
"""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()
_lowercase							 =							logging.get_logger(__name__)



def     lowerCAmelCase__ (		__magic_name__			)	->List[str]:
			__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(__magic_name__ ,      __magic_name__ ,      repo_type="dataset"			) ,      "r"			)			)
			__lowercase						      =					{int(__magic_name__			): 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__ (		__magic_name__			)	->Any:
			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__ (		)	->int:
			__lowercase						      =					"http://images.cocodataset.org/val2017/000000039769.jpg"
			__lowercase						      =					Image.open(requests.get(__magic_name__ ,      stream=__magic_name__			).raw			)
			return im



@torch.no_grad()
def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->str:
			__lowercase      ,				__lowercase						      =					get_convnext_config(__magic_name__			)
			# load original state_dict from URL
			__lowercase						      =					torch.hub.load_state_dict_from_url(__magic_name__			)["model"]
			# rename keys
			for key in state_dict.copy().keys():
						__lowercase						      =					state_dict.pop(__magic_name__			)
						__lowercase						      =					val
			# add prefix to all keys expect classifier head
			for key in state_dict.copy().keys():
						__lowercase						      =					state_dict.pop(__magic_name__			)
						if not key.startswith("classifier"			):
									__lowercase						      =					"convnext." + key
						__lowercase						      =					val

			# load HuggingFace model
			__lowercase						      =					ConvNextForImageClassification(__magic_name__			)
			model.load_state_dict(__magic_name__			)
			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=__magic_name__			)
			__lowercase						      =					image_processor(images=prepare_img() ,      return_tensors="pt"			).pixel_values

			__lowercase						      =					model(__magic_name__			).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] ,      __magic_name__ ,      atol=1E-3			)
			assert logits.shape == expected_shape

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

			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(__magic_name__ ,      __magic_name__			) ,      organization="nielsr" ,      commit_message="Add model" ,      )


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

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







"""simple docstring"""






def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->list:
			__lowercase						      =					len(__magic_name__			)
			__lowercase						      =					len(__magic_name__			)
			__lowercase						      =					(
			    first_input_list_length
			    if first_input_list_length > second_input_list_length
			    else second_input_list_length
			)
			__lowercase						      =					[]
			for char_count in range(__magic_name__			):
						if char_count < first_input_list_length:
									output_result_list.append(first_input_list[char_count]			)
						if char_count < second_input_list_length:
									output_result_list.append(second_input_list[char_count]			)

			return output_result_list


if __name__ == "__main__":
					print(alternative_list_arrange(['''A''', '''B''', '''C'''], [1, 2, 3, 4, 5]), end=''' ''')





