

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     ):
			"""simple docstring"""







			@slow
			def        UpperCamelCase						(      self    )					->  Optional[int]:
							'''simple docstring'''






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

							# encoder-decoder outputs
							A__            =     torch.tensor([[17, 96208, 103471, 2]]    ).to(lowercase    )
							A__            =     torch.tensor(
							    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]    ).to(lowercase    )
							A__            =     model(
							    input_ids=lowercase , attention_mask=lowercase , encoder_outputs=lowercase , decoder_input_ids=lowercase    )
							A__            =     output[0]
							A__            =     torch.Size((1, 14, 250012)    )
							self.assertEqual(output_predited_logis.shape , lowercase    )
							A__            =     torch.tensor(
							    [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]    ).to(lowercase    )
							self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3] , lowercase , atol=1e-4    )    )

							# encoder outputs
							A__            =     model.prophetnet.encoder(lowercase    )[0]
							A__            =     torch.tensor(
							    [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]    ).to(lowercase    )
							A__            =     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
							A__            =     model.prophetnet.decoder(
							    lowercase , encoder_hidden_states=lowercase , )
							A__            =     decoder_outputs[1].view(1 , model.config.ngram , 14 , -1    )
							A__            =     model.lm_head(lowercase    )
							A__            =     predicting_streams_logits[:, 0]
							self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3] , lowercase , atol=1e-4    )    )


			@slow
			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






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

							A__            =     torch.tensor([[17, 96208, 103471, 2]]    ).to(lowercase    )
							A__            =     torch.tensor(
							    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]    ).to(lowercase    )
							A__            =     model(
							    input_ids=lowercase , attention_mask=lowercase , encoder_outputs=lowercase , decoder_input_ids=lowercase    )
							A__            =     output[0]
							A__            =     torch.Size((1, 14, 250012)    )
							self.assertEqual(output_predited_logis.shape , lowercase    )
							# compare the actual values for a slice.
							A__            =     torch.tensor(
							    [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]    ).to(lowercase    )

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


			@slow
			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''simple docstring'''






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

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

							A__            =     (
							    "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__            =     (
							    "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__            =     (
							    "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__            =     tokenizer(
							    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE] , padding=lowercase , max_length=255 , return_tensors="pt"    ).input_ids
							A__            =     input_ids.to(lowercase    )

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

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




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__						=	logging.get_logger(__name__)

lowerCAmelCase__						=	{
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'swiftformer'


			def __init__(      self , lowercase=3 , lowercase=[3, 3, 6, 4] , lowercase=[48, 56, 112, 220] , lowercase=4 , lowercase=[True, True, True, True] , lowercase="gelu" , lowercase=3 , lowercase=2 , lowercase=1 , lowercase=0.0 , lowercase=True , lowercase=1e-5 , lowercase=1e-5 , **lowercase , )					->  Optional[int]:
							'''simple docstring'''






							super().__init__(**lowercase    )
							A__            =     num_channels
							A__            =     depths
							A__            =     embed_dims
							A__            =     mlp_ratio
							A__            =     downsamples
							A__            =     hidden_act
							A__            =     down_patch_size
							A__            =     down_stride
							A__            =     down_pad
							A__            =     drop_path_rate
							A__            =     use_layer_scale
							A__            =     layer_scale_init_value
							A__            =     batch_norm_eps

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    version.parse('1.11'     )


			@property
			def        UpperCamelCase						(      self    )					->  Mapping[str, Mapping[int, str]]:
							'''simple docstring'''






							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]    )


			@property
			def        UpperCamelCase						(      self    )					->  float:
							'''simple docstring'''






							return 1e-4

