
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()
A__       :   Union[str, Any]   						=					logging.get_logger(__name__)



def      _a     (   __UpperCamelCase       :  Tuple    ):
			lowerCAmelCase__ :    str				      =			ConvNextVaConfig()

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

			lowerCAmelCase__ :    Any				      =			1000
			lowerCAmelCase__ :    Tuple				      =			'''imagenet-1k-id2label.json'''
			lowerCAmelCase__ :    Optional[Any]				      =			(1, 1000)

			lowerCAmelCase__ :    Dict				      =			'''huggingface/label-files'''
			lowerCAmelCase__ :    List[str]				      =			num_labels
			lowerCAmelCase__ :    str				      =			json.load(open(hf_hub_download(__UpperCamelCase	,__UpperCamelCase	,repo_type='''dataset'''    )	,'''r'''    )    )
			lowerCAmelCase__ :    int				      =			{int(__UpperCamelCase    ): v for k, v in idalabel.items()}

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

			return config, expected_shape



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

			return name



def      _a     (   ):
			lowerCAmelCase__ :    int				      =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCAmelCase__ :    List[Any]				      =			Image.open(requests.get(__UpperCamelCase	,stream=__UpperCamelCase    ).raw    )
			return im



def      _a     (   __UpperCamelCase       :  Union[str, Any]    ):
			if "224" in checkpoint_url:
						lowerCAmelCase__ :    List[str]				      =			224
						lowerCAmelCase__ :    int				      =			224 / 256
			elif "384" in checkpoint_url:
						lowerCAmelCase__ :    Optional[Any]				      =			384
						lowerCAmelCase__ :    List[str]				      =			None
			else:
						lowerCAmelCase__ :    Any				      =			512
						lowerCAmelCase__ :    str				      =			None

			return ConvNextImageProcessor(
			    size=__UpperCamelCase	,crop_pct=__UpperCamelCase	,image_mean=[0.485, 0.456, 0.406]	,image_std=[0.229, 0.224, 0.225]	,resample=PILImageResampling.BICUBIC	,)



@torch.no_grad()
def      _a     (   __UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  Optional[Any]	,__UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  List[Any]    ):
			print('''Downloading original model from checkpoint...'''    )
			# define ConvNeXTV2 configuration based on URL
			lowerCAmelCase__      ,				lowerCAmelCase__ :    Optional[Any]				      =			get_convnextva_config(__UpperCamelCase    )
			# load original state_dict from URL
			lowerCAmelCase__ :    Any				      =			torch.hub.load_state_dict_from_url(__UpperCamelCase    )['''model''']

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

			# load HuggingFace model
			lowerCAmelCase__ :    str				      =			ConvNextVaForImageClassification(__UpperCamelCase    )
			model.load_state_dict(__UpperCamelCase    )
			model.eval()

			# Check outputs on an image, prepared by ConvNextImageProcessor
			lowerCAmelCase__ :    str				      =			convert_preprocessor(__UpperCamelCase    )
			lowerCAmelCase__ :    Optional[Any]				      =			preprocessor(images=prepare_img()	,return_tensors='''pt'''    )
			lowerCAmelCase__ :    Tuple				      =			model(**__UpperCamelCase    ).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":
						lowerCAmelCase__ :    List[Any]				      =			torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
						lowerCAmelCase__ :    Tuple				      =			torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
						lowerCAmelCase__ :    Tuple				      =			torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
						lowerCAmelCase__ :    Tuple				      =			torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
						lowerCAmelCase__ :    Optional[Any]				      =			torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
						lowerCAmelCase__ :    str				      =			torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
						lowerCAmelCase__ :    List[str]				      =			torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
						lowerCAmelCase__ :    Union[str, Any]				      =			torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
						lowerCAmelCase__ :    Dict				      =			torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
						lowerCAmelCase__ :    int				      =			torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
						lowerCAmelCase__ :    List[Any]				      =			torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
						lowerCAmelCase__ :    Tuple				      =			torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
						lowerCAmelCase__ :    int				      =			torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
						lowerCAmelCase__ :    List[str]				      =			torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
						lowerCAmelCase__ :    Any				      =			torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
						lowerCAmelCase__ :    List[str]				      =			torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
						lowerCAmelCase__ :    Optional[Any]				      =			torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309]    )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
						lowerCAmelCase__ :    Optional[int]				      =			torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826]    )
			else:
						raise ValueError(f'''Unknown URL: {checkpoint_url}'''    )

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

						model.save_pretrained(__UpperCamelCase    )
						preprocessor.save_pretrained(__UpperCamelCase    )

			lowerCAmelCase__ :    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(__UpperCamelCase    )
						preprocessor.push_to_hub(__UpperCamelCase    )


if __name__ == "__main__":
							A__       :   str   						=					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""")

							A__       :   Optional[int]   						=					parser.parse_args()
							convert_convnextva_checkpoint(
							    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
							)





import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


A__       :   Tuple   						=					TypeVar("""T""", bound="""TaskTemplate""")





@dataclass(frozen=__UpperCamelCase					)
class 						lowercase       (  abc.ABC					):
					# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
					__a					=    42
					__a					=    42
					__a					=    42
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								return copy.deepcopy(self    )
					@property
					def lowercase_			(		self    ):
								"""simple docstring"""







								return Features(**self.input_schema ,	**self.label_schema    )
					@property
					@abc.abstractmethod
					def lowercase_			(		self    ):
								"""simple docstring"""







								raise NotImplementedError
					@classmethod
					def lowercase_			(		cls ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			{f.name for f in dataclasses.fields(cls    )}
								return cls(**{k: v for k, v in template_dict.items() if k in field_names}    )



