
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()
snake_case				:	Tuple							   =						logging.get_logger(__name__)






def 						lowerCAmelCase_		(      _snake_case   :						Optional[int]     )       ->	Dict:







	'''simple docstring'''


	__magic_name__	:	List[Any]			     =  ConvNextConfig()

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

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

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

	return config, expected_shape






def 						lowerCAmelCase_		(      _snake_case   :						Tuple     )       ->	Tuple:







	'''simple docstring'''


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

	return name






def 						lowerCAmelCase_		(      )       ->	Optional[int]:







	'''simple docstring'''


	__magic_name__	:	str			     =  "http://images.cocodataset.org/val2017/000000039769.jpg"
	__magic_name__	:	Union[str, Any]			     =  Image.open(requests.get(_snake_case   ,     stream=_snake_case     ).raw     )
	return im






@torch.no_grad()
def 						lowerCAmelCase_		(      _snake_case   :						Optional[Any]   ,     _snake_case   :						Optional[int]     )       ->	List[Any]:







	'''simple docstring'''


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

	# load HuggingFace model
	__magic_name__	:	Dict			     =  ConvNextForImageClassification(_snake_case     )
	model.load_state_dict(_snake_case     )
	model.eval()

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

	__magic_name__	:	Union[str, Any]			     =  model(_snake_case     ).logits

	# note: the logits below were obtained without center cropping
	if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
		__magic_name__	:	Any			     =  torch.tensor([-0.1_210, -0.6_605, 0.1_918]     )
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
		__magic_name__	:	Dict			     =  torch.tensor([-0.4_473, -0.1_847, -0.6_365]     )
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
		__magic_name__	:	int			     =  torch.tensor([0.4_525, 0.7_539, 0.0_308]     )
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
		__magic_name__	:	List[str]			     =  torch.tensor([0.3_561, 0.6_350, -0.0_384]     )
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
		__magic_name__	:	Union[str, Any]			     =  torch.tensor([0.4_174, -0.0_989, 0.1_489]     )
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
		__magic_name__	:	Optional[Any]			     =  torch.tensor([0.2_513, -0.1_349, -0.1_613]     )
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
		__magic_name__	:	str			     =  torch.tensor([1.2_980, 0.3_631, -0.1_198]     )
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
		__magic_name__	:	Optional[Any]			     =  torch.tensor([1.2_963, 0.1_227, 0.1_723]     )
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
		__magic_name__	:	List[Any]			     =  torch.tensor([1.7_956, 0.8_390, 0.2_820]     )
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
		__magic_name__	:	List[Any]			     =  torch.tensor([-0.2_822, -0.0_502, -0.0_878]     )
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
		__magic_name__	:	Optional[int]			     =  torch.tensor([-0.5_672, -0.0_730, -0.4_348]     )
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
		__magic_name__	:	List[str]			     =  torch.tensor([0.2_681, 0.2_365, 0.6_246]     )
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
		__magic_name__	:	Optional[Any]			     =  torch.tensor([-0.2_642, 0.3_931, 0.5_116]     )
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
		__magic_name__	:	Any			     =  torch.tensor([-0.6_677, -0.1_873, -0.8_379]     )
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
		__magic_name__	:	Tuple			     =  torch.tensor([-0.7_749, -0.2_967, -0.6_444]     )
	else:
		raise ValueError(F'''Unknown URL: {checkpoint_url}'''     )

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

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

	print("Pushing model to the hub..."     )
	__magic_name__	:	Optional[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(_snake_case   ,     _snake_case     )   ,     organization="nielsr"   ,     commit_message="Add model"   ,     )


if __name__ == "__main__":
				snake_case				:	Any							   =						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.",
				)

				snake_case				:	Any							   =						parser.parse_args()
				convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


snake_case				:	Optional[int]							   =						{
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case				:	Optional[int]							   =						[
				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "MobileViTV2ForImageClassification",
				    "MobileViTV2ForSemanticSegmentation",
				    "MobileViTV2Model",
				    "MobileViTV2PreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

				snake_case				:	Tuple							   =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
