

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				: 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 __future__ import annotations






class _UpperCamelCase							:
			'''simple docstring'''







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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    vertices
				SCREAMING_SNAKE_CASE				: Tuple			       =    [[0] * vertices for _ in range(a		)]
			def 							__UpperCamelCase	(     self							:	Dict , a							:	list[int]		)							->    None:

				"""simple docstring"""

				print("Vertex \t Distance from Source"		)
				for vertex in range(self.vertices		):
					print(a , "\t\t" , distances_from_source[vertex]		)
			def 							__UpperCamelCase	(     self							:	Tuple , a							:	list[int] , a							:	list[bool]		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    1e7
				SCREAMING_SNAKE_CASE				: int			       =    0

				# Search not nearest vertex not in the shortest path tree
				for vertex in range(self.vertices		):
					if distances_from_source[vertex] < minimum and visited[vertex] is False:
						SCREAMING_SNAKE_CASE				: Optional[int]			       =    distances_from_source[vertex]
						SCREAMING_SNAKE_CASE				: Optional[Any]			       =    vertex
				return min_index






			def 							__UpperCamelCase	(     self							:	Any , a							:	int		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    [int(1e7		)] * self.vertices  # distances from the source
				SCREAMING_SNAKE_CASE				: Tuple			       =    0
				SCREAMING_SNAKE_CASE				: List[Any]			       =    [False] * self.vertices

				for _ in range(self.vertices		):
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.minimum_distance(a , a		)
					SCREAMING_SNAKE_CASE				: List[str]			       =    True

					# Update dist value of the adjacent vertices
					# of the picked vertex only if the current
					# distance is greater than new distance and
					# the vertex in not in the shortest path tree
					for v in range(self.vertices		):
						if (
						    self.graph[u][v] > 0
						    and visited[v] is False
						    and distances[v] > distances[u] + self.graph[u][v]
						):
							SCREAMING_SNAKE_CASE				: int			       =    distances[u] + self.graph[u][v]

				self.print_solution(a		)


if __name__ == "__main__":
			a_         =      Graph(9)
			a_         =      [
			    [0, 4, 0, 0, 0, 0, 0, 8, 0],
			    [4, 0, 8, 0, 0, 0, 0, 11, 0],
			    [0, 8, 0, 7, 0, 4, 0, 0, 2],
			    [0, 0, 7, 0, 9, 14, 0, 0, 0],
			    [0, 0, 0, 9, 0, 10, 0, 0, 0],
			    [0, 0, 4, 14, 10, 0, 2, 0, 0],
			    [0, 0, 0, 0, 0, 2, 0, 1, 6],
			    [8, 11, 0, 0, 0, 0, 1, 0, 7],
			    [0, 0, 2, 0, 0, 0, 6, 7, 0],
			]
			graph.dijkstra(0)