

"""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      )









"""simple docstring"""

import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import EfficientNetForImageClassification, EfficientNetModel
    from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor






class  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case ,				snake_case=13 ,				snake_case=32 ,				snake_case=3 ,				snake_case=[3, 3, 5] ,				snake_case=[32, 16, 24] ,				snake_case=[16, 24, 40] ,				snake_case=[1, 1, 2] ,				snake_case=[1, 1, 2] ,				snake_case=[1, 6, 6] ,				snake_case=True ,				snake_case=True ,				snake_case=37 ,				snake_case="gelu" ,				snake_case=10 ,				):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							parent
        lowerCAmelCase__      :							int    =							batch_size
        lowerCAmelCase__      :							Dict    =							image_size
        lowerCAmelCase__      :							str    =							num_channels
        lowerCAmelCase__      :							Union[str, Any]    =							kernel_sizes
        lowerCAmelCase__      :							int    =							in_channels
        lowerCAmelCase__      :							Dict    =							out_channels
        lowerCAmelCase__      :							str    =							strides
        lowerCAmelCase__      :							int    =							num_block_repeats
        lowerCAmelCase__      :							str    =							expand_ratios
        lowerCAmelCase__      :							List[Any]    =							is_training
        lowerCAmelCase__      :							Any    =							hidden_act
        lowerCAmelCase__      :							Any    =							num_labels
        lowerCAmelCase__      :							int    =							use_labels






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

        lowerCAmelCase__      :							List[str]    =							None
        if self.use_labels:
         lowerCAmelCase__      :							int    =							ids_tensor([self.batch_size] ,				self.num_labels      )

        lowerCAmelCase__      :							List[str]    =							self.get_config()
        return config, pixel_values, labels






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return EfficientNetConfig(
            num_channels=self.num_channels ,				kernel_sizes=self.kernel_sizes ,				in_channels=self.in_channels ,				out_channels=self.out_channels ,				strides=self.strides ,				num_block_repeats=self.num_block_repeats ,				expand_ratios=self.expand_ratios ,				hidden_act=self.hidden_act ,				num_labels=self.num_labels ,				)






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							EfficientNetModel(config=snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							Tuple    =							model(snake_case      )
        # expected last hidden states: B, C, H // 4, W // 4
        self.parent.assertEqual(
            result.last_hidden_state.shape ,				(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4) ,				)






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							EfficientNetForImageClassification(snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							str    =							model(snake_case ,				labels=snake_case      )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							self.prepare_config_and_inputs()
        lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Dict    =							config_and_inputs
        lowerCAmelCase__      :							Dict    =							{"pixel_values": pixel_values}
        return config, inputs_dict






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





       """simple docstring"""


       __UpperCamelCase				:			Any          =	(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
       __UpperCamelCase				:			Dict          =	(
           {'feature-extraction': EfficientNetModel, 'image-classification': EfficientNetForImageClassification}
           if is_torch_available()
           else {}
       )

       __UpperCamelCase				:			str          =	False
       __UpperCamelCase				:			List[Any]          =	False
       __UpperCamelCase				:			Dict          =	False
       __UpperCamelCase				:			Optional[int]          =	False
       __UpperCamelCase				:			List[Any]          =	False






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							EfficientNetModelTester(self      )
        lowerCAmelCase__      :							Tuple    =							ConfigTester(
            self ,				config_class=snake_case ,				has_text_modality=snake_case ,				hidden_size=37      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return






       @unittest.skip(reason="EfficientNet does not use inputs_embeds"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @unittest.skip(reason="EfficientNet does not support input and output embeddings"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @unittest.skip(reason="EfficientNet does not use feedforward chunking"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCAmelCase__      :							Union[str, Any]    =							model_class(snake_case      )
         lowerCAmelCase__      :							Union[str, Any]    =							inspect.signature(model.forward      )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCAmelCase__      :							Union[str, Any]    =							[*signature.parameters.keys()]

         lowerCAmelCase__      :							List[str]    =							["pixel_values"]
         self.assertListEqual(arg_names[:1] ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        def check_hidden_states_output(snake_case ,				snake_case ,				snake_case      ):
         lowerCAmelCase__      :							Tuple    =							model_class(snake_case      )
         model.to(snake_case      )
         model.eval()

         with torch.no_grad():
          lowerCAmelCase__      :							List[str]    =							model(**self._prepare_for_class(snake_case ,				snake_case      )      )

         lowerCAmelCase__      :							Tuple    =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
         lowerCAmelCase__      :							Any    =							sum(config.num_block_repeats      ) * 4
         self.assertEqual(len(snake_case      ) ,				snake_case      )

         # EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]      ) ,				[self.model_tester.image_size // 2, self.model_tester.image_size // 2] ,				)

        lowerCAmelCase__ ,	lowerCAmelCase__      :							Optional[int]    =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCAmelCase__      :							str    =							True
         check_hidden_states_output(snake_case ,				snake_case ,				snake_case      )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCAmelCase__      :							Tuple    =							True

         check_hidden_states_output(snake_case ,				snake_case ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*snake_case      )






       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCAmelCase__      :							str    =							EfficientNetModel.from_pretrained(snake_case      )
         self.assertIsNotNone(snake_case      )






       @is_pipeline_test
       @require_vision
       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        super().test_pipeline_image_classification()




def 			SCREAMING_SNAKE_CASE       (   ) ->	Dict:
 lowerCAmelCase__      :							List[Any]    =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
 return image






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





       """simple docstring"""


       @cached_property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return AutoImageProcessor.from_pretrained("google/efficientnet-b7"      ) if is_vision_available() else None






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							EfficientNetForImageClassification.from_pretrained("google/efficientnet-b7"      ).to(snake_case      )

        lowerCAmelCase__      :							Union[str, Any]    =							self.default_image_processor
        lowerCAmelCase__      :							Tuple    =							prepare_img()
        lowerCAmelCase__      :							Dict    =							image_processor(images=snake_case ,				return_tensors="pt"      ).to(snake_case      )

        # forward pass
        with torch.no_grad():
         lowerCAmelCase__      :							str    =							model(**snake_case      )

        # verify the logits
        lowerCAmelCase__      :							Any    =							torch.Size((1, 1_000)      )
        self.assertEqual(outputs.logits.shape ,				snake_case      )

        lowerCAmelCase__      :							Union[str, Any]    =							torch.tensor([0.0_001, 0.0_002, 0.0_002]      ).to(snake_case      )
        self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				snake_case ,				atol=1e-4      )      )






