


'''simple docstring'''
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       (				UpperCamelCase__       :					Union[str, Any]			):
 __A  :		Optional[Any]       =			SwinvaConfig()
 __A  :		int       =			swinva_name.split('_'			)

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

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

 if "to" in swinva_name:
  __A  :		List[str]       =			(12, 12, 12, 6)

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

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

 __A  :		str       =			img_size
 __A  :		Optional[int]       =			num_classes
 __A  :		str       =			embed_dim
 __A  :		Dict       =			depths
 __A  :		str       =			num_heads
 __A  :		Optional[int]       =			window_size

 return config


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

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

 return name


def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					int			):
 for key in orig_state_dict.copy().keys():
  __A  :		Union[str, Any]       =			orig_state_dict.pop(UpperCamelCase__			)

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

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

 return orig_state_dict


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Optional[Any]			):
 __A  :		Any       =			timm.create_model(UpperCamelCase__,				pretrained=UpperCamelCase__			)
 timm_model.eval()

 __A  :		List[str]       =			get_swinva_config(UpperCamelCase__			)
 __A  :		str       =			SwinvaForImageClassification(UpperCamelCase__			)
 model.eval()

 __A  :		Optional[int]       =			convert_state_dict(timm_model.state_dict(),				UpperCamelCase__			)
 model.load_state_dict(UpperCamelCase__			)

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

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

 __A  :		str       =			timm_model(inputs['pixel_values']			)
 __A  :		Optional[int]       =			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__":
 UpperCAmelCase_ :     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.'
 )

 UpperCAmelCase_ :     Optional[int]								=      parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)










'''simple docstring'''
import math

class 						_lowerCamelCase					:




 '''simple docstring'''


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		List[str]       =			0.0
  __A  :		Optional[int]       =			0.0
  for i in range(len(__lowercase						)						):
   da += math.pow((sample[i] - weights[0][i])      ,      2						)
   da += math.pow((sample[i] - weights[1][i])      ,      2						)
   return 0 if da > da else 1
  return 0







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  for i in range(len(__lowercase						)						):
   weights[j][i] += alpha * (sample[i] - weights[j][i])
  return weights


def 			_lowercase       (				):
 # Training Examples ( m, n )
 __A  :		Union[str, Any]       =			[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

 # weight initialization ( n, C )
 __A  :		Tuple       =			[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

 # training
 __A  :		Optional[int]       =			SelfOrganizingMap()
 __A  :		int       =			3
 __A  :		Optional[Any]       =			0.5

 for _ in range(UpperCamelCase__			):
  for j in range(len(UpperCamelCase__			)			):
   # training sample
   __A  :		Dict       =			training_samples[j]

   # Compute the winning vector
   __A  :		List[Any]       =			self_organizing_map.get_winner(UpperCamelCase__,				UpperCamelCase__			)

   # Update the winning vector
   __A  :		Union[str, Any]       =			self_organizing_map.update(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

    # classify test sample
 __A  :		int       =			[0, 0, 0, 1]
 __A  :		List[Any]       =			self_organizing_map.get_winner(UpperCamelCase__,				UpperCamelCase__			)

 # results
 print(f"""Clusters that the test sample belongs to : {winner}"""			)
 print(f"""Weights that have been trained : {weights}"""			)


# running the main() function
if __name__ == "__main__":
 main()






