


import argparse
import json
from pathlib import Path

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

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase						=		logging.get_logger(__name__)






def 					lowerCamelCase_   (	_a     ,       _a=False	):




		"""simple docstring"""






		lowerCAmelCase__       :      int   					= []
		for i in range(config.num_hidden_layers	):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((f'blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight')	)
				rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias')	)
				rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight')	)
				rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias')	)
				rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight')	)
				rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias')	)
				rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight')	)
				rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias')	)
				rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight')	)
				rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias')	)

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        ('''cls_token''', '''vit.embeddings.cls_token'''),
		        ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
		        ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
		        ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
		    ]	)

		if base_model:
				# layernorm + pooler
				rename_keys.extend(
				    [
				        ('''norm.weight''', '''layernorm.weight'''),
				        ('''norm.bias''', '''layernorm.bias'''),
				        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
				        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
				    ]	)

				# if just the base model, we should remove "vit" from all keys that start with "vit"
				lowerCAmelCase__       :      Tuple   					= [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''	) else pair for pair in rename_keys]
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ('''norm.weight''', '''vit.layernorm.weight'''),
				        ('''norm.bias''', '''vit.layernorm.bias'''),
				        ('''head.weight''', '''classifier.weight'''),
				        ('''head.bias''', '''classifier.bias'''),
				    ]	)

		return rename_keys






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a=False	):




		"""simple docstring"""






		for i in range(config.num_hidden_layers	):
				if base_model:
						lowerCAmelCase__       :      Dict   					= ''''''
				else:
						lowerCAmelCase__       :      List[str]   					= '''vit.'''
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				lowerCAmelCase__       :      Dict   					= state_dict.pop(f'blocks.{i}.attn.qkv.weight'	)
				lowerCAmelCase__       :      Dict   					= state_dict.pop(f'blocks.{i}.attn.qkv.bias'	)
				# next, add query, keys and values (in that order) to the state dict
				lowerCAmelCase__       :      Any   					= in_proj_weight[
				    : config.hidden_size, :
				]
				lowerCAmelCase__       :      Optional[Any]   					= in_proj_bias[: config.hidden_size]
				lowerCAmelCase__       :      int   					= in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCAmelCase__       :      Dict   					= in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowerCAmelCase__       :      Dict   					= in_proj_weight[
				    -config.hidden_size :, :
				]
				lowerCAmelCase__       :      Any   					= in_proj_bias[-config.hidden_size :]






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Union[str, Any]   					= ['''head.weight''', '''head.bias''']
		for k in ignore_keys:
				state_dict.pop(_a     ,       _a	)






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= dct.pop(_a	)
		lowerCAmelCase__       :      Optional[Any]   					= val






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      Dict   					= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase__       :      Tuple   					= Image.open(requests.get(_a     ,       stream=_a	).raw	)
		return im






@torch.no_grad()
def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= ViTConfig()
		lowerCAmelCase__       :      Optional[Any]   					= False
		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		if vit_name[-5:] == "in21k":
				lowerCAmelCase__       :      List[Any]   					= True
				lowerCAmelCase__       :      Any   					= int(vit_name[-12:-10]	)
				lowerCAmelCase__       :      int   					= int(vit_name[-9:-6]	)
		else:
				lowerCAmelCase__       :      Dict   					= 1_000
				lowerCAmelCase__       :      str   					= '''huggingface/label-files'''
				lowerCAmelCase__       :      Dict   					= '''imagenet-1k-id2label.json'''
				lowerCAmelCase__       :      str   					= json.load(open(hf_hub_download(_a     ,       _a     ,       repo_type='''dataset'''	)     ,       '''r'''	)	)
				lowerCAmelCase__       :      Any   					= {int(_a	): v for k, v in idalabel.items()}
				lowerCAmelCase__       :      Optional[Any]   					= idalabel
				lowerCAmelCase__       :      List[str]   					= {v: k for k, v in idalabel.items()}
				lowerCAmelCase__       :      Tuple   					= int(vit_name[-6:-4]	)
				lowerCAmelCase__       :      Union[str, Any]   					= int(vit_name[-3:]	)
		# size of the architecture
		if "deit" in vit_name:
				if vit_name[9:].startswith('''tiny'''	):
						lowerCAmelCase__       :      List[str]   					= 192
						lowerCAmelCase__       :      Tuple   					= 768
						lowerCAmelCase__       :      Optional[int]   					= 12
						lowerCAmelCase__       :      List[Any]   					= 3
				elif vit_name[9:].startswith('''small'''	):
						lowerCAmelCase__       :      Any   					= 384
						lowerCAmelCase__       :      Optional[int]   					= 1_536
						lowerCAmelCase__       :      List[Any]   					= 12
						lowerCAmelCase__       :      Tuple   					= 6
				else:
						pass
		else:
				if vit_name[4:].startswith('''small'''	):
						lowerCAmelCase__       :      List[str]   					= 768
						lowerCAmelCase__       :      Tuple   					= 2_304
						lowerCAmelCase__       :      Any   					= 8
						lowerCAmelCase__       :      Union[str, Any]   					= 8
				elif vit_name[4:].startswith('''base'''	):
						pass
				elif vit_name[4:].startswith('''large'''	):
						lowerCAmelCase__       :      str   					= 1_024
						lowerCAmelCase__       :      Optional[Any]   					= 4_096
						lowerCAmelCase__       :      Optional[int]   					= 24
						lowerCAmelCase__       :      Tuple   					= 16
				elif vit_name[4:].startswith('''huge'''	):
						lowerCAmelCase__       :      Tuple   					= 1_280
						lowerCAmelCase__       :      Tuple   					= 5_120
						lowerCAmelCase__       :      Optional[int]   					= 32
						lowerCAmelCase__       :      List[Any]   					= 16

    # load original model from timm
		lowerCAmelCase__       :      Tuple   					= timm.create_model(_a     ,       pretrained=_a	)
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		lowerCAmelCase__       :      Any   					= timm_model.state_dict()
		if base_model:
				remove_classification_head_(_a	)
		lowerCAmelCase__       :      List[Any]   					= create_rename_keys(_a     ,       _a	)
		for src, dest in rename_keys:
				rename_key(_a     ,       _a     ,       _a	)
		read_in_q_k_v(_a     ,       _a     ,       _a	)

		# load HuggingFace model
		if vit_name[-5:] == "in21k":
				lowerCAmelCase__       :      List[str]   					= ViTModel(_a	).eval()
		else:
				lowerCAmelCase__       :      Any   					= ViTForImageClassification(_a	).eval()
		model.load_state_dict(_a	)

		# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
		if "deit" in vit_name:
				lowerCAmelCase__       :      Dict   					= DeiTImageProcessor(size=config.image_size	)
		else:
				lowerCAmelCase__       :      int   					= ViTImageProcessor(size=config.image_size	)
		lowerCAmelCase__       :      Optional[Any]   					= image_processor(images=prepare_img()     ,       return_tensors='''pt'''	)
		lowerCAmelCase__       :      List[str]   					= encoding['''pixel_values''']
		lowerCAmelCase__       :      List[Any]   					= model(_a	)

		if base_model:
				lowerCAmelCase__       :      Optional[Any]   					= timm_model.forward_features(_a	)
				assert timm_pooled_output.shape == outputs.pooler_output.shape
				assert torch.allclose(_a     ,       outputs.pooler_output     ,       atol=1e-3	)
		else:
				lowerCAmelCase__       :      Union[str, Any]   					= timm_model(_a	)
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(_a     ,       outputs.logits     ,       atol=1e-3	)

		Path(_a	).mkdir(exist_ok=_a	)
		print(f'Saving model {vit_name} to {pytorch_dump_folder_path}'	)
		model.save_pretrained(_a	)
		print(f'Saving image processor to {pytorch_dump_folder_path}'	)
		image_processor.save_pretrained(_a	)


if __name__ == "__main__":
		lowerCamelCase						=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--vit_name''',
		    default='''vit_base_patch16_224''',
		    type=str,
		    help='''Name of the ViT 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						=		parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)








import math





class  _a							:




	def __init__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Any=0		)->	Optional[Any]:  # a graph with Node 0,1,...,N-1
			lowerCAmelCase__       :      Optional[int]   					= n
			lowerCAmelCase__       :      List[Any]   					= [
			    [math.inf for j in range(0		, _SCREAMING_SNAKE_CASE		)] for i in range(0		, _SCREAMING_SNAKE_CASE		)
			]  # adjacency matrix for weight
			lowerCAmelCase__       :      str   					= [
			    [math.inf for j in range(0		, _SCREAMING_SNAKE_CASE		)] for i in range(0		, _SCREAMING_SNAKE_CASE		)
			]  # dp[i][j] stores minimum distance from i to j




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		str		)->	List[str]:
			lowerCAmelCase__       :      Optional[int]   					= w




	def 							UpperCAmelCase__( self						:		List[Any]		)->	Optional[int]:
			for k in range(0		, self.n		):
					for i in range(0		, self.n		):
							for j in range(0		, self.n		):
									lowerCAmelCase__       :      Dict   					= min(self.dp[i][j]		, self.dp[i][k] + self.dp[k][j]		)




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		str		)->	str:
			return self.dp[u][v]


if __name__ == "__main__":
		lowerCamelCase						=		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)




