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()
UpperCamelCase													=		logging.get_logger(__name__)

def     lowercase_       (    _lowerCamelCase							: Union[str, Any]):
    lowercase__				:     List[str]					      =       ConvNextConfig()

    if "tiny" in checkpoint_url:
        lowercase__				:     Tuple					      =       [3, 3, 9, 3]
        lowercase__				:     str					      =       [96, 192, 384, 768]
    if "small" in checkpoint_url:
        lowercase__				:     Optional[Any]					      =       [3, 3, 27, 3]
        lowercase__				:     int					      =       [96, 192, 384, 768]
    if "base" in checkpoint_url:
        lowercase__				:     int					      =       [3, 3, 27, 3]
        lowercase__				:     List[Any]					      =       [128, 256, 512, 1024]
    if "large" in checkpoint_url:
        lowercase__				:     Dict					      =       [3, 3, 27, 3]
        lowercase__				:     Optional[int]					      =       [192, 384, 768, 1536]
    if "xlarge" in checkpoint_url:
        lowercase__				:     str					      =       [3, 3, 27, 3]
        lowercase__				:     Optional[Any]					      =       [256, 512, 1024, 2048]

    if "1k" in checkpoint_url:
        lowercase__				:     int					      =       1000
        lowercase__				:     Any					      =       "imagenet-1k-id2label.json"
        lowercase__				:     Optional[int]					      =       (1, 1000)
    else:
        lowercase__				:     Dict					      =       2_1841
        lowercase__				:     Dict					      =       "imagenet-22k-id2label.json"
        lowercase__				:     Tuple					      =       (1, 2_1841)

    lowercase__				:     Tuple					      =       "huggingface/label-files"
    lowercase__				:     Tuple					      =       num_labels
    lowercase__				:     List[str]					      =       json.load(open(hf_hub_download(_lowerCamelCase	,			_lowerCamelCase	,			repo_type="dataset")	,			"r"))
    lowercase__				:     Union[str, Any]					      =       {int(_lowerCamelCase): 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[9205]
        del idalabel[1_5027]
    lowercase__				:     Any					      =       idalabel
    lowercase__				:     Union[str, Any]					      =       {v: k for k, v in idalabel.items()}
    lowercase__				:     Optional[int]					      =       hidden_sizes
    lowercase__				:     List[Any]					      =       depths

    return config, expected_shape

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

    return name

def     lowercase_       (    ):
    lowercase__				:     Any					      =       "http://images.cocodataset.org/val2017/000000039769.jpg"
    lowercase__				:     str					      =       Image.open(requests.get(_lowerCamelCase	,			stream=_lowerCamelCase).raw)
    return im

@torch.no_grad()
def     lowercase_       (    _lowerCamelCase							: Union[str, Any]	,			_lowerCamelCase							: Optional[int]):
    lowercase__      ,     lowercase__				:     Tuple					      =       get_convnext_config(_lowerCamelCase)
    # load original state_dict from URL
    lowercase__				:     Optional[Any]					      =       torch.hub.load_state_dict_from_url(_lowerCamelCase)["model"]
    # rename keys
    for key in state_dict.copy().keys():
        lowercase__				:     Any					      =       state_dict.pop(_lowerCamelCase)
        lowercase__				:     Tuple					      =       val
    # add prefix to all keys expect classifier head
    for key in state_dict.copy().keys():
        lowercase__				:     List[str]					      =       state_dict.pop(_lowerCamelCase)
        if not key.startswith("classifier"):
            lowercase__				:     List[str]					      =       "convnext." + key
        lowercase__				:     int					      =       val

    # load HuggingFace model
    lowercase__				:     List[str]					      =       ConvNextForImageClassification(_lowerCamelCase)
    model.load_state_dict(_lowerCamelCase)
    model.eval()

    # Check outputs on an image, prepared by ConvNextImageProcessor
    lowercase__				:     str					      =       224 if "224" in checkpoint_url else 384
    lowercase__				:     List[Any]					      =       ConvNextImageProcessor(size=_lowerCamelCase)
    lowercase__				:     Optional[int]					      =       image_processor(images=prepare_img()	,			return_tensors="pt").pixel_values

    lowercase__				:     str					      =       model(_lowerCamelCase).logits

    # note: the logits below were obtained without center cropping
    if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
        lowercase__				:     Union[str, Any]					      =       torch.tensor([-0.1210, -0.6605, 0.1918])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
        lowercase__				:     int					      =       torch.tensor([-0.4473, -0.1847, -0.6365])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
        lowercase__				:     Any					      =       torch.tensor([0.4525, 0.7539, 0.0308])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
        lowercase__				:     List[str]					      =       torch.tensor([0.3561, 0.6350, -0.0384])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
        lowercase__				:     List[str]					      =       torch.tensor([0.4174, -0.0989, 0.1489])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
        lowercase__				:     Union[str, Any]					      =       torch.tensor([0.2513, -0.1349, -0.1613])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
        lowercase__				:     Union[str, Any]					      =       torch.tensor([1.2980, 0.3631, -0.1198])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
        lowercase__				:     Union[str, Any]					      =       torch.tensor([1.2963, 0.1227, 0.1723])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
        lowercase__				:     str					      =       torch.tensor([1.7956, 0.8390, 0.2820])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
        lowercase__				:     Optional[int]					      =       torch.tensor([-0.2822, -0.0502, -0.0878])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
        lowercase__				:     List[str]					      =       torch.tensor([-0.5672, -0.0730, -0.4348])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
        lowercase__				:     Tuple					      =       torch.tensor([0.2681, 0.2365, 0.6246])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
        lowercase__				:     Optional[Any]					      =       torch.tensor([-0.2642, 0.3931, 0.5116])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
        lowercase__				:     Optional[int]					      =       torch.tensor([-0.6677, -0.1873, -0.8379])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
        lowercase__				:     Optional[int]					      =       torch.tensor([-0.7749, -0.2967, -0.6444])
    else:
        raise ValueError(f'''Unknown URL: {checkpoint_url}''')

    assert torch.allclose(logits[0, :3]	,			_lowerCamelCase	,			atol=1E-3)
    assert logits.shape == expected_shape

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

    print("Pushing model to the hub...")
    lowercase__				:     Dict					      =       "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(_lowerCamelCase	,			_lowerCamelCase)	,			organization="nielsr"	,			commit_message="Add model"	,			)


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

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




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 						snake_case_ (	__A ):
   __A			:	Optional[Any]        =       ["image_processor", "tokenizer"]
   __A			:	Tuple        =       "LayoutLMv2ImageProcessor"
   __A			:	Optional[int]        =       ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")






   def __init__(   self       :  Tuple			,		lowercase_       :  List[str]=None			,		lowercase_       :  Dict=None			,		**lowercase_       :  List[str]						)      ->		int:
       lowercase__				:     Optional[Any]					      =       None
       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."			,		lowercase_			,		)
           lowercase__				:     Any					      =       kwargs.pop("feature_extractor"						)

       lowercase__				:     List[str]					      =       image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."						)
       if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."						)

       super().__init__(lowercase_			,		lowercase_						)






   def __call__(   self       :  Union[str, Any]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,		lowercase_       :  Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,		lowercase_       :  Union[List[List[int]], List[List[List[int]]]] = None			,		lowercase_       :  Optional[Union[List[int], List[List[int]]]] = None			,		lowercase_       :  bool = True			,		lowercase_       :  Union[bool, str, PaddingStrategy] = False			,		lowercase_       :  Union[bool, str, TruncationStrategy] = False			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  int = 0			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = True			,		lowercase_       :  Optional[Union[str, TensorType]] = None			,		**lowercase_       :  List[str]			,		)      ->		BatchEncoding:
       # verify input
       if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."						)

       if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."						)

       # first, apply the image processor
       lowercase__				:     List[Any]					      =       self.image_processor(images=lowercase_			,		return_tensors=lowercase_						)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(lowercase_			,		lowercase_						):
               lowercase__				:     List[Any]					      =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
           lowercase__				:     Union[str, Any]					      =       features["words"]

       lowercase__				:     List[str]					      =       self.tokenizer(
           text=text if text is not None else features["words"]			,		text_pair=text_pair if text_pair is not None else None			,		boxes=boxes if boxes is not None else features["boxes"]			,		word_labels=lowercase_			,		add_special_tokens=lowercase_			,		padding=lowercase_			,		truncation=lowercase_			,		max_length=lowercase_			,		stride=lowercase_			,		pad_to_multiple_of=lowercase_			,		return_token_type_ids=lowercase_			,		return_attention_mask=lowercase_			,		return_overflowing_tokens=lowercase_			,		return_special_tokens_mask=lowercase_			,		return_offsets_mapping=lowercase_			,		return_length=lowercase_			,		verbose=lowercase_			,		return_tensors=lowercase_			,		**lowercase_			,		)

       # add pixel values
       lowercase__				:     Optional[int]					      =       features.pop("pixel_values"						)
       if return_overflowing_tokens is True:
           lowercase__				:     Tuple					      =       self.get_overflowing_images(lowercase_			,		encoded_inputs["overflow_to_sample_mapping"]						)
       lowercase__				:     str					      =       images

       return encoded_inputs






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  Optional[int]			,		lowercase_       :  Union[str, Any]						)      ->		Any:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       lowercase__				:     str					      =       []
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]						)

       if len(lowercase_						) != len(lowercase_						):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               F''' {len(lowercase_						)} and {len(lowercase_						)}'''						)

       return images_with_overflow






   def    __UpperCamelCase  (   self       :  Any			,		*lowercase_       :  Tuple			,		**lowercase_       :  List[Any]						)      ->		Union[str, Any]:
       return self.tokenizer.batch_decode(*lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  List[Any]			,		*lowercase_       :  Tuple			,		**lowercase_       :  Union[str, Any]						)      ->		List[Any]:
       return self.tokenizer.decode(*lowercase_			,		**lowercase_						)






   @property
   def    __UpperCamelCase  (   self       :  int						)      ->		Optional[Any]:
       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






   @property
   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Tuple:
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,		lowercase_			,		)
       return self.image_processor_class







   @property
   def    __UpperCamelCase  (   self       :  Dict						)      ->		List[Any]:
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,		lowercase_			,		)
       return self.image_processor



