

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets


@contextmanager
def        _UpperCAmelCase							(	):





    """simple docstring"""


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

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


UpperCamelCase__	:   str								=     "\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n"

UpperCamelCase__	:   str								=     "\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project's README at https://github.com/Tiiiger/bert_score#readme for more information.\n"

UpperCamelCase__	:   Tuple								=     "\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. 'en').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bertscore = datasets.load_metric(\"bertscore\")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")\n    >>> print([round(v, 2) for v in results[\"f1\"]])\n    [1.0, 1.0]\n"





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

  def     lowerCAmelCase__  (       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     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A=None    ,		_A=None    ,		_A=None    ,		_A=False    ,		_A=False    ,		_A=None    ,		_A=64    ,		_A=4    ,		_A=False    ,		_A=False    ,		_A=None    ,		_A=False    ,		):
      SCREAMING_SNAKE_CASE_						=    bert_score.utils.get_hash
      SCREAMING_SNAKE_CASE_						=    bert_score.BERTScorer

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

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

      SCREAMING_SNAKE_CASE_						=    get_hash(
          model=_A    ,		num_layers=_A    ,		idf=_A    ,		rescale_with_baseline=_A    ,		use_custom_baseline=baseline_path is not None    ,		)

      with filter_logging_context():
          if not hasattr(self    ,		'cached_bertscorer') or self.cached_bertscorer.hash != hashcode:
              SCREAMING_SNAKE_CASE_						=    scorer(
                  model_type=_A    ,		num_layers=_A    ,		batch_size=_A    ,		nthreads=_A    ,		all_layers=_A    ,		idf=_A    ,		device=_A    ,		lang=_A    ,		rescale_with_baseline=_A    ,		baseline_path=_A    ,		)

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

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


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





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__	:   Union[str, Any]								=     logging.get_logger(__name__)



class    __snake_case	(     lowerCAmelCase__							):
  __lowerCAmelCase  :						Tuple    			=	['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)
      SCREAMING_SNAKE_CASE_						=    size if size is not None else {'height': 224, 'width': 224}
      SCREAMING_SNAKE_CASE_						=    get_size_dict(_A)
      SCREAMING_SNAKE_CASE_						=    do_resize
      SCREAMING_SNAKE_CASE_						=    do_rescale
      SCREAMING_SNAKE_CASE_						=    do_normalize
      SCREAMING_SNAKE_CASE_						=    size
      SCREAMING_SNAKE_CASE_						=    resample
      SCREAMING_SNAKE_CASE_						=    rescale_factor
      SCREAMING_SNAKE_CASE_						=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      SCREAMING_SNAKE_CASE_						=    image_std if image_std is not None else IMAGENET_STANDARD_STD

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A = PILImageResampling.BILINEAR    ,		_A = None    ,		**_A    ,		):
      SCREAMING_SNAKE_CASE_						=    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     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A = None    ,		**_A):
      return rescale(_A    ,		scale=_A    ,		data_format=_A    ,		**_A)

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A    ,		_A = None    ,		**_A    ,		):
      return normalize(_A    ,		mean=_A    ,		std=_A    ,		data_format=_A    ,		**_A)


  def     lowerCAmelCase__  (       self    ,		_A    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = ChannelDimension.FIRST    ,		**_A    ,		):
      SCREAMING_SNAKE_CASE_						=    do_resize if do_resize is not None else self.do_resize
      SCREAMING_SNAKE_CASE_						=    do_rescale if do_rescale is not None else self.do_rescale
      SCREAMING_SNAKE_CASE_						=    do_normalize if do_normalize is not None else self.do_normalize
      SCREAMING_SNAKE_CASE_						=    resample if resample is not None else self.resample
      SCREAMING_SNAKE_CASE_						=    rescale_factor if rescale_factor is not None else self.rescale_factor
      SCREAMING_SNAKE_CASE_						=    image_mean if image_mean is not None else self.image_mean
      SCREAMING_SNAKE_CASE_						=    image_std if image_std is not None else self.image_std

      SCREAMING_SNAKE_CASE_						=    size if size is not None else self.size
      SCREAMING_SNAKE_CASE_						=    get_size_dict(_A)

      SCREAMING_SNAKE_CASE_						=    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.
      SCREAMING_SNAKE_CASE_						=    [to_numpy_array(_A) for image in images]

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

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

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

      SCREAMING_SNAKE_CASE_						=    [to_channel_dimension_format(_A    ,		_A) for image in images]

      SCREAMING_SNAKE_CASE_						=    {'pixel_values': images}
      return BatchFeature(data=_A    ,		tensor_type=_A)


