





'''simple docstring'''


import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets


@contextmanager
def 		__a():



    '''simple docstring'''





    def filter_log(SCREAMING_SNAKE_CASE_					:   Optional[int]      ):
        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(SCREAMING_SNAKE_CASE_      )
    try:
        yield
    finally:
        logger.removeFilter(SCREAMING_SNAKE_CASE_      )


_SCREAMING_SNAKE_CASE			=							"\\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"

_SCREAMING_SNAKE_CASE			=							"\\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"

_SCREAMING_SNAKE_CASE			=							"\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 				lowerCAmelCase_						(						datasets.Metric						):




 def 							_snake_case				(		self						)      ->							List[Any]:
     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=64     ,       _lowerCAmelCase=4     ,       _lowerCAmelCase=False     ,       _lowerCAmelCase=False     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=False     ,       )      ->							Dict:
     _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						)      ->							Union[str, Any]:
     # 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						)      ->							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						):
         _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 center_crop, 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, is_vision_available, logging


if is_vision_available():
       import PIL


_SCREAMING_SNAKE_CASE			=							logging.get_logger(__name__)

class 				lowerCAmelCase_						(						__magic_name__						):
 __lowerCamelCase  :							Optional[int]    					=   ["pixel_values"]




 def __init__(		self     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = PIL.Image.NEAREST     ,       _lowerCAmelCase = False     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = 1 / 255     ,       _lowerCAmelCase = False     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = True     ,       **_lowerCAmelCase     ,       )      ->							None:
     super().__init__(**_lowerCAmelCase						)
     _lowerCAmelCase		     = size if size is not None else {"height": 346, "width": 346}
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     _lowerCAmelCase		     = crop_size if crop_size is not None else {"height": 289, "width": 289}
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase     ,       param_name="crop_size"						)

     _lowerCAmelCase		     = do_resize
     _lowerCAmelCase		     = size
     _lowerCAmelCase		     = resample
     _lowerCAmelCase		     = do_center_crop
     _lowerCAmelCase		     = crop_size
     _lowerCAmelCase		     = do_rescale
     _lowerCAmelCase		     = rescale_factor
     _lowerCAmelCase		     = rescale_offset
     _lowerCAmelCase		     = do_normalize
     _lowerCAmelCase		     = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     _lowerCAmelCase		     = image_std if image_std is not None else IMAGENET_STANDARD_STD
     _lowerCAmelCase		     = include_top




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = PIL.Image.NEAREST     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							np.ndarray:
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
     return resize(
         _lowerCAmelCase     ,       size=(size["height"], size["width"])     ,       resample=_lowerCAmelCase     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							np.ndarray:
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
     return center_crop(_lowerCAmelCase     ,       size=(size["height"], size["width"])     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							List[str]:
     if offset:
         _lowerCAmelCase		     = (image - 127.5) * scale
         if data_format is not None:
             _lowerCAmelCase		     = to_channel_dimension_format(_lowerCAmelCase     ,       _lowerCAmelCase						)
         _lowerCAmelCase		     = rescaled_image.astype(np.floataa						)
     else:
         _lowerCAmelCase		     = rescale(_lowerCAmelCase     ,       scale=_lowerCAmelCase     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)
     return rescaled_image




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							np.ndarray:
     return normalize(_lowerCAmelCase     ,       mean=_lowerCAmelCase     ,       std=_lowerCAmelCase     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)





 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = ChannelDimension.FIRST     ,       **_lowerCAmelCase     ,       )      ->							PIL.Image.Image:
     _lowerCAmelCase		     = do_resize if do_resize is not None else self.do_resize
     _lowerCAmelCase		     = resample if resample is not None else self.resample
     _lowerCAmelCase		     = do_center_crop if do_center_crop is not None else self.do_center_crop
     _lowerCAmelCase		     = do_rescale if do_rescale is not None else self.do_rescale
     _lowerCAmelCase		     = rescale_factor if rescale_factor is not None else self.rescale_factor
     _lowerCAmelCase		     = rescale_offset if rescale_offset is not None else self.rescale_offset
     _lowerCAmelCase		     = do_normalize if do_normalize is not None else self.do_normalize
     _lowerCAmelCase		     = image_mean if image_mean is not None else self.image_mean
     _lowerCAmelCase		     = image_std if image_std is not None else self.image_std
     _lowerCAmelCase		     = include_top if include_top is not None else self.include_top

     _lowerCAmelCase		     = size if size is not None else self.size
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     _lowerCAmelCase		     = crop_size if crop_size is not None else self.crop_size
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase     ,       param_name="crop_size"						)

     _lowerCAmelCase		     = make_list_of_images(_lowerCAmelCase						)

     if not valid_images(_lowerCAmelCase						):
         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 or resample is None:
         raise ValueError("Size and resample must be specified if do_resize is True."						)

     if do_center_crop and crop_size is None:
         raise ValueError("Crop size must be specified if do_center_crop is True."						)

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

     if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."						)

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

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

     if do_center_crop:
         _lowerCAmelCase		     = [self.center_crop(image=_lowerCAmelCase     ,       size=_lowerCAmelCase						) for image in images]

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

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

     if include_top:
         _lowerCAmelCase		     = [self.normalize(image=_lowerCAmelCase     ,       mean=[0, 0, 0]     ,       std=_lowerCAmelCase						) for image in images]

     _lowerCAmelCase		     = [to_channel_dimension_format(_lowerCAmelCase     ,       _lowerCAmelCase						) for image in images]

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


