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()
lowercase_  							=			logging.get_logger(__name__)







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Union[str, Any]   ):







    '''simple docstring'''
    __snake_case       :				Optional[Any] 			=    ConvNextConfig()

    if "tiny" in checkpoint_url:
        __snake_case       :				Any 			=    [3, 3, 9, 3]
        __snake_case       :				int 			=    [9_6, 1_9_2, 3_8_4, 7_6_8]
    if "small" in checkpoint_url:
        __snake_case       :				str 			=    [3, 3, 2_7, 3]
        __snake_case       :				List[Any] 			=    [9_6, 1_9_2, 3_8_4, 7_6_8]
    if "base" in checkpoint_url:
        __snake_case       :				Dict 			=    [3, 3, 2_7, 3]
        __snake_case       :				Tuple 			=    [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
    if "large" in checkpoint_url:
        __snake_case       :				Optional[int] 			=    [3, 3, 2_7, 3]
        __snake_case       :				Optional[int] 			=    [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
    if "xlarge" in checkpoint_url:
        __snake_case       :				Tuple 			=    [3, 3, 2_7, 3]
        __snake_case       :				int 			=    [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]

    if "1k" in checkpoint_url:
        __snake_case       :				int 			=    1_0_0_0
        __snake_case       :				Union[str, Any] 			=    '''imagenet-1k-id2label.json'''
        __snake_case       :				Any 			=    (1, 1_0_0_0)
    else:
        __snake_case       :				Union[str, Any] 			=    2_1_8_4_1
        __snake_case       :				int 			=    '''imagenet-22k-id2label.json'''
        __snake_case       :				List[str] 			=    (1, 2_1_8_4_1)

    __snake_case       :				List[str] 			=    '''huggingface/label-files'''
    __snake_case       :				Any 			=    num_labels
    __snake_case       :				str 			=    json.load(open(hf_hub_download(_UpperCamelCase		,							_UpperCamelCase		,							repo_type="""dataset"""   )		,							"""r"""   )   )
    __snake_case       :				List[Any] 			=    {int(_UpperCamelCase   ): 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[9_2_0_5]
        del idalabel[1_5_0_2_7]
    __snake_case       :				List[str] 			=    idalabel
    __snake_case       :				Any 			=    {v: k for k, v in idalabel.items()}
    __snake_case       :				Union[str, Any] 			=    hidden_sizes
    __snake_case       :				int 			=    depths

    return config, expected_shape







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Optional[Any]   ):







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

    return name







def 					__lowerCAmelCase				(		):







    '''simple docstring'''
    __snake_case       :				Tuple 			=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __snake_case       :				Union[str, Any] 			=    Image.open(requests.get(_UpperCamelCase		,							stream=_UpperCamelCase   ).raw   )
    return im







@torch.no_grad()
def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Union[str, Any]		,							__SCREAMING_SNAKE_CASE       :					List[str]   ):







    '''simple docstring'''
    __snake_case       :				Union[str, Any] 			=    get_convnext_config(_UpperCamelCase   )
    # load original state_dict from URL
    __snake_case       :				List[Any] 			=    torch.hub.load_state_dict_from_url(_UpperCamelCase   )['''model''']
    # rename keys
    for key in state_dict.copy().keys():
        __snake_case       :				str 			=    state_dict.pop(_UpperCamelCase   )
        __snake_case       :				int 			=    val
    # add prefix to all keys expect classifier head
    for key in state_dict.copy().keys():
        __snake_case       :				Any 			=    state_dict.pop(_UpperCamelCase   )
        if not key.startswith("""classifier"""   ):
            __snake_case       :				Dict 			=    '''convnext.''' + key
        __snake_case       :				List[Any] 			=    val

    # load HuggingFace model
    __snake_case       :				Optional[int] 			=    ConvNextForImageClassification(_UpperCamelCase   )
    model.load_state_dict(_UpperCamelCase   )
    model.eval()

    # Check outputs on an image, prepared by ConvNextImageProcessor
    __snake_case       :				Optional[Any] 			=    2_2_4 if '''224''' in checkpoint_url else 3_8_4
    __snake_case       :				int 			=    ConvNextImageProcessor(size=_UpperCamelCase   )
    __snake_case       :				Union[str, Any] 			=    image_processor(images=prepare_img()		,							return_tensors="""pt"""   ).pixel_values

    __snake_case       :				Any 			=    model(_UpperCamelCase   ).logits

    # note: the logits below were obtained without center cropping
    if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
        __snake_case       :				Optional[int] 			=    torch.tensor([-0.12_10, -0.66_05, 0.19_18]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
        __snake_case       :				Optional[Any] 			=    torch.tensor([-0.44_73, -0.18_47, -0.63_65]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
        __snake_case       :				List[str] 			=    torch.tensor([0.45_25, 0.75_39, 0.03_08]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
        __snake_case       :				Any 			=    torch.tensor([0.35_61, 0.63_50, -0.03_84]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
        __snake_case       :				str 			=    torch.tensor([0.41_74, -0.09_89, 0.14_89]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
        __snake_case       :				Tuple 			=    torch.tensor([0.25_13, -0.13_49, -0.16_13]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
        __snake_case       :				Dict 			=    torch.tensor([1.29_80, 0.36_31, -0.11_98]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
        __snake_case       :				Optional[int] 			=    torch.tensor([1.29_63, 0.12_27, 0.17_23]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
        __snake_case       :				str 			=    torch.tensor([1.79_56, 0.83_90, 0.28_20]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
        __snake_case       :				List[str] 			=    torch.tensor([-0.28_22, -0.05_02, -0.08_78]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
        __snake_case       :				List[Any] 			=    torch.tensor([-0.56_72, -0.07_30, -0.43_48]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
        __snake_case       :				str 			=    torch.tensor([0.26_81, 0.23_65, 0.62_46]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
        __snake_case       :				List[str] 			=    torch.tensor([-0.26_42, 0.39_31, 0.51_16]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
        __snake_case       :				Tuple 			=    torch.tensor([-0.66_77, -0.18_73, -0.83_79]   )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
        __snake_case       :				Any 			=    torch.tensor([-0.77_49, -0.29_67, -0.64_44]   )
    else:
        raise ValueError(F'''Unknown URL: {checkpoint_url}'''   )

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

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

    print("""Pushing model to the hub..."""   )
    __snake_case       :				Union[str, Any] 			=    '''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(_UpperCamelCase		,							_UpperCamelCase   )		,							organization="""nielsr"""		,							commit_message="""Add model"""		,							)


if __name__ == "__main__":
     lowercase_  							=			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.",
     )

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








class 							lowerCAmelCase_		:







		def __init__(    self  :   int       ,		UpperCAmelCase_  :   int					)       ->		None:


						'''simple docstring'''







						_UpperCAmelCase						:  Tuple     					=	data
						_UpperCAmelCase						:  BinaryTreeNode | None     					=	None
						_UpperCAmelCase						:  BinaryTreeNode | None     					=	None


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				if node is None:
								_UpperCAmelCase						:  Union[str, Any]     					=	BinaryTreeNode(_UpperCamelCase			)
								return node

				# binary search tree is not empty,
				# so we will insert it into the tree
				# if new_value is less than value of data in node,
				#  add it to left subtree and proceed recursively
				if new_value < node.data:
								_UpperCAmelCase						:  Any     					=	insert(node.left_child      ,					_UpperCamelCase			)
				else:
								# if new_value is greater than value of data in node,
								#  add it to right subtree and proceed recursively
								_UpperCAmelCase						:  List[Any]     					=	insert(node.right_child      ,					_UpperCamelCase			)
				return node


def 	_A	(   _UpperCamelCase			):  # if node is None,return
				if node:
								_UpperCAmelCase						:  str     					=	inorder(node.left_child			)
								_UpperCAmelCase						:  Union[str, Any]     					=	[*inorder_array, node.data]
								_UpperCAmelCase						:  Optional[int]     					=	inorder_array + inorder(node.right_child			)
				else:
								_UpperCAmelCase						:  Tuple     					=	[]
				return inorder_array


def 	_A	(   ):
				_UpperCAmelCase						:  Dict     					=	insert(_UpperCamelCase      ,					15			)
				insert(_UpperCamelCase      ,					10			)
				insert(_UpperCamelCase      ,					25			)
				insert(_UpperCamelCase      ,					6			)
				insert(_UpperCamelCase      ,					14			)
				insert(_UpperCamelCase      ,					20			)
				insert(_UpperCamelCase      ,					60			)
				return root


def 	_A	(   ):
				# main function
				_UpperCAmelCase						:  Tuple     					=	make_tree()
				print('''Printing values of binary search tree in Inorder Traversal.'''			)
				inorder(_UpperCamelCase			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
