



from heapq import heappop, heappush

import numpy as np


def 			UpperCamelCase(  __UpperCamelCase							:		np.ndarray     ,__UpperCamelCase							:		tuple[int, int]     ,__UpperCamelCase							:		tuple[int, int]     ,__UpperCamelCase							:		bool     ,):
		lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[int]									= grid.shape
		lowerCAmelCase_			:     int									= [-1, 1, 0, 0]
		lowerCAmelCase_			:     Optional[Any]									= [0, 0, -1, 1]
		if allow_diagonal:
				dx += [-1, -1, 1, 1]
				dy += [-1, 1, -1, 1]

		lowerCAmelCase_					,  lowerCAmelCase_			:     Tuple									= [(0, source)], set()
		lowerCAmelCase_			:     int									= np.full((rows, cols)     ,np.inf )
		lowerCAmelCase_			:     List[str]									= 0
		lowerCAmelCase_			:     Tuple									= np.empty((rows, cols)     ,dtype=__UpperCamelCase )
		lowerCAmelCase_			:     Any									= None

		while queue:
				((lowerCAmelCase_)					,  (lowerCAmelCase_))			:     Dict									= heappop(__UpperCamelCase )
				if (x, y) in visited:
						continue
				visited.add((x, y) )

				if (x, y) == destination:
						lowerCAmelCase_			:     Any									= []
						while (x, y) != source:
								path.append((x, y) )
								lowerCAmelCase_					,  lowerCAmelCase_			:     Any									= predecessors[x, y]
						path.append(__UpperCamelCase )  # add the source manually
						path.reverse()
						return matrix[destination], path

				for i in range(len(__UpperCamelCase ) ):
						lowerCAmelCase_					,  lowerCAmelCase_			:     str									= x + dx[i], y + dy[i]
						if 0 <= nx < rows and 0 <= ny < cols:
								lowerCAmelCase_			:     Union[str, Any]									= grid[nx][ny]
								if next_node == 1 and matrix[nx, ny] > dist + 1:
										heappush(__UpperCamelCase     ,(dist + 1, (nx, ny)) )
										lowerCAmelCase_			:     Any									= dist + 1
										lowerCAmelCase_			:     Union[str, Any]									= (x, y)

		return np.inf, []


if __name__ == "__main__":
						import doctest

						doctest.testmod()










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 AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification


def 			UpperCamelCase(  __UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     List[str]									= SwinvaConfig()
		lowerCAmelCase_			:     List[str]									= swinva_name.split('''_''' )

		lowerCAmelCase_			:     str									= name_split[1]
		if "to" in name_split[3]:
				lowerCAmelCase_			:     List[Any]									= int(name_split[3][-3:] )
		else:
				lowerCAmelCase_			:     List[Any]									= int(name_split[3] )
		if "to" in name_split[2]:
				lowerCAmelCase_			:     List[str]									= int(name_split[2][-2:] )
		else:
				lowerCAmelCase_			:     int									= int(name_split[2][6:] )

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

		if "to" in swinva_name:
				lowerCAmelCase_			:     Union[str, Any]									= (12, 12, 12, 6)

		if ("22k" in swinva_name) and ("to" not in swinva_name):
				lowerCAmelCase_			:     Optional[int]									= 21841
				lowerCAmelCase_			:     Any									= '''huggingface/label-files'''
				lowerCAmelCase_			:     Tuple									= '''imagenet-22k-id2label.json'''
				lowerCAmelCase_			:     Any									= json.load(open(hf_hub_download(__UpperCamelCase     ,__UpperCamelCase     ,repo_type='''dataset''' )     ,'''r''' ) )
				lowerCAmelCase_			:     Optional[Any]									= {int(__UpperCamelCase ): v for k, v in idalabel.items()}
				lowerCAmelCase_			:     str									= idalabel
				lowerCAmelCase_			:     List[str]									= {v: k for k, v in idalabel.items()}

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

		lowerCAmelCase_			:     Optional[int]									= img_size
		lowerCAmelCase_			:     Dict									= num_classes
		lowerCAmelCase_			:     Dict									= embed_dim
		lowerCAmelCase_			:     Optional[Any]									= depths
		lowerCAmelCase_			:     Optional[int]									= num_heads
		lowerCAmelCase_			:     Dict									= window_size

		return config


def 			UpperCamelCase(  __UpperCamelCase							:		List[str] ):
		if "patch_embed.proj" in name:
				lowerCAmelCase_			:     Dict									= name.replace('''patch_embed.proj'''     ,'''embeddings.patch_embeddings.projection''' )
		if "patch_embed.norm" in name:
				lowerCAmelCase_			:     List[Any]									= name.replace('''patch_embed.norm'''     ,'''embeddings.norm''' )
		if "layers" in name:
				lowerCAmelCase_			:     int									= '''encoder.''' + name
		if "attn.proj" in name:
				lowerCAmelCase_			:     Union[str, Any]									= name.replace('''attn.proj'''     ,'''attention.output.dense''' )
		if "attn" in name:
				lowerCAmelCase_			:     Optional[Any]									= name.replace('''attn'''     ,'''attention.self''' )
		if "norm1" in name:
				lowerCAmelCase_			:     Union[str, Any]									= name.replace('''norm1'''     ,'''layernorm_before''' )
		if "norm2" in name:
				lowerCAmelCase_			:     Tuple									= name.replace('''norm2'''     ,'''layernorm_after''' )
		if "mlp.fc1" in name:
				lowerCAmelCase_			:     Optional[Any]									= name.replace('''mlp.fc1'''     ,'''intermediate.dense''' )
		if "mlp.fc2" in name:
				lowerCAmelCase_			:     Tuple									= name.replace('''mlp.fc2'''     ,'''output.dense''' )
		if "q_bias" in name:
				lowerCAmelCase_			:     Tuple									= name.replace('''q_bias'''     ,'''query.bias''' )
		if "k_bias" in name:
				lowerCAmelCase_			:     Tuple									= name.replace('''k_bias'''     ,'''key.bias''' )
		if "v_bias" in name:
				lowerCAmelCase_			:     int									= name.replace('''v_bias'''     ,'''value.bias''' )
		if "cpb_mlp" in name:
				lowerCAmelCase_			:     Any									= name.replace('''cpb_mlp'''     ,'''continuous_position_bias_mlp''' )
		if name == "norm.weight":
				lowerCAmelCase_			:     Dict									= '''layernorm.weight'''
		if name == "norm.bias":
				lowerCAmelCase_			:     Any									= '''layernorm.bias'''

		if "head" in name:
				lowerCAmelCase_			:     int									= name.replace('''head'''     ,'''classifier''' )
		else:
				lowerCAmelCase_			:     Union[str, Any]									= '''swinv2.''' + name

		return name


def 			UpperCamelCase(  __UpperCamelCase							:		Any     ,__UpperCamelCase							:		Optional[int] ):
		for key in orig_state_dict.copy().keys():
				lowerCAmelCase_			:     Optional[int]									= orig_state_dict.pop(__UpperCamelCase )

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

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

		return orig_state_dict


def 			UpperCamelCase(  __UpperCamelCase							:		int     ,__UpperCamelCase							:		Dict ):
		lowerCAmelCase_			:     Optional[Any]									= timm.create_model(__UpperCamelCase     ,pretrained=__UpperCamelCase )
		timm_model.eval()

		lowerCAmelCase_			:     List[str]									= get_swinva_config(__UpperCamelCase )
		lowerCAmelCase_			:     Union[str, Any]									= SwinvaForImageClassification(__UpperCamelCase )
		model.eval()

		lowerCAmelCase_			:     str									= convert_state_dict(timm_model.state_dict()     ,__UpperCamelCase )
		model.load_state_dict(__UpperCamelCase )

		lowerCAmelCase_			:     List[Any]									= '''http://images.cocodataset.org/val2017/000000039769.jpg'''

		lowerCAmelCase_			:     Tuple									= AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''     ,'''-''' ) ) )
		lowerCAmelCase_			:     Union[str, Any]									= Image.open(requests.get(__UpperCamelCase     ,stream=__UpperCamelCase ).raw )
		lowerCAmelCase_			:     Optional[Any]									= image_processor(images=__UpperCamelCase     ,return_tensors='''pt''' )

		lowerCAmelCase_			:     List[str]									= timm_model(inputs['''pixel_values'''] )
		lowerCAmelCase_			:     Union[str, Any]									= model(**__UpperCamelCase ).logits

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

		print(f"""Saving model {swinva_name} to {pytorch_dump_folder_path}""" )
		model.save_pretrained(__UpperCamelCase )

		print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
		image_processor.save_pretrained(__UpperCamelCase )

		model.push_to_hub(
		    repo_path_or_name=Path(__UpperCamelCase     ,__UpperCamelCase )     ,organization='''nandwalritik'''     ,commit_message='''Add model'''     ,)


if __name__ == "__main__":
						A__  :  Union[str, Any]								=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--swinv2_name''',
						    default='''swinv2_tiny_patch4_window8_256''',
						    type=str,
						    help='''Name of the Swinv2 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.'''
						)

						A__  :  Optional[Any]								=					parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)





