code: 
  
  
  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):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""		)
  													model.to(_UpperCAmelCase		)
  
  													# encoder-decoder outputs
  													a_					=		torch.tensor([[17, 96_208, 103_471, 2]]		).to(_UpperCAmelCase		)
  													a_					=		torch.tensor(
  													    [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]		).to(_UpperCAmelCase		)
  													a_					=		model(
  													    input_ids=_UpperCAmelCase			,    attention_mask=_UpperCAmelCase			,    encoder_outputs=_UpperCAmelCase			,    decoder_input_ids=_UpperCAmelCase		)
  													a_					=		output[0]
  													a_					=		torch.Size((1, 14, 250_012)		)
  													self.assertEqual(output_predited_logis.shape			,    _UpperCAmelCase		)
  													a_					=		torch.tensor(
  													    [[[-6.3_9_8_6, -8.2_3_9_1, 1_2.5_1_8_9], [-6.3_2_8_9, -8.0_8_6_4, 1_2.6_2_1_1], [-6.2_4_1_8, -8.0_4_4_5, 1_2.7_9_6_8]]]		).to(_UpperCAmelCase		)
  													self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]			,    _UpperCAmelCase			,    atol=1e-4		)		)
  
  													# encoder outputs
  													a_					=		model.prophetnet.encoder(_UpperCAmelCase		)[0]
  													a_					=		torch.tensor(
  													    [[[-1.4_2_6_0, -0.7_6_2_8, 0.8_4_5_3], [-1.4_7_1_9, -0.1_3_9_1, 0.7_8_0_7], [-1.7_6_7_8, 0.0_1_1_4, 0.4_6_4_6]]]		).to(_UpperCAmelCase		)
  													a_					=		torch.Size((1, 4, 1_024)		)
  													self.assertEqual(encoder_outputs.shape			,    _UpperCAmelCase		)
  													self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]			,    _UpperCAmelCase			,    atol=1e-4		)		)
  
  													# decoder outputs
  													a_					=		model.prophetnet.decoder(
  													    _UpperCAmelCase			,    encoder_hidden_states=_UpperCAmelCase			,    )
  													a_					=		decoder_outputs[1].view(1			,    model.config.ngram			,    14			,    -1		)
  													a_					=		model.lm_head(_UpperCAmelCase		)
  													a_					=		predicting_streams_logits[:, 0]
  													self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]			,    _UpperCAmelCase			,    atol=1e-4		)		)
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		XLMProphetNetForConditionalGeneration.from_pretrained(
  													    """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""		)
  													model.to(_UpperCAmelCase		)
  
  													a_					=		torch.tensor([[17, 96_208, 103_471, 2]]		).to(_UpperCAmelCase		)
  													a_					=		torch.tensor(
  													    [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]		).to(_UpperCAmelCase		)
  													a_					=		model(
  													    input_ids=_UpperCAmelCase			,    attention_mask=_UpperCAmelCase			,    encoder_outputs=_UpperCAmelCase			,    decoder_input_ids=_UpperCAmelCase		)
  													a_					=		output[0]
  													a_					=		torch.Size((1, 14, 250_012)		)
  													self.assertEqual(output_predited_logis.shape			,    _UpperCAmelCase		)
  													# compare the actual values for a slice.
  													a_					=		torch.tensor(
  													    [[[-9.2_2_5_3, -9.7_1_7_3, -6.3_5_2_9], [-7.6_7_0_1, -9.0_1_4_5, -1.9_3_8_2], [-8.0_1_9_5, -7.0_0_0_4, -0.1_5_2_3]]]		).to(_UpperCAmelCase		)
  
  													self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]			,    _UpperCAmelCase			,    atol=1e-4		)		)
  
  
  
  
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		XLMProphetNetForConditionalGeneration.from_pretrained(
  													    """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""		)
  													model.to(_UpperCAmelCase		)
  													a_					=		512
  
  													a_					=		XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""		)
  
  													a_					=		(
  													    """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."""
  													)
  													a_					=		(
  													    """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."""
  													)
  													a_					=		(
  													    """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。"""
  													)
  
  													a_					=		tokenizer(
  													    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]			,    padding=_UpperCAmelCase			,    max_length=255			,    return_tensors="""pt"""		).input_ids
  													a_					=		input_ids.to(_UpperCAmelCase		)
  
  													a_					=		model.generate(
  													    _UpperCAmelCase			,    num_beams=10			,    length_penalty=1.0			,    no_repeat_ngram_size=3			,    early_stopping=_UpperCAmelCase		)
  													a_					=		[tokenizer.decode(_UpperCAmelCase			,    skip_special_tokens=_UpperCAmelCase		) for g in summary_ids]
  													a_					=		"""Microsoft to end Windows 7 free support after January 14, 2020"""
  													a_					=		"""Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
  													a_					=		"""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]			,    _UpperCAmelCase			,    )
  
  													a_					=		model.generate(
  													    _UpperCAmelCase			,    num_beams=1			,    length_penalty=1.0			,    no_repeat_ngram_size=3			,    early_stopping=_UpperCAmelCase		)
  													a_					=		[
  													    tokenizer.convert_ids_to_tokens(_UpperCAmelCase			,    skip_special_tokens=_UpperCAmelCase		) for g in summary_ids_beama
  													]
  													a_					=		"""▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7""".split(""" """		)
  													a_					=		"""▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года""".split(
  													    """ """		)
  													a_					=		"""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]			,    _UpperCAmelCase			,    )
style_context: 
  
  
  import json
  import os
  import unittest
  
  from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
  from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_tokenizers, require_torch
  from transformers.utils import cached_property
  
  from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
  
  
  
  
  
  @require_tokenizers
  class        lowercase_				( UpperCamelCase__			,unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		BartTokenizer
  							snake_case_        =		BartTokenizerFast
  							snake_case_        =		True
  							snake_case_        =		filter_roberta_detectors
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().setUp()
  													a_					=		[
  													    """l""",
  													    """o""",
  													    """w""",
  													    """e""",
  													    """r""",
  													    """s""",
  													    """t""",
  													    """i""",
  													    """d""",
  													    """n""",
  													    """\u0120""",
  													    """\u0120l""",
  													    """\u0120n""",
  													    """\u0120lo""",
  													    """\u0120low""",
  													    """er""",
  													    """\u0120lowest""",
  													    """\u0120newer""",
  													    """\u0120wider""",
  													    """<unk>""",
  													]
  													a_					=		dict(zip(_UpperCAmelCase			,    range(len(_UpperCAmelCase		)		)		)		)
  													a_					=		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
  													a_					=		{"""unk_token""": """<unk>"""}
  
  													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""vocab_file"""]		)
  													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""merges_file"""]		)
  													with open(self.vocab_file			,    """w"""			,    encoding="""utf-8"""		) as fp:
  																			fp.write(json.dumps(_UpperCAmelCase		) + """\n"""		)
  													with open(self.merges_file			,    """w"""			,    encoding="""utf-8"""		) as fp:
  																			fp.write("""\n""".join(_UpperCAmelCase		)		)
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													kwargs.update(self.special_tokens_map		)
  													return self.tokenizer_class.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													kwargs.update(self.special_tokens_map		)
  													return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return "lower newer", "lower newer"
  
  
  							@cached_property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return BartTokenizer.from_pretrained("""facebook/bart-large"""		)
  
  
  							@cached_property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return BartTokenizerFast.from_pretrained("""facebook/bart-large"""		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
  													a_					=		[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]
  
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(_UpperCAmelCase			,    max_length=len(_UpperCAmelCase		)			,    padding=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			self.assertIsInstance(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  																			self.assertEqual((2, 9)			,    batch.input_ids.shape		)
  																			self.assertEqual((2, 9)			,    batch.attention_mask.shape		)
  																			a_					=		batch.input_ids.tolist()[0]
  																			self.assertListEqual(_UpperCAmelCase			,    _UpperCAmelCase		)
  																			# Test that special tokens are reset
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(_UpperCAmelCase			,    padding=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			# check if input_ids are returned and no labels
  																			self.assertIn("""input_ids"""			,    _UpperCAmelCase		)
  																			self.assertIn("""attention_mask"""			,    _UpperCAmelCase		)
  																			self.assertNotIn("""labels"""			,    _UpperCAmelCase		)
  																			self.assertNotIn("""decoder_attention_mask"""			,    _UpperCAmelCase		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		[
  													    """Summary of the text.""",
  													    """Another summary.""",
  													]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(text_target=_UpperCAmelCase			,    max_length=32			,    padding="""max_length"""			,    return_tensors="""pt"""		)
  																			self.assertEqual(32			,    targets["""input_ids"""].shape[1]		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(
  																			    ["""I am a small frog""" * 1_024, """I am a small frog"""]			,    padding=_UpperCAmelCase			,    truncation=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			self.assertIsInstance(_UpperCAmelCase			,    _UpperCAmelCase		)
  																			self.assertEqual(batch.input_ids.shape			,    (2, 1_024)		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		["""A long paragraph for summarization."""]
  													a_					=		[
  													    """Summary of the text.""",
  													]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			a_					=		tokenizer(text_target=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			a_					=		inputs["""input_ids"""]
  																			a_					=		targets["""input_ids"""]
  																			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
  																			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
  																			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
  																			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													pass
  
  
  
  
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  																			with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
  																									a_					=		self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase			,    **_UpperCAmelCase		)
  																									a_					=		self.tokenizer_class.from_pretrained(_UpperCAmelCase			,    **_UpperCAmelCase		)
  																									a_					=		"""A, <mask> AllenNLP sentence."""
  																									a_					=		tokenizer_r.encode_plus(_UpperCAmelCase			,    add_special_tokens=_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)
  																									a_					=		tokenizer_p.encode_plus(_UpperCAmelCase			,    add_special_tokens=_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)
  
  																									# token_type_ids should put 0 everywhere
  																									self.assertEqual(sum(tokens_r["""token_type_ids"""]		)			,    sum(tokens_p["""token_type_ids"""]		)		)
  
  																									# attention_mask should put 1 everywhere, so sum over length should be 1
  																									self.assertEqual(
  																									    sum(tokens_r["""attention_mask"""]		) / len(tokens_r["""attention_mask"""]		)			,    sum(tokens_p["""attention_mask"""]		) / len(tokens_p["""attention_mask"""]		)			,    )
  
  																									a_					=		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
  																									a_					=		tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]		)
  
  																									# Rust correctly handles the space before the mask while python doesnt
  																									self.assertSequenceEqual(tokens_p["""input_ids"""]			,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
  																									self.assertSequenceEqual(tokens_r["""input_ids"""]			,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
  
  																									self.assertSequenceEqual(
  																									    _UpperCAmelCase			,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)
  																									self.assertSequenceEqual(
  																									    _UpperCAmelCase			,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)