__=(512, 512, 512, 512, 512, 512, 512),							lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2),							lowerCAmelCase__=(10, 3, 3, 3, 3, 2, 2),							lowerCAmelCase__=False,							lowerCAmelCase__=128,							lowerCAmelCase__=16,							lowerCAmelCase__=False,							lowerCAmelCase__=True,							lowerCAmelCase__=0.05,							lowerCAmelCase__=10,							lowerCAmelCase__=2,							lowerCAmelCase__=0.0,							lowerCAmelCase__=10,							lowerCAmelCase__=0,							lowerCAmelCase__="sum",							lowerCAmelCase__=False,							lowerCAmelCase__=False,							lowerCAmelCase__=256,							lowerCAmelCase__=0,							lowerCAmelCase__=1,							lowerCAmelCase__=2,							**lowerCAmelCase__,							)   ->   Any:
            super().__init__(**lowerCAmelCase__,							pad_token_id=lowerCAmelCase__,							bos_token_id=lowerCAmelCase__,							eos_token_id=lowerCAmelCase__)
            snake_case_       =      hidden_size
            snake_case_       =      feat_extract_norm
            snake_case_       =      feat_extract_activation
            snake_case_       =      list(lowerCAmelCase__)
            snake_case_       =      list(lowerCAmelCase__)
            snake_case_       =      list(lowerCAmelCase__)
            snake_case_       =      conv_bias
            snake_case_       =      num_conv_pos_embeddings
            snake_case_       =      num_conv_pos_embedding_groups
            snake_case_       =      len(self.conv_dim)
            snake_case_       =      num_hidden_layers
            snake_case_       =      intermediate_size
            snake_case_       =      hidden_act
            snake_case_       =      num_attention_heads
            snake_case_       =      hidden_dropout
            snake_case_       =      attention_dropout
            snake_case_       =      activation_dropout
            snake_case_       =      feat_proj_layer_norm
            snake_case_       =      feat_proj_dropout
            snake_case_       =      final_dropout
            snake_case_       =      layerdrop
            snake_case_       =      layer_norm_eps
            snake_case_       =      initializer_range
            snake_case_       =      vocab_size
            snake_case_       =      do_stable_layer_norm
            snake_case_       =      use_weighted_layer_sum
            snake_case_       =      classifier_proj_size

            if (
                (len(self.conv_stride) != self.num_feat_extract_layers)
                or (len(self.conv_kernel) != self.num_feat_extract_layers)
                or (len(self.conv_dim) != self.num_feat_extract_layers)
            ):
                 raise ValueError(
                     'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
                     ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
                     f' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,'
                     f' `len(config.conv_kernel) = {len(self.conv_kernel)}`.')

            # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
            snake_case_       =      apply_spec_augment
            snake_case_       =      mask_time_prob
            snake_case_       =      mask_time_length
            snake_case_       =      mask_time_min_masks
            snake_case_       =      mask_feature_prob
            snake_case_       =      mask_feature_length
            snake_case_       =      mask_feature_min_masks

            # ctc loss
            snake_case_       =      ctc_loss_reduction
            snake_case_       =      ctc_zero_infinity







       @property
       def 	a_		(				self)   ->   Tuple:
            return functools.reduce(operator.mul,							self.conv_stride,							1)


"""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__)

