

'''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()
lowerCAmelCase_        =	logging.get_logger(__name__)

def 			__magic_name__				(       A							)	->						List[str]:
	snake_case							       = ConvNextVaConfig()

	if "atto" in checkpoint_url:
		snake_case							       = [2, 2, 6, 2]
		snake_case							       = [4_0, 8_0, 1_6_0, 3_2_0]
	if "femto" in checkpoint_url:
		snake_case							       = [2, 2, 6, 2]
		snake_case							       = [4_8, 9_6, 1_9_2, 3_8_4]
	if "pico" in checkpoint_url:
		snake_case							       = [2, 2, 6, 2]
		snake_case							       = [6_4, 1_2_8, 2_5_6, 5_1_2]
	if "nano" in checkpoint_url:
		snake_case							       = [2, 2, 8, 2]
		snake_case							       = [8_0, 1_6_0, 3_2_0, 6_4_0]
	if "tiny" in checkpoint_url:
		snake_case							       = [3, 3, 9, 3]
		snake_case							       = [9_6, 1_9_2, 3_8_4, 7_6_8]
	if "base" in checkpoint_url:
		snake_case							       = [3, 3, 2_7, 3]
		snake_case							       = [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
	if "large" in checkpoint_url:
		snake_case							       = [3, 3, 2_7, 3]
		snake_case							       = [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
	if "huge" in checkpoint_url:
		snake_case							       = [3, 3, 2_7, 3]
		snake_case							       = [3_5_2, 7_0_4, 1_4_0_8, 2_8_1_6]

	snake_case							       = 1_0_0_0
	snake_case							       = 'imagenet-1k-id2label.json'
	snake_case							       = (1, 1_0_0_0)

	snake_case							       = 'huggingface/label-files'
	snake_case							       = num_labels
	snake_case							       = json.load(open(hf_hub_download(lowercase_    ,      lowercase_    ,      repo_type='dataset'							)    ,      'r'							)							)
	snake_case							       = {int(lowercase_							): v for k, v in idalabel.items()}

	snake_case							       = idalabel
	snake_case							       = {v: k for k, v in idalabel.items()}
	snake_case							       = hidden_sizes
	snake_case							       = depths

	return config, expected_shape

def 			__magic_name__				(       A							)	->						int:
	if "downsample_layers.0.0" in name:
		snake_case							       = name.replace('downsample_layers.0.0'    ,      'embeddings.patch_embeddings'							)
	if "downsample_layers.0.1" in name:
		snake_case							       = name.replace('downsample_layers.0.1'    ,      'embeddings.norm'							)  # we rename to layernorm later on
	if "downsample_layers.1.0" in name:
		snake_case							       = name.replace('downsample_layers.1.0'    ,      'stages.1.downsampling_layer.0'							)
	if "downsample_layers.1.1" in name:
		snake_case							       = name.replace('downsample_layers.1.1'    ,      'stages.1.downsampling_layer.1'							)
	if "downsample_layers.2.0" in name:
		snake_case							       = name.replace('downsample_layers.2.0'    ,      'stages.2.downsampling_layer.0'							)
	if "downsample_layers.2.1" in name:
		snake_case							       = name.replace('downsample_layers.2.1'    ,      'stages.2.downsampling_layer.1'							)
	if "downsample_layers.3.0" in name:
		snake_case							       = name.replace('downsample_layers.3.0'    ,      'stages.3.downsampling_layer.0'							)
	if "downsample_layers.3.1" in name:
		snake_case							       = 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.
		snake_case							       = name[: len('stages.0'							)] + '.layers' + name[len('stages.0'							) :]
	if "gamma" in name:
		snake_case							       = name.replace('gamma'    ,      'weight'							)
	if "beta" in name:
		snake_case							       = name.replace('beta'    ,      'bias'							)
	if "stages" in name:
		snake_case							       = name.replace('stages'    ,      'encoder.stages'							)
	if "norm" in name:
		snake_case							       = name.replace('norm'    ,      'layernorm'							)
	if "head" in name:
		snake_case							       = name.replace('head'    ,      'classifier'							)

	return name

def 			__magic_name__				(       )	->						int:
	snake_case							       = 'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case							       = Image.open(requests.get(lowercase_    ,      stream=lowercase_							).raw							)
	return im

def 			__magic_name__				(       A							)	->						List[str]:
	if "224" in checkpoint_url:
		snake_case							       = 2_2_4
		snake_case							       = 2_2_4 / 2_5_6
	elif "384" in checkpoint_url:
		snake_case							       = 3_8_4
		snake_case							       = None
	else:
		snake_case							       = 5_1_2
		snake_case							       = None

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

@torch.no_grad()
def 			__magic_name__				(       A    ,      A    ,      A    ,      A							)	->						Tuple:
	print('Downloading original model from checkpoint...'							)
	# define ConvNeXTV2 configuration based on URL
	snake_case       ,       snake_case							       = get_convnextva_config(lowercase_							)
	# load original state_dict from URL
	snake_case							       = torch.hub.load_state_dict_from_url(lowercase_							)['model']

	print('Converting model parameters...'							)
	# rename keys
	for key in state_dict.copy().keys():
		snake_case							       = state_dict.pop(lowercase_							)
		snake_case							       = val
	# add prefix to all keys expect classifier head
	for key in state_dict.copy().keys():
		snake_case							       = state_dict.pop(lowercase_							)
		if not key.startswith('classifier'							):
			snake_case							       = 'convnextv2.' + key
		snake_case							       = val

	# load HuggingFace model
	snake_case							       = ConvNextVaForImageClassification(lowercase_							)
	model.load_state_dict(lowercase_							)
	model.eval()

	# Check outputs on an image, prepared by ConvNextImageProcessor
	snake_case							       = convert_preprocessor(lowercase_							)
	snake_case							       = preprocessor(images=prepare_img()    ,      return_tensors='pt'							)
	snake_case							       = model(**lowercase_							).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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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":
		snake_case							       = 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]    ,      lowercase_    ,      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(lowercase_							):
			os.mkdir(lowercase_							)

		model.save_pretrained(lowercase_							)
		preprocessor.save_pretrained(lowercase_							)

	snake_case							       = '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(lowercase_							)
		preprocessor.push_to_hub(lowercase_							)


if __name__ == "__main__":
						lowerCAmelCase_        =	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")

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










"""simple docstring"""
from .stack import Stack



def 				lowerCAmelCase_(      lowercase_   :							str							)		->				bool:
				_lowerCamelCase					     = Stack()
				_lowerCamelCase					     = {'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
				for bracket in parentheses:
								if bracket in bracket_pairs:
												stack.push(lowercase_							)
								elif bracket in (")", "]", "}"):
												if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
																return False
				return stack.is_empty()


if __name__ == "__main__":
			from doctest import testmod

			testmod()

			__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					['''((()))''', '''((())''', '''(()))''']
			print('''Balanced parentheses demonstration:\n''')
			for example in examples:
						__SCREAMING_SNAKE_CASE     :  Any										=					'''''' if balanced_parentheses(example) else '''not '''
						print(F"""{example} is {not_str}balanced""")
