


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_														=      logging.get_logger(__name__)







def 							UpperCAmelCase				(     UpperCAmelCase      )->		Tuple:







					'''simple docstring'''



					SCREAMING_SNAKE_CASE_	      =       ConvNextVaConfig()

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

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

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

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

					return config, expected_shape







def 							UpperCAmelCase				(     UpperCAmelCase      )->		int:







					'''simple docstring'''



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

					return name







def 							UpperCAmelCase				(     )->		Tuple:







					'''simple docstring'''



					SCREAMING_SNAKE_CASE_	      =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					SCREAMING_SNAKE_CASE_	      =       Image.open(requests.get(UpperCAmelCase				,stream=UpperCAmelCase      ).raw      )
					return im







def 							UpperCAmelCase				(     UpperCAmelCase      )->		Any:







					'''simple docstring'''



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

					return ConvNextImageProcessor(
					    size=UpperCAmelCase				,crop_pct=UpperCAmelCase				,image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]				,image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]				,resample=PILImageResampling.BICUBIC				,)







@torch.no_grad()
def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase      )->		int:







					'''simple docstring'''



					print('''Downloading original model from checkpoint...'''      )
					# define ConvNeXTV2 configuration based on URL
					SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       get_convnextva_config(UpperCAmelCase      )
					# load original state_dict from URL
					SCREAMING_SNAKE_CASE_	      =       torch.hub.load_state_dict_from_url(UpperCAmelCase      )['''model''']

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

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

					# Check outputs on an image, prepared by ConvNextImageProcessor
					SCREAMING_SNAKE_CASE_	      =       convert_preprocessor(UpperCAmelCase      )
					SCREAMING_SNAKE_CASE_	      =       preprocessor(images=prepare_img()				,return_tensors='''pt'''      )
					SCREAMING_SNAKE_CASE_	      =       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":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9]      )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
										SCREAMING_SNAKE_CASE_	      =       torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6]      )
					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      )

					SCREAMING_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(UpperCAmelCase      )
										preprocessor.push_to_hub(UpperCAmelCase      )


if __name__ == "__main__":
		A_														=      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_														=      parser.parse_args()
		convert_convnextva_checkpoint(
		    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
		)






def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		int:







					'''simple docstring'''



					return (input_a, input_a).count(0      ) % 2







def 							UpperCAmelCase				(     )->		None:







					'''simple docstring'''



					assert xor_gate(0				,0      ) == 0
					assert xor_gate(0				,1      ) == 1
					assert xor_gate(1				,0      ) == 1
					assert xor_gate(1				,1      ) == 0


if __name__ == "__main__":
		print(xor_gate(0, 0))
		print(xor_gate(0, 1))


