






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

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						__magic_name__    :     Dict     		=      ConvNextConfig()

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

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

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

						return config, expected_shape

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







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

						return name

def 					UpperCamelCase		( ):






						"""simple docstring"""







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

@torch.no_grad()
def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







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

						# load HuggingFace model
						__magic_name__    :     Any     		=      ConvNextForImageClassification(_A				)
						model.load_state_dict(_A				)
						model.eval()

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

						__magic_name__    :     Tuple     		=      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":
												__magic_name__    :     Dict     		=      torch.tensor([-0.1210, -0.6605, 0.1918]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
												__magic_name__    :     Tuple     		=      torch.tensor([-0.4473, -0.1847, -0.6365]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
												__magic_name__    :     List[str]     		=      torch.tensor([0.4525, 0.7539, 0.0308]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
												__magic_name__    :     str     		=      torch.tensor([0.3561, 0.6350, -0.0384]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
												__magic_name__    :     Dict     		=      torch.tensor([0.4174, -0.0989, 0.1489]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
												__magic_name__    :     Optional[int]     		=      torch.tensor([0.2513, -0.1349, -0.1613]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
												__magic_name__    :     str     		=      torch.tensor([1.2980, 0.3631, -0.1198]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
												__magic_name__    :     Union[str, Any]     		=      torch.tensor([1.2963, 0.1227, 0.1723]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
												__magic_name__    :     Tuple     		=      torch.tensor([1.7956, 0.8390, 0.2820]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
												__magic_name__    :     Optional[int]     		=      torch.tensor([-0.2822, -0.0502, -0.0878]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
												__magic_name__    :     List[str]     		=      torch.tensor([-0.5672, -0.0730, -0.4348]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
												__magic_name__    :     List[str]     		=      torch.tensor([0.2681, 0.2365, 0.6246]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
												__magic_name__    :     int     		=      torch.tensor([-0.2642, 0.3931, 0.5116]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
												__magic_name__    :     Union[str, Any]     		=      torch.tensor([-0.6677, -0.1873, -0.8379]				)
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
												__magic_name__    :     List[str]     		=      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..."""				)
						__magic_name__    :     List[str]     		=      """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__":
		__magic_name__:  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.",
		)

		__magic_name__:  int  	=			parser.parse_args()
		convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








__magic_name__:  Tuple  	=			[
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "be-e6", "bh-e12", "cd-e2", "ce-e4",
     "de-e1", "df-e8", "dg-e5", "dh-e10", "ef-e3", "eg-e2", "fg-e6", "gh-e6", "hi-e3"],
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "be-e6", "cd-e2", "de-e1", "df-e8",
     "ef-e3", "eg-e2", "fg-e6"],
    ["ab-e1", "ac-e3", "bc-e4", "bd-e2", "de-e1", "df-e8", "dg-e5", "ef-e3", "eg-e2",
     "eh-e12", "fg-e6", "fh-e10", "gh-e6"],
    ["ab-e1", "ac-e3", "bc-e4", "bd-e2", "bh-e12", "cd-e2", "df-e8", "dh-e10"],
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "cd-e2", "ce-e4", "de-e1", "df-e8",
     "dg-e5", "ef-e3", "eg-e2", "fg-e6"]
]

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						__magic_name__    :     str     		=      set()
						for row in edge_array:
												for item in row:
																		distinct_edge.add(item[0]				)
						return list(_A				)

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						__magic_name__    :     Union[str, Any]     		=      ["""0"""] * len(_A				)
						for i, row in enumerate(_A				):
												for item in row:
																		if distinct_edge in item[0]:
																								__magic_name__    :     Optional[Any]     		=      """1"""
																								break
						return "".join(_A				)

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						__magic_name__    :     List[str]     		=      get_distinct_edge(_A				)
						__magic_name__    :     List[str]     		=      {}

						for item in distinct_edge:
												__magic_name__    :     Optional[Any]     		=      get_bitcode(_A,					_A				)
												# print('bit',bit)
												# bt=''.join(bit)
												__magic_name__    :     Union[str, Any]     		=      bit.count("""1"""				)
												__magic_name__    :     Dict     		=      [s, bit]
						# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
						__magic_name__    :     List[Any]     		=      [
						    [k, v[0], v[1]]
						    for k, v in sorted(frequency_table.items(),					key=lambda _A				: v[1][0],					reverse=_A				)
						]
						return sorted_frequency_table

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						__magic_name__    :     str     		=      {}
						for _, item in enumerate(_A				):
												nodes.setdefault(item[2],					[]				).append(item[0]				)
						return nodes

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						__magic_name__    :     Optional[int]     		=      {}
						for key, value in nodes.items():
												__magic_name__    :     Tuple     		=      value
						return cluster

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						return [i * 100 / len(_A				) for i in cluster]

def 					UpperCamelCase		( ):






						"""simple docstring"""







						print("""\nNodes\n"""				)
						for key, value in nodes.items():
												print(_A,					_A				)
						print("""\nSupport\n"""				)
						print(_A				)
						print("""\n Cluster \n"""				)
						for key, value in sorted(cluster.items(),					reverse=_A				):
												print(_A,					_A				)
						print("""\n Graph\n"""				)
						for key, value in graph.items():
												print(_A,					_A				)
						print("""\n Edge List of Frequent subgraphs \n"""				)
						for edge_list in freq_subgraph_edge_list:
												print(_A				)

def 					UpperCamelCase		( _A,					_A,					_A,					_A				):






						"""simple docstring"""







						for i in cluster[ca]:
												__magic_name__    :     Any     		=      0
												__magic_name__    :     Any     		=      ca + 1
												while ca < max(cluster.keys()				):
																		for j in cluster[ca]:
																								if int(_A,					2				) & int(_A,					2				) == int(_A,					2				):
																														if tuple(nodes[i]				) in graph:
																																				graph[tuple(nodes[i]				)].append(nodes[j]				)
																														else:
																																				__magic_name__    :     int     		=      [nodes[j]]
																														count += 1
																		if count == 0:
																								__magic_name__    :     Optional[Any]     		=      ca + 1
																		else:
																								break

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						__magic_name__    :     List[str]     		=      cluster[max(cluster.keys()				)]
						__magic_name__    :     List[Any]     		=      """Header"""
						__magic_name__    :     Tuple     		=      {}
						for i in x:
												if (["Header"],) in graph:
																		graph[(["Header"],)].append(x[i]				)
												else:
																		__magic_name__    :     List[str]     		=      [x[i]]
						for i in x:
												__magic_name__    :     Tuple     		=      [["""Header"""]]
						__magic_name__    :     Union[str, Any]     		=      1
						while i < max(_A				) - 1:
												create_edge(_A,					_A,					_A,					_A				)
												__magic_name__    :     Optional[Any]     		=      i + 1
						return graph

def 					UpperCamelCase		( _A,					_A,					_A,					_A=None				):






						"""simple docstring"""







						__magic_name__    :     str     		=      (path or []) + [start]
						if start == end:
												paths.append(_A				)
						for node in graph[start]:
												if tuple(_A				) not in path:
																		my_dfs(_A,					tuple(_A				),					_A,					_A				)

def 					UpperCamelCase		( _A,					_A,					_A				):






						"""simple docstring"""







						__magic_name__    :     Union[str, Any]     		=      int(s / 100 * (len(_A				) - 1)				)
						for i in cluster[k]:
												my_dfs(_A,					tuple(cluster[k][i]				),					(["""Header"""],)				)

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						__magic_name__    :     str     		=      []
						for edges in paths:
												__magic_name__    :     List[Any]     		=      []
												for j in range(len(_A				) - 1				):
																		__magic_name__    :     Any     		=      list(edges[j]				)
																		for e in temp:
																								__magic_name__    :     Optional[Any]     		=      (e[0], e[1])
																								el.append(_A				)
												freq_sub_el.append(_A				)
						return freq_sub_el

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						for i in range(len(_A				)				):
												for j in range(len(edge_array[i]				)				):
																		__magic_name__    :     Any     		=      edge_array[i][j].split("""-"""				)
																		__magic_name__    :     Optional[int]     		=      t


if __name__ == "__main__":
		preprocess(edge_array)
		__magic_name__:  Optional[int]  	=			get_frequency_table(edge_array)
		__magic_name__:  str  	=			get_nodes(frequency_table)
		__magic_name__:  List[str]  	=			get_cluster(nodes)
		__magic_name__:  Optional[Any]  	=			get_support(cluster)
		__magic_name__:  Dict  	=			construct_graph(cluster, nodes)
		find_freq_subgraph_given_support(60, cluster, graph)
		__magic_name__:  list  	=			[]
		__magic_name__:  Dict  	=			freq_subgraphs_edge_list(paths)
		print_all()
