
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   __UpperCamelCase				(    unittest.TestCase   ):






     """simple docstring"""







     @slow
     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						XLMProphetNetForConditionalGeneration.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''				)
       model.to(_A				)

       # encoder-decoder outputs
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						torch.tensor([[17, 9_6208, 10_3471, 2]]				).to(_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						torch.tensor(
           [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]				).to(_A				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						model(
           input_ids=_A      ,		attention_mask=_A      ,		encoder_outputs=_A      ,		decoder_input_ids=_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						output[0]
       __SCREAMING_SNAKE_CASE     :					str			=						torch.Size((1, 14, 25_0012)				)
       self.assertEqual(output_predited_logis.shape      ,		_A				)
       __SCREAMING_SNAKE_CASE     :					str			=						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(_A				)
       self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]      ,		_A      ,		atol=1e-4				)				)

       # encoder outputs
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						model.prophetnet.encoder(_A				)[0]
       __SCREAMING_SNAKE_CASE     :					Tuple			=						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(_A				)
       __SCREAMING_SNAKE_CASE     :					Dict			=						torch.Size((1, 4, 1024)				)
       self.assertEqual(encoder_outputs.shape      ,		_A				)
       self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]      ,		_A      ,		atol=1e-4				)				)

       # decoder outputs
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						model.prophetnet.decoder(
           _A      ,		encoder_hidden_states=_A      ,		)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						decoder_outputs[1].view(1      ,		model.config.ngram      ,		14      ,		-1				)
       __SCREAMING_SNAKE_CASE     :					Tuple			=						model.lm_head(_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						predicting_streams_logits[:, 0]
       self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]      ,		_A      ,		atol=1e-4				)				)



     @slow
     def 				UpperCAmelCase__    (							self    :    Any				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						XLMProphetNetForConditionalGeneration.from_pretrained(
           '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''				)
       model.to(_A				)

       __SCREAMING_SNAKE_CASE     :					Dict			=						torch.tensor([[17, 9_6208, 10_3471, 2]]				).to(_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						torch.tensor(
           [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]				).to(_A				)
       __SCREAMING_SNAKE_CASE     :					Dict			=						model(
           input_ids=_A      ,		attention_mask=_A      ,		encoder_outputs=_A      ,		decoder_input_ids=_A				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						output[0]
       __SCREAMING_SNAKE_CASE     :					str			=						torch.Size((1, 14, 25_0012)				)
       self.assertEqual(output_predited_logis.shape      ,		_A				)
       # compare the actual values for a slice.
       __SCREAMING_SNAKE_CASE     :					Tuple			=						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(_A				)

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




     @slow
     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						XLMProphetNetForConditionalGeneration.from_pretrained(
           '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''				)
       model.to(_A				)
       __SCREAMING_SNAKE_CASE     :					Dict			=						512

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''				)

       __SCREAMING_SNAKE_CASE     :					Dict			=						(
           '''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.'''
       )
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						(
           '''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.'''
       )
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						(
           '''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。'''
       )

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						tokenizer(
           [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]      ,		padding=_A      ,		max_length=255      ,		return_tensors='''pt'''				).input_ids
       __SCREAMING_SNAKE_CASE     :					Tuple			=						input_ids.to(_A				)

       __SCREAMING_SNAKE_CASE     :					Any			=						model.generate(
           _A      ,		num_beams=10      ,		length_penalty=1.0      ,		no_repeat_ngram_size=3      ,		early_stopping=_A				)
       __SCREAMING_SNAKE_CASE     :					Any			=						[tokenizer.decode(_A      ,		skip_special_tokens=_A				) for g in summary_ids]
       __SCREAMING_SNAKE_CASE     :					Tuple			=						'''Microsoft to end Windows 7 free support after January 14, 2020'''
       __SCREAMING_SNAKE_CASE     :					Dict			=						'''Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'''
       __SCREAMING_SNAKE_CASE     :					Tuple			=						'''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]      ,		_A      ,		)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						model.generate(
           _A      ,		num_beams=1      ,		length_penalty=1.0      ,		no_repeat_ngram_size=3      ,		early_stopping=_A				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						[
           tokenizer.convert_ids_to_tokens(_A      ,		skip_special_tokens=_A				) for g in summary_ids_beama
       ]
       __SCREAMING_SNAKE_CASE     :					Any			=						'''▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'''.split(''' '''				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						'''▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'''.split(
           ''' '''				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						'''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]      ,		_A      ,		)


import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowercase_											=	"""
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""





class   __UpperCamelCase				(    unittest.TestCase				,		lowerCAmelCase__   ):






     """simple docstring"""







     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						load_tool('''summarization'''				)
       self.tool.setup()
       __SCREAMING_SNAKE_CASE     :					Any			=						load_tool('''summarization'''      ,		remote=_A				)



     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						self.tool(_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.remote_tool(_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						self.tool(text=_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)




     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						self.remote_tool(text=_A				)
       self.assertEqual(
           _A      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)
