




'''simple docstring'''



import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


UpperCamelCase_       =							'''\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",
    author = "Lin, Chin-Yew  and
      Och, Franz Josef",
    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",
    month = "aug 23{--}aug 27",
    year = "2004",
    address = "Geneva, Switzerland",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/C04-1072",
    pages = "501--507",
}
'''

UpperCamelCase_       =							'''\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,
the better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
'''

UpperCamelCase_       =							'''
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    \'bleu\': bleu score,
    \'precisions\': geometric mean of n-gram precisions,
    \'brevity_penalty\': brevity penalty,
    \'length_ratio\': ratio of lengths,
    \'translation_length\': translation_length,
    \'reference_length\': reference_length
Examples:

    >>> predictions = [
    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample
    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)
    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric("bleu")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results["bleu"])
    1.0
'''






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





       '''simple docstring'''






       def     UpperCamelCase_				(	self   ):







             '''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'   ),
                     }   ),				codebase_urls=['https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py'],				reference_urls=[
                     'https://en.wikipedia.org/wiki/BLEU',
                     'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
                 ],				)







       def     UpperCamelCase_				(	self,				A,				A,				A=4,				A=False   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		compute_bleu(
                 reference_corpus=UpperCamelCase_,				translation_corpus=UpperCamelCase_,				max_order=UpperCamelCase_,				smooth=UpperCamelCase_   )
             (SCREAMING_SNAKE_CASE)					:	Dict   =		score
             return {
                 "bleu": bleu,
                 "precisions": precisions,
                 "brevity_penalty": bp,
                 "length_ratio": ratio,
                 "translation_length": translation_length,
                 "reference_length": reference_length,
             }






"""simple docstring"""





import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
     FlaxRobertaPreLayerNormForCausalLM,
     FlaxRobertaPreLayerNormForMaskedLM,
     FlaxRobertaPreLayerNormForMultipleChoice,
     FlaxRobertaPreLayerNormForQuestionAnswering,
     FlaxRobertaPreLayerNormForSequenceClassification,
     FlaxRobertaPreLayerNormForTokenClassification,
     FlaxRobertaPreLayerNormModel,
 )



class 						snake_case_(				unittest.TestCase	):






     def __init__(		self     :    List[Any]    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    List[Any]=1_3    , UpperCamelCase_     :    Tuple=7    , UpperCamelCase_     :    List[Any]=True    , UpperCamelCase_     :    int=True    , UpperCamelCase_     :    Union[str, Any]=True    , UpperCamelCase_     :    Optional[Any]=True    , UpperCamelCase_     :    List[str]=9_9    , UpperCamelCase_     :    str=3_2    , UpperCamelCase_     :    Union[str, Any]=5    , UpperCamelCase_     :    int=4    , UpperCamelCase_     :    Optional[Any]=3_7    , UpperCamelCase_     :    Optional[int]="gelu"    , UpperCamelCase_     :    Any=0.1    , UpperCamelCase_     :    List[str]=0.1    , UpperCamelCase_     :    str=5_1_2    , UpperCamelCase_     :    Optional[Any]=1_6    , UpperCamelCase_     :    Union[str, Any]=2    , UpperCamelCase_     :    Any=0.02    , UpperCamelCase_     :    Union[str, Any]=4    , ):
           lowerCAmelCase    :		str				=      parent
           lowerCAmelCase    :		List[str]				=      batch_size
           lowerCAmelCase    :		int				=      seq_length
           lowerCAmelCase    :		str				=      is_training
           lowerCAmelCase    :		Tuple				=      use_attention_mask
           lowerCAmelCase    :		Dict				=      use_token_type_ids
           lowerCAmelCase    :		Optional[int]				=      use_labels
           lowerCAmelCase    :		Optional[Any]				=      vocab_size
           lowerCAmelCase    :		Optional[int]				=      hidden_size
           lowerCAmelCase    :		Optional[Any]				=      num_hidden_layers
           lowerCAmelCase    :		str				=      num_attention_heads
           lowerCAmelCase    :		Optional[Any]				=      intermediate_size
           lowerCAmelCase    :		int				=      hidden_act
           lowerCAmelCase    :		int				=      hidden_dropout_prob
           lowerCAmelCase    :		Tuple				=      attention_probs_dropout_prob
           lowerCAmelCase    :		str				=      max_position_embeddings
           lowerCAmelCase    :		str				=      type_vocab_size
           lowerCAmelCase    :		str				=      type_sequence_label_size
           lowerCAmelCase    :		Any				=      initializer_range
           lowerCAmelCase    :		int				=      num_choices






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		Tuple				=      ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size )

           lowerCAmelCase    :		Optional[int]				=      None
           if self.use_attention_mask:
                 lowerCAmelCase    :		Union[str, Any]				=      random_attention_mask([self.batch_size, self.seq_length] )

           lowerCAmelCase    :		Union[str, Any]				=      None
           if self.use_token_type_ids:
                 lowerCAmelCase    :		Dict				=      ids_tensor([self.batch_size, self.seq_length]    , self.type_vocab_size )

           lowerCAmelCase    :		Union[str, Any]				=      RobertaPreLayerNormConfig(
               vocab_size=self.vocab_size    , hidden_size=self.hidden_size    , num_hidden_layers=self.num_hidden_layers    , num_attention_heads=self.num_attention_heads    , intermediate_size=self.intermediate_size    , hidden_act=self.hidden_act    , hidden_dropout_prob=self.hidden_dropout_prob    , attention_probs_dropout_prob=self.attention_probs_dropout_prob    , max_position_embeddings=self.max_position_embeddings    , type_vocab_size=self.type_vocab_size    , is_decoder=UpperCamelCase_    , initializer_range=self.initializer_range    , )

           return config, input_ids, token_type_ids, attention_mask






     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		List[str]				=      self.prepare_config_and_inputs()
           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		Optional[Any]				=      config_and_inputs
           lowerCAmelCase    :		Optional[Any]				=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		int				=      self.prepare_config_and_inputs()
           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      config_and_inputs

           lowerCAmelCase    :		str				=      True
           lowerCAmelCase    :		Optional[Any]				=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
           lowerCAmelCase    :		str				=      ids_tensor([self.batch_size, self.seq_length]    , vocab_size=2 )

           return (
               config,
               input_ids,
               token_type_ids,
               encoder_hidden_states,
               encoder_attention_mask,
           )



@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 						snake_case_(				a__							,    unittest.TestCase	):
     __UpperCamelCase     =    True

     __UpperCamelCase     =    (
         (
             FlaxRobertaPreLayerNormModel,
             FlaxRobertaPreLayerNormForCausalLM,
             FlaxRobertaPreLayerNormForMaskedLM,
             FlaxRobertaPreLayerNormForSequenceClassification,
             FlaxRobertaPreLayerNormForTokenClassification,
             FlaxRobertaPreLayerNormForMultipleChoice,
             FlaxRobertaPreLayerNormForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )






     def    lowerCamelCase__   (		self     :    List[Any] ):
           lowerCAmelCase    :		Any				=      FlaxRobertaPreLayerNormModelTester(self )






     @slow
     def    lowerCamelCase__   (		self     :    List[str] ):
           for model_class_name in self.all_model_classes:
                 lowerCAmelCase    :		Optional[int]				=      model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''    , from_pt=UpperCamelCase_ )
                 lowerCAmelCase    :		int				=      model(np.ones((1, 1) ) )
                 self.assertIsNotNone(UpperCamelCase_ )



@require_flax
class 						snake_case_(				unittest.TestCase	):






     @slow
     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		str				=      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''    , from_pt=UpperCamelCase_ )

           lowerCAmelCase    :		Any				=      np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]    , dtype=jnp.intaa )
           lowerCAmelCase    :		Union[str, Any]				=      model(UpperCamelCase_ )[0]
           lowerCAmelCase    :		str				=      [1, 1_1, 5_0_2_6_5]
           self.assertEqual(list(output.shape )    , UpperCamelCase_ )
           # compare the actual values for a slice.
           lowerCAmelCase    :		Optional[Any]				=      np.array(
               [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]    , dtype=np.floataa )
           self.assertTrue(np.allclose(output[:, :3, :3]    , UpperCamelCase_    , atol=1E-4 ) )






     @slow
     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		Dict				=      FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''    , from_pt=UpperCamelCase_ )

           lowerCAmelCase    :		str				=      np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]    , dtype=jnp.intaa )
           lowerCAmelCase    :		str				=      model(UpperCamelCase_ )[0]
           # compare the actual values for a slice.
           lowerCAmelCase    :		str				=      np.array(
               [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]    , dtype=np.floataa )
           self.assertTrue(np.allclose(output[:, :3, :3]    , UpperCamelCase_    , atol=1E-4 ) )


