code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import argparse
  import json
  from pathlib import Path
  
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  lowerCAmelCase					 =	logging.get_logger(__name__)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__			)		->			str:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    ConvNextConfig()
  
  		if "tiny" in checkpoint_url:
  				__lowercase=    [3, 3, 9, 3]
  				__lowercase=    [9_6, 1_9_2, 3_8_4, 7_6_8]
  		if "small" in checkpoint_url:
  				__lowercase=    [3, 3, 2_7, 3]
  				__lowercase=    [9_6, 1_9_2, 3_8_4, 7_6_8]
  		if "base" in checkpoint_url:
  				__lowercase=    [3, 3, 2_7, 3]
  				__lowercase=    [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
  		if "large" in checkpoint_url:
  				__lowercase=    [3, 3, 2_7, 3]
  				__lowercase=    [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
  		if "xlarge" in checkpoint_url:
  				__lowercase=    [3, 3, 2_7, 3]
  				__lowercase=    [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
  
  		if "1k" in checkpoint_url:
  				__lowercase=    1_0_0_0
  				__lowercase=    'imagenet-1k-id2label.json'
  				__lowercase=    (1, 1_0_0_0)
  		else:
  				__lowercase=    2_1_8_4_1
  				__lowercase=    'imagenet-22k-id2label.json'
  				__lowercase=    (1, 2_1_8_4_1)
  
  		__lowercase=    'huggingface/label-files'
  		__lowercase=    num_labels
  		__lowercase=    json.load(open(hf_hub_download(lowerCAmelCase__     ,				lowerCAmelCase__     ,				repo_type='dataset'			)     ,				'r'			)			)
  		__lowercase=    {int(lowerCAmelCase__			): v for k, v in idalabel.items()}
  		if "1k" not in checkpoint_url:
  				# this dataset contains 21843 labels but the model only has 21841
  				# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
  				del idalabel[9_2_0_5]
  				del idalabel[1_5_0_2_7]
  		__lowercase=    idalabel
  		__lowercase=    {v: k for k, v in idalabel.items()}
  		__lowercase=    hidden_sizes
  		__lowercase=    depths
  
  		return config, expected_shape
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__			)		->			str:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if "downsample_layers.0.0" in name:
  				__lowercase=    name.replace('downsample_layers.0.0'     ,				'embeddings.patch_embeddings'			)
  		if "downsample_layers.0.1" in name:
  				__lowercase=    name.replace('downsample_layers.0.1'     ,				'embeddings.norm'			)  # we rename to layernorm later on
  		if "downsample_layers.1.0" in name:
  				__lowercase=    name.replace('downsample_layers.1.0'     ,				'stages.1.downsampling_layer.0'			)
  		if "downsample_layers.1.1" in name:
  				__lowercase=    name.replace('downsample_layers.1.1'     ,				'stages.1.downsampling_layer.1'			)
  		if "downsample_layers.2.0" in name:
  				__lowercase=    name.replace('downsample_layers.2.0'     ,				'stages.2.downsampling_layer.0'			)
  		if "downsample_layers.2.1" in name:
  				__lowercase=    name.replace('downsample_layers.2.1'     ,				'stages.2.downsampling_layer.1'			)
  		if "downsample_layers.3.0" in name:
  				__lowercase=    name.replace('downsample_layers.3.0'     ,				'stages.3.downsampling_layer.0'			)
  		if "downsample_layers.3.1" in name:
  				__lowercase=    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.
  				__lowercase=    name[: len('stages.0'			)] + '.layers' + name[len('stages.0'			) :]
  		if "stages" in name:
  				__lowercase=    name.replace('stages'     ,				'encoder.stages'			)
  		if "norm" in name:
  				__lowercase=    name.replace('norm'     ,				'layernorm'			)
  		if "gamma" in name:
  				__lowercase=    name.replace('gamma'     ,				'layer_scale_parameter'			)
  		if "head" in name:
  				__lowercase=    name.replace('head'     ,				'classifier'			)
  
  		return name
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			Any:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    'http://images.cocodataset.org/val2017/000000039769.jpg'
  		__lowercase=    Image.open(requests.get(lowerCAmelCase__     ,				stream=lowerCAmelCase__			).raw			)
  		return im
  
  
  
  
  
  
  
  @torch.no_grad()
  def  _lowerCamelCase(			lowercase__     ,				lowercase__			)		->			Union[str, Any]:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    get_convnext_config(lowerCAmelCase__			)
  		# load original state_dict from URL
  		__lowercase=    torch.hub.load_state_dict_from_url(lowerCAmelCase__			)['model']
  		# rename keys
  		for key in state_dict.copy().keys():
  				__lowercase=    state_dict.pop(lowerCAmelCase__			)
  				__lowercase=    val
  		# add prefix to all keys expect classifier head
  		for key in state_dict.copy().keys():
  				__lowercase=    state_dict.pop(lowerCAmelCase__			)
  				if not key.startswith('classifier'			):
  						__lowercase=    'convnext.' + key
  				__lowercase=    val
  
  		# load HuggingFace model
  		__lowercase=    ConvNextForImageClassification(lowerCAmelCase__			)
  		model.load_state_dict(lowerCAmelCase__			)
  		model.eval()
  
  		# Check outputs on an image, prepared by ConvNextImageProcessor
  		__lowercase=    2_2_4 if '224' in checkpoint_url else 3_8_4
  		__lowercase=    ConvNextImageProcessor(size=lowerCAmelCase__			)
  		__lowercase=    image_processor(images=prepare_img()     ,				return_tensors='pt'			).pixel_values
  
  		__lowercase=    model(lowerCAmelCase__			).logits
  
  		# note: the logits below were obtained without center cropping
  		if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
  				__lowercase=    torch.tensor([-0.1210, -0.6605, 0.1918]			)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
  				__lowercase=    torch.tensor([-0.4473, -0.1847, -0.6365]			)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
  				__lowercase=    torch.tensor([0.4525, 0.7539, 0.0308]			)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
  				__lowercase=    torch.tensor([0.3561, 0.6350, -0.0384]			)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
  				__lowercase=    torch.tensor([0.4174, -0.0989, 0.1489]			)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
  				__lowercase=    torch.tensor([0.2513, -0.1349, -0.1613]			)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
  				__lowercase=    torch.tensor([1.2980, 0.3631, -0.1198]			)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
  				__lowercase=    torch.tensor([1.2963, 0.1227, 0.1723]			)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
  				__lowercase=    torch.tensor([1.7956, 0.8390, 0.2820]			)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
  				__lowercase=    torch.tensor([-0.2822, -0.0502, -0.0878]			)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
  				__lowercase=    torch.tensor([-0.5672, -0.0730, -0.4348]			)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
  				__lowercase=    torch.tensor([0.2681, 0.2365, 0.6246]			)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
  				__lowercase=    torch.tensor([-0.2642, 0.3931, 0.5116]			)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
  				__lowercase=    torch.tensor([-0.6677, -0.1873, -0.8379]			)
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
  				__lowercase=    torch.tensor([-0.7749, -0.2967, -0.6444]			)
  		else:
  				raise ValueError(F'Unknown URL: {checkpoint_url}'			)
  
  		assert torch.allclose(logits[0, :3]     ,				lowerCAmelCase__     ,				atol=1E-3			)
  		assert logits.shape == expected_shape
  
  		Path(lowerCAmelCase__			).mkdir(exist_ok=lowerCAmelCase__			)
  		print(F'Saving model to {pytorch_dump_folder_path}'			)
  		model.save_pretrained(lowerCAmelCase__			)
  		print(F'Saving image processor to {pytorch_dump_folder_path}'			)
  		image_processor.save_pretrained(lowerCAmelCase__			)
  
  		print('Pushing model to the hub...'			)
  		__lowercase=    'convnext'
  		if "tiny" in checkpoint_url:
  				model_name += "-tiny"
  		elif "small" in checkpoint_url:
  				model_name += "-small"
  		elif "base" in checkpoint_url:
  				model_name += "-base"
  		elif "xlarge" in checkpoint_url:
  				model_name += "-xlarge"
  		elif "large" in checkpoint_url:
  				model_name += "-large"
  		if "224" in checkpoint_url:
  				model_name += "-224"
  		elif "384" in checkpoint_url:
  				model_name += "-384"
  		if "22k" in checkpoint_url and "1k" not in checkpoint_url:
  				model_name += "-22k"
  		if "22k" in checkpoint_url and "1k" in checkpoint_url:
  				model_name += "-22k-1k"
  
  		model.push_to_hub(
  		    repo_path_or_name=Path(lowerCAmelCase__     ,				lowerCAmelCase__			)     ,				organization='nielsr'     ,				commit_message='Add model'     ,				)
  
  
  if __name__ == "__main__":
  	lowerCAmelCase					 =	argparse.ArgumentParser()
  	# Required parameters
  	parser.add_argument(
  	    '''--checkpoint_url''',
  	    default='''https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth''',
  	    type=str,
  	    help='''URL of the original ConvNeXT checkpoint you\'d like to convert.''',
  	)
  	parser.add_argument(
  	    '''--pytorch_dump_folder_path''',
  	    default=None,
  	    type=str,
  	    required=True,
  	    help='''Path to the output PyTorch model directory.''',
  	)
  
  	lowerCAmelCase					 =	parser.parse_args()
  	convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
  
  
  
  
  
  
  
style_context: 
  
  
  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				):
  
  
  							@slow
  							def     _A (self      ):
  									__lowercase=    XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'      )
  									model.to(lowerCAmelCase      )
  
  									# encoder-decoder outputs
  									__lowercase=    torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]      ).to(lowerCAmelCase      )
  									__lowercase=    torch.tensor(
  									    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]      ).to(lowerCAmelCase      )
  									__lowercase=    model(
  									    input_ids=lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       encoder_outputs=lowerCAmelCase   ,       decoder_input_ids=lowerCAmelCase      )
  									__lowercase=    output[0]
  									__lowercase=    torch.Size((1, 1_4, 2_5_0_0_1_2)      )
  									self.assertEqual(output_predited_logis.shape   ,       lowerCAmelCase      )
  									__lowercase=    torch.tensor(
  									    [[[-6.39_86, -8.23_91, 12.51_89], [-6.32_89, -8.08_64, 12.62_11], [-6.24_18, -8.04_45, 12.79_68]]]      ).to(lowerCAmelCase      )
  									self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]   ,       lowerCAmelCase   ,       atol=1E-4      )      )
  
  									# encoder outputs
  									__lowercase=    model.prophetnet.encoder(lowerCAmelCase      )[0]
  									__lowercase=    torch.tensor(
  									    [[[-1.42_60, -0.76_28, 0.84_53], [-1.47_19, -0.13_91, 0.78_07], [-1.76_78, 0.01_14, 0.46_46]]]      ).to(lowerCAmelCase      )
  									__lowercase=    torch.Size((1, 4, 1_0_2_4)      )
  									self.assertEqual(encoder_outputs.shape   ,       lowerCAmelCase      )
  									self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]   ,       lowerCAmelCase   ,       atol=1E-4      )      )
  
  									# decoder outputs
  									__lowercase=    model.prophetnet.decoder(
  									    lowerCAmelCase   ,       encoder_hidden_states=lowerCAmelCase   ,       )
  									__lowercase=    decoder_outputs[1].view(1   ,       model.config.ngram   ,       1_4   ,       -1      )
  									__lowercase=    model.lm_head(lowerCAmelCase      )
  									__lowercase=    predicting_streams_logits[:, 0]
  									self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]   ,       lowerCAmelCase   ,       atol=1E-4      )      )
  
  
  							@slow
  							def     _A (self      ):
  									__lowercase=    XLMProphetNetForConditionalGeneration.from_pretrained(
  									    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'      )
  									model.to(lowerCAmelCase      )
  
  									__lowercase=    torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]      ).to(lowerCAmelCase      )
  									__lowercase=    torch.tensor(
  									    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]      ).to(lowerCAmelCase      )
  									__lowercase=    model(
  									    input_ids=lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       encoder_outputs=lowerCAmelCase   ,       decoder_input_ids=lowerCAmelCase      )
  									__lowercase=    output[0]
  									__lowercase=    torch.Size((1, 1_4, 2_5_0_0_1_2)      )
  									self.assertEqual(output_predited_logis.shape   ,       lowerCAmelCase      )
  									# compare the actual values for a slice.
  									__lowercase=    torch.tensor(
  									    [[[-9.22_53, -9.71_73, -6.35_29], [-7.67_01, -9.01_45, -1.93_82], [-8.01_95, -7.00_04, -0.15_23]]]      ).to(lowerCAmelCase      )
  
  									self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]   ,       lowerCAmelCase   ,       atol=1E-4      )      )
  
  
  
  
  
  
  
  							@slow
  							def     _A (self      ):
  									__lowercase=    XLMProphetNetForConditionalGeneration.from_pretrained(
  									    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'      )
  									model.to(lowerCAmelCase      )
  									__lowercase=    5_1_2
  
  									__lowercase=    XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'      )
  
  									__lowercase=    (
  									    '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.'
  									)
  									__lowercase=    (
  									    '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.'
  									)
  									__lowercase=    (
  									    '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。'
  									)
  
  									__lowercase=    tokenizer(
  									    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]   ,       padding=lowerCAmelCase   ,       max_length=2_5_5   ,       return_tensors='pt'      ).input_ids
  									__lowercase=    input_ids.to(lowerCAmelCase      )
  
  									__lowercase=    model.generate(
  									    lowerCAmelCase   ,       num_beams=1_0   ,       length_penalty=1.0   ,       no_repeat_ngram_size=3   ,       early_stopping=lowerCAmelCase      )
  									__lowercase=    [tokenizer.decode(lowerCAmelCase   ,       skip_special_tokens=lowerCAmelCase      ) for g in summary_ids]
  									__lowercase=    'Microsoft to end Windows 7 free support after January 14, 2020'
  									__lowercase=    'Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'
  									__lowercase=    '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   ,       )
  
  									__lowercase=    model.generate(
  									    lowerCAmelCase   ,       num_beams=1   ,       length_penalty=1.0   ,       no_repeat_ngram_size=3   ,       early_stopping=lowerCAmelCase      )
  									__lowercase=    [
  									    tokenizer.convert_ids_to_tokens(lowerCAmelCase   ,       skip_special_tokens=lowerCAmelCase      ) for g in summary_ids_beama
  									]
  									__lowercase=    '▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'.split(' '      )
  									__lowercase=    '▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'.split(
  									    ' '      )
  									__lowercase=    '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   ,       )
  
  
  
  
  
  
  