

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def 	snake_case_      ()			->	Union[str, Any]:
   def filter_log(__A     :						int     ):
      return False if "This IS expected if you are initializing" in record.msg else True

   __lowerCAmelCase  :			Optional[Any]					    =						datasets.utils.logging.get_logger("""transformers.modeling_utils"""     )
   logger.addFilter(__A     )
   try:
      yield
   finally:
      logger.removeFilter(__A     )


__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 SCREAMING_SNAKE_CASE	(  datasets.Metric ):
  """simple docstring"""






  def        SCREAMING_SNAKE_CASE    (			self :    Optional[Any]    )							->	int:






     """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 :    List[Any]       ,							lowerCAmelCase :    Union[str, Any]       ,							lowerCAmelCase :    int       ,							lowerCAmelCase :    List[Any]=None       ,							lowerCAmelCase :    Tuple=None       ,							lowerCAmelCase :    Tuple=None       ,							lowerCAmelCase :    Tuple=False       ,							lowerCAmelCase :    Dict=False       ,							lowerCAmelCase :    Union[str, Any]=None       ,							lowerCAmelCase :    List[str]=64       ,							lowerCAmelCase :    int=4       ,							lowerCAmelCase :    Optional[Any]=False       ,							lowerCAmelCase :    Dict=False       ,							lowerCAmelCase :    Union[str, Any]=None       ,							lowerCAmelCase :    Union[str, Any]=False       ,							)							->	Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :			int					    =						bert_score.utils.get_hash
     __lowerCAmelCase  :			Optional[int]					    =						bert_score.BERTScorer

     if version.parse(bert_score.__version__    ) >= version.parse("""0.3.10"""    ):
        __lowerCAmelCase  :			str					    =						functools.partial(lowerCAmelCase       ,							use_fast_tokenizer=lowerCAmelCase    )
        __lowerCAmelCase  :			Union[str, Any]					    =						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  :			List[Any]					    =						bert_score.utils.langamodel[lang.lower()]

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

     __lowerCAmelCase  :			Tuple					    =						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  :			Union[str, Any]					    =						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))  :			List[str]					    =						self.cached_bertscorer.score(
         cands=lowerCAmelCase       ,							refs=lowerCAmelCase       ,							verbose=lowerCAmelCase       ,							batch_size=lowerCAmelCase       ,							)
     __lowerCAmelCase  :			List[Any]					    =						{
         """precision""": P.tolist(),
         """recall""": R.tolist(),
         """f1""": F.tolist(),
         """hashcode""": hashcode,
     }
     return output_dict







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[int]       ,							lowerCAmelCase :    List[Any]=None       ,							lowerCAmelCase :    int=None       ,							**lowerCAmelCase :    int    )							->	Dict:






     """simple docstring"""







     if references is not None:
        __lowerCAmelCase  :			Optional[int]					    =						[[ref] if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ) else ref for ref in references]
     super().add_batch(predictions=lowerCAmelCase       ,							references=lowerCAmelCase       ,							**lowerCAmelCase    )







  def        SCREAMING_SNAKE_CASE    (			self :    List[Any]       ,							lowerCAmelCase :    Tuple=None       ,							lowerCAmelCase :    Dict=None       ,							**lowerCAmelCase :    Any    )							->	Dict:






     """simple docstring"""







     if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        __lowerCAmelCase  :			Optional[Any]					    =						[reference]
     super().add(prediction=lowerCAmelCase       ,							reference=lowerCAmelCase       ,							**lowerCAmelCase    )










"""simple docstring"""
import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import FlavaImageProcessor, FlavaProcessor
      from transformers.models.flava.image_processing_flava import (
          FLAVA_CODEBOOK_MEAN,
          FLAVA_CODEBOOK_STD,
          FLAVA_IMAGE_MEAN,
          FLAVA_IMAGE_STD,
      )



@require_vision
class 		a_      (	unittest.TestCase ):







      '''simple docstring'''

      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Union[str, Any]     				=						tempfile.mkdtemp()

       # fmt: off
       lowerCamelCase__     :			List[str]     				=						['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
       # fmt: on
       lowerCamelCase__     :			List[Any]     				=						os.path.join(self.tmpdirname,     VOCAB_FILES_NAMES['vocab_file']					)

       with open(self.vocab_file,     'w',     encoding='utf-8'					) as fp:
        fp.write(''.join([x + '\n' for x in vocab_tokens]					)					)

       lowerCamelCase__     :			List[Any]     				=						{
           'image_mean': FLAVA_IMAGE_MEAN,
           'image_std': FLAVA_IMAGE_STD,
           'do_normalize': True,
           'do_resize': True,
           'size': 2_2_4,
           'do_center_crop': True,
           'crop_size': 2_2_4,
           'input_size_patches': 1_4,
           'total_mask_patches': 7_5,
           'mask_group_max_patches': None,
           'mask_group_min_patches': 1_6,
           'mask_group_min_aspect_ratio': 0.3,
           'mask_group_max_aspect_ratio': None,
           'codebook_do_resize': True,
           'codebook_size': 1_1_2,
           'codebook_do_center_crop': True,
           'codebook_crop_size': 1_1_2,
           'codebook_do_map_pixels': True,
           'codebook_do_normalize': True,
           'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
           'codebook_image_std': FLAVA_CODEBOOK_STD,
       }

       lowerCamelCase__     :			List[str]     				=						os.path.join(self.tmpdirname,     lowerCamelCase_					)
       with open(self.image_processor_file,     'w',     encoding='utf-8'					) as fp:
        json.dump(lowerCamelCase_,     lowerCamelCase_					)




      def 			a__							(self,     **lowerCamelCase_					):

       '''simple docstring'''



       return BertTokenizer.from_pretrained(self.tmpdirname,     **lowerCamelCase_					)




      def 			a__							(self,     **lowerCamelCase_					):

       '''simple docstring'''



       return BertTokenizerFast.from_pretrained(self.tmpdirname,     **lowerCamelCase_					)




      def 			a__							(self,     **lowerCamelCase_					):

       '''simple docstring'''



       return FlavaImageProcessor.from_pretrained(self.tmpdirname,     **lowerCamelCase_					)




      def 			a__							(self					):

       '''simple docstring'''



       shutil.rmtree(self.tmpdirname					)




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						[np.random.randint(2_5_5,     size=(3, 3_0, 4_0_0),     dtype=np.uinta					)]

       lowerCamelCase__     :			Union[str, Any]     				=						[Image.fromarray(np.moveaxis(lowerCamelCase_,     0,     -1					)					) for x in image_inputs]

       return image_inputs




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Union[str, Any]     				=						self.get_tokenizer()
       lowerCamelCase__     :			Dict     				=						self.get_rust_tokenizer()
       lowerCamelCase__     :			Union[str, Any]     				=						self.get_image_processor()

       lowerCamelCase__     :			Tuple     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)
       processor_slow.save_pretrained(self.tmpdirname					)
       lowerCamelCase__     :			str     				=						FlavaProcessor.from_pretrained(self.tmpdirname,     use_fast=lowerCamelCase_					)

       lowerCamelCase__     :			List[str]     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)
       processor_fast.save_pretrained(self.tmpdirname					)
       lowerCamelCase__     :			List[str]     				=						FlavaProcessor.from_pretrained(self.tmpdirname					)

       self.assertEqual(processor_slow.tokenizer.get_vocab(),     tokenizer_slow.get_vocab()					)
       self.assertEqual(processor_fast.tokenizer.get_vocab(),     tokenizer_fast.get_vocab()					)
       self.assertEqual(tokenizer_slow.get_vocab(),     tokenizer_fast.get_vocab()					)
       self.assertIsInstance(processor_slow.tokenizer,     lowerCamelCase_					)
       self.assertIsInstance(processor_fast.tokenizer,     lowerCamelCase_					)

       self.assertEqual(processor_slow.image_processor.to_json_string(),     image_processor.to_json_string()					)
       self.assertEqual(processor_fast.image_processor.to_json_string(),     image_processor.to_json_string()					)
       self.assertIsInstance(processor_slow.image_processor,     lowerCamelCase_					)
       self.assertIsInstance(processor_fast.image_processor,     lowerCamelCase_					)




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						FlavaProcessor(tokenizer=self.get_tokenizer(),     image_processor=self.get_image_processor()					)
       processor.save_pretrained(self.tmpdirname					)

       lowerCamelCase__     :			List[str]     				=						self.get_tokenizer(bos_token='(BOS)',     eos_token='(EOS)'					)
       lowerCamelCase__     :			List[str]     				=						self.get_image_processor(do_normalize=lowerCamelCase_,     padding_value=1.0					)

       lowerCamelCase__     :			Dict     				=						FlavaProcessor.from_pretrained(
           self.tmpdirname,     bos_token='(BOS)',     eos_token='(EOS)',     do_normalize=lowerCamelCase_,     padding_value=1.0					)

       self.assertEqual(processor.tokenizer.get_vocab(),     tokenizer_add_kwargs.get_vocab()					)
       self.assertIsInstance(processor.tokenizer,     lowerCamelCase_					)

       self.assertEqual(processor.image_processor.to_json_string(),     image_processor_add_kwargs.to_json_string()					)
       self.assertIsInstance(processor.image_processor,     lowerCamelCase_					)




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Tuple     				=						self.get_image_processor()
       lowerCamelCase__     :			Union[str, Any]     				=						self.get_tokenizer()

       lowerCamelCase__     :			Optional[Any]     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)

       lowerCamelCase__     :			Tuple     				=						self.prepare_image_inputs()

       lowerCamelCase__     :			List[str]     				=						image_processor(lowerCamelCase_,     return_tensors='np'					)
       lowerCamelCase__     :			Optional[Any]     				=						processor(images=lowerCamelCase_,     return_tensors='np'					)

       for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum(),     input_processor[key].sum(),     delta=1e-2					)

       # With rest of the args
       random.seed(1_2_3_4					)
       lowerCamelCase__     :			Optional[Any]     				=						image_processor(
           lowerCamelCase_,     return_image_mask=lowerCamelCase_,     return_codebook_pixels=lowerCamelCase_,     return_tensors='np'					)
       random.seed(1_2_3_4					)
       lowerCamelCase__     :			Optional[Any]     				=						processor(
           images=lowerCamelCase_,     return_image_mask=lowerCamelCase_,     return_codebook_pixels=lowerCamelCase_,     return_tensors='np'					)

       for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum(),     input_processor[key].sum(),     delta=1e-2					)




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Dict     				=						self.get_image_processor()
       lowerCamelCase__     :			Union[str, Any]     				=						self.get_tokenizer()

       lowerCamelCase__     :			Tuple     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)

       lowerCamelCase__     :			Optional[int]     				=						'lower newer'

       lowerCamelCase__     :			Union[str, Any]     				=						processor(text=lowerCamelCase_					)

       lowerCamelCase__     :			Union[str, Any]     				=						tokenizer(lowerCamelCase_					)

       for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key],     encoded_processor[key]					)




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Any     				=						self.get_image_processor()
       lowerCamelCase__     :			Optional[int]     				=						self.get_tokenizer()

       lowerCamelCase__     :			List[str]     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)

       lowerCamelCase__     :			str     				=						'lower newer'
       lowerCamelCase__     :			Union[str, Any]     				=						self.prepare_image_inputs()

       lowerCamelCase__     :			Optional[int]     				=						processor(text=lowerCamelCase_,     images=lowerCamelCase_					)

       self.assertListEqual(list(inputs.keys()					),     ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']					)

       # add extra args
       lowerCamelCase__     :			Tuple     				=						processor(text=lowerCamelCase_,     images=lowerCamelCase_,     return_codebook_pixels=lowerCamelCase_,     return_image_mask=lowerCamelCase_					)

       self.assertListEqual(
           list(inputs.keys()					),     [
               'input_ids',
               'token_type_ids',
               'attention_mask',
               'pixel_values',
               'codebook_pixel_values',
               'bool_masked_pos',
           ],     )

       # test if it raises when no input is passed
       with pytest.raises(lowerCamelCase_					):
        processor()




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			int     				=						self.get_image_processor()
       lowerCamelCase__     :			List[str]     				=						self.get_tokenizer()

       lowerCamelCase__     :			Tuple     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)

       lowerCamelCase__     :			str     				=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       lowerCamelCase__     :			str     				=						processor.batch_decode(lowerCamelCase_					)
       lowerCamelCase__     :			List[Any]     				=						tokenizer.batch_decode(lowerCamelCase_					)

       self.assertListEqual(lowerCamelCase_,     lowerCamelCase_					)





      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						self.get_image_processor()
       lowerCamelCase__     :			Union[str, Any]     				=						self.get_tokenizer()

       lowerCamelCase__     :			Union[str, Any]     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)

       lowerCamelCase__     :			List[Any]     				=						'lower newer'
       lowerCamelCase__     :			Optional[int]     				=						self.prepare_image_inputs()

       lowerCamelCase__     :			List[str]     				=						processor(text=lowerCamelCase_,     images=lowerCamelCase_					)

       self.assertListEqual(list(inputs.keys()					),     processor.model_input_names					)

