






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()
__UpperCAmelCase										=		logging.get_logger(__name__)




def   lowercase__   (  __snake_case					:							Optional[int]						):


					'''simple docstring'''


					UpperCAmelCase_					:   Optional[Any]   =		ConvNextConfig()

					if "tiny" in checkpoint_url:
										UpperCAmelCase_					:   Any   =		[3, 3, 9, 3]
										UpperCAmelCase_					:   List[Any]   =		[96, 192, 384, 768]
					if "small" in checkpoint_url:
										UpperCAmelCase_					:   List[Any]   =		[3, 3, 27, 3]
										UpperCAmelCase_					:   Optional[Any]   =		[96, 192, 384, 768]
					if "base" in checkpoint_url:
										UpperCAmelCase_					:   Optional[Any]   =		[3, 3, 27, 3]
										UpperCAmelCase_					:   int   =		[128, 256, 512, 1_024]
					if "large" in checkpoint_url:
										UpperCAmelCase_					:   int   =		[3, 3, 27, 3]
										UpperCAmelCase_					:   Tuple   =		[192, 384, 768, 1_536]
					if "xlarge" in checkpoint_url:
										UpperCAmelCase_					:   Any   =		[3, 3, 27, 3]
										UpperCAmelCase_					:   Any   =		[256, 512, 1_024, 2_048]

					if "1k" in checkpoint_url:
										UpperCAmelCase_					:   Tuple   =		1_000
										UpperCAmelCase_					:   Dict   =		'imagenet-1k-id2label.json'
										UpperCAmelCase_					:   Tuple   =		(1, 1_000)
					else:
										UpperCAmelCase_					:   Tuple   =		21_841
										UpperCAmelCase_					:   List[str]   =		'imagenet-22k-id2label.json'
										UpperCAmelCase_					:   Union[str, Any]   =		(1, 21_841)

					UpperCAmelCase_					:   Union[str, Any]   =		'huggingface/label-files'
					UpperCAmelCase_					:   str   =		num_labels
					UpperCAmelCase_					:   Any   =		json.load(open(hf_hub_download(__snake_case  , __snake_case  , repo_type='dataset'						)  , 'r'						)						)
					UpperCAmelCase_					:   Dict   =		{int(__snake_case						): 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_205]
										del idalabel[15_027]
					UpperCAmelCase_					:   str   =		idalabel
					UpperCAmelCase_					:   Tuple   =		{v: k for k, v in idalabel.items()}
					UpperCAmelCase_					:   Any   =		hidden_sizes
					UpperCAmelCase_					:   Optional[int]   =		depths

					return config, expected_shape




def   lowercase__   (  __snake_case					:							Optional[int]						):


					'''simple docstring'''


					if "downsample_layers.0.0" in name:
										UpperCAmelCase_					:   Union[str, Any]   =		name.replace('downsample_layers.0.0'  , 'embeddings.patch_embeddings'						)
					if "downsample_layers.0.1" in name:
										UpperCAmelCase_					:   Optional[Any]   =		name.replace('downsample_layers.0.1'  , 'embeddings.norm'						)  # we rename to layernorm later on
					if "downsample_layers.1.0" in name:
										UpperCAmelCase_					:   int   =		name.replace('downsample_layers.1.0'  , 'stages.1.downsampling_layer.0'						)
					if "downsample_layers.1.1" in name:
										UpperCAmelCase_					:   Optional[Any]   =		name.replace('downsample_layers.1.1'  , 'stages.1.downsampling_layer.1'						)
					if "downsample_layers.2.0" in name:
										UpperCAmelCase_					:   Optional[int]   =		name.replace('downsample_layers.2.0'  , 'stages.2.downsampling_layer.0'						)
					if "downsample_layers.2.1" in name:
										UpperCAmelCase_					:   Tuple   =		name.replace('downsample_layers.2.1'  , 'stages.2.downsampling_layer.1'						)
					if "downsample_layers.3.0" in name:
										UpperCAmelCase_					:   Tuple   =		name.replace('downsample_layers.3.0'  , 'stages.3.downsampling_layer.0'						)
					if "downsample_layers.3.1" in name:
										UpperCAmelCase_					:   List[Any]   =		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.
										UpperCAmelCase_					:   List[Any]   =		name[: len('stages.0'						)] + '.layers' + name[len('stages.0'						) :]
					if "stages" in name:
										UpperCAmelCase_					:   int   =		name.replace('stages'  , 'encoder.stages'						)
					if "norm" in name:
										UpperCAmelCase_					:   Any   =		name.replace('norm'  , 'layernorm'						)
					if "gamma" in name:
										UpperCAmelCase_					:   Dict   =		name.replace('gamma'  , 'layer_scale_parameter'						)
					if "head" in name:
										UpperCAmelCase_					:   Dict   =		name.replace('head'  , 'classifier'						)

					return name




def   lowercase__   (  ):


					'''simple docstring'''


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




@torch.no_grad()
def   lowercase__   (  __snake_case					:							Dict  , __snake_case					:							Optional[int]						):


					'''simple docstring'''


					UpperCAmelCase_ ,						UpperCAmelCase_					:   List[Any]   =		get_convnext_config(__snake_case						)
					# load original state_dict from URL
					UpperCAmelCase_					:   Union[str, Any]   =		torch.hub.load_state_dict_from_url(__snake_case						)['model']
					# rename keys
					for key in state_dict.copy().keys():
										UpperCAmelCase_					:   int   =		state_dict.pop(__snake_case						)
										UpperCAmelCase_					:   List[Any]   =		val
					# add prefix to all keys expect classifier head
					for key in state_dict.copy().keys():
										UpperCAmelCase_					:   str   =		state_dict.pop(__snake_case						)
										if not key.startswith('classifier'						):
															UpperCAmelCase_					:   Union[str, Any]   =		'convnext.' + key
										UpperCAmelCase_					:   List[Any]   =		val

					# load HuggingFace model
					UpperCAmelCase_					:   int   =		ConvNextForImageClassification(__snake_case						)
					model.load_state_dict(__snake_case						)
					model.eval()

					# Check outputs on an image, prepared by ConvNextImageProcessor
					UpperCAmelCase_					:   List[Any]   =		224 if '224' in checkpoint_url else 384
					UpperCAmelCase_					:   Dict   =		ConvNextImageProcessor(size=__snake_case						)
					UpperCAmelCase_					:   List[Any]   =		image_processor(images=prepare_img()  , return_tensors='pt'						).pixel_values

					UpperCAmelCase_					:   Any   =		model(__snake_case						).logits

					# note: the logits below were obtained without center cropping
					if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
										UpperCAmelCase_					:   Tuple   =		torch.tensor([-0.1210, -0.6605, 0.1918]						)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
										UpperCAmelCase_					:   List[str]   =		torch.tensor([-0.4473, -0.1847, -0.6365]						)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
										UpperCAmelCase_					:   Tuple   =		torch.tensor([0.4525, 0.7539, 0.0308]						)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
										UpperCAmelCase_					:   Any   =		torch.tensor([0.3561, 0.6350, -0.0384]						)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
										UpperCAmelCase_					:   Optional[int]   =		torch.tensor([0.4174, -0.0989, 0.1489]						)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
										UpperCAmelCase_					:   Optional[int]   =		torch.tensor([0.2513, -0.1349, -0.1613]						)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
										UpperCAmelCase_					:   Union[str, Any]   =		torch.tensor([1.2980, 0.3631, -0.1198]						)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
										UpperCAmelCase_					:   List[str]   =		torch.tensor([1.2963, 0.1227, 0.1723]						)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
										UpperCAmelCase_					:   Optional[int]   =		torch.tensor([1.7956, 0.8390, 0.2820]						)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
										UpperCAmelCase_					:   Union[str, Any]   =		torch.tensor([-0.2822, -0.0502, -0.0878]						)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
										UpperCAmelCase_					:   Optional[int]   =		torch.tensor([-0.5672, -0.0730, -0.4348]						)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
										UpperCAmelCase_					:   List[Any]   =		torch.tensor([0.2681, 0.2365, 0.6246]						)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
										UpperCAmelCase_					:   Any   =		torch.tensor([-0.2642, 0.3931, 0.5116]						)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
										UpperCAmelCase_					:   Dict   =		torch.tensor([-0.6677, -0.1873, -0.8379]						)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
										UpperCAmelCase_					:   Any   =		torch.tensor([-0.7749, -0.2967, -0.6444]						)
					else:
										raise ValueError(F"Unknown URL: {checkpoint_url}"						)

					assert torch.allclose(logits[0, :3]  , __snake_case  , atol=1E-3						)
					assert logits.shape == expected_shape

					Path(__snake_case						).mkdir(exist_ok=__snake_case						)
					print(F"Saving model to {pytorch_dump_folder_path}"						)
					model.save_pretrained(__snake_case						)
					print(F"Saving image processor to {pytorch_dump_folder_path}"						)
					image_processor.save_pretrained(__snake_case						)

					print('Pushing model to the hub...'						)
					UpperCAmelCase_					:   Any   =		'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(__snake_case  , __snake_case						)  , organization='nielsr'  , commit_message='Add model'  , )


if __name__ == "__main__":
		__UpperCAmelCase										=		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.',
		)

		__UpperCAmelCase										=		parser.parse_args()
		convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)














import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 						lowerCamelCase   (_snake_case				,						unittest.TestCase    ):







		'''simple docstring'''



		_snake_case					:					Tuple 		=						BertweetTokenizer
		_snake_case					:					List[str] 		=						False



		def       __UpperCAmelCase    (							self     )			->							Optional[int]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							UpperCAmelCase_					:   Optional[int]   =		['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
							UpperCAmelCase_					:   Dict   =		dict(zip(_UpperCamelCase       ,	range(len(_UpperCamelCase     )     )     )     )
							UpperCAmelCase_					:   List[Any]   =		['#version: 0.2', 'a m</w>']
							UpperCAmelCase_					:   Union[str, Any]   =		{'unk_token': '<unk>'}

							UpperCAmelCase_					:   Optional[Any]   =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['vocab_file']     )
							UpperCAmelCase_					:   List[str]   =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['merges_file']     )
							with open(self.vocab_file       ,	'w'       ,	encoding='utf-8'     ) as fp:
												for token in vocab_tokens:
																	fp.write(f"{token} {vocab_tokens[token]}\n"     )
							with open(self.merges_file       ,	'w'       ,	encoding='utf-8'     ) as fp:
												fp.write('\n'.join(_UpperCamelCase     )     )



		def       __UpperCAmelCase    (							self       ,	**_UpperCamelCase     )			->							List[Any]:
							kwargs.update(self.special_tokens_map     )
							return BertweetTokenizer.from_pretrained(self.tmpdirname       ,	**_UpperCamelCase     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							List[Any]:
							UpperCAmelCase_					:   Any   =		'I am VinAI Research'
							UpperCAmelCase_					:   Any   =		'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
							return input_text, output_text



		def       __UpperCAmelCase    (							self     )			->							Tuple:
							UpperCAmelCase_					:   Union[str, Any]   =		BertweetTokenizer(self.vocab_file       ,	self.merges_file       ,	**self.special_tokens_map     )
							UpperCAmelCase_					:   Optional[Any]   =		'I am VinAI Research'
							UpperCAmelCase_					:   str   =		'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
							UpperCAmelCase_					:   Any   =		tokenizer.tokenize(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase       ,	_UpperCamelCase     )

							UpperCAmelCase_					:   Optional[int]   =		tokens + [tokenizer.unk_token]

							UpperCAmelCase_					:   List[str]   =		[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase     )       ,	_UpperCamelCase     )






