





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()
__A       :					Optional[Any]     					=     logging.get_logger(__name__)





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__				)						->       Tuple:
			UpperCAmelCase     	=       ConvNextVaConfig()

			if "atto" in checkpoint_url:
						UpperCAmelCase     	=       [2, 2, 6, 2]
						UpperCAmelCase     	=       [40, 80, 160, 320]
			if "femto" in checkpoint_url:
						UpperCAmelCase     	=       [2, 2, 6, 2]
						UpperCAmelCase     	=       [48, 96, 192, 384]
			if "pico" in checkpoint_url:
						UpperCAmelCase     	=       [2, 2, 6, 2]
						UpperCAmelCase     	=       [64, 128, 256, 512]
			if "nano" in checkpoint_url:
						UpperCAmelCase     	=       [2, 2, 8, 2]
						UpperCAmelCase     	=       [80, 160, 320, 640]
			if "tiny" in checkpoint_url:
						UpperCAmelCase     	=       [3, 3, 9, 3]
						UpperCAmelCase     	=       [96, 192, 384, 768]
			if "base" in checkpoint_url:
						UpperCAmelCase     	=       [3, 3, 27, 3]
						UpperCAmelCase     	=       [128, 256, 512, 1024]
			if "large" in checkpoint_url:
						UpperCAmelCase     	=       [3, 3, 27, 3]
						UpperCAmelCase     	=       [192, 384, 768, 1536]
			if "huge" in checkpoint_url:
						UpperCAmelCase     	=       [3, 3, 27, 3]
						UpperCAmelCase     	=       [352, 704, 1408, 2816]

			UpperCAmelCase     	=       1000
			UpperCAmelCase     	=       '''imagenet-1k-id2label.json'''
			UpperCAmelCase     	=       (1, 1000)

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

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

			return config, expected_shape





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__				)						->       Any:

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

			return name





def 		__SCREAMING_SNAKE_CASE						(       )						->       Optional[Any]:
			UpperCAmelCase     	=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			UpperCAmelCase     	=       Image.open(requests.get(UpperCamelCase__    ,    stream=UpperCamelCase__				).raw				)
			return im





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__				)						->       List[Any]:

			if "224" in checkpoint_url:
						UpperCAmelCase     	=       224
						UpperCAmelCase     	=       224 / 256
			elif "384" in checkpoint_url:
						UpperCAmelCase     	=       384
						UpperCAmelCase     	=       None
			else:
						UpperCAmelCase     	=       512
						UpperCAmelCase     	=       None

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





@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)						->       Dict:
			print('''Downloading original model from checkpoint...'''				)
			# define ConvNeXTV2 configuration based on URL
			UpperCAmelCase	,    UpperCAmelCase     	=       get_convnextva_config(UpperCamelCase__				)
			# load original state_dict from URL
			UpperCAmelCase     	=       torch.hub.load_state_dict_from_url(UpperCamelCase__				)['''model''']

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

			# load HuggingFace model
			UpperCAmelCase     	=       ConvNextVaForImageClassification(UpperCamelCase__				)
			model.load_state_dict(UpperCamelCase__				)
			model.eval()

			# Check outputs on an image, prepared by ConvNextImageProcessor
			UpperCAmelCase     	=       convert_preprocessor(UpperCamelCase__				)
			UpperCAmelCase     	=       preprocessor(images=prepare_img()    ,    return_tensors='''pt'''				)
			UpperCAmelCase     	=       model(**UpperCamelCase__				).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":
						UpperCAmelCase     	=       torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
						UpperCAmelCase     	=       torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
						UpperCAmelCase     	=       torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
						UpperCAmelCase     	=       torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
						UpperCAmelCase     	=       torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
						UpperCAmelCase     	=       torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
						UpperCAmelCase     	=       torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
						UpperCAmelCase     	=       torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
						UpperCAmelCase     	=       torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
						UpperCAmelCase     	=       torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
						UpperCAmelCase     	=       torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
						UpperCAmelCase     	=       torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
						UpperCAmelCase     	=       torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
						UpperCAmelCase     	=       torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
						UpperCAmelCase     	=       torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
						UpperCAmelCase     	=       torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
						UpperCAmelCase     	=       torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
						UpperCAmelCase     	=       torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]				)
			else:
						raise ValueError(F"""Unknown URL: {checkpoint_url}"""				)

			assert torch.allclose(logits[0, :5]    ,    UpperCamelCase__    ,    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(UpperCamelCase__				):
									os.mkdir(UpperCamelCase__				)

						model.save_pretrained(UpperCamelCase__				)
						preprocessor.save_pretrained(UpperCamelCase__				)

			UpperCAmelCase     	=       '''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(UpperCamelCase__				)
						preprocessor.push_to_hub(UpperCamelCase__				)


if __name__ == "__main__":
				__A       :					List[Any]     					=     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")

				__A       :					Union[str, Any]     					=     parser.parse_args()
				convert_convnextva_checkpoint(
				    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
				)










import numpy as np





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__="utf-8"    ,    UpperCamelCase__="surrogatepass"				)						->       Optional[Any]:







			'''simple docstring'''

			UpperCAmelCase     	=       bin(int.from_bytes(text.encode(UpperCamelCase__    ,    UpperCamelCase__				)    ,    '''big'''				)				)[2:]
			return bits.zfill(8 * ((len(UpperCamelCase__				) + 7) // 8)				)





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__="utf-8"    ,    UpperCamelCase__="surrogatepass"				)						->       str:







			'''simple docstring'''

			UpperCAmelCase     	=       int(UpperCamelCase__    ,    2				)
			return n.to_bytes((n.bit_length() + 7) // 8    ,    '''big'''				).decode(UpperCamelCase__    ,    UpperCamelCase__				) or "\0"





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__				)						->       Tuple:







			'''simple docstring'''

			if size_par + len(UpperCamelCase__				) <= 2**size_par - (len(UpperCamelCase__				) - 1):
						raise ValueError('''size of parity don\'t match with size of data'''				)

			UpperCAmelCase     	=       []
			UpperCAmelCase     	=       []
			UpperCAmelCase     	=       [bin(UpperCamelCase__				)[2:] for x in range(1    ,    size_par + len(UpperCamelCase__				) + 1				)]

			# sorted information data for the size of the output data
			UpperCAmelCase     	=       []
			# data position template + parity
			UpperCAmelCase     	=       []
			# parity bit counter
			UpperCAmelCase     	=       0
			# counter position of data bits
			UpperCAmelCase     	=       0

			for x in range(1    ,    size_par + len(UpperCamelCase__				) + 1				):
						# Performs a template of bit positions - who should be given,
						# and who should be parity
						if qtd_bp < size_par:
									if (np.log(UpperCamelCase__				) / np.log(2				)).is_integer():
												data_out_gab.append('''P'''				)
												UpperCAmelCase     	=       qtd_bp + 1
									else:
												data_out_gab.append('''D'''				)
						else:
									data_out_gab.append('''D'''				)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data[cont_data]				)
									cont_data += 1
						else:
									data_ord.append(UpperCamelCase__				)

    # Calculates parity
			UpperCAmelCase     	=       0  # parity bit counter
			for bp in range(1    ,    size_par + 1				):
						# Bit counter one for a given parity
						UpperCAmelCase     	=       0
						# counter to control the loop reading
						UpperCAmelCase     	=       0
						for x in data_ord:
									if x is not None:
												try:
															UpperCAmelCase     	=       (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCAmelCase     	=       '''0'''
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(cont_bo % 2				)

						qtd_bp += 1

			# Mount the message
			UpperCAmelCase     	=       0  # parity bit counter
			for x in range(0    ,    size_par + len(UpperCamelCase__				)				):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]				)				)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]				)

			return data_out





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__				)						->       Optional[int]:







			'''simple docstring'''

			UpperCAmelCase     	=       []
			# Parity bit counter
			UpperCAmelCase     	=       0
			# Counter p data bit reading
			UpperCAmelCase     	=       0
			# list of parity received
			UpperCAmelCase     	=       []
			UpperCAmelCase     	=       []

			for x in range(1    ,    len(UpperCamelCase__				) + 1				):
						# Performs a template of bit positions - who should be given,
						#  and who should be parity
						if qtd_bp < size_par and (np.log(UpperCamelCase__				) / np.log(2				)).is_integer():
									data_out_gab.append('''P'''				)
									UpperCAmelCase     	=       qtd_bp + 1
						else:
									data_out_gab.append('''D'''				)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_output.append(data[cont_data]				)
						else:
									parity_received.append(data[cont_data]				)
						cont_data += 1

			# -----------calculates the parity with the data
			UpperCAmelCase     	=       []
			UpperCAmelCase     	=       []
			UpperCAmelCase     	=       [bin(UpperCamelCase__				)[2:] for x in range(1    ,    size_par + len(UpperCamelCase__				) + 1				)]

			#  sorted information data for the size of the output data
			UpperCAmelCase     	=       []
			# Data position feedback + parity
			UpperCAmelCase     	=       []
			# Parity bit counter
			UpperCAmelCase     	=       0
			# Counter p data bit reading
			UpperCAmelCase     	=       0

			for x in range(1    ,    size_par + len(UpperCamelCase__				) + 1				):
						# Performs a template position of bits - who should be given,
						# and who should be parity
						if qtd_bp < size_par and (np.log(UpperCamelCase__				) / np.log(2				)).is_integer():
									data_out_gab.append('''P'''				)
									UpperCAmelCase     	=       qtd_bp + 1
						else:
									data_out_gab.append('''D'''				)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data_output[cont_data]				)
									cont_data += 1
						else:
									data_ord.append(UpperCamelCase__				)

    # Calculates parity
			UpperCAmelCase     	=       0  # parity bit counter
			for bp in range(1    ,    size_par + 1				):
						# Bit counter one for a certain parity
						UpperCAmelCase     	=       0
						# Counter to control loop reading
						UpperCAmelCase     	=       0
						for x in data_ord:
									if x is not None:
												try:
															UpperCAmelCase     	=       (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCAmelCase     	=       '''0'''
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(str(cont_bo % 2				)				)

						qtd_bp += 1

			# Mount the message
			UpperCAmelCase     	=       0  # Parity bit counter
			for x in range(0    ,    size_par + len(UpperCamelCase__				)				):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]				)				)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]				)

			UpperCAmelCase     	=       parity_received == parity
			return data_output, ack



