



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()
_lowercase				:					Optional[Any]     					=logging.get_logger(__name__)




def    UpperCAmelCase					(  lowercase__						:					str	):



			'''simple docstring'''





			a__					=  ConvNextVaConfig()

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

			a__					=  1000
			a__					=  """imagenet-1k-id2label.json"""
			a__					=  (1, 1000)

			a__					=  """huggingface/label-files"""
			a__					=  num_labels
			a__					=  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					repo_type="""dataset"""	)				,					"""r"""	)	)
			a__					=  {int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}

			a__					=  idalabel
			a__					=  {v: k for k, v in idalabel.items()}
			a__					=  hidden_sizes
			a__					=  depths

			return config, expected_shape




def    UpperCAmelCase					(  lowercase__						:					str	):



			'''simple docstring'''





			if "downsample_layers.0.0" in name:
						a__					=  name.replace("""downsample_layers.0.0"""				,					"""embeddings.patch_embeddings"""	)
			if "downsample_layers.0.1" in name:
						a__					=  name.replace("""downsample_layers.0.1"""				,					"""embeddings.norm"""	)  # we rename to layernorm later on
			if "downsample_layers.1.0" in name:
						a__					=  name.replace("""downsample_layers.1.0"""				,					"""stages.1.downsampling_layer.0"""	)
			if "downsample_layers.1.1" in name:
						a__					=  name.replace("""downsample_layers.1.1"""				,					"""stages.1.downsampling_layer.1"""	)
			if "downsample_layers.2.0" in name:
						a__					=  name.replace("""downsample_layers.2.0"""				,					"""stages.2.downsampling_layer.0"""	)
			if "downsample_layers.2.1" in name:
						a__					=  name.replace("""downsample_layers.2.1"""				,					"""stages.2.downsampling_layer.1"""	)
			if "downsample_layers.3.0" in name:
						a__					=  name.replace("""downsample_layers.3.0"""				,					"""stages.3.downsampling_layer.0"""	)
			if "downsample_layers.3.1" in name:
						a__					=  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.
						a__					=  name[: len("""stages.0"""	)] + """.layers""" + name[len("""stages.0"""	) :]
			if "gamma" in name:
						a__					=  name.replace("""gamma"""				,					"""weight"""	)
			if "beta" in name:
						a__					=  name.replace("""beta"""				,					"""bias"""	)
			if "stages" in name:
						a__					=  name.replace("""stages"""				,					"""encoder.stages"""	)
			if "norm" in name:
						a__					=  name.replace("""norm"""				,					"""layernorm"""	)
			if "head" in name:
						a__					=  name.replace("""head"""				,					"""classifier"""	)

			return name




def    UpperCAmelCase					(  ):



			'''simple docstring'''





			a__					=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
			a__					=  Image.open(requests.get(_SCREAMING_SNAKE_CASE				,					stream=_SCREAMING_SNAKE_CASE	).raw	)
			return im




def    UpperCAmelCase					(  lowercase__						:					Tuple	):



			'''simple docstring'''





			if "224" in checkpoint_url:
						a__					=  224
						a__					=  224 / 256
			elif "384" in checkpoint_url:
						a__					=  384
						a__					=  None
			else:
						a__					=  512
						a__					=  None

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




@torch.no_grad()
def    UpperCAmelCase					(  lowercase__						:					Optional[Any]				,					lowercase__						:					int				,					lowercase__						:					Tuple				,					lowercase__						:					Union[str, Any]	):



			'''simple docstring'''





			print("""Downloading original model from checkpoint..."""	)
			# define ConvNeXTV2 configuration based on URL
			a__				,       a__					=  get_convnextva_config(_SCREAMING_SNAKE_CASE	)
			# load original state_dict from URL
			a__					=  torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE	)["""model"""]

			print("""Converting model parameters..."""	)
			# rename keys
			for key in state_dict.copy().keys():
						a__					=  state_dict.pop(_SCREAMING_SNAKE_CASE	)
						a__					=  val
			# add prefix to all keys expect classifier head
			for key in state_dict.copy().keys():
						a__					=  state_dict.pop(_SCREAMING_SNAKE_CASE	)
						if not key.startswith("""classifier"""	):
									a__					=  """convnextv2.""" + key
						a__					=  val

			# load HuggingFace model
			a__					=  ConvNextVaForImageClassification(_SCREAMING_SNAKE_CASE	)
			model.load_state_dict(_SCREAMING_SNAKE_CASE	)
			model.eval()

			# Check outputs on an image, prepared by ConvNextImageProcessor
			a__					=  convert_preprocessor(_SCREAMING_SNAKE_CASE	)
			a__					=  preprocessor(images=prepare_img()				,					return_tensors="""pt"""	)
			a__					=  model(**_SCREAMING_SNAKE_CASE	).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":
						a__					=  torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
						a__					=  torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
						a__					=  torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
						a__					=  torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
						a__					=  torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
						a__					=  torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
						a__					=  torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
						a__					=  torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
						a__					=  torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
						a__					=  torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
						a__					=  torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
						a__					=  torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
						a__					=  torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
						a__					=  torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
						a__					=  torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
						a__					=  torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
						a__					=  torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]	)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
						a__					=  torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]	)
			else:
						raise ValueError(f'Unknown URL: {checkpoint_url}'	)

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

						model.save_pretrained(_SCREAMING_SNAKE_CASE	)
						preprocessor.save_pretrained(_SCREAMING_SNAKE_CASE	)

			a__					=  """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(_SCREAMING_SNAKE_CASE	)
						preprocessor.push_to_hub(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
	_lowercase				:					Tuple     					=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""")

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








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_		  =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_		  =			{
    """facebook/sam-vit-huge""": """https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json""",
    """facebook/sam-vit-large""": """https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json""",
    """facebook/sam-vit-base""": """https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json""",
}

class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     def __init__(				self		:       List[str]		,lowerCamelCase__		:       int=256		,lowerCamelCase__		:       List[Any]=1024		,lowerCamelCase__		:       Any=16		,lowerCamelCase__		:       Union[str, Any]=16		,lowerCamelCase__		:       int=4		,lowerCamelCase__		:       Tuple="gelu"		,lowerCamelCase__		:       Dict=1e-6		,**lowerCamelCase__		:       int		,)	->						List[str]:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		hidden_size
           SCREAMING_SNAKE_CASE						=		image_size
           SCREAMING_SNAKE_CASE						=		patch_size
           SCREAMING_SNAKE_CASE						=		image_size // patch_size
           SCREAMING_SNAKE_CASE						=		mask_input_channels
           SCREAMING_SNAKE_CASE						=		num_point_embeddings
           SCREAMING_SNAKE_CASE						=		hidden_act
           SCREAMING_SNAKE_CASE						=		layer_norm_eps

class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     def __init__(				self		:       str		,lowerCamelCase__		:       str=256		,lowerCamelCase__		:       Optional[Any]="relu"		,lowerCamelCase__		:       str=2048		,lowerCamelCase__		:       Union[str, Any]=2		,lowerCamelCase__		:       int=8		,lowerCamelCase__		:       Union[str, Any]=2		,lowerCamelCase__		:       List[Any]=3		,lowerCamelCase__		:       int=3		,lowerCamelCase__		:       Optional[Any]=256		,lowerCamelCase__		:       Tuple=1e-6		,**lowerCamelCase__		:       List[str]		,)	->						Dict:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		hidden_size
           SCREAMING_SNAKE_CASE						=		hidden_act
           SCREAMING_SNAKE_CASE						=		mlp_dim
           SCREAMING_SNAKE_CASE						=		num_hidden_layers
           SCREAMING_SNAKE_CASE						=		num_attention_heads
           SCREAMING_SNAKE_CASE						=		attention_downsample_rate
           SCREAMING_SNAKE_CASE						=		num_multimask_outputs
           SCREAMING_SNAKE_CASE						=		iou_head_depth
           SCREAMING_SNAKE_CASE						=		iou_head_hidden_dim
           SCREAMING_SNAKE_CASE						=		layer_norm_eps

class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     def __init__(				self		:       Union[str, Any]		,lowerCamelCase__		:       Optional[Any]=768		,lowerCamelCase__		:       Union[str, Any]=256		,lowerCamelCase__		:       Optional[int]=12		,lowerCamelCase__		:       str=12		,lowerCamelCase__		:       Tuple=3		,lowerCamelCase__		:       Optional[Any]=1024		,lowerCamelCase__		:       Optional[int]=16		,lowerCamelCase__		:       Optional[Any]="gelu"		,lowerCamelCase__		:       int=1e-0_6		,lowerCamelCase__		:       Optional[Any]=0.0		,lowerCamelCase__		:       Tuple=1e-1_0		,lowerCamelCase__		:       Any=True		,lowerCamelCase__		:       List[Any]=4.0		,lowerCamelCase__		:       Optional[int]=True		,lowerCamelCase__		:       Union[str, Any]=True		,lowerCamelCase__		:       List[str]=14		,lowerCamelCase__		:       List[str]=[2, 5, 8, 11]		,lowerCamelCase__		:       Tuple=128		,lowerCamelCase__		:       Dict=None		,**lowerCamelCase__		:       Optional[Any]		,)	->						str:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__			)

           SCREAMING_SNAKE_CASE						=		hidden_size
           SCREAMING_SNAKE_CASE						=		output_channels
           SCREAMING_SNAKE_CASE						=		num_hidden_layers
           SCREAMING_SNAKE_CASE						=		num_attention_heads
           SCREAMING_SNAKE_CASE						=		num_channels
           SCREAMING_SNAKE_CASE						=		image_size
           SCREAMING_SNAKE_CASE						=		patch_size
           SCREAMING_SNAKE_CASE						=		hidden_act
           SCREAMING_SNAKE_CASE						=		layer_norm_eps
           SCREAMING_SNAKE_CASE						=		attention_dropout
           SCREAMING_SNAKE_CASE						=		initializer_range
           SCREAMING_SNAKE_CASE						=		qkv_bias
           SCREAMING_SNAKE_CASE						=		mlp_ratio
           SCREAMING_SNAKE_CASE						=		use_abs_pos
           SCREAMING_SNAKE_CASE						=		use_rel_pos
           SCREAMING_SNAKE_CASE						=		window_size
           SCREAMING_SNAKE_CASE						=		global_attn_indexes
           SCREAMING_SNAKE_CASE						=		num_pos_feats
           SCREAMING_SNAKE_CASE						=		int(hidden_size * mlp_ratio			) if mlp_dim is None else mlp_dim

class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     __snake_case       :      Any						      =     "sam"
     __snake_case       :      Optional[Any]						      =     True






     def __init__(				self		:       Union[str, Any]		,lowerCamelCase__		:       List[Any]=None		,lowerCamelCase__		:       int=None		,lowerCamelCase__		:       int=None		,lowerCamelCase__		:       int=0.02		,**lowerCamelCase__		:       int		,)	->						str:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		vision_config if vision_config is not None else {}
           SCREAMING_SNAKE_CASE						=		prompt_encoder_config if prompt_encoder_config is not None else {}
           SCREAMING_SNAKE_CASE						=		mask_decoder_config if mask_decoder_config is not None else {}

           if isinstance(lowerCamelCase__		,lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		vision_config.to_dict()
           if isinstance(lowerCamelCase__		,lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		prompt_encoder_config.to_dict()
           if isinstance(lowerCamelCase__		,lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		mask_decoder_config.to_dict()

           SCREAMING_SNAKE_CASE						=		SamVisionConfig(**lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		SamPromptEncoderConfig(**lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		SamMaskDecoderConfig(**lowerCamelCase__			)
           SCREAMING_SNAKE_CASE						=		initializer_range






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Union[str, Any]			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		copy.deepcopy(self.__dict__			)
           SCREAMING_SNAKE_CASE						=		self.vision_config.to_dict()
           SCREAMING_SNAKE_CASE						=		self.prompt_encoder_config.to_dict()
           SCREAMING_SNAKE_CASE						=		self.mask_decoder_config.to_dict()
           SCREAMING_SNAKE_CASE						=		self.__class__.model_type
           return output


