






"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
						import torch

						from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer




@require_torch
class        __lowercase(       unittest.TestCase  ):






						@slow
						def 		lowerCAmelCase   (					self)			->  Optional[Any]:




											"""simple docstring"""





											lowerCamelCase__				=XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')
											model.to(_SCREAMING_SNAKE_CASE)

											# encoder-decoder outputs
											lowerCamelCase__				=torch.tensor([[17, 9_62_08, 10_34_71, 2]]).to(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=torch.tensor(
											    [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]).to(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=model(
											    input_ids=_SCREAMING_SNAKE_CASE    ,						attention_mask=_SCREAMING_SNAKE_CASE    ,						encoder_outputs=_SCREAMING_SNAKE_CASE    ,						decoder_input_ids=_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=output[0]
											lowerCamelCase__				=torch.Size((1, 14, 25_00_12))
											self.assertEqual(output_predited_logis.shape    ,						_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=torch.tensor(
											    [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]).to(_SCREAMING_SNAKE_CASE)
											self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]    ,						_SCREAMING_SNAKE_CASE    ,						atol=1E-4))

											# encoder outputs
											lowerCamelCase__				=model.prophetnet.encoder(_SCREAMING_SNAKE_CASE)[0]
											lowerCamelCase__				=torch.tensor(
											    [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]).to(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=torch.Size((1, 4, 10_24))
											self.assertEqual(encoder_outputs.shape    ,						_SCREAMING_SNAKE_CASE)
											self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]    ,						_SCREAMING_SNAKE_CASE    ,						atol=1E-4))

											# decoder outputs
											lowerCamelCase__				=model.prophetnet.decoder(
											    _SCREAMING_SNAKE_CASE    ,						encoder_hidden_states=_SCREAMING_SNAKE_CASE    ,						)
											lowerCamelCase__				=decoder_outputs[1].view(1    ,						model.config.ngram    ,						14    ,						-1)
											lowerCamelCase__				=model.lm_head(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=predicting_streams_logits[:, 0]
											self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]    ,						_SCREAMING_SNAKE_CASE    ,						atol=1E-4))






						@slow
						def 		lowerCAmelCase   (					self)			->  Tuple:




											"""simple docstring"""





											lowerCamelCase__				=XLMProphetNetForConditionalGeneration.from_pretrained(
											    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg')
											model.to(_SCREAMING_SNAKE_CASE)

											lowerCamelCase__				=torch.tensor([[17, 9_62_08, 10_34_71, 2]]).to(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=torch.tensor(
											    [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]).to(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=model(
											    input_ids=_SCREAMING_SNAKE_CASE    ,						attention_mask=_SCREAMING_SNAKE_CASE    ,						encoder_outputs=_SCREAMING_SNAKE_CASE    ,						decoder_input_ids=_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=output[0]
											lowerCamelCase__				=torch.Size((1, 14, 25_00_12))
											self.assertEqual(output_predited_logis.shape    ,						_SCREAMING_SNAKE_CASE)
											# compare the actual values for a slice.
											lowerCamelCase__				=torch.tensor(
											    [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]).to(_SCREAMING_SNAKE_CASE)

											self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]    ,						_SCREAMING_SNAKE_CASE    ,						atol=1E-4))






						@slow
						def 		lowerCAmelCase   (					self)			->  str:




											"""simple docstring"""





											lowerCamelCase__				=XLMProphetNetForConditionalGeneration.from_pretrained(
											    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg')
											model.to(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=5_12

											lowerCamelCase__				=XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-ntg')

											lowerCamelCase__				=(
											    'Microsoft Corporation intends to officially end free support for the Windows 7 operating system after'
											    ' January 14, 2020, according to the official portal of the organization. From that day, users of this'
											    ' system will not be able to receive security updates, which could make their computers vulnerable to'
											    ' cyber attacks.'
											)
											lowerCamelCase__				=(
											    'орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7'
											    ' после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи'
											    ' этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми'
											    ' к кибератакам.'
											)
											lowerCamelCase__				=(
											    '根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。'
											)

											lowerCamelCase__				=tokenizer(
											    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]    ,						padding=_SCREAMING_SNAKE_CASE    ,						max_length=2_55    ,						return_tensors='pt').input_ids
											lowerCamelCase__				=input_ids.to(_SCREAMING_SNAKE_CASE)

											lowerCamelCase__				=model.generate(
											    _SCREAMING_SNAKE_CASE    ,						num_beams=10    ,						length_penalty=1.0    ,						no_repeat_ngram_size=3    ,						early_stopping=_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=[tokenizer.decode(_SCREAMING_SNAKE_CASE    ,						skip_special_tokens=_SCREAMING_SNAKE_CASE) for g in summary_ids]
											lowerCamelCase__				='Microsoft to end Windows 7 free support after January 14, 2020'
											lowerCamelCase__				='Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'
											lowerCamelCase__				='微软打算终止对Windows 7操作系统的免费支持'
											self.assertListEqual(
											    [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]    ,						_SCREAMING_SNAKE_CASE    ,						)

											lowerCamelCase__				=model.generate(
											    _SCREAMING_SNAKE_CASE    ,						num_beams=1    ,						length_penalty=1.0    ,						no_repeat_ngram_size=3    ,						early_stopping=_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=[
											    tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE    ,						skip_special_tokens=_SCREAMING_SNAKE_CASE) for g in summary_ids_beama
											]
											lowerCamelCase__				='▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'.split(' ')
											lowerCamelCase__				='▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'.split(
											    ' ')
											lowerCamelCase__				='微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持'.split(' ')
											self.assertListEqual(
											    [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]    ,						_SCREAMING_SNAKE_CASE    ,						)







"""simple docstring"""



import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


_snake_case       :      Dict       =					"\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n"

_snake_case       :      List[str]       =					"\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n"

_snake_case       :      Dict       =					"\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer 'id' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - 'id': id of the question-answer pair (see above),\n            - 'answers': a list of Dict {'text': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    'exact': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'total': Number of score considered\n    'HasAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'HasAns_f1': The F-score of predicted tokens versus the gold answer\n    'HasAns_total': Number of score considered\n    'NoAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'NoAns_f1': The F-score of predicted tokens versus the gold answer\n    'NoAns_total': Number of score considered\n    'best_exact': Best exact match (with varying threshold)\n    'best_exact_thresh': No-answer probability threshold associated to the best exact match\n    'best_f1': Best F1 (with varying threshold)\n    'best_f1_thresh': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22', 'no_answer_probability': 0.}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_v2_metric = datasets.load_metric(\"squad_v2\")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact': 100.0, 'f1': 100.0, 'total': 1, 'HasAns_exact': 100.0, 'HasAns_f1': 100.0, 'HasAns_total': 1, 'best_exact': 100.0, 'best_exact_thresh': 0.0, 'best_f1': 100.0, 'best_f1_thresh': 0.0}\n"




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






						def 		lowerCAmelCase   (					self)			->  int:




											"""simple docstring"""





											return datasets.MetricInfo(
											    description=_DESCRIPTION    ,						citation=_CITATION    ,						inputs_description=_KWARGS_DESCRIPTION    ,						features=datasets.Features(
											        {
											            'predictions': {
											                'id': datasets.Value('string'),
											                'prediction_text': datasets.Value('string'),
											                'no_answer_probability': datasets.Value('float32'),
											            },
											            'references': {
											                'id': datasets.Value('string'),
											                'answers': datasets.features.Sequence(
											                    {'text': datasets.Value('string'), 'answer_start': datasets.Value('int32')}),
											            },
											        })    ,						codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/']    ,						reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/']    ,						)






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE=1.0)			->  Optional[int]:




											"""simple docstring"""





											lowerCamelCase__				={p['id']: p['no_answer_probability'] for p in predictions}
											lowerCamelCase__				=[{'paragraphs': [{'qas': references}]}]
											lowerCamelCase__				={p['id']: p['prediction_text'] for p in predictions}

											lowerCamelCase__				=make_qid_to_has_ans(_SCREAMING_SNAKE_CASE)  # maps qid to True/False
											lowerCamelCase__				=[k for k, v in qid_to_has_ans.items() if v]
											lowerCamelCase__				=[k for k, v in qid_to_has_ans.items() if not v]

											lowerCamelCase__				,     lowerCamelCase__				=get_raw_scores(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=apply_no_ans_threshold(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=apply_no_ans_threshold(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=make_eval_dict(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)

											if has_ans_qids:
																lowerCamelCase__				=make_eval_dict(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						qid_list=_SCREAMING_SNAKE_CASE)
																merge_eval(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						'HasAns')
											if no_ans_qids:
																lowerCamelCase__				=make_eval_dict(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						qid_list=_SCREAMING_SNAKE_CASE)
																merge_eval(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						'NoAns')
											find_all_best_thresh(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
											return dict(_SCREAMING_SNAKE_CASE)