






import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def 					snake_case_						(   lowerCAmelCase_				:  List[str]					):
							__lowercase					:							Dict			=   SwinConfig()
							__lowercase					:							List[Any]			=   swin_name.split("""_"""					)

							__lowercase					:							Dict			=   name_split[1]
							__lowercase					:							List[str]			=   int(name_split[4]					)
							__lowercase					:							Tuple			=   int(name_split[3][-1]					)

							if model_size == "tiny":
														__lowercase					:							Union[str, Any]			=   96
														__lowercase					:							Dict			=   (2, 2, 6, 2)
														__lowercase					:							Any			=   (3, 6, 12, 24)
							elif model_size == "small":
														__lowercase					:							List[Any]			=   96
														__lowercase					:							str			=   (2, 2, 18, 2)
														__lowercase					:							Optional[int]			=   (3, 6, 12, 24)
							elif model_size == "base":
														__lowercase					:							str			=   128
														__lowercase					:							List[str]			=   (2, 2, 18, 2)
														__lowercase					:							str			=   (4, 8, 16, 32)
							else:
														__lowercase					:							Optional[Any]			=   192
														__lowercase					:							Optional[int]			=   (2, 2, 18, 2)
														__lowercase					:							Dict			=   (6, 12, 24, 48)

							if "in22k" in swin_name:
														__lowercase					:							Dict			=   21841
							else:
														__lowercase					:							Dict			=   1000
														__lowercase					:							Union[str, Any]			=   """huggingface/label-files"""
														__lowercase					:							int			=   """imagenet-1k-id2label.json"""
														__lowercase					:							List[Any]			=   json.load(open(hf_hub_download(lowerCAmelCase_				,				lowerCAmelCase_				,				repo_type="""dataset"""					)				,				"""r"""					)					)
														__lowercase					:							int			=   {int(lowerCAmelCase_					): v for k, v in idalabel.items()}
														__lowercase					:							str			=   idalabel
														__lowercase					:							List[str]			=   {v: k for k, v in idalabel.items()}

							__lowercase					:							List[Any]			=   img_size
							__lowercase					:							str			=   num_classes
							__lowercase					:							List[str]			=   embed_dim
							__lowercase					:							Optional[int]			=   depths
							__lowercase					:							Any			=   num_heads
							__lowercase					:							List[Any]			=   window_size

							return config
def 					snake_case_						(   lowerCAmelCase_				:  List[str]					):
							if "patch_embed.proj" in name:
														__lowercase					:							List[str]			=   name.replace("""patch_embed.proj"""				,				"""embeddings.patch_embeddings.projection"""					)
							if "patch_embed.norm" in name:
														__lowercase					:							Optional[int]			=   name.replace("""patch_embed.norm"""				,				"""embeddings.norm"""					)
							if "layers" in name:
														__lowercase					:							Any			=   """encoder.""" + name
							if "attn.proj" in name:
														__lowercase					:							str			=   name.replace("""attn.proj"""				,				"""attention.output.dense"""					)
							if "attn" in name:
														__lowercase					:							Dict			=   name.replace("""attn"""				,				"""attention.self"""					)
							if "norm1" in name:
														__lowercase					:							List[Any]			=   name.replace("""norm1"""				,				"""layernorm_before"""					)
							if "norm2" in name:
														__lowercase					:							int			=   name.replace("""norm2"""				,				"""layernorm_after"""					)
							if "mlp.fc1" in name:
														__lowercase					:							Tuple			=   name.replace("""mlp.fc1"""				,				"""intermediate.dense"""					)
							if "mlp.fc2" in name:
														__lowercase					:							Optional[Any]			=   name.replace("""mlp.fc2"""				,				"""output.dense"""					)

							if name == "norm.weight":
														__lowercase					:							Any			=   """layernorm.weight"""
							if name == "norm.bias":
														__lowercase					:							Union[str, Any]			=   """layernorm.bias"""

							if "head" in name:
														__lowercase					:							int			=   name.replace("""head"""				,				"""classifier"""					)
							else:
														__lowercase					:							Optional[int]			=   """swin.""" + name

							return name
def 					snake_case_						(   lowerCAmelCase_				:  Optional[int]				,				lowerCAmelCase_				:  Optional[int]					):
							for key in orig_state_dict.copy().keys():
														__lowercase					:							List[Any]			=   orig_state_dict.pop(lowerCAmelCase_					)

														if "mask" in key:
																					continue
														elif "qkv" in key:
																					__lowercase					:							Any			=   key.split("""."""					)
																					__lowercase					:							int			=   int(key_split[1]					)
																					__lowercase					:							Dict			=   int(key_split[3]					)
																					__lowercase					:							int			=   model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																					if "weight" in key:
																												__lowercase					:							List[Any]			=   val[:dim, :]
																												__lowercase					:							int			=   val[
																												    dim : dim * 2, :
																												]
																												__lowercase					:							Any			=   val[-dim:, :]
																					else:
																												__lowercase					:							int			=   val[
																												    :dim
																												]
																												__lowercase					:							Union[str, Any]			=   val[
																												    dim : dim * 2
																												]
																												__lowercase					:							Optional[Any]			=   val[
																												    -dim:
																												]
														else:
																					__lowercase					:							Dict			=   val

							return orig_state_dict
def 					snake_case_						(   lowerCAmelCase_				:  Any				,				lowerCAmelCase_				:  Dict					):
							__lowercase					:							Optional[Any]			=   timm.create_model(lowerCAmelCase_				,				pretrained=lowerCAmelCase_					)
							timm_model.eval()

							__lowercase					:							Optional[Any]			=   get_swin_config(lowerCAmelCase_					)
							__lowercase					:							int			=   SwinForImageClassification(lowerCAmelCase_					)
							model.eval()

							__lowercase					:							int			=   convert_state_dict(timm_model.state_dict()				,				lowerCAmelCase_					)
							model.load_state_dict(lowerCAmelCase_					)

							__lowercase					:							Optional[int]			=   """http://images.cocodataset.org/val2017/000000039769.jpg"""

							__lowercase					:							str			=   AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""				,				"""-"""					)					)					)
							__lowercase					:							Dict			=   Image.open(requests.get(lowerCAmelCase_				,				stream=lowerCAmelCase_					).raw					)
							__lowercase					:							List[str]			=   image_processor(images=lowerCAmelCase_				,				return_tensors="""pt"""					)

							__lowercase					:							int			=   timm_model(inputs["""pixel_values"""]					)
							__lowercase					:							Dict			=   model(**lowerCAmelCase_					).logits

							assert torch.allclose(lowerCAmelCase_				,				lowerCAmelCase_				,				atol=1e-3					)

							print(F"Saving model {swin_name} to {pytorch_dump_folder_path}"					)
							model.save_pretrained(lowerCAmelCase_					)

							print(F"Saving image processor to {pytorch_dump_folder_path}"					)
							image_processor.save_pretrained(lowerCAmelCase_					)


if __name__ == "__main__":
	lowerCamelCase       :       str        =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--swin_name''',
	    default='''swin_tiny_patch4_window7_224''',
	    type=str,
	    help='''Name of the Swin timm model you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
	)

	lowerCamelCase       :       Dict        =				parser.parse_args()
	convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)







import math

class 						lowerCAmelCase   :

					'''simple docstring'''

					def __init__(     self :		Tuple						,   __a :		int=0							)	->     Optional[Any]:  # a graph with Node 0,1,...,N-1


												"""simple docstring"""

												__lowercase					:							Any			=   n
												__lowercase					:							Optional[int]			=   [
												    [math.inf for j in range(0						,   __a							)] for i in range(0						,   __a							)
												]  # adjacency matrix for weight
												__lowercase					:							Dict			=   [
												    [math.inf for j in range(0						,   __a							)] for i in range(0						,   __a							)
												]  # dp[i][j] stores minimum distance from i to j



					def 					lowerCAmelCase (     self :		int						,   __a :		Optional[int]						,   __a :		Tuple						,   __a :		Dict							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							Any			=   w



					def 					lowerCAmelCase (     self :		List[str]							)	->     Tuple:


												"""simple docstring"""

												for k in range(0						,   self.n							):
																			for i in range(0						,   self.n							):
																										for j in range(0						,   self.n							):
																																	__lowercase					:							List[str]			=   min(self.dp[i][j]						,   self.dp[i][k] + self.dp[k][j]							)



					def 					lowerCAmelCase (     self :		Any						,   __a :		Any						,   __a :		str							)	->     int:


												"""simple docstring"""

												return self.dp[u][v]


if __name__ == "__main__":
	lowerCamelCase       :       int        =				Graph(5)
	graph.add_edge(0, 2, 9)
	graph.add_edge(0, 4, 10)
	graph.add_edge(1, 3, 5)
	graph.add_edge(2, 3, 7)
	graph.add_edge(3, 0, 10)
	graph.add_edge(3, 1, 2)
	graph.add_edge(3, 2, 1)
	graph.add_edge(3, 4, 6)
	graph.add_edge(4, 1, 3)
	graph.add_edge(4, 2, 4)
	graph.add_edge(4, 3, 9)
	graph.floyd_warshall()
	graph.show_min(1, 4)
	graph.show_min(0, 3)