





from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__lowerCAmelCase							:     Optional[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}
}
"""

__lowerCAmelCase							:     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.
"""

__lowerCAmelCase							:     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        UpperCAmelCase     (	datasets.Metric    ):
 def 					UpperCAmelCase_							(    self				:int							)->							Dict:
     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 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:Dict						,	lowercase_				:Union[str, Any]						,	lowercase_				:Optional[int] = 1						,	lowercase_				:List[Any] = 4						,	)->							Optional[int]:
     return {
         "google_bleu": gleu_score.corpus_gleu(
             list_of_references=lowercase_						,	hypotheses=lowercase_						,	min_len=lowercase_						,	max_len=lowercase_							)
     }










'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.distilbert.modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertModel,
  )






class        UpperCAmelCase     :
 def __init__(    self				:str						,	lowercase_				:str						,	)->							str:
     A__    =      parent
     A__    =      13
     A__    =      7
     A__    =      True
     A__    =      True
     A__    =      False
     A__    =      True
     A__    =      99
     A__    =      32
     A__    =      2
     A__    =      4
     A__    =      37
     A__    =      "gelu"
     A__    =      0.1
     A__    =      0.1
     A__    =      5_12
     A__    =      16
     A__    =      2
     A__    =      0.0_2
     A__    =      3
     A__    =      4
     A__    =      None
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							int:
     A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size							)

     A__    =      None
     if self.use_input_mask:
         A__    =      random_attention_mask([self.batch_size, self.seq_length]							)

     A__    =      None
     A__    =      None
     A__    =      None
     if self.use_labels:
         A__    =      ids_tensor([self.batch_size]						,	self.type_sequence_label_size							)
         A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels							)
         A__    =      ids_tensor([self.batch_size]						,	self.num_choices							)

     A__    =      DistilBertConfig(
         vocab_size=self.vocab_size						,	dim=self.hidden_size						,	n_layers=self.num_hidden_layers						,	n_heads=self.num_attention_heads						,	hidden_dim=self.intermediate_size						,	hidden_act=self.hidden_act						,	dropout=self.hidden_dropout_prob						,	attention_dropout=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	initializer_range=self.initializer_range						,	)

     return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Optional[int]						,	lowercase_				:List[str]						,	lowercase_				:Any						,	lowercase_				:Union[str, Any]						,	lowercase_				:Optional[int]						,	lowercase_				:str							)->							List[str]:
     A__    =      TFDistilBertModel(config=lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}

     A__    =      model(lowercase_							)

     A__    =      [input_ids, input_mask]

     A__    =      model(lowercase_							)

     self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)							)
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:str						,	lowercase_				:Optional[Any]						,	lowercase_				:Optional[int]						,	lowercase_				:Optional[int]						,	lowercase_				:Optional[int]						,	lowercase_				:Union[str, Any]							)->							Optional[int]:
     A__    =      TFDistilBertForMaskedLM(config=lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)							)
 def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:str						,	lowercase_				:str						,	lowercase_				:Optional[int]						,	lowercase_				:str						,	lowercase_				:List[Any]						,	lowercase_				:Union[str, Any]							)->							Optional[int]:
     A__    =      TFDistilBertForQuestionAnswering(config=lowercase_							)
     A__    =      {
         "input_ids": input_ids,
         "attention_mask": input_mask,
     }
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.start_logits.shape						,	(self.batch_size, self.seq_length)							)
     self.parent.assertEqual(result.end_logits.shape						,	(self.batch_size, self.seq_length)							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:Optional[int]						,	lowercase_				:Any						,	lowercase_				:Dict						,	lowercase_				:Tuple						,	lowercase_				:Optional[Any]						,	lowercase_				:Optional[int]							)->							Any:
     A__    =      self.num_labels
     A__    =      TFDistilBertForSequenceClassification(lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)							)
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Optional[Any]						,	lowercase_				:List[Any]						,	lowercase_				:Dict						,	lowercase_				:Tuple						,	lowercase_				:int						,	lowercase_				:Union[str, Any]							)->							str:
     A__    =      self.num_choices
     A__    =      TFDistilBertForMultipleChoice(lowercase_							)
     A__    =      tf.tile(tf.expand_dims(lowercase_						,	1							)						,	(1, self.num_choices, 1)							)
     A__    =      tf.tile(tf.expand_dims(lowercase_						,	1							)						,	(1, self.num_choices, 1)							)
     A__    =      {
         "input_ids": multiple_choice_inputs_ids,
         "attention_mask": multiple_choice_input_mask,
     }
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)							)
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Any						,	lowercase_				:List[str]						,	lowercase_				:Any						,	lowercase_				:int						,	lowercase_				:List[Any]						,	lowercase_				:Tuple							)->							Tuple:
     A__    =      self.num_labels
     A__    =      TFDistilBertForTokenClassification(lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)							)



 def 					UpperCAmelCase_							(    self				:Any							)->							Union[str, Any]:
     A__    =      self.prepare_config_and_inputs()
     ((A__),     (A__),     (A__),     (A__),     (A__),     (A__))    =      config_and_inputs
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_tf
class        UpperCAmelCase     (	UpperCamelCase__      ,	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    (
     (
         TFDistilBertModel,
         TFDistilBertForMaskedLM,
         TFDistilBertForQuestionAnswering,
         TFDistilBertForSequenceClassification,
         TFDistilBertForTokenClassification,
         TFDistilBertForMultipleChoice,
     )
     if is_tf_available()
     else None
 )
 __lowercase       		=    (
     {
         """feature-extraction""": TFDistilBertModel,
         """fill-mask""": TFDistilBertForMaskedLM,
         """question-answering""": TFDistilBertForQuestionAnswering,
         """text-classification""": TFDistilBertForSequenceClassification,
         """token-classification""": TFDistilBertForTokenClassification,
         """zero-shot""": TFDistilBertForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 __lowercase       		=    False
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
     A__    =      TFDistilBertModelTester(self							)
     A__    =      ConfigTester(self						,	config_class=lowercase_						,	dim=37							)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     self.config_tester.run_common_tests()
 def 					UpperCAmelCase_							(    self				:int							)->							Tuple:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_model(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Optional[Any]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_masked_lm(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:str							)->							str:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_question_answering(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Dict:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:str							)->							int:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_token_classification(*lowercase_							)



 @slow
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Dict:
     for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]							):
         A__    =      TFDistilBertModel.from_pretrained(lowercase_							)
         self.assertIsNotNone(lowercase_							)






@require_tf
class        UpperCAmelCase     (	unittest.TestCase    ):
 @slow
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Any:
     A__    =      TFDistilBertModel.from_pretrained("distilbert-base-uncased"							)
     A__    =      tf.constant([[0, 1, 2, 3, 4, 5]]							)
     A__    =      model(lowercase_							)[0]

     A__    =      [1, 6, 7_68]
     self.assertEqual(output.shape						,	lowercase_							)

     A__    =      tf.constant(
         [
             [
                 [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
                 [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
                 [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
             ]
         ]							)
     tf.debugging.assert_near(output[:, :3, :3]						,	lowercase_						,	atol=1E-4							)



