code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import csv
  import dataclasses
  import json
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  lowerCAmelCase					 =	logging.get_logger(__name__)
  
  
  
  
  
  @dataclass
  class    A					:
  							UpperCamelCase_     :							Any =42
  							UpperCamelCase_     :							Any =42
  							UpperCamelCase_     :							Any =None
  							UpperCamelCase_     :							Optional[int] =None
  
  
  							def     _A (self      ):
  									return json.dumps(dataclasses.asdict(self      )   ,       indent=2      ) + "\n"
  
  
  
  
  
  @dataclass(frozen=UpperCAmelCase_				)
  class    A					:
  							UpperCamelCase_     :							Any =42
  							UpperCamelCase_     :							Any =None
  							UpperCamelCase_     :							Dict =None
  							UpperCamelCase_     :							int =None
  
  
  							def     _A (self      ):
  									return json.dumps(dataclasses.asdict(self      )      ) + "\n"
  
  
  
  
  
  class    A					:
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									raise NotImplementedError()
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									raise NotImplementedError()
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									raise NotImplementedError()
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									raise NotImplementedError()
  
  
  							def     _A (self      ):
  									raise NotImplementedError()
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									if len(self.get_labels()      ) > 1:
  											__lowercase=    self.get_labels()[int(example.label      )]
  									return example
  
  
  
  
  
  
  
  							@classmethod
  							def     _A (cls   ,       lowerCAmelCase   ,       lowerCAmelCase=None      ):
  									with open(lowerCAmelCase   ,       'r'   ,       encoding='utf-8-sig'      ) as f:
  											return list(csv.reader(lowerCAmelCase   ,       delimiter='\t'   ,       quotechar=lowerCAmelCase      )      )
  
  
  
  
  
  class    A					(     UpperCAmelCase_				):
  
  
  							def __init__(self   ,       lowerCAmelCase=None   ,       lowerCAmelCase=None   ,       lowerCAmelCase="classification"   ,       lowerCAmelCase=False      ):
  									__lowercase=    [] if labels is None else labels
  									__lowercase=    [] if examples is None else examples
  									__lowercase=    mode
  									__lowercase=    verbose
  
  
  							def __len__(self      ):
  									return len(self.examples      )
  
  
  							def __getitem__(self   ,       lowerCAmelCase      ):
  									if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ):
  											return SingleSentenceClassificationProcessor(labels=self.labels   ,       examples=self.examples[idx]      )
  									return self.examples[idx]
  
  
  							@classmethod
  							def     _A (cls   ,       lowerCAmelCase   ,       lowerCAmelCase=""   ,       lowerCAmelCase=0   ,       lowerCAmelCase=1   ,       lowerCAmelCase=None   ,       lowerCAmelCase=False   ,       **lowerCAmelCase      ):
  									__lowercase=    cls(**lowerCAmelCase      )
  									processor.add_examples_from_csv(
  									    lowerCAmelCase   ,       split_name=lowerCAmelCase   ,       column_label=lowerCAmelCase   ,       column_text=lowerCAmelCase   ,       column_id=lowerCAmelCase   ,       skip_first_row=lowerCAmelCase   ,       overwrite_labels=lowerCAmelCase   ,       overwrite_examples=lowerCAmelCase   ,       )
  									return processor
  
  
  							@classmethod
  							def     _A (cls   ,       lowerCAmelCase   ,       lowerCAmelCase=None   ,       **lowerCAmelCase      ):
  									__lowercase=    cls(**lowerCAmelCase      )
  									processor.add_examples(lowerCAmelCase   ,       labels=lowerCAmelCase      )
  									return processor
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase=""   ,       lowerCAmelCase=0   ,       lowerCAmelCase=1   ,       lowerCAmelCase=None   ,       lowerCAmelCase=False   ,       lowerCAmelCase=False   ,       lowerCAmelCase=False   ,       ):
  									__lowercase=    self._read_tsv(lowerCAmelCase      )
  									if skip_first_row:
  											__lowercase=    lines[1:]
  									__lowercase=    []
  									__lowercase=    []
  									__lowercase=    []
  									for i, line in enumerate(lowerCAmelCase      ):
  											texts.append(line[column_text]      )
  											labels.append(line[column_label]      )
  											if column_id is not None:
  													ids.append(line[column_id]      )
  											else:
  													__lowercase=    f'{split_name}-{i}' if split_name else str(lowerCAmelCase      )
  													ids.append(lowerCAmelCase      )
  
  									return self.add_examples(
  									    lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       overwrite_labels=lowerCAmelCase   ,       overwrite_examples=lowerCAmelCase      )
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase=None   ,       lowerCAmelCase=None   ,       lowerCAmelCase=False   ,       lowerCAmelCase=False      ):
  									if labels is not None and len(lowerCAmelCase      ) != len(lowerCAmelCase      ):
  											raise ValueError(
  											    f'Text and labels have mismatched lengths {len(lowerCAmelCase      )} and {len(lowerCAmelCase      )}'      )
  									if ids is not None and len(lowerCAmelCase      ) != len(lowerCAmelCase      ):
  											raise ValueError(f'Text and ids have mismatched lengths {len(lowerCAmelCase      )} and {len(lowerCAmelCase      )}'      )
  									if ids is None:
  											__lowercase=    [None] * len(lowerCAmelCase      )
  									if labels is None:
  											__lowercase=    [None] * len(lowerCAmelCase      )
  									__lowercase=    []
  									__lowercase=    set()
  									for text_or_text_and_label, label, guid in zip(lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  											if isinstance(lowerCAmelCase   ,       (tuple, list)      ) and label is None:
  													__lowercase,							__lowercase=    text_or_text_and_label
  											else:
  													__lowercase=    text_or_text_and_label
  											added_labels.add(lowerCAmelCase      )
  											examples.append(InputExample(guid=lowerCAmelCase   ,       text_a=lowerCAmelCase   ,       text_b=lowerCAmelCase   ,       label=lowerCAmelCase      )      )
  
  									# Update examples
  									if overwrite_examples:
  											__lowercase=    examples
  									else:
  											self.examples.extend(lowerCAmelCase      )
  
  									# Update labels
  									if overwrite_labels:
  											__lowercase=    list(lowerCAmelCase      )
  									else:
  											__lowercase=    list(set(self.labels      ).union(lowerCAmelCase      )      )
  
  									return self.examples
  
  
  
  
  
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase=None   ,       lowerCAmelCase=False   ,       lowerCAmelCase=0   ,       lowerCAmelCase=True   ,       lowerCAmelCase=None   ,       ):
  									if max_length is None:
  											__lowercase=    tokenizer.max_len
  
  									__lowercase=    {label: i for i, label in enumerate(self.labels      )}
  
  									__lowercase=    []
  									for ex_index, example in enumerate(self.examples      ):
  											if ex_index % 1_0_0_0_0 == 0:
  													logger.info(f'Tokenizing example {ex_index}'      )
  
  											__lowercase=    tokenizer.encode(
  											    example.text_a   ,       add_special_tokens=lowerCAmelCase   ,       max_length=min(lowerCAmelCase   ,       tokenizer.max_len      )   ,       )
  											all_input_ids.append(lowerCAmelCase      )
  
  									__lowercase=    max(len(lowerCAmelCase      ) for input_ids in all_input_ids      )
  
  									__lowercase=    []
  									for ex_index, (input_ids, example) in enumerate(zip(lowerCAmelCase   ,       self.examples      )      ):
  											if ex_index % 1_0_0_0_0 == 0:
  													logger.info(f'Writing example {ex_index}/{len(self.examples      )}'      )
  											# The mask has 1 for real tokens and 0 for padding tokens. Only real
  											# tokens are attended to.
  											__lowercase=    [1 if mask_padding_with_zero else 0] * len(lowerCAmelCase      )
  
  											# Zero-pad up to the sequence length.
  											__lowercase=    batch_length - len(lowerCAmelCase      )
  											if pad_on_left:
  													__lowercase=    ([pad_token] * padding_length) + input_ids
  													__lowercase=    ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
  											else:
  													__lowercase=    input_ids + ([pad_token] * padding_length)
  													__lowercase=    attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
  											if len(lowerCAmelCase      ) != batch_length:
  													raise ValueError(f'Error with input length {len(lowerCAmelCase      )} vs {batch_length}'      )
  											if len(lowerCAmelCase      ) != batch_length:
  													raise ValueError(f'Error with input length {len(lowerCAmelCase      )} vs {batch_length}'      )
  
  											if self.mode == "classification":
  													__lowercase=    label_map[example.label]
  											elif self.mode == "regression":
  													__lowercase=    float(example.label      )
  											else:
  													raise ValueError(self.mode      )
  
  											if ex_index < 5 and self.verbose:
  													logger.info('*** Example ***'      )
  													logger.info(f'guid: {example.guid}'      )
  													logger.info(f'input_ids: {" ".join([str(lowerCAmelCase      ) for x in input_ids]      )}'      )
  													logger.info(f'attention_mask: {" ".join([str(lowerCAmelCase      ) for x in attention_mask]      )}'      )
  													logger.info(f'label: {example.label} (id = {label})'      )
  
  											features.append(InputFeatures(input_ids=lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       label=lowerCAmelCase      )      )
  
  									if return_tensors is None:
  											return features
  									elif return_tensors == "tf":
  											if not is_tf_available():
  													raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'      )
  											import tensorflow as tf
  
  											def gen():
  													for ex in features:
  															yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)
  
  											__lowercase=    tf.data.Dataset.from_generator(
  											    lowerCAmelCase   ,       ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)   ,       ({'input_ids': tf.TensorShape([None]      ), 'attention_mask': tf.TensorShape([None]      )}, tf.TensorShape([]      ))   ,       )
  											return dataset
  									elif return_tensors == "pt":
  											if not is_torch_available():
  													raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported'      )
  											import torch
  											from torch.utils.data import TensorDataset
  
  											__lowercase=    torch.tensor([f.input_ids for f in features]   ,       dtype=torch.long      )
  											__lowercase=    torch.tensor([f.attention_mask for f in features]   ,       dtype=torch.long      )
  											if self.mode == "classification":
  													__lowercase=    torch.tensor([f.label for f in features]   ,       dtype=torch.long      )
  											elif self.mode == "regression":
  													__lowercase=    torch.tensor([f.label for f in features]   ,       dtype=torch.float      )
  
  											__lowercase=    TensorDataset(lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  											return dataset
  									else:
  											raise ValueError('return_tensors should be one of \'tf\' or \'pt\''      )
  
  
  
  
  
  
  
style_context: 
  
  
  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    A					(     unittest.TestCase				):
  
  
  							@slow
  							def     _A (self      ):
  									__lowercase=    XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'      )
  									model.to(lowerCAmelCase      )
  
  									# encoder-decoder outputs
  									__lowercase=    torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]      ).to(lowerCAmelCase      )
  									__lowercase=    torch.tensor(
  									    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]      ).to(lowerCAmelCase      )
  									__lowercase=    model(
  									    input_ids=lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       encoder_outputs=lowerCAmelCase   ,       decoder_input_ids=lowerCAmelCase      )
  									__lowercase=    output[0]
  									__lowercase=    torch.Size((1, 1_4, 2_5_0_0_1_2)      )
  									self.assertEqual(output_predited_logis.shape   ,       lowerCAmelCase      )
  									__lowercase=    torch.tensor(
  									    [[[-6.39_86, -8.23_91, 12.51_89], [-6.32_89, -8.08_64, 12.62_11], [-6.24_18, -8.04_45, 12.79_68]]]      ).to(lowerCAmelCase      )
  									self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]   ,       lowerCAmelCase   ,       atol=1E-4      )      )
  
  									# encoder outputs
  									__lowercase=    model.prophetnet.encoder(lowerCAmelCase      )[0]
  									__lowercase=    torch.tensor(
  									    [[[-1.42_60, -0.76_28, 0.84_53], [-1.47_19, -0.13_91, 0.78_07], [-1.76_78, 0.01_14, 0.46_46]]]      ).to(lowerCAmelCase      )
  									__lowercase=    torch.Size((1, 4, 1_0_2_4)      )
  									self.assertEqual(encoder_outputs.shape   ,       lowerCAmelCase      )
  									self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]   ,       lowerCAmelCase   ,       atol=1E-4      )      )
  
  									# decoder outputs
  									__lowercase=    model.prophetnet.decoder(
  									    lowerCAmelCase   ,       encoder_hidden_states=lowerCAmelCase   ,       )
  									__lowercase=    decoder_outputs[1].view(1   ,       model.config.ngram   ,       1_4   ,       -1      )
  									__lowercase=    model.lm_head(lowerCAmelCase      )
  									__lowercase=    predicting_streams_logits[:, 0]
  									self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]   ,       lowerCAmelCase   ,       atol=1E-4      )      )
  
  
  							@slow
  							def     _A (self      ):
  									__lowercase=    XLMProphetNetForConditionalGeneration.from_pretrained(
  									    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'      )
  									model.to(lowerCAmelCase      )
  
  									__lowercase=    torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]      ).to(lowerCAmelCase      )
  									__lowercase=    torch.tensor(
  									    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]      ).to(lowerCAmelCase      )
  									__lowercase=    model(
  									    input_ids=lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       encoder_outputs=lowerCAmelCase   ,       decoder_input_ids=lowerCAmelCase      )
  									__lowercase=    output[0]
  									__lowercase=    torch.Size((1, 1_4, 2_5_0_0_1_2)      )
  									self.assertEqual(output_predited_logis.shape   ,       lowerCAmelCase      )
  									# compare the actual values for a slice.
  									__lowercase=    torch.tensor(
  									    [[[-9.22_53, -9.71_73, -6.35_29], [-7.67_01, -9.01_45, -1.93_82], [-8.01_95, -7.00_04, -0.15_23]]]      ).to(lowerCAmelCase      )
  
  									self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]   ,       lowerCAmelCase   ,       atol=1E-4      )      )
  
  
  
  
  
  
  
  							@slow
  							def     _A (self      ):
  									__lowercase=    XLMProphetNetForConditionalGeneration.from_pretrained(
  									    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'      )
  									model.to(lowerCAmelCase      )
  									__lowercase=    5_1_2
  
  									__lowercase=    XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'      )
  
  									__lowercase=    (
  									    '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.'
  									)
  									__lowercase=    (
  									    '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.'
  									)
  									__lowercase=    (
  									    '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。'
  									)
  
  									__lowercase=    tokenizer(
  									    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]   ,       padding=lowerCAmelCase   ,       max_length=2_5_5   ,       return_tensors='pt'      ).input_ids
  									__lowercase=    input_ids.to(lowerCAmelCase      )
  
  									__lowercase=    model.generate(
  									    lowerCAmelCase   ,       num_beams=1_0   ,       length_penalty=1.0   ,       no_repeat_ngram_size=3   ,       early_stopping=lowerCAmelCase      )
  									__lowercase=    [tokenizer.decode(lowerCAmelCase   ,       skip_special_tokens=lowerCAmelCase      ) for g in summary_ids]
  									__lowercase=    'Microsoft to end Windows 7 free support after January 14, 2020'
  									__lowercase=    'Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'
  									__lowercase=    '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]   ,       lowerCAmelCase   ,       )
  
  									__lowercase=    model.generate(
  									    lowerCAmelCase   ,       num_beams=1   ,       length_penalty=1.0   ,       no_repeat_ngram_size=3   ,       early_stopping=lowerCAmelCase      )
  									__lowercase=    [
  									    tokenizer.convert_ids_to_tokens(lowerCAmelCase   ,       skip_special_tokens=lowerCAmelCase      ) for g in summary_ids_beama
  									]
  									__lowercase=    '▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'.split(' '      )
  									__lowercase=    '▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'.split(
  									    ' '      )
  									__lowercase=    '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]   ,       lowerCAmelCase   ,       )
  
  
  
  
  
  
  