






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()
a 			=							logging.get_logger(__name__)
def    a__    (	A       ):
   lowercase__    :						List[str]       =			ConvNextConfig()

   if "tiny" in checkpoint_url:
      lowercase__    :						str       =			[3, 3, 9, 3]
      lowercase__    :						Dict       =			[96, 192, 384, 768]
   if "small" in checkpoint_url:
      lowercase__    :						Optional[Any]       =			[3, 3, 27, 3]
      lowercase__    :						Tuple       =			[96, 192, 384, 768]
   if "base" in checkpoint_url:
      lowercase__    :						Tuple       =			[3, 3, 27, 3]
      lowercase__    :						Any       =			[128, 256, 512, 1_024]
   if "large" in checkpoint_url:
      lowercase__    :						Union[str, Any]       =			[3, 3, 27, 3]
      lowercase__    :						Optional[Any]       =			[192, 384, 768, 1_536]
   if "xlarge" in checkpoint_url:
      lowercase__    :						List[str]       =			[3, 3, 27, 3]
      lowercase__    :						Tuple       =			[256, 512, 1_024, 2_048]

   if "1k" in checkpoint_url:
      lowercase__    :						Tuple       =			1_000
      lowercase__    :						Dict       =			"""imagenet-1k-id2label.json"""
      lowercase__    :						List[str]       =			(1, 1_000)
   else:
      lowercase__    :						Optional[Any]       =			21_841
      lowercase__    :						Union[str, Any]       =			"""imagenet-22k-id2label.json"""
      lowercase__    :						Optional[int]       =			(1, 21_841)

   lowercase__    :						int       =			"""huggingface/label-files"""
   lowercase__    :						str       =			num_labels
   lowercase__    :						List[Any]       =			json.load(open(hf_hub_download(lowercase__		,					lowercase__		,					repo_type="""dataset"""       )		,					"""r"""       )       )
   lowercase__    :						str       =			{int(lowercase__       ): 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_205]
      del idalabel[15_027]
   lowercase__    :						Dict       =			idalabel
   lowercase__    :						int       =			{v: k for k, v in idalabel.items()}
   lowercase__    :						Dict       =			hidden_sizes
   lowercase__    :						List[Any]       =			depths

   return config, expected_shape
def    a__    (	A       ):







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

   return name
def    a__    (	):
   lowercase__    :						List[Any]       =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   lowercase__    :						Optional[Any]       =			Image.open(requests.get(lowercase__		,					stream=lowercase__       ).raw       )
   return im
@torch.no_grad()
def    a__    (	A		,					A       ):
   lowercase__  ,       lowercase__    :						Union[str, Any]       =			get_convnext_config(lowercase__       )
   # load original state_dict from URL
   lowercase__    :						List[Any]       =			torch.hub.load_state_dict_from_url(lowercase__       )["""model"""]
   # rename keys
   for key in state_dict.copy().keys():
      lowercase__    :						str       =			state_dict.pop(lowercase__       )
      lowercase__    :						Optional[int]       =			val
   # add prefix to all keys expect classifier head
   for key in state_dict.copy().keys():
      lowercase__    :						str       =			state_dict.pop(lowercase__       )
      if not key.startswith("""classifier"""       ):
         lowercase__    :						Dict       =			"""convnext.""" + key
      lowercase__    :						List[Any]       =			val

   # load HuggingFace model
   lowercase__    :						Optional[Any]       =			ConvNextForImageClassification(lowercase__       )
   model.load_state_dict(lowercase__       )
   model.eval()

   # Check outputs on an image, prepared by ConvNextImageProcessor
   lowercase__    :						List[Any]       =			224 if """224""" in checkpoint_url else 384
   lowercase__    :						Optional[Any]       =			ConvNextImageProcessor(size=lowercase__       )
   lowercase__    :						Union[str, Any]       =			image_processor(images=prepare_img()		,					return_tensors="""pt"""       ).pixel_values

   lowercase__    :						Tuple       =			model(lowercase__       ).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__    :						int       =			torch.tensor([-0.1_2_1_0, -0.6_6_0_5, 0.1_9_1_8]       )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
      lowercase__    :						int       =			torch.tensor([-0.4_4_7_3, -0.1_8_4_7, -0.6_3_6_5]       )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
      lowercase__    :						Tuple       =			torch.tensor([0.4_5_2_5, 0.7_5_3_9, 0.0_3_0_8]       )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
      lowercase__    :						List[str]       =			torch.tensor([0.3_5_6_1, 0.6_3_5_0, -0.0_3_8_4]       )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
      lowercase__    :						Any       =			torch.tensor([0.4_1_7_4, -0.0_9_8_9, 0.1_4_8_9]       )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
      lowercase__    :						Optional[int]       =			torch.tensor([0.2_5_1_3, -0.1_3_4_9, -0.1_6_1_3]       )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
      lowercase__    :						Union[str, Any]       =			torch.tensor([1.2_9_8_0, 0.3_6_3_1, -0.1_1_9_8]       )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
      lowercase__    :						List[str]       =			torch.tensor([1.2_9_6_3, 0.1_2_2_7, 0.1_7_2_3]       )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
      lowercase__    :						List[str]       =			torch.tensor([1.7_9_5_6, 0.8_3_9_0, 0.2_8_2_0]       )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
      lowercase__    :						List[str]       =			torch.tensor([-0.2_8_2_2, -0.0_5_0_2, -0.0_8_7_8]       )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
      lowercase__    :						str       =			torch.tensor([-0.5_6_7_2, -0.0_7_3_0, -0.4_3_4_8]       )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
      lowercase__    :						Dict       =			torch.tensor([0.2_6_8_1, 0.2_3_6_5, 0.6_2_4_6]       )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
      lowercase__    :						str       =			torch.tensor([-0.2_6_4_2, 0.3_9_3_1, 0.5_1_1_6]       )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
      lowercase__    :						Optional[int]       =			torch.tensor([-0.6_6_7_7, -0.1_8_7_3, -0.8_3_7_9]       )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
      lowercase__    :						Optional[Any]       =			torch.tensor([-0.7_7_4_9, -0.2_9_6_7, -0.6_4_4_4]       )
   else:
      raise ValueError(F'''Unknown URL: {checkpoint_url}'''       )

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

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

   print("""Pushing model to the hub..."""       )
   lowercase__    :						Tuple       =			"""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(lowercase__		,					lowercase__       )		,					organization="""nielsr"""		,					commit_message="""Add model"""		,					)


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

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







'''simple docstring'''


from dataclasses import dataclass
from typing import Callable, Optional

import datasets







@dataclass
class 						UpperCAmelCase						(		datasets.BuilderConfig):


 '''simple docstring'''




 __magic_name__       :			Optional[Callable]	=   None
 __magic_name__       :			Optional[dict]	=   None
 __magic_name__       :			Optional[datasets.Features]	=   None




 def    lowercase_	( self)			->   List[Any]:



      """simple docstring"""







      assert self.generator is not None, "generator must be specified"

      if self.gen_kwargs is None:
           a_									={}







class 						UpperCAmelCase						(		datasets.GeneratorBasedBuilder):


 '''simple docstring'''




 __magic_name__       :			Tuple	=   GeneratorConfig




 def    lowercase_	( self)			->   Tuple:



      """simple docstring"""







      return datasets.DatasetInfo(features=self.config.features)




 def    lowercase_	( self				,	lowerCAmelCase_)			->   int:



      """simple docstring"""







      return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,	gen_kwargs=self.config.gen_kwargs)]




 def    lowercase_	( self				,	**lowerCAmelCase_)			->   List[Any]:



      """simple docstring"""







      for idx, ex in enumerate(self.config.generator(**lowerCAmelCase_)):
           yield idx, ex


