




"""simple docstring"""

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import OwlViTImageProcessor






class  __a				(	unittest.TestCase					):





       """simple docstring"""


       def __init__(      self ,				snake_case ,				snake_case=7 ,				snake_case=3 ,				snake_case=18 ,				snake_case=30 ,				snake_case=400 ,				snake_case=True ,				snake_case=None ,				snake_case=True ,				snake_case=None ,				snake_case=True ,				snake_case=[0.48_145_466, 0.4_578_275, 0.40_821_073] ,				snake_case=[0.26_862_954, 0.26_130_258, 0.27_577_711] ,				snake_case=True ,				):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							parent
        lowerCAmelCase__      :							str    =							batch_size
        lowerCAmelCase__      :							Union[str, Any]    =							num_channels
        lowerCAmelCase__      :							str    =							image_size
        lowerCAmelCase__      :							Dict    =							min_resolution
        lowerCAmelCase__      :							Any    =							max_resolution
        lowerCAmelCase__      :							str    =							do_resize
        lowerCAmelCase__      :							str    =							size if size is not None else {"height": 18, "width": 18}
        lowerCAmelCase__      :							List[Any]    =							do_center_crop
        lowerCAmelCase__      :							Tuple    =							crop_size if crop_size is not None else {"height": 18, "width": 18}
        lowerCAmelCase__      :							List[str]    =							do_normalize
        lowerCAmelCase__      :							Union[str, Any]    =							image_mean
        lowerCAmelCase__      :							int    =							image_std
        lowerCAmelCase__      :							str    =							do_convert_rgb






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
        }






@require_torch
@require_vision
class  __a				(	__magic_name__					,  unittest.TestCase					):





       """simple docstring"""


       __UpperCamelCase				:			Tuple          =	OwlViTImageProcessor if is_vision_available() else None






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							OwlViTImageProcessingTester(self      )






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return self.image_processor_tester.prepare_image_processor_dict()






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							self.image_processing_class(**self.image_processor_dict      )
        self.assertTrue(hasattr(snake_case ,				"do_resize"      )      )
        self.assertTrue(hasattr(snake_case ,				"size"      )      )
        self.assertTrue(hasattr(snake_case ,				"do_center_crop"      )      )
        self.assertTrue(hasattr(snake_case ,				"center_crop"      )      )
        self.assertTrue(hasattr(snake_case ,				"do_normalize"      )      )
        self.assertTrue(hasattr(snake_case ,				"image_mean"      )      )
        self.assertTrue(hasattr(snake_case ,				"image_std"      )      )
        self.assertTrue(hasattr(snake_case ,				"do_convert_rgb"      )      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							self.image_processing_class.from_dict(self.image_processor_dict      )
        self.assertEqual(image_processor.size ,				{"height": 18, "width": 18}      )
        self.assertEqual(image_processor.crop_size ,				{"height": 18, "width": 18}      )

        lowerCAmelCase__      :							Union[str, Any]    =							self.image_processing_class.from_dict(self.image_processor_dict ,				size=42 ,				crop_size=84      )
        self.assertEqual(image_processor.size ,				{"height": 42, "width": 42}      )
        self.assertEqual(image_processor.crop_size ,				{"height": 84, "width": 84}      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							self.image_processing_class(**self.image_processor_dict      )
        # create random PIL images
        lowerCAmelCase__      :							List[str]    =							prepare_image_inputs(self.image_processor_tester ,				equal_resolution=snake_case      )

        for image in image_inputs:
         self.assertIsInstance(snake_case ,				Image.Image      )

        # Test not batched input
        lowerCAmelCase__      :							str    =							image_processing(image_inputs[0] ,				return_tensors="pt"      ).pixel_values
        self.assertEqual(
            encoded_images.shape ,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ) ,				)

        # Test batched
        lowerCAmelCase__      :							Tuple    =							image_processing(snake_case ,				return_tensors="pt"      ).pixel_values
        self.assertEqual(
            encoded_images.shape ,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ) ,				)






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							self.image_processing_class(**self.image_processor_dict      )
        # create random numpy tensors
        lowerCAmelCase__      :							Union[str, Any]    =							prepare_image_inputs(self.image_processor_tester ,				equal_resolution=snake_case ,				numpify=snake_case      )
        for image in image_inputs:
         self.assertIsInstance(snake_case ,				np.ndarray      )

        # Test not batched input
        lowerCAmelCase__      :							Tuple    =							image_processing(image_inputs[0] ,				return_tensors="pt"      ).pixel_values
        self.assertEqual(
            encoded_images.shape ,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ) ,				)

        # Test batched
        lowerCAmelCase__      :							Optional[Any]    =							image_processing(snake_case ,				return_tensors="pt"      ).pixel_values
        self.assertEqual(
            encoded_images.shape ,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ) ,				)






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							self.image_processing_class(**self.image_processor_dict      )
        # create random PyTorch tensors
        lowerCAmelCase__      :							Union[str, Any]    =							prepare_image_inputs(self.image_processor_tester ,				equal_resolution=snake_case ,				torchify=snake_case      )
        for image in image_inputs:
         self.assertIsInstance(snake_case ,				torch.Tensor      )

        # Test not batched input
        lowerCAmelCase__      :							List[Any]    =							image_processing(image_inputs[0] ,				return_tensors="pt"      ).pixel_values
        self.assertEqual(
            encoded_images.shape ,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ) ,				)

        # Test batched
        lowerCAmelCase__      :							Dict    =							image_processing(snake_case ,				return_tensors="pt"      ).pixel_values
        self.assertEqual(
            encoded_images.shape ,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ) ,				)









"""simple docstring"""

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



@contextmanager
def 			SCREAMING_SNAKE_CASE       (   ) ->	Any:
 def filter_log(lowercase__      ):
  return False if "This IS expected if you are initializing" in record.msg else True

 lowerCAmelCase__      :							Tuple    =							datasets.utils.logging.get_logger("transformers.modeling_utils"      )
 logger.addFilter(lowercase__      )
 try:
  yield
 finally:
  logger.removeFilter(lowercase__      )


_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  __a				(	datasets.Metric					):





       """simple docstring"""


       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """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  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				snake_case=False ,				snake_case=False ,				snake_case=None ,				snake_case=64 ,				snake_case=4 ,				snake_case=False ,				snake_case=False ,				snake_case=None ,				snake_case=False ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							bert_score.utils.get_hash
        lowerCAmelCase__      :							List[Any]    =							bert_score.BERTScorer

        if version.parse(bert_score.__version__      ) >= version.parse("0.3.10"      ):
         lowerCAmelCase__      :							str    =							functools.partial(snake_case ,				use_fast_tokenizer=snake_case      )
         lowerCAmelCase__      :							Tuple    =							functools.partial(snake_case ,				use_fast_tokenizer=snake_case      )
        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__      :							Dict    =							bert_score.utils.langamodel[lang.lower()]

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

        lowerCAmelCase__      :							Optional[int]    =							get_hash(
            model=snake_case ,				num_layers=snake_case ,				idf=snake_case ,				rescale_with_baseline=snake_case ,				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__      :							Dict    =							scorer(
              model_type=snake_case ,				num_layers=snake_case ,				batch_size=snake_case ,				nthreads=snake_case ,				all_layers=snake_case ,				idf=snake_case ,				device=snake_case ,				lang=snake_case ,				rescale_with_baseline=snake_case ,				baseline_path=snake_case ,				)

        ((lowerCAmelCase__) ,	(lowerCAmelCase__) ,	(lowerCAmelCase__))      :							int    =							self.cached_bertscorer.score(
            cands=snake_case ,				refs=snake_case ,				verbose=snake_case ,				batch_size=snake_case ,				)
        lowerCAmelCase__      :							List[str]    =							{
            "precision": P.tolist(),
            "recall": R.tolist(),
            "f1": F.tolist(),
            "hashcode": hashcode,
        }
        return output_dict






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case=None ,				snake_case=None ,				**snake_case      ):





        """simple docstring"""

        if references is not None:
         lowerCAmelCase__      :							Union[str, Any]    =							[[ref] if isinstance(snake_case ,				snake_case      ) else ref for ref in references]
        super().add_batch(predictions=snake_case ,				references=snake_case ,				**snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case=None ,				snake_case=None ,				**snake_case      ):





        """simple docstring"""

        if isinstance(snake_case ,				snake_case      ):
         lowerCAmelCase__      :							str    =							[reference]
        super().add(prediction=snake_case ,				reference=snake_case ,				**snake_case      )






