





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()
UpperCamelCase__ :			List[Any]       	=			logging.get_logger(__name__)




def  A_(      A ):
	UpperCAmelCase_		    =  ConvNextVaConfig()

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

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

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

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

	return config, expected_shape




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

	return name




def  A_(      ):
	UpperCAmelCase_		    =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
	UpperCAmelCase_		    =  Image.open(requests.get(A					,   stream=A ).raw )
	return im




def  A_(      A ):
	if "224" in checkpoint_url:
		UpperCAmelCase_		    =  224
		UpperCAmelCase_		    =  224 / 256
	elif "384" in checkpoint_url:
		UpperCAmelCase_		    =  384
		UpperCAmelCase_		    =  None
	else:
		UpperCAmelCase_		    =  512
		UpperCAmelCase_		    =  None

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




@torch.no_grad()
def  A_(      A					,   A					,   A					,   A ):
	print("""Downloading original model from checkpoint...""" )
	# define ConvNeXTV2 configuration based on URL
	UpperCAmelCase_    ,						UpperCAmelCase_		    =  get_convnextva_config(A )
	# load original state_dict from URL
	UpperCAmelCase_		    =  torch.hub.load_state_dict_from_url(A )["""model"""]

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

	# load HuggingFace model
	UpperCAmelCase_		    =  ConvNextVaForImageClassification(A )
	model.load_state_dict(A )
	model.eval()

	# Check outputs on an image, prepared by ConvNextImageProcessor
	UpperCAmelCase_		    =  convert_preprocessor(A )
	UpperCAmelCase_		    =  preprocessor(images=prepare_img()					,   return_tensors="""pt""" )
	UpperCAmelCase_		    =  model(**A ).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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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":
		UpperCAmelCase_		    =  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]					,   A					,   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(A ):
			os.mkdir(A )

		model.save_pretrained(A )
		preprocessor.save_pretrained(A )

	UpperCAmelCase_		    =  """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(A )
		preprocessor.push_to_hub(A )


if __name__ == "__main__":
				UpperCamelCase__ :			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""")

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









from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
				import pyximport

				pyximport.install(setup_args={"""include_dirs""": np.get_include()})
				from . import algos_graphormer  # noqa E402




def  A_(      A					,   A = 512 ):
	UpperCAmelCase_		    =  x.shape[1] if len(x.shape ) > 1 else 1
	UpperCAmelCase_		    =  1 + np.arange(0					,   feature_num * offset					,   A					,   dtype=np.intaa )
	UpperCAmelCase_		    =  x + feature_offset
	return x




def  A_(      A					,   A=True ):
	requires_backends(A					,   ["""cython"""] )

	if keep_features and "edge_attr" in item.keys():  # edge_attr
		UpperCAmelCase_		    =  np.asarray(item["""edge_attr"""]					,   dtype=np.intaa )
	else:
		UpperCAmelCase_		    =  np.ones((len(item["""edge_index"""][0] ), 1)					,   dtype=np.intaa )  # same embedding for all

	if keep_features and "node_feat" in item.keys():  # input_nodes
		UpperCAmelCase_		    =  np.asarray(item["""node_feat"""]					,   dtype=np.intaa )
	else:
		UpperCAmelCase_		    =  np.ones((item["""num_nodes"""], 1)					,   dtype=np.intaa )  # same embedding for all

	UpperCAmelCase_		    =  np.asarray(item["""edge_index"""]					,   dtype=np.intaa )

	UpperCAmelCase_		    =  convert_to_single_emb(A ) + 1
	UpperCAmelCase_		    =  item["""num_nodes"""]

	if len(edge_attr.shape ) == 1:
		UpperCAmelCase_		    =  edge_attr[:, None]
	UpperCAmelCase_		    =  np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,   dtype=np.intaa )
	UpperCAmelCase_		    =  convert_to_single_emb(A ) + 1

	# node adj matrix [num_nodes, num_nodes] bool
	UpperCAmelCase_		    =  np.zeros([num_nodes, num_nodes]					,   dtype=A )
	UpperCAmelCase_		    =  True

	UpperCAmelCase_    ,						UpperCAmelCase_		    =  algos_graphormer.floyd_warshall(A )
	UpperCAmelCase_		    =  np.amax(A )

	UpperCAmelCase_		    =  algos_graphormer.gen_edge_input(A					,   A					,   A )
	UpperCAmelCase_		    =  np.zeros([num_nodes + 1, num_nodes + 1]					,   dtype=np.single )  # with graph token

	# combine
	UpperCAmelCase_		    =  input_nodes + 1  # we shift all indices by one for padding
	UpperCAmelCase_		    =  attn_bias
	UpperCAmelCase_		    =  attn_edge_type
	UpperCAmelCase_		    =  shortest_path_result.astype(np.intaa ) + 1  # we shift all indices by one for padding
	UpperCAmelCase_		    =  np.sum(A					,   axis=1 ).reshape(-1 ) + 1  # we shift all indices by one for padding
	UpperCAmelCase_		    =  item["""in_degree"""]  # for undirected graph
	UpperCAmelCase_		    =  input_edges + 1  # we shift all indices by one for padding
	if "labels" not in item:
		UpperCAmelCase_		    =  item["""y"""]

	return item







class 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	str			,       __lowercase     :	int=20			,       __lowercase     :	Union[str, Any]=False		):






						'''simple docstring'''







						if not is_cython_available():
							raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""		)

						UpperCAmelCase_		    =  spatial_pos_max
						UpperCAmelCase_		    =  on_the_fly_processing





					def __call__(    self     :	List[str]			,       __lowercase     :	List[dict]		):






						'''simple docstring'''







						if self.on_the_fly_processing:
							UpperCAmelCase_		    =  [preprocess_item(__lowercase		) for i in features]

						if not isinstance(features[0]			,       __lowercase		):
							UpperCAmelCase_		    =  [vars(__lowercase		) for f in features]
						UpperCAmelCase_		    =  {}

						UpperCAmelCase_		    =  max(len(i["""input_nodes"""]		) for i in features		)
						UpperCAmelCase_		    =  len(features[0]["""input_nodes"""][0]		)
						UpperCAmelCase_		    =  len(features[0]["""attn_edge_type"""][0][0]		)
						UpperCAmelCase_		    =  max(len(i["""input_edges"""][0][0]		) for i in features		)
						UpperCAmelCase_		    =  len(features[0]["""input_edges"""][0][0][0]		)
						UpperCAmelCase_		    =  len(__lowercase		)

						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       max_node_num + 1			,       max_node_num + 1			,       dtype=torch.float		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(
						    __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)

						for ix, f in enumerate(__lowercase		):
							for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
								UpperCAmelCase_		    =  torch.tensor(f[k]		)

							if len(f["""attn_bias"""][1:, 1:][f["""spatial_pos"""] >= self.spatial_pos_max]		) > 0:
								UpperCAmelCase_		    =  float("""-inf"""		)

							UpperCAmelCase_		    =  f["""attn_bias"""]
							UpperCAmelCase_		    =  f[
							    """attn_edge_type"""
							]
							UpperCAmelCase_		    =  f["""spatial_pos"""]
							UpperCAmelCase_		    =  f["""in_degree"""]
							UpperCAmelCase_		    =  f["""input_nodes"""]
							UpperCAmelCase_		    =  f["""input_edges"""]

						UpperCAmelCase_		    =  batch["""in_degree"""]

						UpperCAmelCase_		    =  features[0]["""labels"""]
						if len(__lowercase		) == 1:  # one task
							if isinstance(sample[0]			,       __lowercase		):  # regression
								UpperCAmelCase_		    =  torch.from_numpy(np.concatenate([i["""labels"""] for i in features]		)		)
							else:  # binary classification
								UpperCAmelCase_		    =  torch.from_numpy(np.concatenate([i["""labels"""] for i in features]		)		)
						else:  # multi task classification, left to float to keep the NaNs
							UpperCAmelCase_		    =  torch.from_numpy(np.stack([i["""labels"""] for i in features]			,       axis=0		)		)

						return batch


