


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 							__lowercase     (				__lowerCAmelCase							:       Union[str, Any]     ):
							a__    =  SwinvaConfig()
							a__    =  swinva_name.split('_'     )

							a__    =  name_split[1]
							if "to" in name_split[3]:
														a__    =  int(name_split[3][-3:]     )
							else:
														a__    =  int(name_split[3]     )
							if "to" in name_split[2]:
														a__    =  int(name_split[2][-2:]     )
							else:
														a__    =  int(name_split[2][6:]     )

							if model_size == "tiny":
														a__    =  9_6
														a__    =  (2, 2, 6, 2)
														a__    =  (3, 6, 1_2, 2_4)
							elif model_size == "small":
														a__    =  9_6
														a__    =  (2, 2, 1_8, 2)
														a__    =  (3, 6, 1_2, 2_4)
							elif model_size == "base":
														a__    =  1_2_8
														a__    =  (2, 2, 1_8, 2)
														a__    =  (4, 8, 1_6, 3_2)
							else:
														a__    =  1_9_2
														a__    =  (2, 2, 1_8, 2)
														a__    =  (6, 1_2, 2_4, 4_8)

							if "to" in swinva_name:
														a__    =  (1_2, 1_2, 1_2, 6)

							if ("22k" in swinva_name) and ("to" not in swinva_name):
														a__    =  2_1_8_4_1
														a__    =  'huggingface/label-files'
														a__    =  'imagenet-22k-id2label.json'
														a__    =  json.load(open(hf_hub_download(__lowerCAmelCase      ,					__lowerCAmelCase      ,					repo_type='dataset'     )      ,					'r'     )     )
														a__    =  {int(__lowerCAmelCase     ): v for k, v in idalabel.items()}
														a__    =  idalabel
														a__    =  {v: k for k, v in idalabel.items()}

							else:
														a__    =  1_0_0_0
														a__    =  'huggingface/label-files'
														a__    =  'imagenet-1k-id2label.json'
														a__    =  json.load(open(hf_hub_download(__lowerCAmelCase      ,					__lowerCAmelCase      ,					repo_type='dataset'     )      ,					'r'     )     )
														a__    =  {int(__lowerCAmelCase     ): v for k, v in idalabel.items()}
														a__    =  idalabel
														a__    =  {v: k for k, v in idalabel.items()}

							a__    =  img_size
							a__    =  num_classes
							a__    =  embed_dim
							a__    =  depths
							a__    =  num_heads
							a__    =  window_size

							return config






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

							if "head" in name:
														a__    =  name.replace('head'      ,					'classifier'     )
							else:
														a__    =  'swinv2.' + name

							return name






def 							__lowercase     (				__lowerCAmelCase							:       Optional[int]      ,					__lowerCAmelCase							:       Optional[int]     ):
							for key in orig_state_dict.copy().keys():
														a__    =  orig_state_dict.pop(__lowerCAmelCase     )

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

																					if "weight" in key:
																												a__    =  val[:dim, :]
																												a__    =  val[dim : dim * 2, :]
																												a__    =  val[-dim:, :]
																					else:
																												a__    =  val[:dim]
																												a__    =  val[
																												    dim : dim * 2
																												]
																												a__    =  val[-dim:]
														else:
																					a__    =  val

							return orig_state_dict






def 							__lowercase     (				__lowerCAmelCase							:       Tuple      ,					__lowerCAmelCase							:       List[Any]     ):
							a__    =  timm.create_model(__lowerCAmelCase      ,					pretrained=__lowerCAmelCase     )
							timm_model.eval()

							a__    =  get_swinva_config(__lowerCAmelCase     )
							a__    =  SwinvaForImageClassification(__lowerCAmelCase     )
							model.eval()

							a__    =  convert_state_dict(timm_model.state_dict()      ,					__lowerCAmelCase     )
							model.load_state_dict(__lowerCAmelCase     )

							a__    =  'http://images.cocodataset.org/val2017/000000039769.jpg'

							a__    =  AutoImageProcessor.from_pretrained('microsoft/{}'.format(swinva_name.replace('_'      ,					'-'     )     )     )
							a__    =  Image.open(requests.get(__lowerCAmelCase      ,					stream=__lowerCAmelCase     ).raw     )
							a__    =  image_processor(images=__lowerCAmelCase      ,					return_tensors='pt'     )

							a__    =  timm_model(inputs['pixel_values']     )
							a__    =  model(**__lowerCAmelCase     ).logits

							assert torch.allclose(__lowerCAmelCase      ,					__lowerCAmelCase      ,					atol=1E-3     )

							print(F'Saving model {swinva_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     )

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


if __name__ == "__main__":
						snake_case							: 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.'''
						)

						snake_case							: Any      =   parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)








import unittest

from knapsack import greedy_knapsack as kp







class  snake_case_     (unittest.TestCase   ):
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Union[str, Any]:
												a__    =  [10, 20, 30, 40, 50, 60]
												a__    =  [2, 4, 6, 8, 10, 12]
												a__    =  1_00
												self.assertEqual(kp.calc_profit(__snake_case    ,__snake_case    ,__snake_case   )    ,2_10   )
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[int]:
												self.assertRaisesRegex(__snake_case    ,'max_weight must greater than zero.'   )
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					int:
												self.assertRaisesRegex(__snake_case    ,'Weight can not be negative.'   )
					def 				lowerCamelCase__(   self     :str   )      ->					List[str]:
												self.assertRaisesRegex(__snake_case    ,'Profit can not be negative.'   )
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[Any]:
												self.assertRaisesRegex(__snake_case    ,'max_weight must greater than zero.'   )



					def 				lowerCamelCase__(   self     :int   )      ->					List[Any]:
												self.assertRaisesRegex(
												    __snake_case    ,'The length of profit and weight must be same.'   )


if __name__ == "__main__":
						unittest.main()




