




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     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased""")
									model.to(_lowercase)

									# encoder-decoder outputs
									__lowerCAmelCase					=  torch.tensor([[17, 96208, 103471, 2]]).to(_lowercase)
									__lowerCAmelCase					=  torch.tensor(
									    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]).to(_lowercase)
									__lowerCAmelCase					=  model(
									    input_ids=_lowercase,     attention_mask=_lowercase,     encoder_outputs=_lowercase,     decoder_input_ids=_lowercase)
									__lowerCAmelCase					=  output[0]
									__lowerCAmelCase					=  torch.Size((1, 14, 250012))
									self.assertEqual(output_predited_logis.shape,     _lowercase)
									__lowerCAmelCase					=  torch.tensor(
									    [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]).to(_lowercase)
									self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3],     _lowercase,     atol=1e-4))

									# encoder outputs
									__lowerCAmelCase					=  model.prophetnet.encoder(_lowercase)[0]
									__lowerCAmelCase					=  torch.tensor(
									    [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]).to(_lowercase)
									__lowerCAmelCase					=  torch.Size((1, 4, 1024))
									self.assertEqual(encoder_outputs.shape,     _lowercase)
									self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3],     _lowercase,     atol=1e-4))

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




					@slow
					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  XLMProphetNetForConditionalGeneration.from_pretrained(
									    """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg""")
									model.to(_lowercase)

									__lowerCAmelCase					=  torch.tensor([[17, 96208, 103471, 2]]).to(_lowercase)
									__lowerCAmelCase					=  torch.tensor(
									    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]).to(_lowercase)
									__lowerCAmelCase					=  model(
									    input_ids=_lowercase,     attention_mask=_lowercase,     encoder_outputs=_lowercase,     decoder_input_ids=_lowercase)
									__lowerCAmelCase					=  output[0]
									__lowerCAmelCase					=  torch.Size((1, 14, 250012))
									self.assertEqual(output_predited_logis.shape,     _lowercase)
									# compare the actual values for a slice.
									__lowerCAmelCase					=  torch.tensor(
									    [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]).to(_lowercase)

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







					@slow
					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  XLMProphetNetForConditionalGeneration.from_pretrained(
									    """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg""")
									model.to(_lowercase)
									__lowerCAmelCase					=  512

									__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=_lowercase,     max_length=255,     return_tensors="""pt""").input_ids
									__lowerCAmelCase					=  input_ids.to(_lowercase)

									__lowerCAmelCase					=  model.generate(
									    _lowercase,     num_beams=10,     length_penalty=1.0,     no_repeat_ngram_size=3,     early_stopping=_lowercase)
									__lowerCAmelCase					=  [tokenizer.decode(_lowercase,     skip_special_tokens=_lowercase) 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],     _lowercase,     )

									__lowerCAmelCase					=  model.generate(
									    _lowercase,     num_beams=1,     length_penalty=1.0,     no_repeat_ngram_size=3,     early_stopping=_lowercase)
									__lowerCAmelCase					=  [
									    tokenizer.convert_ids_to_tokens(_lowercase,     skip_special_tokens=_lowercase) 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],     _lowercase,     )











import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     str:







				'''simple docstring'''


				__lowerCAmelCase					=  min(re.search("""^ +"""						,			UpperCamelCase__   ).end() if t.startswith(""" """   ) else 0 for t in string.splitlines()   )
				return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(UpperCamelCase__   )]   )


__A						:		str          =						"\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

__A						:		int          =						"\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


__A						:		Any          =						"\\n# Begin of markdown\n\nSome cool dataset card\n"

class 		lowercase_       (	unittest.TestCase				):




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase					=  Path(_lowercase) / """README.md"""
													with open(_lowercase,     """w+""") as readme_file:
																	readme_file.write(_lowercase)
													__lowerCAmelCase					=  DatasetMetadata.from_readme(_lowercase)
													self.assertDictEqual(_lowercase,     {"""language""": ["""zh""", """en"""], """task_ids""": ["""sentiment-classification"""]})

													with open(_lowercase,     """w+""") as readme_file:
																	readme_file.write(_lowercase)
													__lowerCAmelCase					=  DatasetMetadata.from_readme(_lowercase)
													self.assertDictEqual(_lowercase,     {})

													with open(_lowercase,     """w+""") as readme_file:
																	readme_file.write(_lowercase)
													__lowerCAmelCase					=  DatasetMetadata.from_readme(_lowercase)
													self.assertEqual(_lowercase,     {})







					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  _dedent(
									    """\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            """)
									assert DatasetMetadata.from_yaml_string(_lowercase)

									__lowerCAmelCase					=  _dedent(
									    """\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            """)
									with self.assertRaises(_lowercase):
													DatasetMetadata.from_yaml_string(_lowercase)

									__lowerCAmelCase					=  _dedent(
									    """\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            """)
									assert DatasetMetadata.from_yaml_string(_lowercase)





