
from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__lowerCamelCase							=       """\
@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}
}
"""

__lowerCamelCase							=       """\
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.
"""

__lowerCamelCase							=       """\
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   UpperCAmelCase		(						datasets.Metric ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	MetricInfo:


          '''simple docstring'''


          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 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					List[List[List[str]]]    ,						snake_case__		:					List[List[str]]    ,						snake_case__		:					int = 1    ,						snake_case__		:					int = 4    ,						)		->	Dict[str, float]:


          '''simple docstring'''


          return {
              "google_bleu": gleu_score.corpus_gleu(
                  list_of_references=snake_case__    ,						hypotheses=snake_case__    ,						min_len=snake_case__    ,						max_len=snake_case__ )
          }







import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
   from PIL import Image

   from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor






@require_tokenizers
@require_vision
class   UpperCAmelCase		(						unittest.TestCase ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	List[str]:


          '''simple docstring'''


          snake_case       :   int							=      tempfile.mkdtemp()

          # fmt: off
          snake_case       :   Optional[int]							=      ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
          # fmt: on
          snake_case       :   List[str]							=      os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["vocab_file"] )
          with open(self.vocab_file    ,						"w"    ,						encoding="utf-8" ) as vocab_writer:
                 vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

          snake_case       :   int							=      {
              "do_resize": True,
              "size": {"height": 18, "width": 18},
              "do_normalize": True,
              "image_mean": [0.5, 0.5, 0.5],
              "image_std": [0.5, 0.5, 0.5],
          }
          snake_case       :   Optional[Any]							=      os.path.join(self.tmpdirname    ,						snake_case__ )
          with open(self.image_processor_file    ,						"w"    ,						encoding="utf-8" ) as fp:
                 json.dump(snake_case__    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						**snake_case__		:					str )		->	Optional[int]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						**snake_case__		:					List[str] )		->	int:


          '''simple docstring'''


          return ViTImageProcessor.from_pretrained(self.tmpdirname    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Dict:


          '''simple docstring'''


          shutil.rmtree(self.tmpdirname )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	str:


          '''simple docstring'''


          snake_case       :   List[Any]							=      [np.random.randint(2_55    ,						size=(3, 30, 4_00)    ,						dtype=np.uinta )]

          snake_case       :   Optional[int]							=      [Image.fromarray(np.moveaxis(snake_case__    ,						0    ,						-1 ) ) for x in image_inputs]

          return image_inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.get_tokenizer()
          snake_case       :   Optional[Any]							=      self.get_image_processor()

          snake_case       :   Optional[Any]							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          processor.save_pretrained(self.tmpdirname )
          snake_case       :   Any							=      VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

          self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer.get_vocab() )
          self.assertIsInstance(processor.tokenizer    ,						(BertTokenizer, BertTokenizerFast) )

          self.assertEqual(processor.image_processor.to_json_string()    ,						image_processor.to_json_string() )
          self.assertIsInstance(processor.image_processor    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   str							=      VisionTextDualEncoderProcessor(
              tokenizer=self.get_tokenizer()    ,						image_processor=self.get_image_processor() )
          processor.save_pretrained(self.tmpdirname )

          snake_case       :   Optional[int]							=      self.get_tokenizer(bos_token="(BOS)"    ,						eos_token="(EOS)" )
          snake_case       :   Tuple							=      self.get_image_processor(do_normalize=snake_case__    ,						padding_value=1.0 )

          snake_case       :   List[str]							=      VisionTextDualEncoderProcessor.from_pretrained(
              self.tmpdirname    ,						bos_token="(BOS)"    ,						eos_token="(EOS)"    ,						do_normalize=snake_case__    ,						padding_value=1.0 )

          self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer_add_kwargs.get_vocab() )
          self.assertIsInstance(processor.tokenizer    ,						(BertTokenizer, BertTokenizerFast) )

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	int:


          '''simple docstring'''


          snake_case       :   str							=      self.get_image_processor()
          snake_case       :   Optional[int]							=      self.get_tokenizer()

          snake_case       :   List[Any]							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   Optional[Any]							=      self.prepare_image_inputs()

          snake_case       :   str							=      image_processor(snake_case__    ,						return_tensors="np" )
          snake_case       :   Any							=      processor(images=snake_case__    ,						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 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.get_image_processor()
          snake_case       :   int							=      self.get_tokenizer()

          snake_case       :   Any							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   Tuple							=      "lower newer"

          snake_case       :   Tuple							=      processor(text=snake_case__ )

          snake_case       :   Union[str, Any]							=      tokenizer(snake_case__ )

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.get_image_processor()
          snake_case       :   Dict							=      self.get_tokenizer()

          snake_case       :   Dict							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   int							=      "lower newer"
          snake_case       :   Dict							=      self.prepare_image_inputs()

          snake_case       :   Union[str, Any]							=      processor(text=snake_case__    ,						images=snake_case__ )

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

          # test if it raises when no input is passed
          with self.assertRaises(snake_case__ ):
                 processor()



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Tuple:


          '''simple docstring'''


          snake_case       :   Tuple							=      self.get_image_processor()
          snake_case       :   Optional[Any]							=      self.get_tokenizer()

          snake_case       :   Tuple							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

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

          snake_case       :   List[Any]							=      processor.batch_decode(snake_case__ )
          snake_case       :   Union[str, Any]							=      tokenizer.batch_decode(snake_case__ )

          self.assertListEqual(snake_case__    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	List[str]:


          '''simple docstring'''


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

          snake_case       :   Any							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   Optional[Any]							=      "lower newer"
          snake_case       :   List[Any]							=      self.prepare_image_inputs()

          snake_case       :   Tuple							=      processor(text=snake_case__    ,						images=snake_case__ )

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





