



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 			__magic_name__		(					unittest.TestCase     ):







							@slow
							def 							__lowercase    (							self      : int	):
									_a  :   Dict				 =							XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'	)
									model.to(_UpperCAmelCase	)

									# encoder-decoder outputs
									_a  :   int				 =							torch.tensor([[17, 96208, 103471, 2]]	).to(_UpperCAmelCase	)
									_a  :   Any				 =							torch.tensor(
									    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]	).to(_UpperCAmelCase	)
									_a  :   str				 =							model(
									    input_ids=_UpperCAmelCase			,attention_mask=_UpperCAmelCase			,encoder_outputs=_UpperCAmelCase			,decoder_input_ids=_UpperCAmelCase	)
									_a  :   Optional[int]				 =							output[0]
									_a  :   str				 =							torch.Size((1, 14, 250012)	)
									self.assertEqual(output_predited_logis.shape			,_UpperCAmelCase	)
									_a  :   Dict				 =							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
									_a  :   Any				 =							model.prophetnet.encoder(_UpperCAmelCase	)[0]
									_a  :   Union[str, Any]				 =							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	)
									_a  :   int				 =							torch.Size((1, 4, 1024)	)
									self.assertEqual(encoder_outputs.shape			,_UpperCAmelCase	)
									self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]			,_UpperCAmelCase			,atol=1E-4	)	)

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







							@slow
							def 							__lowercase    (							self      : List[Any]	):
									_a  :   Union[str, Any]				 =							XLMProphetNetForConditionalGeneration.from_pretrained(
									    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'	)
									model.to(_UpperCAmelCase	)

									_a  :   Optional[int]				 =							torch.tensor([[17, 96208, 103471, 2]]	).to(_UpperCAmelCase	)
									_a  :   Union[str, Any]				 =							torch.tensor(
									    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]	).to(_UpperCAmelCase	)
									_a  :   List[str]				 =							model(
									    input_ids=_UpperCAmelCase			,attention_mask=_UpperCAmelCase			,encoder_outputs=_UpperCAmelCase			,decoder_input_ids=_UpperCAmelCase	)
									_a  :   Union[str, Any]				 =							output[0]
									_a  :   str				 =							torch.Size((1, 14, 250012)	)
									self.assertEqual(output_predited_logis.shape			,_UpperCAmelCase	)
									# compare the actual values for a slice.
									_a  :   List[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 							__lowercase    (							self      : List[str]	):
									_a  :   Optional[Any]				 =							XLMProphetNetForConditionalGeneration.from_pretrained(
									    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'	)
									model.to(_UpperCAmelCase	)
									_a  :   int				 =							512

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

									_a  :   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  :   Tuple				 =							(
									    '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  :   List[Any]				 =							(
									    '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  :   Union[str, Any]				 =							tokenizer(
									    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]			,padding=_UpperCAmelCase			,max_length=255			,return_tensors='pt'	).input_ids
									_a  :   Tuple				 =							input_ids.to(_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							model.generate(
									    _UpperCAmelCase			,num_beams=10			,length_penalty=1.0			,no_repeat_ngram_size=3			,early_stopping=_UpperCAmelCase	)
									_a  :   List[str]				 =							[tokenizer.decode(_UpperCAmelCase			,skip_special_tokens=_UpperCAmelCase	) for g in summary_ids]
									_a  :   int				 =							'Microsoft to end Windows 7 free support after January 14, 2020'
									_a  :   Optional[Any]				 =							'Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'
									_a  :   Dict				 =							'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  :   Union[str, Any]				 =							model.generate(
									    _UpperCAmelCase			,num_beams=1			,length_penalty=1.0			,no_repeat_ngram_size=3			,early_stopping=_UpperCAmelCase	)
									_a  :   Union[str, Any]				 =							[
									    tokenizer.convert_ids_to_tokens(_UpperCAmelCase			,skip_special_tokens=_UpperCAmelCase	) for g in summary_ids_beama
									]
									_a  :   Optional[Any]				 =							'▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'.split(' '	)
									_a  :   List[Any]				 =							'▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'.split(
									    ' '	)
									_a  :   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]			,_UpperCAmelCase			,)









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase		    =     {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     ['''T5Tokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     ['''T5TokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''T5EncoderModel''',
							    '''T5ForConditionalGeneration''',
							    '''T5Model''',
							    '''T5PreTrainedModel''',
							    '''load_tf_weights_in_t5''',
							    '''T5ForQuestionAnswering''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFT5EncoderModel''',
							    '''TFT5ForConditionalGeneration''',
							    '''TFT5Model''',
							    '''TFT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''FlaxT5EncoderModel''',
							    '''FlaxT5ForConditionalGeneration''',
							    '''FlaxT5Model''',
							    '''FlaxT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta import TaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta_fast import TaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ta import (
														    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TaEncoderModel,
														    TaForConditionalGeneration,
														    TaForQuestionAnswering,
														    TaModel,
														    TaPreTrainedModel,
														    load_tf_weights_in_ta,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_ta import (
														    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTaEncoderModel,
														    TFTaForConditionalGeneration,
														    TFTaModel,
														    TFTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_ta import (
														    FlaxTaEncoderModel,
														    FlaxTaForConditionalGeneration,
														    FlaxTaModel,
														    FlaxTaPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase		    =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




