





'''simple docstring'''



from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


snake_case_ :	Union[str, Any]             =   """\
@misc{wu2016googles,
      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

snake_case_ :	Union[str, Any]             =   """\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the 'GLEU score'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score's range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""

snake_case_ :	str             =   """\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    'google_bleu': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.44

    Example 2:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.61

    Example 3:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results[\"google_bleu\"], 2))
        0.53

    Example 4:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results[\"google_bleu\"], 2))
        0.4
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,      _KWARGS_DESCRIPTION	)
class      A_			(	datasets.Metric	):

			'''simple docstring'''





			def    a						(       self ):
								return datasets.MetricInfo(
								    description=_DESCRIPTION      ,  citation=_CITATION      ,  inputs_description=_KWARGS_DESCRIPTION      ,  features=datasets.Features(
								        {
								            "predictions": datasets.Sequence(datasets.Value("string"      ,  id="token" )      ,  id="sequence" ),
								            "references": datasets.Sequence(
								                datasets.Sequence(datasets.Value("string"      ,  id="token" )      ,  id="sequence" )      ,  id="references" ),
								        } )      ,  )




			def    a						(       self      ,  A_      ,  A_      ,  A_ = 1      ,  A_ = 4      ,  ):
								return {
								    "google_bleu": gleu_score.corpus_gleu(
								        list_of_references=UpperCAmelCase__      ,  hypotheses=UpperCAmelCase__      ,  min_len=UpperCAmelCase__      ,  max_len=UpperCAmelCase__ )
								}









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 ViTImageProcessor


class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    def __init__(     self :      Tuple   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      int=13   ,					UpperCAmelCase__ :      Union[str, Any]=3   ,					UpperCAmelCase__ :      str=224   ,					UpperCAmelCase__ :      str=30   ,					UpperCAmelCase__ :      Tuple=400   ,					UpperCAmelCase__ :      int=True   ,					UpperCAmelCase__ :      Dict=None   ,					UpperCAmelCase__ :      List[Any]=True   ,					UpperCAmelCase__ :      Union[str, Any]=[0.5, 0.5, 0.5]   ,					UpperCAmelCase__ :      Tuple=[0.5, 0.5, 0.5]   ,					)		->str:





           '''simple docstring'''

           A__ 					=       size if size is not None else {'''height''': 18, '''width''': 18}
           A__ 					=       parent
           A__ 					=       batch_size
           A__ 					=       num_channels
           A__ 					=       image_size
           A__ 					=       min_resolution
           A__ 					=       max_resolution
           A__ 					=       do_resize
           A__ 					=       size
           A__ 					=       do_normalize
           A__ 					=       image_mean
           A__ 					=       image_std





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





           '''simple docstring'''

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


@require_torch
@require_vision
class 							UpperCamelCase_       (					UpperCAmelCase__						,   unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = ViTImageProcessor if is_vision_available() else None


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->str:





           '''simple docstring'''

           A__ 					=       EfficientFormerImageProcessorTester(self)


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->int:





           '''simple docstring'''

           return self.image_proc_tester.prepare_image_processor_dict()


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





           '''simple docstring'''

           A__ 					=       self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(UpperCAmelCase__   ,					'''image_mean'''))
           self.assertTrue(hasattr(UpperCAmelCase__   ,					'''image_std'''))
           self.assertTrue(hasattr(UpperCAmelCase__   ,					'''do_normalize'''))
           self.assertTrue(hasattr(UpperCAmelCase__   ,					'''do_resize'''))
           self.assertTrue(hasattr(UpperCAmelCase__   ,					'''size'''))


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->Dict:





           '''simple docstring'''

           pass


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





           '''simple docstring'''

           A__ 					=       self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           A__ 					=       prepare_image_inputs(self.image_proc_tester   ,					equal_resolution=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__   ,					Image.Image)

           # Test not batched input
           A__ 					=       image_processor(image_inputs[0]   ,					return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape   ,					(
                   1,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )   ,					)

           # Test batched
           A__ 					=       image_processor(UpperCAmelCase__   ,					return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape   ,					(
                   self.image_proc_tester.batch_size,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )   ,					)


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->List[Any]:





           '''simple docstring'''

           A__ 					=       self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           A__ 					=       prepare_image_inputs(self.image_proc_tester   ,					equal_resolution=UpperCAmelCase__   ,					numpify=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__   ,					np.ndarray)

           # Test not batched input
           A__ 					=       image_processor(image_inputs[0]   ,					return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape   ,					(
                   1,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )   ,					)

           # Test batched
           A__ 					=       image_processor(UpperCAmelCase__   ,					return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape   ,					(
                   self.image_proc_tester.batch_size,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )   ,					)





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





           '''simple docstring'''

           A__ 					=       self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           A__ 					=       prepare_image_inputs(self.image_proc_tester   ,					equal_resolution=UpperCAmelCase__   ,					torchify=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__   ,					torch.Tensor)

           # Test not batched input
           A__ 					=       image_processor(image_inputs[0]   ,					return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape   ,					(
                   1,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )   ,					)

           # Test batched
           A__ 					=       image_processor(UpperCAmelCase__   ,					return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape   ,					(
                   self.image_proc_tester.batch_size,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )   ,					)


