

'''simple docstring'''

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 lowerCamelCase   (    unittest.TestCase				):

				@slow
				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased' )
					model.to(lowercase_ )

					# encoder-decoder outputs
					snake_case							=					torch.tensor([[17, 96208, 103471, 2]] ).to(lowercase_ )
					snake_case							=					torch.tensor(
					    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]] ).to(lowercase_ )
					snake_case							=					model(
					    input_ids=lowercase_,						attention_mask=lowercase_,						encoder_outputs=lowercase_,						decoder_input_ids=lowercase_ )
					snake_case							=					output[0]
					snake_case							=					torch.Size((1, 14, 250012) )
					self.assertEqual(output_predited_logis.shape,						lowercase_ )
					snake_case							=					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
					snake_case							=					model.prophetnet.encoder(lowercase_ )[0]
					snake_case							=					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_ )
					snake_case							=					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
					snake_case							=					model.prophetnet.decoder(
					    lowercase_,						encoder_hidden_states=lowercase_,						)
					snake_case							=					decoder_outputs[1].view(1,						model.config.ngram,						14,						-1 )
					snake_case							=					model.lm_head(lowercase_ )
					snake_case							=					predicting_streams_logits[:, 0]
					self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3],						lowercase_,						atol=1E-4 ) )

				@slow
				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					XLMProphetNetForConditionalGeneration.from_pretrained(
					    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg' )
					model.to(lowercase_ )

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

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

					snake_case							=					(
					    '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.'
					)
					snake_case							=					(
					    '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.'
					)
					snake_case							=					(
					    '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。'
					)

					snake_case							=					tokenizer(
					    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE],						padding=lowercase_,						max_length=255,						return_tensors='pt' ).input_ids
					snake_case							=					input_ids.to(lowercase_ )

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

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








'''simple docstring'''

from typing import List, Union

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


lowerCAmelCase_     =      logging.get_logger(__name__)

lowerCAmelCase_     =      {
    "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/config.json",
    # See all LED models at https://huggingface.co/models?filter=led
}







class lowerCamelCase   (    __lowerCAmelCase				):
				snake_case_							     = '''led'''
				snake_case_							     = {
				    '''num_attention_heads''': '''encoder_attention_heads''',
				    '''hidden_size''': '''d_model''',
				    '''attention_probs_dropout_prob''': '''attention_dropout''',
				    '''initializer_range''': '''init_std''',
				}

				def __init__(       self,						lowercase_=50265,						lowercase_=16384,						lowercase_=1024,						lowercase_=12,						lowercase_=4096,						lowercase_=16,						lowercase_=12,						lowercase_=4096,						lowercase_=16,						lowercase_=0.0,						lowercase_=0.0,						lowercase_=True,						lowercase_=True,						lowercase_="gelu",						lowercase_=1024,						lowercase_=0.1,						lowercase_=0.0,						lowercase_=0.0,						lowercase_=0.02,						lowercase_=2,						lowercase_=0.0,						lowercase_=1,						lowercase_=0,						lowercase_=2,						lowercase_ = 512,						**lowercase_,						)    ->							Union[str, Any]:
					snake_case							=					vocab_size
					snake_case							=					max_encoder_position_embeddings
					snake_case							=					max_decoder_position_embeddings
					snake_case							=					d_model
					snake_case							=					encoder_ffn_dim
					snake_case							=					encoder_layers
					snake_case							=					encoder_attention_heads
					snake_case							=					decoder_ffn_dim
					snake_case							=					decoder_layers
					snake_case							=					decoder_attention_heads
					snake_case							=					dropout
					snake_case							=					attention_dropout
					snake_case							=					activation_dropout
					snake_case							=					activation_function
					snake_case							=					init_std
					snake_case							=					encoder_layerdrop
					snake_case							=					decoder_layerdrop
					snake_case							=					classifier_dropout
					snake_case							=					use_cache
					snake_case							=					encoder_layers
					snake_case							=					attention_window

					super().__init__(
					    pad_token_id=lowercase_,						bos_token_id=lowercase_,						eos_token_id=lowercase_,						is_encoder_decoder=lowercase_,						decoder_start_token_id=lowercase_,						**lowercase_,						)





