
"""simple docstring"""




import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets

@contextmanager
def     UpperCamelCase_    (						):
					def filter_log(SCREAMING_SNAKE_CASE_     ):
										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(a_     )
					try:
										yield
					finally:
										logger.removeFilter(a_     )


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'

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'

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 	UpperCamelCase      (			datasets.Metric ):

				"""simple docstring"""



				def 				A   (							self     )       ->       Union[str, Any]:
									"""simple docstring"""







									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						,      lowercase__						,      lowercase__						,      lowercase__=None						,      lowercase__=None						,      lowercase__=None						,      lowercase__=False						,      lowercase__=False						,      lowercase__=None						,      lowercase__=64						,      lowercase__=4						,      lowercase__=False						,      lowercase__=False						,      lowercase__=None						,      lowercase__=False						,      )       ->       Optional[Any]:
									"""simple docstring"""







									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(lowerCamelCase_						,      use_fast_tokenizer=lowerCamelCase_     )
														SCREAMING_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"
														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=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:
																			SCREAMING_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_						,      )

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


				def 				A   (							self						,      lowercase__=None						,      lowercase__=None						,      **lowercase__     )       ->       Optional[int]:
									"""simple docstring"""







									if references is not None:
														SCREAMING_SNAKE_CASE           =      [[ref] if isinstance(lowerCamelCase_						,      lowerCamelCase_     ) else ref for ref in references]
									super().add_batch(predictions=lowerCamelCase_						,      references=lowerCamelCase_						,      **lowerCamelCase_     )




				def 				A   (							self						,      lowercase__=None						,      lowercase__=None						,      **lowercase__     )       ->       Any:
									"""simple docstring"""







									if isinstance(lowerCamelCase_						,      lowerCamelCase_     ):
														SCREAMING_SNAKE_CASE           =      [reference]
									super().add(prediction=lowerCamelCase_						,      reference=lowerCamelCase_						,      **lowerCamelCase_     )


"""simple docstring"""







from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION






class      __a      (       lowerCAmelCase_					,						lowerCAmelCase_      ):







       def __init__(       self		):



          '''simple docstring'''

          super().__init__()

          self.register_buffer("watermark_image"				,torch.zeros((62, 62, 4)		)		)
          _UpperCAmelCase 						=       None







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_=None		):



          '''simple docstring'''

          _UpperCAmelCase 						=       images[0].height
          _UpperCAmelCase 						=       images[0].width

          _UpperCAmelCase 						=       sample_size or h

          _UpperCAmelCase 						=       min(h / sample_size				,w / sample_size		)
          _UpperCAmelCase						,				_UpperCAmelCase 						=       (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

          _UpperCAmelCase						,				_UpperCAmelCase 						=       1024**2, img_w * img_h
          _UpperCAmelCase 						=       (Sa / Sa) ** 0.5
          _UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase 						=       int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)

          if self.watermark_image_as_pil is None:
             _UpperCAmelCase 						=       self.watermark_image.to(torch.uinta		).cpu().numpy()
             _UpperCAmelCase 						=       Image.fromarray(lowerCamelCase_				,mode="RGBA"		)
             _UpperCAmelCase 						=       watermark_image

          _UpperCAmelCase 						=       self.watermark_image_as_pil.resize(
              (wm_size, wm_size)				,PIL_INTERPOLATION["bicubic"]				,reducing_gap=lowerCamelCase_		)

          for pil_img in images:
             pil_img.paste(lowerCamelCase_				,box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)				,mask=wm_img.split()[-1]		)

          return images

