


"""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()
__snake_case												=							logging.get_logger(__name__)
def    A_ ( _lowerCAmelCase  :       Union[str, Any]							):






     """simple docstring"""

     _a     						=					ConvNextConfig()

     if "tiny" in checkpoint_url:
          _a     						=					[3, 3, 9, 3]
          _a     						=					[96, 1_92, 3_84, 7_68]
     if "small" in checkpoint_url:
          _a     						=					[3, 3, 27, 3]
          _a     						=					[96, 1_92, 3_84, 7_68]
     if "base" in checkpoint_url:
          _a     						=					[3, 3, 27, 3]
          _a     						=					[1_28, 2_56, 5_12, 10_24]
     if "large" in checkpoint_url:
          _a     						=					[3, 3, 27, 3]
          _a     						=					[1_92, 3_84, 7_68, 15_36]
     if "xlarge" in checkpoint_url:
          _a     						=					[3, 3, 27, 3]
          _a     						=					[2_56, 5_12, 10_24, 20_48]

     if "1k" in checkpoint_url:
          _a     						=					10_00
          _a     						=					'''imagenet-1k-id2label.json'''
          _a     						=					(1, 10_00)
     else:
          _a     						=					2_18_41
          _a     						=					'''imagenet-22k-id2label.json'''
          _a     						=					(1, 2_18_41)

     _a     						=					'''huggingface/label-files'''
     _a     						=					num_labels
     _a     						=					json.load(open(hf_hub_download(_a,  _a,  repo_type='''dataset'''							),  '''r'''							)							)
     _a     						=					{int(_a							): 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[92_05]
          del idalabel[1_50_27]
     _a     						=					idalabel
     _a     						=					{v: k for k, v in idalabel.items()}
     _a     						=					hidden_sizes
     _a     						=					depths

     return config, expected_shape
def    A_ ( _lowerCAmelCase  :       str							):






     """simple docstring"""

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

     return name
def    A_ ( ):






     """simple docstring"""

     _a     						=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _a     						=					Image.open(requests.get(_a,  stream=_a							).raw							)
     return im
@torch.no_grad()
def    A_ ( _lowerCAmelCase  :       Optional[int],  _lowerCAmelCase  :       Union[str, Any]							):






     """simple docstring"""

     _a     						=					get_convnext_config(_a							)
     # load original state_dict from URL
     _a     						=					torch.hub.load_state_dict_from_url(_a							)['''model''']
     # rename keys
     for key in state_dict.copy().keys():
          _a     						=					state_dict.pop(_a							)
          _a     						=					val
     # add prefix to all keys expect classifier head
     for key in state_dict.copy().keys():
          _a     						=					state_dict.pop(_a							)
          if not key.startswith('''classifier'''							):
               _a     						=					'''convnext.''' + key
          _a     						=					val

     # load HuggingFace model
     _a     						=					ConvNextForImageClassification(_a							)
     model.load_state_dict(_a							)
     model.eval()

     # Check outputs on an image, prepared by ConvNextImageProcessor
     _a     						=					2_24 if '''224''' in checkpoint_url else 3_84
     _a     						=					ConvNextImageProcessor(size=_a							)
     _a     						=					image_processor(images=prepare_img(),  return_tensors='''pt'''							).pixel_values

     _a     						=					model(_a							).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     						=					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":
          _a     						=					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":
          _a     						=					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":
          _a     						=					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":
          _a     						=					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":
          _a     						=					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":
          _a     						=					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":
          _a     						=					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":
          _a     						=					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":
          _a     						=					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":
          _a     						=					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":
          _a     						=					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":
          _a     						=					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":
          _a     						=					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":
          _a     						=					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],  _a,  atol=1e-3							)
     assert logits.shape == expected_shape

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

     print('''Pushing model to the hub...'''							)
     _a     						=					'''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(_a,  _a							),  organization='''nielsr''',  commit_message='''Add model''',  )


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

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



from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase		=						{'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
		from .tokenization_phobert import PhobertTokenizer

else:
		import sys

		lowerCamelCase		=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




