






"""simple docstring"""


import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE      =      logging.get_logger(__name__)


def 					UpperCAmelCase			(				a__   )     ->   Any:





						'''simple docstring'''






						lowerCAmelCase      :Optional[Any]				  = ConvNextVaConfig()

						if "atto" in checkpoint_url:
												lowerCAmelCase      :List[str]				  = [2, 2, 6, 2]
												lowerCAmelCase      :int				  = [40, 80, 1_60, 3_20]
						if "femto" in checkpoint_url:
												lowerCAmelCase      :str				  = [2, 2, 6, 2]
												lowerCAmelCase      :Tuple				  = [48, 96, 1_92, 3_84]
						if "pico" in checkpoint_url:
												lowerCAmelCase      :Optional[int]				  = [2, 2, 6, 2]
												lowerCAmelCase      :Tuple				  = [64, 1_28, 2_56, 5_12]
						if "nano" in checkpoint_url:
												lowerCAmelCase      :Union[str, Any]				  = [2, 2, 8, 2]
												lowerCAmelCase      :List[Any]				  = [80, 1_60, 3_20, 6_40]
						if "tiny" in checkpoint_url:
												lowerCAmelCase      :List[str]				  = [3, 3, 9, 3]
												lowerCAmelCase      :Tuple				  = [96, 1_92, 3_84, 7_68]
						if "base" in checkpoint_url:
												lowerCAmelCase      :Union[str, Any]				  = [3, 3, 27, 3]
												lowerCAmelCase      :Tuple				  = [1_28, 2_56, 5_12, 10_24]
						if "large" in checkpoint_url:
												lowerCAmelCase      :Any				  = [3, 3, 27, 3]
												lowerCAmelCase      :List[Any]				  = [1_92, 3_84, 7_68, 15_36]
						if "huge" in checkpoint_url:
												lowerCAmelCase      :List[Any]				  = [3, 3, 27, 3]
												lowerCAmelCase      :Optional[int]				  = [3_52, 7_04, 14_08, 28_16]

						lowerCAmelCase      :Dict				  = 10_00
						lowerCAmelCase      :Tuple				  = 'imagenet-1k-id2label.json'
						lowerCAmelCase      :Union[str, Any]				  = (1, 10_00)

						lowerCAmelCase      :Dict				  = 'huggingface/label-files'
						lowerCAmelCase      :List[Any]				  = num_labels
						lowerCAmelCase      :Union[str, Any]				  = json.load(open(hf_hub_download(_A    ,			_A    ,			repo_type='dataset'   )    ,			'r'   )   )
						lowerCAmelCase      :str				  = {int(_A   ): v for k, v in idalabel.items()}

						lowerCAmelCase      :Any				  = idalabel
						lowerCAmelCase      :str				  = {v: k for k, v in idalabel.items()}
						lowerCAmelCase      :List[str]				  = hidden_sizes
						lowerCAmelCase      :List[str]				  = depths

						return config, expected_shape


def 					UpperCAmelCase			(				a__   )     ->   Optional[Any]:





						'''simple docstring'''






						if "downsample_layers.0.0" in name:
												lowerCAmelCase      :Dict				  = name.replace('downsample_layers.0.0'    ,			'embeddings.patch_embeddings'   )
						if "downsample_layers.0.1" in name:
												lowerCAmelCase      :Optional[int]				  = name.replace('downsample_layers.0.1'    ,			'embeddings.norm'   )  # we rename to layernorm later on
						if "downsample_layers.1.0" in name:
												lowerCAmelCase      :List[Any]				  = name.replace('downsample_layers.1.0'    ,			'stages.1.downsampling_layer.0'   )
						if "downsample_layers.1.1" in name:
												lowerCAmelCase      :Union[str, Any]				  = name.replace('downsample_layers.1.1'    ,			'stages.1.downsampling_layer.1'   )
						if "downsample_layers.2.0" in name:
												lowerCAmelCase      :Union[str, Any]				  = name.replace('downsample_layers.2.0'    ,			'stages.2.downsampling_layer.0'   )
						if "downsample_layers.2.1" in name:
												lowerCAmelCase      :List[str]				  = name.replace('downsample_layers.2.1'    ,			'stages.2.downsampling_layer.1'   )
						if "downsample_layers.3.0" in name:
												lowerCAmelCase      :Optional[int]				  = name.replace('downsample_layers.3.0'    ,			'stages.3.downsampling_layer.0'   )
						if "downsample_layers.3.1" in name:
												lowerCAmelCase      :str				  = name.replace('downsample_layers.3.1'    ,			'stages.3.downsampling_layer.1'   )
						if "stages" in name and "downsampling_layer" not in name:
												# stages.0.0. for instance should be renamed to stages.0.layers.0.
												lowerCAmelCase      :Dict				  = name[: len('stages.0'   )] + '.layers' + name[len('stages.0'   ) :]
						if "gamma" in name:
												lowerCAmelCase      :Any				  = name.replace('gamma'    ,			'weight'   )
						if "beta" in name:
												lowerCAmelCase      :List[Any]				  = name.replace('beta'    ,			'bias'   )
						if "stages" in name:
												lowerCAmelCase      :Optional[int]				  = name.replace('stages'    ,			'encoder.stages'   )
						if "norm" in name:
												lowerCAmelCase      :Union[str, Any]				  = name.replace('norm'    ,			'layernorm'   )
						if "head" in name:
												lowerCAmelCase      :Tuple				  = name.replace('head'    ,			'classifier'   )

						return name


def 					UpperCAmelCase			(				)     ->   Any:





						'''simple docstring'''






						lowerCAmelCase      :Optional[Any]				  = 'http://images.cocodataset.org/val2017/000000039769.jpg'
						lowerCAmelCase      :Dict				  = Image.open(requests.get(_A    ,			stream=_A   ).raw   )
						return im


def 					UpperCAmelCase			(				a__   )     ->   int:





						'''simple docstring'''






						if "224" in checkpoint_url:
												lowerCAmelCase      :Optional[int]				  = 2_24
												lowerCAmelCase      :str				  = 2_24 / 2_56
						elif "384" in checkpoint_url:
												lowerCAmelCase      :Dict				  = 3_84
												lowerCAmelCase      :Optional[Any]				  = None
						else:
												lowerCAmelCase      :Optional[int]				  = 5_12
												lowerCAmelCase      :Dict				  = None

						return ConvNextImageProcessor(
						    size=_A    ,			crop_pct=_A    ,			image_mean=[0.485, 0.456, 0.406]    ,			image_std=[0.229, 0.224, 0.225]    ,			resample=PILImageResampling.BICUBIC    ,			)


@torch.no_grad()
def 					UpperCAmelCase			(				a__    ,			a__    ,			a__    ,			a__   )     ->   List[Any]:





						'''simple docstring'''






						print('Downloading original model from checkpoint...'   )
						# define ConvNeXTV2 configuration based on URL
						lowerCAmelCase  ,   lowerCAmelCase      :str				  = get_convnextva_config(_A   )
						# load original state_dict from URL
						lowerCAmelCase      :Optional[Any]				  = torch.hub.load_state_dict_from_url(_A   )['model']

						print('Converting model parameters...'   )
						# rename keys
						for key in state_dict.copy().keys():
												lowerCAmelCase      :Any				  = state_dict.pop(_A   )
												lowerCAmelCase      :Any				  = val
						# add prefix to all keys expect classifier head
						for key in state_dict.copy().keys():
												lowerCAmelCase      :str				  = state_dict.pop(_A   )
												if not key.startswith('classifier'   ):
																		lowerCAmelCase      :List[str]				  = 'convnextv2.' + key
												lowerCAmelCase      :Optional[int]				  = val

						# load HuggingFace model
						lowerCAmelCase      :Tuple				  = ConvNextVaForImageClassification(_A   )
						model.load_state_dict(_A   )
						model.eval()

						# Check outputs on an image, prepared by ConvNextImageProcessor
						lowerCAmelCase      :Optional[Any]				  = convert_preprocessor(_A   )
						lowerCAmelCase      :List[Any]				  = preprocessor(images=prepare_img()    ,			return_tensors='pt'   )
						lowerCAmelCase      :Optional[Any]				  = model(**_A   ).logits

						# note: the logits below were obtained without center cropping
						if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
												lowerCAmelCase      :int				  = torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
												lowerCAmelCase      :int				  = torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
												lowerCAmelCase      :Optional[int]				  = torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
												lowerCAmelCase      :Any				  = torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
												lowerCAmelCase      :Tuple				  = torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
												lowerCAmelCase      :Optional[int]				  = torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
												lowerCAmelCase      :Union[str, Any]				  = torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
												lowerCAmelCase      :List[Any]				  = torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
												lowerCAmelCase      :Union[str, Any]				  = torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
												lowerCAmelCase      :Optional[int]				  = torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
												lowerCAmelCase      :Optional[int]				  = torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
												lowerCAmelCase      :Tuple				  = torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
												lowerCAmelCase      :List[str]				  = torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
												lowerCAmelCase      :Tuple				  = torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
												lowerCAmelCase      :List[str]				  = torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
												lowerCAmelCase      :Union[str, Any]				  = torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
												lowerCAmelCase      :List[Any]				  = torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309]   )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
												lowerCAmelCase      :Union[str, Any]				  = torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826]   )
						else:
												raise ValueError(F"""Unknown URL: {checkpoint_url}"""   )

						assert torch.allclose(logits[0, :5]    ,			_A    ,			atol=1e-3   )
						assert logits.shape == expected_shape
						print('Model outputs match the original results!'   )

						if save_model:
												print('Saving model to local...'   )
												# Create folder to save model
												if not os.path.isdir(_A   ):
																		os.mkdir(_A   )

												model.save_pretrained(_A   )
												preprocessor.save_pretrained(_A   )

						lowerCAmelCase      :str				  = 'convnextv2'
						if "atto" in checkpoint_url:
												model_name += "-atto"
						if "femto" in checkpoint_url:
												model_name += "-femto"
						if "pico" in checkpoint_url:
												model_name += "-pico"
						if "nano" in checkpoint_url:
												model_name += "-nano"
						elif "tiny" in checkpoint_url:
												model_name += "-tiny"
						elif "base" in checkpoint_url:
												model_name += "-base"
						elif "large" in checkpoint_url:
												model_name += "-large"
						elif "huge" in checkpoint_url:
												model_name += "-huge"
						if "22k" in checkpoint_url and "1k" not in checkpoint_url:
												model_name += "-22k"
						elif "22k" in checkpoint_url and "1k" in checkpoint_url:
												model_name += "-22k-1k"
						elif "1k" in checkpoint_url:
												model_name += "-1k"
						if "224" in checkpoint_url:
												model_name += "-224"
						elif "384" in checkpoint_url:
												model_name += "-384"
						elif "512" in checkpoint_url:
												model_name += "-512"

						if push_to_hub:
												print(F"""Pushing {model_name} to the hub..."""   )
												model.push_to_hub(_A   )
												preprocessor.push_to_hub(_A   )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE      =      argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--checkpoint_url',
		    default='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt',
		    type=str,
		    help='URL of the original ConvNeXTV2 checkpoint you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default='model',
		    type=str,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument('--save_model', action='store_true', help='Save model to local')
		parser.add_argument('--push_to_hub', action='store_true', help='Push model and image preprocessor to the hub')

		__SCREAMING_SNAKE_CASE      =      parser.parse_args()
		convert_convnextva_checkpoint(
		    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
		)







"""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 	__UpperCamelCase    (    unittest.TestCase	):
					@slow
					def 					UpperCAmelCase__   (       self       :  Tuple      )  ->					Union[str, Any]:
											lowerCAmelCase      :Any				  = XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'      )
											model.to(UpperCAmelCase      )

											# encoder-decoder outputs
											lowerCAmelCase      :Any				  = torch.tensor([[17, 9_6208, 10_3471, 2]]      ).to(UpperCAmelCase      )
											lowerCAmelCase      :Optional[int]				  = torch.tensor(
											    [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]      ).to(UpperCAmelCase      )
											lowerCAmelCase      :Tuple				  = model(
											    input_ids=UpperCAmelCase    ,   attention_mask=UpperCAmelCase    ,   encoder_outputs=UpperCAmelCase    ,   decoder_input_ids=UpperCAmelCase      )
											lowerCAmelCase      :Tuple				  = output[0]
											lowerCAmelCase      :Any				  = torch.Size((1, 14, 25_0012)      )
											self.assertEqual(output_predited_logis.shape    ,   UpperCAmelCase      )
											lowerCAmelCase      :Optional[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(UpperCAmelCase      )
											self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]    ,   UpperCAmelCase    ,   atol=1e-4      )      )

											# encoder outputs
											lowerCAmelCase      :int				  = model.prophetnet.encoder(UpperCAmelCase      )[0]
											lowerCAmelCase      :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(UpperCAmelCase      )
											lowerCAmelCase      :Optional[Any]				  = 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
											lowerCAmelCase      :Tuple				  = model.prophetnet.decoder(
											    UpperCAmelCase    ,   encoder_hidden_states=UpperCAmelCase    ,   )
											lowerCAmelCase      :Optional[Any]				  = decoder_outputs[1].view(1    ,   model.config.ngram    ,   14    ,   -1      )
											lowerCAmelCase      :Any				  = model.lm_head(UpperCAmelCase      )
											lowerCAmelCase      :Optional[int]				  = predicting_streams_logits[:, 0]
											self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]    ,   UpperCAmelCase    ,   atol=1e-4      )      )
					@slow
					def 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :Union[str, Any]				  = XLMProphetNetForConditionalGeneration.from_pretrained(
											    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'      )
											model.to(UpperCAmelCase      )

											lowerCAmelCase      :Tuple				  = torch.tensor([[17, 9_6208, 10_3471, 2]]      ).to(UpperCAmelCase      )
											lowerCAmelCase      :Any				  = torch.tensor(
											    [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]      ).to(UpperCAmelCase      )
											lowerCAmelCase      :Tuple				  = model(
											    input_ids=UpperCAmelCase    ,   attention_mask=UpperCAmelCase    ,   encoder_outputs=UpperCAmelCase    ,   decoder_input_ids=UpperCAmelCase      )
											lowerCAmelCase      :Dict				  = output[0]
											lowerCAmelCase      :List[str]				  = torch.Size((1, 14, 25_0012)      )
											self.assertEqual(output_predited_logis.shape    ,   UpperCAmelCase      )
											# compare the actual values for a slice.
											lowerCAmelCase      :List[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(UpperCAmelCase      )

											self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]    ,   UpperCAmelCase    ,   atol=1e-4      )      )
					@slow
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					Optional[int]:
											lowerCAmelCase      :str				  = XLMProphetNetForConditionalGeneration.from_pretrained(
											    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'      )
											model.to(UpperCAmelCase      )
											lowerCAmelCase      :Any				  = 512

											lowerCAmelCase      :Union[str, Any]				  = XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'      )

											lowerCAmelCase      :Optional[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.'
											)
											lowerCAmelCase      :Union[str, Any]				  = (
											    '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.'
											)
											lowerCAmelCase      :Optional[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。'
											)

											lowerCAmelCase      :List[Any]				  = tokenizer(
											    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]    ,   padding=UpperCAmelCase    ,   max_length=255    ,   return_tensors='pt'      ).input_ids
											lowerCAmelCase      :List[str]				  = input_ids.to(UpperCAmelCase      )

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

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