






"""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()
_UpperCamelCase   :						Dict        = logging.get_logger(__name__)




def 				a (     UpperCamelCase_							:				int		)  ->		Tuple:
 __A	:       Tuple       =	ConvNextConfig()

 if "tiny" in checkpoint_url:
  __A	:       List[str]       =	[3, 3, 9, 3]
  __A	:       Union[str, Any]       =	[9_6, 1_9_2, 3_8_4, 7_6_8]
 if "small" in checkpoint_url:
  __A	:       List[str]       =	[3, 3, 2_7, 3]
  __A	:       Dict       =	[9_6, 1_9_2, 3_8_4, 7_6_8]
 if "base" in checkpoint_url:
  __A	:       Union[str, Any]       =	[3, 3, 2_7, 3]
  __A	:       List[str]       =	[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
 if "large" in checkpoint_url:
  __A	:       Tuple       =	[3, 3, 2_7, 3]
  __A	:       str       =	[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
 if "xlarge" in checkpoint_url:
  __A	:       Any       =	[3, 3, 2_7, 3]
  __A	:       Union[str, Any]       =	[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]

 if "1k" in checkpoint_url:
  __A	:       Tuple       =	1_0_0_0
  __A	:       int       =	'imagenet-1k-id2label.json'
  __A	:       Optional[int]       =	(1, 1_0_0_0)
 else:
  __A	:       Union[str, Any]       =	2_1_8_4_1
  __A	:       int       =	'imagenet-22k-id2label.json'
  __A	:       str       =	(1, 2_1_8_4_1)

 __A	:       List[str]       =	'huggingface/label-files'
 __A	:       Optional[Any]       =	num_labels
 __A	:       List[Any]       =	json.load(open(hf_hub_download(UpperCamelCase_     ,	UpperCamelCase_     ,	repo_type='dataset'		)     ,	'r'		)		)
 __A	:       Dict       =	{int(UpperCamelCase_		): 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]
 __A	:       Optional[int]       =	idalabel
 __A	:       str       =	{v: k for k, v in idalabel.items()}
 __A	:       Dict       =	hidden_sizes
 __A	:       List[str]       =	depths

 return config, expected_shape




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

 return name




def 				a (     )  ->		List[str]:
 __A	:       Tuple       =	'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A	:       Dict       =	Image.open(requests.get(UpperCamelCase_     ,	stream=UpperCamelCase_		).raw		)
 return im




@torch.no_grad()
def 				a (     UpperCamelCase_							:				str     ,	UpperCamelCase_							:				str		)  ->		Dict:
 __A	:       Optional[Any]       =	get_convnext_config(UpperCamelCase_		)
 # load original state_dict from URL
 __A	:       Any       =	torch.hub.load_state_dict_from_url(UpperCamelCase_		)['model']
 # rename keys
 for key in state_dict.copy().keys():
  __A	:       List[Any]       =	state_dict.pop(UpperCamelCase_		)
  __A	:       Optional[Any]       =	val
 # add prefix to all keys expect classifier head
 for key in state_dict.copy().keys():
  __A	:       int       =	state_dict.pop(UpperCamelCase_		)
  if not key.startswith('classifier'		):
   __A	:       Tuple       =	'convnext.' + key
  __A	:       List[str]       =	val

 # load HuggingFace model
 __A	:       str       =	ConvNextForImageClassification(UpperCamelCase_		)
 model.load_state_dict(UpperCamelCase_		)
 model.eval()

 # Check outputs on an image, prepared by ConvNextImageProcessor
 __A	:       List[Any]       =	2_2_4 if '224' in checkpoint_url else 3_8_4
 __A	:       Union[str, Any]       =	ConvNextImageProcessor(size=UpperCamelCase_		)
 __A	:       List[str]       =	image_processor(images=prepare_img()     ,	return_tensors='pt'		).pixel_values

 __A	:       str       =	model(UpperCamelCase_		).logits

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

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

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

 print('Pushing model to the hub...'		)
 __A	:       Optional[int]       =	'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(UpperCamelCase_     ,	UpperCamelCase_		)     ,	organization='nielsr'     ,	commit_message='Add model'     ,	)


if __name__ == "__main__":
   _UpperCamelCase   :						List[Any]        = 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   :						str        = parser.parse_args()
   convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)













"""simple docstring"""


from __future__ import annotations

from typing import Any






class __a   :



  def __init__(self  :		Optional[Any]				,			lowercase_  :		Any				,			lowercase_  :		Any   )		->							None:  # noqa: A002
   __A	:       Optional[Any]       =	item
   __A	:       Union[str, Any]       =	next






class __a   :



  def __init__(self  :		Tuple   )		->							None:
   __A	:       Node | None       =	None
   __A	:       Dict       =	0



  def 						A_				(self  :		str				,			lowercase_  :		Any   )		->							None:
   __A	:       Dict       =	Node(lowercase_				,			self.head   )
   self.size += 1



  def 						A_				(self  :		Optional[int]   )		->							Any:
   # Switched 'self.is_empty()' to 'self.head is None'
   # because mypy was considering the possibility that 'self.head'
   # can be None in below else part and giving error
   if self.head is None:
    return None
   else:
    __A	:       Dict       =	self.head.item
    __A	:       List[str]       =	self.head.next
    self.size -= 1
    return item



  def 						A_				(self  :		List[str]   )		->							bool:
   return self.head is None



  def __str__(self  :		Any   )		->							str:
   if self.is_empty():
    return ""
   else:
    __A	:       List[str]       =	self.head
    __A	:       Union[str, Any]       =	''
    __A	:       list[str]       =	[]
    while iterate:
     item_list.append(str(iterate.item   )   )
     __A	:       List[str]       =	iterate.next

    __A	:       str       =	' --> '.join(lowercase_   )

    return item_str



  def __len__(self  :		Union[str, Any]   )		->							int:
   return self.size





