

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()
a_         =      logging.get_logger(__name__)

def 	lowerCamelCase__	(   _a):
	SCREAMING_SNAKE_CASE				: int			       =    ConvNextConfig()

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

	if "1k" in checkpoint_url:
		SCREAMING_SNAKE_CASE				: int			       =    1000
		SCREAMING_SNAKE_CASE				: Any			       =    "imagenet-1k-id2label.json"
		SCREAMING_SNAKE_CASE				: List[Any]			       =    (1, 1000)
	else:
		SCREAMING_SNAKE_CASE				: Tuple			       =    21841
		SCREAMING_SNAKE_CASE				: str			       =    "imagenet-22k-id2label.json"
		SCREAMING_SNAKE_CASE				: str			       =    (1, 21841)

	SCREAMING_SNAKE_CASE				: Tuple			       =    "huggingface/label-files"
	SCREAMING_SNAKE_CASE				: List[Any]			       =    num_labels
	SCREAMING_SNAKE_CASE				: str			       =    json.load(open(hf_hub_download(_a							,     _a							,     repo_type="dataset")							,     "r"))
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    {int(_a): 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[15027]
	SCREAMING_SNAKE_CASE				: Dict			       =    idalabel
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    {v: k for k, v in idalabel.items()}
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    hidden_sizes
	SCREAMING_SNAKE_CASE				: Tuple			       =    depths

	return config, expected_shape

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

	return name

def 	lowerCamelCase__	(   ):
	SCREAMING_SNAKE_CASE				: Dict			       =    "http://images.cocodataset.org/val2017/000000039769.jpg"
	SCREAMING_SNAKE_CASE				: List[str]			       =    Image.open(requests.get(_a							,     stream=_a).raw)
	return im

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

	# load HuggingFace model
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    ConvNextForImageClassification(_a)
	model.load_state_dict(_a)
	model.eval()

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

	SCREAMING_SNAKE_CASE				: int			       =    model(_a).logits

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

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

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

	print("Pushing model to the hub...")
	SCREAMING_SNAKE_CASE				: int			       =    "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(_a							,     _a)							,     organization="nielsr"							,     commit_message="Add model"							,     )


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

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


from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	Optional[int] , a							:	Any		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    vocab
				SCREAMING_SNAKE_CASE				: str			       =    normalizers.BertNormalizer(
				    clean_text=a , handle_chinese_chars=a , strip_accents=a , lowercase=a , )
				try:
					import rjieba
				except ImportError:
					raise ImportError(
					    "You need to install rjieba to use RoFormerTokenizer. "
					    "See https://pypi.org/project/rjieba/ for installation."		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    rjieba
			def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	int , a							:	NormalizedString		)							->    List[NormalizedString]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    []

				# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
				for token, start, end in self.jieba.tokenize(str(a		) , hmm=a		):
					if token in self.vocab:
						splits.append(normalized_string[start:end]		)
					else:
						SCREAMING_SNAKE_CASE				: Any			       =    self.normalizers.normalize_str(a		).split()
						for token in token_list:
							if token:
								SCREAMING_SNAKE_CASE				: Optional[int]			       =    start + len(a		)
								splits.append(normalized_string[start:end]		)
								SCREAMING_SNAKE_CASE				: List[Any]			       =    end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

				return splits






			def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	PreTokenizedString		)							->    Tuple:

				"""simple docstring"""

				pretok.split(self.jieba_split		)