



"""simple docstring"""
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()
_lowerCAmelCase					:List[Any]  =      logging.get_logger(__name__)





def 				lowerCamelCase_		(UpperCamelCase__	:	Tuple							):
    _UpperCAmelCase							:						Union[str, Any]       							=					ConvNextVaConfig()

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

    _UpperCAmelCase							:						Tuple       							=					1000
    _UpperCAmelCase							:						Tuple       							=					'''imagenet-1k-id2label.json'''
    _UpperCAmelCase							:						List[str]       							=					(1, 1000)

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

    _UpperCAmelCase							:						List[str]       							=					idalabel
    _UpperCAmelCase							:						Tuple       							=					{v: k for k, v in idalabel.items()}
    _UpperCAmelCase							:						int       							=					hidden_sizes
    _UpperCAmelCase							:						Optional[int]       							=					depths

    return config, expected_shape





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

    return name





def 				lowerCamelCase_		():
    _UpperCAmelCase							:						Tuple       							=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    _UpperCAmelCase							:						Optional[Any]       							=					Image.open(requests.get(UpperCamelCase__  , stream=UpperCamelCase__							).raw							)
    return im





def 				lowerCamelCase_		(UpperCamelCase__	:	int							):
    if "224" in checkpoint_url:
        _UpperCAmelCase							:						Any       							=					224
        _UpperCAmelCase							:						List[Any]       							=					224 / 256
    elif "384" in checkpoint_url:
        _UpperCAmelCase							:						Union[str, Any]       							=					384
        _UpperCAmelCase							:						List[Any]       							=					None
    else:
        _UpperCAmelCase							:						Tuple       							=					512
        _UpperCAmelCase							:						str       							=					None

    return ConvNextImageProcessor(
        size=UpperCamelCase__  , crop_pct=UpperCamelCase__  , image_mean=[0.485, 0.456, 0.406]  , image_std=[0.229, 0.224, 0.225]  , resample=PILImageResampling.BICUBIC  , )





@torch.no_grad()
def 				lowerCamelCase_		(UpperCamelCase__	:	Union[str, Any]  , UpperCamelCase__	:	Dict  , UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	Optional[Any]							):
    print('''Downloading original model from checkpoint...'''							)
    # define ConvNeXTV2 configuration based on URL
    _UpperCAmelCase							:						Any       							=					get_convnextva_config(UpperCamelCase__							)
    # load original state_dict from URL
    _UpperCAmelCase							:						List[str]       							=					torch.hub.load_state_dict_from_url(UpperCamelCase__							)['''model''']

    print('''Converting model parameters...'''							)
    # rename keys
    for key in state_dict.copy().keys():
        _UpperCAmelCase							:						str       							=					state_dict.pop(UpperCamelCase__							)
        _UpperCAmelCase							:						Tuple       							=					val
    # add prefix to all keys expect classifier head
    for key in state_dict.copy().keys():
        _UpperCAmelCase							:						Union[str, Any]       							=					state_dict.pop(UpperCamelCase__							)
        if not key.startswith('''classifier'''							):
            _UpperCAmelCase							:						Optional[int]       							=					'''convnextv2.''' + key
        _UpperCAmelCase							:						List[Any]       							=					val

    # load HuggingFace model
    _UpperCAmelCase							:						Dict       							=					ConvNextVaForImageClassification(UpperCamelCase__							)
    model.load_state_dict(UpperCamelCase__							)
    model.eval()

    # Check outputs on an image, prepared by ConvNextImageProcessor
    _UpperCAmelCase							:						List[Any]       							=					convert_preprocessor(UpperCamelCase__							)
    _UpperCAmelCase							:						Union[str, Any]       							=					preprocessor(images=prepare_img()  , return_tensors='''pt'''							)
    _UpperCAmelCase							:						Any       							=					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":
        _UpperCAmelCase							:						Union[str, Any]       							=					torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
        _UpperCAmelCase							:						str       							=					torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
        _UpperCAmelCase							:						Union[str, Any]       							=					torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
        _UpperCAmelCase							:						Dict       							=					torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
        _UpperCAmelCase							:						Optional[Any]       							=					torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
        _UpperCAmelCase							:						List[Any]       							=					torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
        _UpperCAmelCase							:						Dict       							=					torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
        _UpperCAmelCase							:						str       							=					torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
        _UpperCAmelCase							:						Optional[int]       							=					torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
        _UpperCAmelCase							:						List[str]       							=					torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
        _UpperCAmelCase							:						str       							=					torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
        _UpperCAmelCase							:						List[Any]       							=					torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
        _UpperCAmelCase							:						Any       							=					torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
        _UpperCAmelCase							:						Optional[Any]       							=					torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
        _UpperCAmelCase							:						str       							=					torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
        _UpperCAmelCase							:						List[str]       							=					torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
        _UpperCAmelCase							:						str       							=					torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]							)
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
        _UpperCAmelCase							:						List[str]       							=					torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]							)
    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__							)

    _UpperCAmelCase							:						int       							=					'''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__":
  _lowerCAmelCase					:Any  =      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')

  _lowerCAmelCase					:Optional[Any]  =      parser.parse_args()
  convert_convnextva_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
  )







"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
  import tensorflow as tf
class 							_UpperCAmelCase							(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]   ):





     '''simple docstring'''



     def __init__(      self ,			A=None ,			**A	)							->      List[str]:
         super().__init__(features=A	)
         _UpperCAmelCase							:						Any       							=					tf_tensor_kwargs
         import tensorflow as tf  # noqa: import tf at initialization





     def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
         import tensorflow as tf

         if isinstance(A ,			A	) and column:
             if all(
                 isinstance(A ,			tf.Tensor	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
                 return tf.stack(A	)
             elif all(
                 isinstance(A ,			(tf.Tensor, tf.RaggedTensor)	) and x.ndim == 1 and x.dtype == column[0].dtype
                 for x in column	):
                 # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                 return tf.ragged.stack(A	)

         return column





     def 				__lowerCAmelCase			(      self ,			A	)							->      int:
         import tensorflow as tf

         if value is None:
             return value

         _UpperCAmelCase							:						Any       							=					{}

         if isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.integer	):
             _UpperCAmelCase							:						Union[str, Any]       							=					{'''dtype''': tf.intaa}
         elif isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.floating	):
             _UpperCAmelCase							:						Dict       							=					{'''dtype''': tf.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
             import PIL.Image

             if isinstance(A ,			PIL.Image.Image	):
                 _UpperCAmelCase							:						Optional[int]       							=					np.asarray(A	)

         return tf.convert_to_tensor(A ,			**{**default_dtype, **self.tf_tensor_kwargs}	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      Tuple:
         import tensorflow as tf

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
             import torch

             if isinstance(A ,			torch.Tensor	):
                 return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
         if hasattr(A ,			'''__array__'''	) and not isinstance(A ,			tf.Tensor	):
             _UpperCAmelCase							:						List[str]       							=					data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(A ,			np.ndarray	):
             if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                 return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
         elif isinstance(A ,			(list, tuple)	):
             return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
         return self._tensorize(A	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[int]:
         return map_nested(self._recursive_tensorize ,			A ,			map_list=A	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
         _UpperCAmelCase							:						str       							=					self.numpy_arrow_extractor().extract_row(A	)
         _UpperCAmelCase							:						List[Any]       							=					self.python_features_decoder.decode_row(A	)
         return self.recursive_tensorize(A	)





     def 				__lowerCAmelCase			(      self ,			A	)							->      "tf.Tensor":
         _UpperCAmelCase							:						Union[str, Any]       							=					self.numpy_arrow_extractor().extract_column(A	)
         _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_column(A ,			pa_table.column_names[0]	)
         _UpperCAmelCase							:						str       							=					self.recursive_tensorize(A	)
         _UpperCAmelCase							:						Optional[Any]       							=					self._consolidate(A	)
         return column





     def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
         _UpperCAmelCase							:						Dict       							=					self.numpy_arrow_extractor().extract_batch(A	)
         _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_batch(A	)
         _UpperCAmelCase							:						Union[str, Any]       							=					self.recursive_tensorize(A	)
         for column_name in batch:
             _UpperCAmelCase							:						str       							=					self._consolidate(batch[column_name]	)
         return batch


