
'''simple docstring'''
import datasets

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


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

__lowerCAmelCase												=	'\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality 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,\nthe 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\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'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\nrepresenting 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\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'

__lowerCAmelCase												=	'\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    \'bleu\': bleu score,\n    \'precisions\': geometric mean of n-gram precisions,\n    \'brevity_penalty\': brevity penalty,\n    \'length_ratio\': ratio of lengths,\n    \'translation_length\': translation_length,\n    \'reference_length\': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample\n    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)\n    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric("bleu")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results["bleu"])\n    1.0\n'


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







							'''simple docstring'''

							def 	lowercase							(self  )					->       Tuple:
														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 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=4  ,  UpperCAmelCase=False  )					->       List[str]:
														_snake_case       =  compute_bleu(
														    reference_corpus=UpperCAmelCase  ,  translation_corpus=UpperCAmelCase  ,  max_order=UpperCAmelCase  ,  smooth=UpperCAmelCase  )
														((_snake_case),							(_snake_case),							(_snake_case),							(_snake_case),							(_snake_case),							(_snake_case))       =  score
														return {
														    "bleu": bleu,
														    "precisions": precisions,
														    "brevity_penalty": bp,
														    "length_ratio": ratio,
														    "translation_length": translation_length,
														    "reference_length": reference_length,
														}

'''simple docstring'''
import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    DebertaForMaskedLM,
				    DebertaForQuestionAnswering,
				    DebertaForSequenceClassification,
				    DebertaForTokenClassification,
				    DebertaModel,
				)
				from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase=13  ,  UpperCAmelCase=7  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=99  ,  UpperCAmelCase=32  ,  UpperCAmelCase=5  ,  UpperCAmelCase=4  ,  UpperCAmelCase=37  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=512  ,  UpperCAmelCase=16  ,  UpperCAmelCase=2  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=False  ,  UpperCAmelCase=True  ,  UpperCAmelCase="None"  ,  UpperCAmelCase=3  ,  UpperCAmelCase=4  ,  UpperCAmelCase=None  ,  )					->       Optional[Any]:
														_snake_case       =  parent
														_snake_case       =  batch_size
														_snake_case       =  seq_length
														_snake_case       =  is_training
														_snake_case       =  use_input_mask
														_snake_case       =  use_token_type_ids
														_snake_case       =  use_labels
														_snake_case       =  vocab_size
														_snake_case       =  hidden_size
														_snake_case       =  num_hidden_layers
														_snake_case       =  num_attention_heads
														_snake_case       =  intermediate_size
														_snake_case       =  hidden_act
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  max_position_embeddings
														_snake_case       =  type_vocab_size
														_snake_case       =  type_sequence_label_size
														_snake_case       =  initializer_range
														_snake_case       =  num_labels
														_snake_case       =  num_choices
														_snake_case       =  relative_attention
														_snake_case       =  position_biased_input
														_snake_case       =  pos_att_type
														_snake_case       =  scope

							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size  )

														_snake_case       =  None
														if self.use_input_mask:
																					_snake_case       =  ids_tensor([self.batch_size, self.seq_length]  ,  vocab_size=2  )

														_snake_case       =  None
														if self.use_token_type_ids:
																					_snake_case       =  ids_tensor([self.batch_size, self.seq_length]  ,  self.type_vocab_size  )

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

														_snake_case       =  self.get_config()

														return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

							def 	lowercase							(self  )					->       int:
														return DebertaConfig(
														    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  ,  initializer_range=self.initializer_range  ,  relative_attention=self.relative_attention  ,  position_biased_input=self.position_biased_input  ,  pos_att_type=self.pos_att_type  ,  )

							def 	lowercase							(self  )					->       int:
														_snake_case       =  self.get_config()
														_snake_case       =  300
														return config

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       Dict:
														self.parent.assertListEqual(list(result.loss.size()  )  ,  []  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[Any]:
														_snake_case       =  DebertaModel(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  )[0]
														_snake_case       =  model(UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  )[0]
														_snake_case       =  model(UpperCAmelCase  )[0]

														self.parent.assertListEqual(list(sequence_output.size()  )  ,  [self.batch_size, self.seq_length, self.hidden_size]  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       str:
														_snake_case       =  DebertaForMaskedLM(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  ,  labels=UpperCAmelCase  )

														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[str]:
														_snake_case       =  self.num_labels
														_snake_case       =  DebertaForSequenceClassification(UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  ,  labels=UpperCAmelCase  )
														self.parent.assertListEqual(list(result.logits.size()  )  ,  [self.batch_size, self.num_labels]  )
														self.check_loss_output(UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Any:
														_snake_case       =  self.num_labels
														_snake_case       =  DebertaForTokenClassification(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  ,  labels=UpperCAmelCase  )
														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.num_labels)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Any:
														_snake_case       =  DebertaForQuestionAnswering(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(
														    UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  ,  start_positions=UpperCAmelCase  ,  end_positions=UpperCAmelCase  ,  )
														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 	lowercase							(self  )					->       Tuple:
														_snake_case       =  self.prepare_config_and_inputs()
														(
														    (
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							
														)       =  config_and_inputs
														_snake_case       =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
														return config, inputs_dict


@require_torch
class       _lowerCAmelCase   (   __snake_case					,							__snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						(
							    (
							        DebertaModel,
							        DebertaForMaskedLM,
							        DebertaForSequenceClassification,
							        DebertaForTokenClassification,
							        DebertaForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCAmelCase_ 						=						(
							    {
							        "feature-extraction": DebertaModel,
							        "fill-mask": DebertaForMaskedLM,
							        "question-answering": DebertaForQuestionAnswering,
							        "text-classification": DebertaForSequenceClassification,
							        "token-classification": DebertaForTokenClassification,
							        "zero-shot": DebertaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase_ 						=						True
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  DebertaModelTester(self  )
														_snake_case       =  ConfigTester(self  ,  config_class=UpperCAmelCase  ,  hidden_size=37  )

							def 	lowercase							(self  )					->       List[str]:
														self.config_tester.run_common_tests()

							def 	lowercase							(self  )					->       Tuple:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_model(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       str:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_for_sequence_classification(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       int:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_for_masked_lm(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_for_question_answering(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       str:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_for_token_classification(*UpperCAmelCase  )





							@slow
							def 	lowercase							(self  )					->       Tuple:
														for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_snake_case       =  DebertaModel.from_pretrained(UpperCAmelCase  )
																					self.assertIsNotNone(UpperCAmelCase  )


@require_torch
@require_sentencepiece
@require_tokenizers
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							@unittest.skip(reason="""Model not available yet"""  )
							def 	lowercase							(self  )					->       Any:
														pass





							@slow
							def 	lowercase							(self  )					->       Dict:
														_snake_case       =  DebertaModel.from_pretrained("""microsoft/deberta-base"""  )

														_snake_case       =  torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  )
														_snake_case       =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
														with torch.no_grad():
																					_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  )[0]
														# compare the actual values for a slice.
														_snake_case       =  torch.tensor(
														    [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]  )
														self.assertTrue(torch.allclose(output[:, 1:4, 1:4]  ,  UpperCAmelCase  ,  atol=1e-4  )  ,  f"""{output[:, 1:4, 1:4]}"""  )