






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					    =	logging.get_logger(__name__)
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						ConvNextVaConfig()

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

    _lowercase     			=						1000
    _lowercase     			=						'''imagenet-1k-id2label.json'''
    _lowercase     			=						(1, 1000)

    _lowercase     			=						'''huggingface/label-files'''
    _lowercase     			=						num_labels
    _lowercase     			=						json.load(open(hf_hub_download(_lowercase,   _lowercase,   repo_type='dataset'   ),   'r'   )   )
    _lowercase     			=						{int(_lowercase   ): v for k, v in idalabel.items()}

    _lowercase     			=						idalabel
    _lowercase     			=						{v: k for k, v in idalabel.items()}
    _lowercase     			=						hidden_sizes
    _lowercase     			=						depths

    return config, expected_shape
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    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 "gamma" in name:
        _lowercase     			=						name.replace('gamma',   'weight'   )
    if "beta" in name:
        _lowercase     			=						name.replace('beta',   'bias'   )
    if "stages" in name:
        _lowercase     			=						name.replace('stages',   'encoder.stages'   )
    if "norm" in name:
        _lowercase     			=						name.replace('norm',   'layernorm'   )
    if "head" in name:
        _lowercase     			=						name.replace('head',   'classifier'   )

    return name
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    _lowercase     			=						Image.open(requests.get(_lowercase,   stream=_lowercase   ).raw   )
    return im
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    if "224" in checkpoint_url:
        _lowercase     			=						224
        _lowercase     			=						224 / 256
    elif "384" in checkpoint_url:
        _lowercase     			=						384
        _lowercase     			=						None
    else:
        _lowercase     			=						512
        _lowercase     			=						None

    return ConvNextImageProcessor(
        size=_lowercase,   crop_pct=_lowercase,   image_mean=[0.485, 0.456, 0.406],   image_std=[0.229, 0.224, 0.225],   resample=PILImageResampling.BICUBIC,   )
@torch.no_grad()
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    print('Downloading original model from checkpoint...'   )
    # define ConvNeXTV2 configuration based on URL
    _lowercase     			=						get_convnextva_config(_lowercase   )
    # load original state_dict from URL
    _lowercase     			=						torch.hub.load_state_dict_from_url(_lowercase   )['''model''']

    print('Converting model parameters...'   )
    # rename keys
    for key in state_dict.copy().keys():
        _lowercase     			=						state_dict.pop(_lowercase   )
        _lowercase     			=						val
    # add prefix to all keys expect classifier head
    for key in state_dict.copy().keys():
        _lowercase     			=						state_dict.pop(_lowercase   )
        if not key.startswith('classifier'   ):
            _lowercase     			=						'''convnextv2.''' + key
        _lowercase     			=						val

    # load HuggingFace model
    _lowercase     			=						ConvNextVaForImageClassification(_lowercase   )
    model.load_state_dict(_lowercase   )
    model.eval()

    # Check outputs on an image, prepared by ConvNextImageProcessor
    _lowercase     			=						convert_preprocessor(_lowercase   )
    _lowercase     			=						preprocessor(images=prepare_img(),   return_tensors='pt'   )
    _lowercase     			=						model(**_lowercase   ).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":
        _lowercase     			=						torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
        _lowercase     			=						torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
        _lowercase     			=						torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
        _lowercase     			=						torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
        _lowercase     			=						torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
        _lowercase     			=						torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
        _lowercase     			=						torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
        _lowercase     			=						torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
        _lowercase     			=						torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
        _lowercase     			=						torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
        _lowercase     			=						torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
        _lowercase     			=						torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
        _lowercase     			=						torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
        _lowercase     			=						torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
        _lowercase     			=						torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
        _lowercase     			=						torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
        _lowercase     			=						torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
        _lowercase     			=						torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826]   )
    else:
        raise ValueError(f'''Unknown URL: {checkpoint_url}'''   )

    assert torch.allclose(logits[0, :5],   _lowercase,   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(_lowercase   ):
            os.mkdir(_lowercase   )

        model.save_pretrained(_lowercase   )
        preprocessor.save_pretrained(_lowercase   )

    _lowercase     			=						'''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(_lowercase   )
        preprocessor.push_to_hub(_lowercase   )


if __name__ == "__main__":
   lowerCamelCase					    =	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					    =	parser.parse_args()
   convert_convnextva_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
   )




import numpy as np

def       _a	(					SCREAMING_SNAKE_CASE						:					Tuple  ):







 """simple docstring"""



 UpperCamelCase__       :     str 							= np.exp(SCREAMING_SNAKE_CASE  )

 # Add up the all the exponentials
 UpperCamelCase__       :     Optional[int] 							= np.sum(SCREAMING_SNAKE_CASE  )

 # Divide every exponent by the sum of all exponents
 UpperCamelCase__       :     Optional[Any] 							= exponent_vector / sum_of_exponents

 return softmax_vector


if __name__ == "__main__":
       print(softmax((0,)))


