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     snake_case_			(						unittest.TestCase			):






   @slow
   def  __UpperCamelCase	( self							:	Any    )   ->		str:
       lowercase__					:  List[Any]          =							XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"    )
       model.to(UpperCamelCase_    )

       # encoder-decoder outputs
       lowercase__					:  Any          =							torch.tensor([[17, 9_62_08, 10_34_71, 2]]    ).to(UpperCamelCase_    )
       lowercase__					:  Optional[Any]          =							torch.tensor(
           [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]    ).to(UpperCamelCase_    )
       lowercase__					:  Union[str, Any]          =							model(
           input_ids=UpperCamelCase_      ,		attention_mask=UpperCamelCase_      ,		encoder_outputs=UpperCamelCase_      ,		decoder_input_ids=UpperCamelCase_    )
       lowercase__					:  Dict          =							output[0]
       lowercase__					:  str          =							torch.Size((1, 14, 25_00_12)    )
       self.assertEqual(output_predited_logis.shape      ,		UpperCamelCase_    )
       lowercase__					:  int          =							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(UpperCamelCase_    )
       self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]      ,		UpperCamelCase_      ,		atol=1E-4    )    )

       # encoder outputs
       lowercase__					:  Optional[Any]          =							model.prophetnet.encoder(UpperCamelCase_    )[0]
       lowercase__					:  Optional[int]          =							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(UpperCamelCase_    )
       lowercase__					:  Tuple          =							torch.Size((1, 4, 10_24)    )
       self.assertEqual(encoder_outputs.shape      ,		UpperCamelCase_    )
       self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]      ,		UpperCamelCase_      ,		atol=1E-4    )    )

       # decoder outputs
       lowercase__					:  int          =							model.prophetnet.decoder(
           UpperCamelCase_      ,		encoder_hidden_states=UpperCamelCase_      ,		)
       lowercase__					:  int          =							decoder_outputs[1].view(1      ,		model.config.ngram      ,		14      ,		-1    )
       lowercase__					:  Optional[Any]          =							model.lm_head(UpperCamelCase_    )
       lowercase__					:  List[str]          =							predicting_streams_logits[:, 0]
       self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]      ,		UpperCamelCase_      ,		atol=1E-4    )    )






   @slow
   def  __UpperCamelCase	( self							:	Optional[int]    )   ->		Optional[Any]:
       lowercase__					:  Tuple          =							XLMProphetNetForConditionalGeneration.from_pretrained(
           "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"    )
       model.to(UpperCamelCase_    )

       lowercase__					:  Any          =							torch.tensor([[17, 9_62_08, 10_34_71, 2]]    ).to(UpperCamelCase_    )
       lowercase__					:  Any          =							torch.tensor(
           [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]    ).to(UpperCamelCase_    )
       lowercase__					:  str          =							model(
           input_ids=UpperCamelCase_      ,		attention_mask=UpperCamelCase_      ,		encoder_outputs=UpperCamelCase_      ,		decoder_input_ids=UpperCamelCase_    )
       lowercase__					:  Any          =							output[0]
       lowercase__					:  List[Any]          =							torch.Size((1, 14, 25_00_12)    )
       self.assertEqual(output_predited_logis.shape      ,		UpperCamelCase_    )
       # compare the actual values for a slice.
       lowercase__					:  Optional[Any]          =							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(UpperCamelCase_    )

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







   @slow
   def  __UpperCamelCase	( self							:	int    )   ->		int:
       lowercase__					:  Union[str, Any]          =							XLMProphetNetForConditionalGeneration.from_pretrained(
           "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"    )
       model.to(UpperCamelCase_    )
       lowercase__					:  Optional[Any]          =							5_12

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

       lowercase__					:  Tuple          =							(
           '''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__					:  str          =							(
           '''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__					:  Dict          =							(
           '''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__					:  Dict          =							tokenizer(
           [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]      ,		padding=UpperCamelCase_      ,		max_length=2_55      ,		return_tensors="pt"    ).input_ids
       lowercase__					:  Any          =							input_ids.to(UpperCamelCase_    )

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

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





'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase	=    logging.get_logger(__name__)

__lowercase	=    {
    '''google/pegasus-x-base''': '''https://huggingface.co/google/pegasus-x-base/resolve/main/config.json''',
    '''google/pegasus-x-large''': '''https://huggingface.co/google/pegasus-x-large/resolve/main/config.json''',
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}

class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Dict								=		'''pegasus_x'''
	_UpperCamelCase							:  Optional[int]								=		['''past_key_values''']
	_UpperCamelCase							:  str								=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




	def __init__(						self				: Any					,						UpperCamelCase_				: Tuple=96103					,						UpperCamelCase_				: List[str]=16384					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: Dict=4096					,						UpperCamelCase_				: str=16					,						UpperCamelCase_				: Union[str, Any]=16					,						UpperCamelCase_				: str=4096					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Dict=0.0					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: List[str]="gelu"					,						UpperCamelCase_				: Dict=1024					,						UpperCamelCase_				: str=0.1					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Optional[Any]=0.0					,						UpperCamelCase_				: str=0.0_2					,						UpperCamelCase_				: Optional[Any]=0					,						UpperCamelCase_				: Tuple=True					,						UpperCamelCase_				: Tuple=0					,						UpperCamelCase_				: Optional[int]=1					,						UpperCamelCase_				: List[Any]=1					,						UpperCamelCase_				: List[Any]=32					,						UpperCamelCase_				: Optional[int]=512					,						UpperCamelCase_				: Any=True					,						**UpperCamelCase_				: Dict					,						):
			lowerCAmelCase_   :       List[Any]    							=vocab_size
			lowerCAmelCase_   :       Any    							=max_position_embeddings
			lowerCAmelCase_   :       int    							=d_model
			lowerCAmelCase_   :       List[Any]    							=encoder_ffn_dim
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=encoder_attention_heads
			lowerCAmelCase_   :       List[Any]    							=decoder_ffn_dim
			lowerCAmelCase_   :       List[Any]    							=decoder_layers
			lowerCAmelCase_   :       List[str]    							=decoder_attention_heads
			lowerCAmelCase_   :       str    							=dropout
			lowerCAmelCase_   :       Any    							=attention_dropout
			lowerCAmelCase_   :       Any    							=activation_dropout
			lowerCAmelCase_   :       Optional[int]    							=activation_function
			lowerCAmelCase_   :       List[Any]    							=init_std
			lowerCAmelCase_   :       List[str]    							=encoder_layerdrop
			lowerCAmelCase_   :       Optional[int]    							=decoder_layerdrop
			lowerCAmelCase_   :       int    							=use_cache
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=scale_embedding  # scale factor will be sqrt(d_model) if True

			lowerCAmelCase_   :       Optional[int]    							=num_global_tokens
			lowerCAmelCase_   :       List[str]    							=block_size
			lowerCAmelCase_   :       Optional[int]    							=stagger_local_blocks

			super().__init__(
			    pad_token_id=UpperCamelCase_					,						eos_token_id=UpperCamelCase_					,						is_encoder_decoder=UpperCamelCase_					,						decoder_start_token_id=UpperCamelCase_					,						forced_eos_token_id=UpperCamelCase_					,						**UpperCamelCase_					,						)




	@property
	def 	__A      (						self				: List[str]      ):
			return self.encoder_attention_heads





	@property
	def 	__A      (						self				: int      ):
			return self.d_model






