


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 __UpperCAmelCase	(			self      :List[Any]     )	-> List[Any]:




             '''simple docstring'''
             _a   :			Tuple					 =XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""     )
             model.to(SCREAMING_SNAKE_CASE     )

             # encoder-decoder outputs
             _a   :			str					 =torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]     ).to(SCREAMING_SNAKE_CASE     )
             _a   :			Optional[int]					 =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(SCREAMING_SNAKE_CASE     )
             _a   :			Dict					 =model(
                 input_ids=SCREAMING_SNAKE_CASE					,						attention_mask=SCREAMING_SNAKE_CASE					,						encoder_outputs=SCREAMING_SNAKE_CASE					,						decoder_input_ids=SCREAMING_SNAKE_CASE     )
             _a   :			List[Any]					 =output[0]
             _a   :			Tuple					 =torch.Size((1, 1_4, 2_5_0_0_1_2)     )
             self.assertEqual(output_predited_logis.shape					,						SCREAMING_SNAKE_CASE     )
             _a   :			Optional[Any]					 =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(SCREAMING_SNAKE_CASE     )
             self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]					,						SCREAMING_SNAKE_CASE					,						atol=1e-4     )     )

             # encoder outputs
             _a   :			Optional[Any]					 =model.prophetnet.encoder(SCREAMING_SNAKE_CASE     )[0]
             _a   :			Union[str, Any]					 =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(SCREAMING_SNAKE_CASE     )
             _a   :			str					 =torch.Size((1, 4, 1_0_2_4)     )
             self.assertEqual(encoder_outputs.shape					,						SCREAMING_SNAKE_CASE     )
             self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]					,						SCREAMING_SNAKE_CASE					,						atol=1e-4     )     )

             # decoder outputs
             _a   :			List[Any]					 =model.prophetnet.decoder(
                 SCREAMING_SNAKE_CASE					,						encoder_hidden_states=SCREAMING_SNAKE_CASE					,						)
             _a   :			Any					 =decoder_outputs[1].view(1					,						model.config.ngram					,						1_4					,						-1     )
             _a   :			Optional[int]					 =model.lm_head(SCREAMING_SNAKE_CASE     )
             _a   :			List[Any]					 =predicting_streams_logits[:, 0]
             self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]					,						SCREAMING_SNAKE_CASE					,						atol=1e-4     )     )






      @slow
      def __UpperCAmelCase	(			self      :str     )	-> Any:




             '''simple docstring'''
             _a   :			List[str]					 =XLMProphetNetForConditionalGeneration.from_pretrained(
                 """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""     )
             model.to(SCREAMING_SNAKE_CASE     )

             _a   :			Any					 =torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]     ).to(SCREAMING_SNAKE_CASE     )
             _a   :			Tuple					 =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(SCREAMING_SNAKE_CASE     )
             _a   :			str					 =model(
                 input_ids=SCREAMING_SNAKE_CASE					,						attention_mask=SCREAMING_SNAKE_CASE					,						encoder_outputs=SCREAMING_SNAKE_CASE					,						decoder_input_ids=SCREAMING_SNAKE_CASE     )
             _a   :			List[str]					 =output[0]
             _a   :			Tuple					 =torch.Size((1, 1_4, 2_5_0_0_1_2)     )
             self.assertEqual(output_predited_logis.shape					,						SCREAMING_SNAKE_CASE     )
             # compare the actual values for a slice.
             _a   :			Union[str, Any]					 =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(SCREAMING_SNAKE_CASE     )

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






      @slow
      def __UpperCAmelCase	(			self      :Optional[int]     )	-> List[Any]:




             '''simple docstring'''
             _a   :			Tuple					 =XLMProphetNetForConditionalGeneration.from_pretrained(
                 """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""     )
             model.to(SCREAMING_SNAKE_CASE     )
             _a   :			Tuple					 =5_1_2

             _a   :			Tuple					 =XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""     )

             _a   :			Optional[int]					 =(
                 """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   :			Dict					 =(
                 """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   :			str					 =(
                 """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   :			Optional[int]					 =tokenizer(
                 [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]					,						padding=SCREAMING_SNAKE_CASE					,						max_length=2_5_5					,						return_tensors="""pt"""     ).input_ids
             _a   :			str					 =input_ids.to(SCREAMING_SNAKE_CASE     )

             _a   :			Optional[Any]					 =model.generate(
                 SCREAMING_SNAKE_CASE					,						num_beams=1_0					,						length_penalty=1.0					,						no_repeat_ngram_size=3					,						early_stopping=SCREAMING_SNAKE_CASE     )
             _a   :			Optional[int]					 =[tokenizer.decode(SCREAMING_SNAKE_CASE					,						skip_special_tokens=SCREAMING_SNAKE_CASE     ) for g in summary_ids]
             _a   :			List[str]					 ="""Microsoft to end Windows 7 free support after January 14, 2020"""
             _a   :			Any					 ="""Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
             _a   :			int					 ="""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					,						)

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







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class A__	(						UpperCAmelCase__				):
      __UpperCamelCase	:  Optional[Any]     	=							["image_processor", "tokenizer"]
      __UpperCamelCase	:  Optional[Any]     	=							"EfficientNetImageProcessor"
      __UpperCamelCase	:  Union[str, Any]     	=							("BertTokenizer", "BertTokenizerFast")






      def __init__(			self      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :Optional[int]     )	-> List[str]:




             '''simple docstring'''
             super().__init__(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






      def __call__(			self      :str					,						SCREAMING_SNAKE_CASE      :List[Any]=None					,						SCREAMING_SNAKE_CASE      :Dict=None					,						SCREAMING_SNAKE_CASE      :Union[str, Any]="max_length"					,						SCREAMING_SNAKE_CASE      :Any=6_4					,						SCREAMING_SNAKE_CASE      :List[str]=None					,						**SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Optional[int]:




             '''simple docstring'''
             if text is None and images is None:
                    raise ValueError("""You have to specify either text or images. Both cannot be none."""     )

             if text is not None:
                    _a   :			Dict					 =self.tokenizer(
                        SCREAMING_SNAKE_CASE					,						padding=SCREAMING_SNAKE_CASE					,						max_length=SCREAMING_SNAKE_CASE					,						return_tensors=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )

             if images is not None:
                    _a   :			Any					 =self.image_processor(SCREAMING_SNAKE_CASE					,						return_tensors=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )

             if text is not None and images is not None:
                    _a   :			Dict					 =image_features.pixel_values
                    return encoding
             elif text is not None:
                    return encoding
             else:
                    return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE     )					,						tensor_type=SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Any					,						*SCREAMING_SNAKE_CASE      :Optional[int]					,						**SCREAMING_SNAKE_CASE      :str     )	-> Dict:




             '''simple docstring'''
             return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :int					,						*SCREAMING_SNAKE_CASE      :List[str]					,						**SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Union[str, Any]:




             '''simple docstring'''
             return self.tokenizer.decode(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      @property
      def __UpperCAmelCase	(			self      :Tuple     )	-> Tuple:




             '''simple docstring'''
             _a   :			int					 =self.tokenizer.model_input_names
             _a   :			Union[str, Any]					 =self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )


