




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()
a_      						=   logging.get_logger(__name__)




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		Any:

    """simple docstring"""


    __UpperCamelCase     						=  ConvNextVaConfig()

    if "atto" in checkpoint_url:
        __UpperCamelCase     						=  [2, 2, 6, 2]
        __UpperCamelCase     						=  [40, 80, 1_60, 3_20]
    if "femto" in checkpoint_url:
        __UpperCamelCase     						=  [2, 2, 6, 2]
        __UpperCamelCase     						=  [48, 96, 1_92, 3_84]
    if "pico" in checkpoint_url:
        __UpperCamelCase     						=  [2, 2, 6, 2]
        __UpperCamelCase     						=  [64, 1_28, 2_56, 5_12]
    if "nano" in checkpoint_url:
        __UpperCamelCase     						=  [2, 2, 8, 2]
        __UpperCamelCase     						=  [80, 1_60, 3_20, 6_40]
    if "tiny" in checkpoint_url:
        __UpperCamelCase     						=  [3, 3, 9, 3]
        __UpperCamelCase     						=  [96, 1_92, 3_84, 7_68]
    if "base" in checkpoint_url:
        __UpperCamelCase     						=  [3, 3, 27, 3]
        __UpperCamelCase     						=  [1_28, 2_56, 5_12, 10_24]
    if "large" in checkpoint_url:
        __UpperCamelCase     						=  [3, 3, 27, 3]
        __UpperCamelCase     						=  [1_92, 3_84, 7_68, 15_36]
    if "huge" in checkpoint_url:
        __UpperCamelCase     						=  [3, 3, 27, 3]
        __UpperCamelCase     						=  [3_52, 7_04, 14_08, 28_16]

    __UpperCamelCase     						=  10_00
    __UpperCamelCase     						=  '''imagenet-1k-id2label.json'''
    __UpperCamelCase     						=  (1, 10_00)

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

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

    return config, expected_shape




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


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

    return name




def     __SCREAMING_SNAKE_CASE      (     )				->		Optional[Any]:

    """simple docstring"""


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




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		Any:

    """simple docstring"""


    if "224" in checkpoint_url:
        __UpperCamelCase     						=  2_24
        __UpperCamelCase     						=  2_24 / 2_56
    elif "384" in checkpoint_url:
        __UpperCamelCase     						=  3_84
        __UpperCamelCase     						=  None
    else:
        __UpperCamelCase     						=  5_12
        __UpperCamelCase     						=  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     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_					)				->		Optional[int]:

    """simple docstring"""


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

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

    # load HuggingFace model
    __UpperCamelCase     						=  ConvNextVaForImageClassification(lowercase_					)
    model.load_state_dict(lowercase_					)
    model.eval()

    # Check outputs on an image, prepared by ConvNextImageProcessor
    __UpperCamelCase     						=  convert_preprocessor(lowercase_					)
    __UpperCamelCase     						=  preprocessor(images=prepare_img()    ,   return_tensors='''pt'''					)
    __UpperCamelCase     						=  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":
        __UpperCamelCase     						=  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     						=  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     						=  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     						=  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     						=  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     						=  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     						=  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     						=  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     						=  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     						=  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     						=  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     						=  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     						=  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     						=  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     						=  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     						=  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     						=  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     						=  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]    ,   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_					)

    __UpperCamelCase     						=  '''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__":
 a_      						=   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")

 a_      						=   parser.parse_args()
 convert_convnextva_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
 )












import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
 from .tokenization_deberta_va import DebertaVaTokenizer
else:
 a_      						=   None

a_      						=   logging.get_logger(__name__)

a_      						=   {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"}

a_      						=   {
    "vocab_file": {
        "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"
        ),
        "microsoft/deberta-v2-xxlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"
        ),
    }
}

a_      						=   {
    "microsoft/deberta-v2-xlarge": 512,
    "microsoft/deberta-v2-xxlarge": 512,
    "microsoft/deberta-v2-xlarge-mnli": 512,
    "microsoft/deberta-v2-xxlarge-mnli": 512,
}

a_      						=   {
    "microsoft/deberta-v2-xlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False},
}

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       int			=    VOCAB_FILES_NAMES
      lowerCAmelCase__	:       Any			=    PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__	:       Optional[Any]			=    PRETRAINED_INIT_CONFIGURATION
      lowerCAmelCase__	:       int			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__	:       Dict			=    DebertaVaTokenizer







      def __init__(   self    :       int       ,				snake_case    :       int=None       ,				snake_case    :       Optional[int]=None       ,				snake_case    :       Any=False       ,				snake_case    :       Optional[int]=False       ,				snake_case    :       Dict="[CLS]"       ,				snake_case    :       Any="[SEP]"       ,				snake_case    :       Union[str, Any]="[UNK]"       ,				snake_case    :       List[Any]="[SEP]"       ,				snake_case    :       Union[str, Any]="[PAD]"       ,				snake_case    :       Tuple="[CLS]"       ,				snake_case    :       Dict="[MASK]"       ,				**snake_case    :       int       ,				):
          super().__init__(
              snake_case       ,				tokenizer_file=snake_case       ,				do_lower_case=snake_case       ,				bos_token=snake_case       ,				eos_token=snake_case       ,				unk_token=snake_case       ,				sep_token=snake_case       ,				pad_token=snake_case       ,				cls_token=snake_case       ,				mask_token=snake_case       ,				split_by_punct=snake_case       ,				**snake_case       ,				)

          __UpperCamelCase     						=  do_lower_case
          __UpperCamelCase     						=  split_by_punct
          __UpperCamelCase     						=  vocab_file
          __UpperCamelCase     						=  False if not self.vocab_file else True







      def 					snake_case		(   self    :       Optional[int]       ,				snake_case    :       int       ,				snake_case    :       Tuple=None ):

          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __UpperCamelCase     						=  [self.cls_token_id]
          __UpperCamelCase     						=  [self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep







      def 					snake_case		(   self    :       Any       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Optional[Any]=None       ,				snake_case    :       Union[str, Any]=False ):

          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=snake_case       ,				token_ids_a=snake_case       ,				already_has_special_tokens=snake_case )

          if token_ids_a is not None:
              return [1] + ([0] * len(snake_case )) + [1] + ([0] * len(snake_case )) + [1]
          return [1] + ([0] * len(snake_case )) + [1]







      def 					snake_case		(   self    :       Optional[int]       ,				snake_case    :       Optional[int]       ,				snake_case    :       Optional[Any]=None ):
          __UpperCamelCase     						=  [self.sep_token_id]
          __UpperCamelCase     						=  [self.cls_token_id]
          if token_ids_a is None:
              return len(cls + token_ids_a + sep ) * [0]
          return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







      def 					snake_case		(   self    :       List[str]       ,				snake_case    :       str       ,				snake_case    :       Optional[str] = None ):
          if not self.can_save_slow_tokenizer:
              raise ValueError(
                  '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                  '''tokenizer.''' )

          if not os.path.isdir(snake_case ):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
              return
          __UpperCamelCase     						=  os.path.join(
              snake_case       ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case ):
              copyfile(self.vocab_file       ,				snake_case )

          return (out_vocab_file,)






