





"""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:	Any							      =logging.get_logger(__name__)




def 			__snake_case						(      __A    )		->				int:
   lowercase    :			Union[str, Any]  =       ConvNextVaConfig()

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

   lowercase    :			Optional[Any]  =       1000
   lowercase    :			str  =       """imagenet-1k-id2label.json"""
   lowercase    :			List[str]  =       (1, 1000)

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

   lowercase    :			List[Any]  =       idalabel
   lowercase    :			Union[str, Any]  =       {v: k for k, v in idalabel.items()}
   lowercase    :			int  =       hidden_sizes
   lowercase    :			Dict  =       depths

   return config, expected_shape




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

   return name




def 			__snake_case						(      )		->				str:
   lowercase    :			Optional[Any]  =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
   lowercase    :			Any  =       Image.open(requests.get(__A ,stream=__A    ).raw    )
   return im




def 			__snake_case						(      __A    )		->				List[str]:
   if "224" in checkpoint_url:
      lowercase    :			List[str]  =       224
      lowercase    :			Tuple  =       224 / 256
   elif "384" in checkpoint_url:
      lowercase    :			Optional[int]  =       384
      lowercase    :			List[Any]  =       None
   else:
      lowercase    :			str  =       512
      lowercase    :			List[Any]  =       None

   return ConvNextImageProcessor(
       size=__A ,crop_pct=__A ,image_mean=[0.4_85, 0.4_56, 0.4_06] ,image_std=[0.2_29, 0.2_24, 0.2_25] ,resample=PILImageResampling.BICUBIC ,)




@torch.no_grad()
def 			__snake_case						(      __A ,__A ,__A ,__A    )		->				List[str]:
   print("""Downloading original model from checkpoint..."""    )
   # define ConvNeXTV2 configuration based on URL
   lowercase  ,					lowercase    :			int  =       get_convnextva_config(__A    )
   # load original state_dict from URL
   lowercase    :			Any  =       torch.hub.load_state_dict_from_url(__A    )["""model"""]

   print("""Converting model parameters..."""    )
   # rename keys
   for key in state_dict.copy().keys():
      lowercase    :			Tuple  =       state_dict.pop(__A    )
      lowercase    :			Tuple  =       val
   # add prefix to all keys expect classifier head
   for key in state_dict.copy().keys():
      lowercase    :			List[str]  =       state_dict.pop(__A    )
      if not key.startswith("""classifier"""    ):
         lowercase    :			Any  =       """convnextv2.""" + key
      lowercase    :			Dict  =       val

   # load HuggingFace model
   lowercase    :			str  =       ConvNextVaForImageClassification(__A    )
   model.load_state_dict(__A    )
   model.eval()

   # Check outputs on an image, prepared by ConvNextImageProcessor
   lowercase    :			int  =       convert_preprocessor(__A    )
   lowercase    :			Tuple  =       preprocessor(images=prepare_img() ,return_tensors="""pt"""    )
   lowercase    :			Optional[int]  =       model(**__A    ).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    :			int  =       torch.tensor([-0.39_30, 0.17_47, -0.52_46, 0.41_77, 0.42_95]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
      lowercase    :			int  =       torch.tensor([-0.17_27, -0.53_41, -0.78_18, -0.47_45, -0.65_66]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
      lowercase    :			int  =       torch.tensor([-0.03_33, 0.15_63, -0.91_37, 0.10_54, 0.03_81]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
      lowercase    :			int  =       torch.tensor([-0.17_44, -0.15_55, -0.07_13, 0.09_50, -0.14_31]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
      lowercase    :			Optional[Any]  =       torch.tensor([0.99_96, 0.19_66, -0.43_86, -0.34_72, 0.66_61]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
      lowercase    :			Tuple  =       torch.tensor([-0.25_53, -0.67_08, -0.13_59, 0.25_18, -0.24_88]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
      lowercase    :			str  =       torch.tensor([-0.06_73, -0.56_27, -0.37_53, -0.27_22, 0.01_78]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
      lowercase    :			List[str]  =       torch.tensor([-0.63_77, -0.74_58, -0.21_50, 0.11_84, -0.05_97]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
      lowercase    :			List[str]  =       torch.tensor([1.07_99, 0.23_22, -0.88_60, 1.02_19, 0.62_31]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
      lowercase    :			Tuple  =       torch.tensor([0.37_66, 0.49_17, -1.14_26, 0.99_42, 0.60_24]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
      lowercase    :			Optional[Any]  =       torch.tensor([0.42_20, -0.69_19, -0.43_17, -0.28_81, -0.66_09]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
      lowercase    :			Union[str, Any]  =       torch.tensor([0.10_82, -0.82_86, -0.50_95, 0.46_81, -0.80_85]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
      lowercase    :			Optional[int]  =       torch.tensor([-0.24_19, -0.62_21, 0.21_76, -0.09_80, -0.75_27]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
      lowercase    :			List[str]  =       torch.tensor([0.03_91, -0.43_71, 0.37_86, 0.12_51, -0.27_84]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
      lowercase    :			Any  =       torch.tensor([-0.05_04, 0.56_36, -0.17_29, -0.65_07, -0.39_49]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
      lowercase    :			Dict  =       torch.tensor([0.35_60, 0.94_86, 0.31_49, -0.26_67, -0.51_38]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
      lowercase    :			Any  =       torch.tensor([-0.24_69, -0.45_50, -0.58_53, -0.08_10, 0.03_09]    )
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
      lowercase    :			Any  =       torch.tensor([-0.30_90, 0.08_02, -0.06_82, -0.19_79, -0.28_26]    )
   else:
      raise ValueError(F'''Unknown URL: {checkpoint_url}'''    )

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

      model.save_pretrained(__A    )
      preprocessor.save_pretrained(__A    )

   lowercase    :			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(__A    )
      preprocessor.push_to_hub(__A    )


if __name__ == "__main__":
   lowerCAmelCase:	Union[str, 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:	List[Any]							      =parser.parse_args()
   convert_convnextva_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
   )









"""simple docstring"""
from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class lowerCamelCase__  (    __UpperCamelCase				):
   __UpperCAmelCase        =					"""MarkupLMFeatureExtractor"""
   __UpperCAmelCase        =					("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
   __UpperCAmelCase        =					True



   def __call__(       self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case = True      ,   snake_case = False      ,   snake_case = None      ,   snake_case = None      ,   snake_case = 0      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = False      ,   snake_case = False      ,   snake_case = False      ,   snake_case = False      ,   snake_case = True      ,   snake_case = None      ,   **snake_case      ,   ) ->    BatchEncoding:







      """simple docstring"""
      # first, create nodes and xpaths
      if self.parse_html:
         if html_strings is None:
            raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""	)

         if nodes is not None or xpaths is not None or node_labels is not None:
            raise ValueError(
                """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""	)

         lowercase    :			List[Any]  =       self.feature_extractor(snake_case	)
         lowercase    :			Union[str, Any]  =       features["""nodes"""]
         lowercase    :			List[Any]  =       features["""xpaths"""]
      else:
         if html_strings is not None:
            raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""	)
         if nodes is None or xpaths is None:
            raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""	)

        # # second, apply the tokenizer
      if questions is not None and self.parse_html:
         if isinstance(snake_case      ,   snake_case	):
            lowercase    :			Dict  =       [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

      lowercase    :			Dict  =       self.tokenizer(
          text=questions if questions is not None else nodes      ,   text_pair=nodes if questions is not None else None      ,   xpaths=snake_case      ,   node_labels=snake_case      ,   add_special_tokens=snake_case      ,   padding=snake_case      ,   truncation=snake_case      ,   max_length=snake_case      ,   stride=snake_case      ,   pad_to_multiple_of=snake_case      ,   return_token_type_ids=snake_case      ,   return_attention_mask=snake_case      ,   return_overflowing_tokens=snake_case      ,   return_special_tokens_mask=snake_case      ,   return_offsets_mapping=snake_case      ,   return_length=snake_case      ,   verbose=snake_case      ,   return_tensors=snake_case      ,   **snake_case      ,   )

      return encoded_inputs



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    Optional[Any]:







      """simple docstring"""
      return self.tokenizer.batch_decode(*snake_case      ,   **snake_case	)



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    Tuple:







      """simple docstring"""
      return self.tokenizer.decode(*snake_case      ,   **snake_case	)





   @property
   def    _UpperCAmelCase	(       self	) ->    Dict:







      """simple docstring"""
      lowercase    :			Any  =       self.tokenizer.model_input_names
      return tokenizer_input_names


