



import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()






def 					a(   A :					str				,  A :					List[str]				,  A :					List[Any] )	-> int:
					"""simple docstring"""





					a        =     ConvBertConfig.from_json_file(A )
					a        =     ConvBertModel(A )

					a        =     load_tf_weights_in_convbert(A				,  A				,  A )
					model.save_pretrained(A )

					a        =     TFConvBertModel.from_pretrained(A				,  from_pt=A )
					tf_model.save_pretrained(A )


if __name__ == "__main__":
				_lowercase:				List[Any]       =	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
				)
				parser.add_argument(
				    "--convbert_config_file",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        "The config json file corresponding to the pre-trained ConvBERT model. \n"
				        "This specifies the model architecture."
				    ),
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				_lowercase:				List[str]       =	parser.parse_args()
				convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)





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 			_lowercase							(     unittest.TestCase			):





	"""simple docstring"""




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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

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

						# encoder outputs
						a        =     model.prophetnet.encoder(lowerCamelCase_	)[0]
						a        =     torch.tensor(
						    [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]	).to(lowerCamelCase_	)
						a        =     torch.Size((1, 4, 1024)	)
						self.assertEqual(encoder_outputs.shape	, lowerCamelCase_	)
						self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]	, lowerCamelCase_	, atol=1E-4	)	)

						# decoder outputs
						a        =     model.prophetnet.decoder(
						    lowerCamelCase_	, encoder_hidden_states=lowerCamelCase_	, )
						a        =     decoder_outputs[1].view(1	, model.config.ngram	, 14	, -1	)
						a        =     model.lm_head(lowerCamelCase_	)
						a        =     predicting_streams_logits[:, 0]
						self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]	, lowerCamelCase_	, atol=1E-4	)	)




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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

						a        =     torch.tensor([[17, 96208, 103471, 2]]	).to(lowerCamelCase_	)
						a        =     torch.tensor(
						    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]	).to(lowerCamelCase_	)
						a        =     model(
						    input_ids=lowerCamelCase_	, attention_mask=lowerCamelCase_	, encoder_outputs=lowerCamelCase_	, decoder_input_ids=lowerCamelCase_	)
						a        =     output[0]
						a        =     torch.Size((1, 14, 250012)	)
						self.assertEqual(output_predited_logis.shape	, lowerCamelCase_	)
						# 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(lowerCamelCase_	)

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




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     XLMProphetNetForConditionalGeneration.from_pretrained(
						    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"	)
						model.to(lowerCamelCase_	)
						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=lowerCamelCase_	, max_length=255	, return_tensors="pt"	).input_ids
						a        =     input_ids.to(lowerCamelCase_	)

						a        =     model.generate(
						    lowerCamelCase_	, num_beams=10	, length_penalty=1.0	, no_repeat_ngram_size=3	, early_stopping=lowerCamelCase_	)
						a        =     [tokenizer.decode(lowerCamelCase_	, skip_special_tokens=lowerCamelCase_	) 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]	, lowerCamelCase_	, )

						a        =     model.generate(
						    lowerCamelCase_	, num_beams=1	, length_penalty=1.0	, no_repeat_ngram_size=3	, early_stopping=lowerCamelCase_	)
						a        =     [
						    tokenizer.convert_ids_to_tokens(lowerCamelCase_	, skip_special_tokens=lowerCamelCase_	) 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]	, lowerCamelCase_	, )
