



"""simple docstring"""
import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



@contextmanager
def 				lowerCAmelCase_(      )		->				int:






				def filter_log(lowercase_   :							List[str]							):
								return False if "This IS expected if you are initializing" in record.msg else True

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


__SCREAMING_SNAKE_CASE     :  List[str]										=					'''\
@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}
}
'''

__SCREAMING_SNAKE_CASE     :  Tuple										=					'''\
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.
'''

__SCREAMING_SNAKE_CASE     :  List[str]										=					'''
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 							lowerCamelCase_(      datasets.Metric			):





					'''simple docstring'''



					def 	snake_case__					(			self      ):
									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 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__=False ,				lowerCamelCase__=False ,				lowerCamelCase__=None ,				lowerCamelCase__=6_4 ,				lowerCamelCase__=4 ,				lowerCamelCase__=False ,				lowerCamelCase__=False ,				lowerCamelCase__=None ,				lowerCamelCase__=False ,				):
									_lowerCamelCase					     = bert_score.utils.get_hash
									_lowerCamelCase					     = bert_score.BERTScorer

									if version.parse(bert_score.__version__      ) >= version.parse('''0.3.10'''      ):
													_lowerCamelCase					     = functools.partial(lowerCamelCase__ ,				use_fast_tokenizer=lowerCamelCase__      )
													_lowerCamelCase					     = 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"
													_lowerCamelCase					     = bert_score.utils.langamodel[lang.lower()]

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

									_lowerCamelCase					     = 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:
																	_lowerCamelCase					     = 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__ ,				)

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

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





					def 	snake_case__					(			self ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				**lowerCamelCase__      ):
									# References can be strings or lists of strings
									# Let's change strings to lists of strings with one element
									if isinstance(lowerCamelCase__ ,				lowerCamelCase__      ):
													_lowerCamelCase					     = [reference]
									super().add(prediction=lowerCamelCase__ ,				reference=lowerCamelCase__ ,				**lowerCamelCase__      )








"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_UpperCamelCase     			=  logging.get_logger(__name__)







class 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			['''pixel_values''']






    def __init__( self	,A = True	,A = None	,A = PILImageResampling.BILINEAR	,A = True	,A = 1 / 255	,A = True	,A = None	,A = None	,**A	,):
          super().__init__(**A					)
          UpperCAmelCase							=       size if size is not None else {"""height""": 224, """width""": 224}
          UpperCAmelCase							=       get_size_dict(A					)
          UpperCAmelCase							=       do_resize
          UpperCAmelCase							=       do_rescale
          UpperCAmelCase							=       do_normalize
          UpperCAmelCase							=       size
          UpperCAmelCase							=       resample
          UpperCAmelCase							=       rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase							=       image_std if image_std is not None else IMAGENET_STANDARD_STD






    def 				_UpperCamelCase							( self	,A	,A	,A = PILImageResampling.BILINEAR	,A = None	,**A	,):
          UpperCAmelCase							=       get_size_dict(A					)
          if "height" not in size or "width" not in size:
                raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''					)
          return resize(
              A	,size=(size["""height"""], size["""width"""])	,resample=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A = None	,**A					):
          return rescale(A	,scale=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A = None	,**A	,):
          return normalize(A	,mean=A	,std=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = ChannelDimension.FIRST	,**A	,):
          UpperCAmelCase							=       do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase							=       do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase							=       do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase							=       resample if resample is not None else self.resample
          UpperCAmelCase							=       rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase							=       image_std if image_std is not None else self.image_std

          UpperCAmelCase							=       size if size is not None else self.size
          UpperCAmelCase							=       get_size_dict(A					)

          UpperCAmelCase							=       make_list_of_images(A					)

          if not valid_images(A					):
                raise ValueError(
                    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

          if do_resize and size is None:
                raise ValueError("""Size must be specified if do_resize is True."""					)

          if do_rescale and rescale_factor is None:
                raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

          # All transformations expect numpy arrays.
          UpperCAmelCase							=       [to_numpy_array(A					) for image in images]

          if do_resize:
                UpperCAmelCase							=       [self.resize(image=A	,size=A	,resample=A					) for image in images]

          if do_rescale:
                UpperCAmelCase							=       [self.rescale(image=A	,scale=A					) for image in images]

          if do_normalize:
                UpperCAmelCase							=       [self.normalize(image=A	,mean=A	,std=A					) for image in images]

          UpperCAmelCase							=       [to_channel_dimension_format(A	,A					) for image in images]

          UpperCAmelCase							=       {"""pixel_values""": images}
          return BatchFeature(data=A	,tensor_type=A					)



