





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 SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):






						@slow
						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     Tuple:







							"""simple docstring"""


							snake_case_		:      Any           =						XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'						)
							model.to(_A						)

							# encoder-decoder outputs
							snake_case_		:      Any           =						torch.tensor([[17, 96208, 103471, 2]]						).to(_A						)
							snake_case_		:      Dict           =						torch.tensor(
							    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]						).to(_A						)
							snake_case_		:      Optional[Any]           =						model(
							    input_ids=_A		, attention_mask=_A		, encoder_outputs=_A		, decoder_input_ids=_A						)
							snake_case_		:      Tuple           =						output[0]
							snake_case_		:      List[Any]           =						torch.Size((1, 14, 250012)						)
							self.assertEqual(output_predited_logis.shape		, _A						)
							snake_case_		:      int           =						torch.tensor(
							    [[[-6.3_9_8_6, -8.2_3_9_1, 1_2.5_1_8_9], [-6.3_2_8_9, -8.0_8_6_4, 1_2.6_2_1_1], [-6.2_4_1_8, -8.0_4_4_5, 1_2.7_9_6_8]]]						).to(_A						)
							self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]		, _A		, atol=1E-4						)						)

							# encoder outputs
							snake_case_		:      Dict           =						model.prophetnet.encoder(_A						)[0]
							snake_case_		:      int           =						torch.tensor(
							    [[[-1.4_2_6_0, -0.7_6_2_8, 0.8_4_5_3], [-1.4_7_1_9, -0.1_3_9_1, 0.7_8_0_7], [-1.7_6_7_8, 0.0_1_1_4, 0.4_6_4_6]]]						).to(_A						)
							snake_case_		:      Optional[Any]           =						torch.Size((1, 4, 1024)						)
							self.assertEqual(encoder_outputs.shape		, _A						)
							self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]		, _A		, atol=1E-4						)						)

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






						@slow
						def UpperCAmelCase_					(   self   :   List[str]						)				->     Union[str, Any]:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						XLMProphetNetForConditionalGeneration.from_pretrained(
							    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'						)
							model.to(_A						)

							snake_case_		:      str           =						torch.tensor([[17, 96208, 103471, 2]]						).to(_A						)
							snake_case_		:      Optional[Any]           =						torch.tensor(
							    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]						).to(_A						)
							snake_case_		:      Any           =						model(
							    input_ids=_A		, attention_mask=_A		, encoder_outputs=_A		, decoder_input_ids=_A						)
							snake_case_		:      Optional[int]           =						output[0]
							snake_case_		:      Union[str, Any]           =						torch.Size((1, 14, 250012)						)
							self.assertEqual(output_predited_logis.shape		, _A						)
							# compare the actual values for a slice.
							snake_case_		:      Any           =						torch.tensor(
							    [[[-9.2_2_5_3, -9.7_1_7_3, -6.3_5_2_9], [-7.6_7_0_1, -9.0_1_4_5, -1.9_3_8_2], [-8.0_1_9_5, -7.0_0_0_4, -0.1_5_2_3]]]						).to(_A						)

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






						@slow
						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     Optional[Any]:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						XLMProphetNetForConditionalGeneration.from_pretrained(
							    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'						)
							model.to(_A						)
							snake_case_		:      Dict           =						512

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

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

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

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




'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def      __magic_name__							(			A				)      ->		str:
	snake_case							=					args.serialization_dir
	snake_case							=					args.pruning_method
	snake_case							=					args.threshold

	snake_case							=					torch.load(os.path.join(A       , 'pytorch_model.bin'				)       , map_location='cpu'				)

	snake_case							=					0  # Number of remaining (not pruned) params in the encoder
	snake_case							=					0  # Number of params in the encoder

	print('name'.ljust(6_0       , ' '				)       , 'Remaining Weights %'       , 'Remaining Weight'				)
	for name, param in st.items():
		if "encoder" not in name:
			continue

		if "mask_scores" in name:
			if pruning_method == "topK":
				snake_case							=					TopKBinarizer.apply(A       , A				).sum().item()
			elif pruning_method == "sigmoied_threshold":
				snake_case							=					ThresholdBinarizer.apply(A       , A       , A				).sum().item()
			elif pruning_method == "l0":
				snake_case			,			snake_case							=					-0.1, 1.1
				snake_case							=					torch.sigmoid(A				)
				snake_case							=					s * (r - l) + l
				snake_case							=					s_bar.clamp(min=0.0       , max=1.0				)
				snake_case							=					(mask > 0.0).sum().item()
			else:
				raise ValueError('Unknown pruning method'				)
			remaining_count += mask_ones
			print(name.ljust(6_0       , ' '				)       , str(round(1_0_0 * mask_ones / param.numel()       , 3				)				).ljust(2_0       , ' '				)       , str(A				)				)
		else:
			encoder_count += param.numel()
			if "bias" in name or "LayerNorm" in name:
				remaining_count += param.numel()

	print(''				)
	print('Remaining Weights (global) %: '       , 1_0_0 * remaining_count / encoder_count				)


if __name__ == "__main__":
						lowerCAmelCase_     =      argparse.ArgumentParser()

						parser.add_argument(
						    "--pruning_method",
						    choices=["l0", "topK", "sigmoied_threshold"],
						    type=str,
						    required=True,
						    help=(
						        "Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"
						        " pruning)"
						    ),
						)
						parser.add_argument(
						    "--threshold",
						    type=float,
						    required=False,
						    help=(
						        "For `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
						        "For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
						        "Not needed for `l0`"
						    ),
						)
						parser.add_argument(
						    "--serialization_dir",
						    type=str,
						    required=True,
						    help="Folder containing the model that was previously fine-pruned",
						)

						lowerCAmelCase_     =      parser.parse_args()

						main(args)





