


'''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  __lowerCAmelCase (__lowerCAmelCase   ):
		_UpperCAmelCase		:       Union[str, Any]								=      SwinvaConfig()
		_UpperCAmelCase		:       Optional[Any]								=      swinva_name.split("_"   )

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

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

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

		if ("22k" in swinva_name) and ("to" not in swinva_name):
				_UpperCAmelCase		:       List[Any]								=      21_841
				_UpperCAmelCase		:       str								=      "huggingface/label-files"
				_UpperCAmelCase		:       List[str]								=      "imagenet-22k-id2label.json"
				_UpperCAmelCase		:       Tuple								=      json.load(open(hf_hub_download(__lowerCAmelCase      , __lowerCAmelCase      , repo_type="dataset"   )      , "r"   )   )
				_UpperCAmelCase		:       List[str]								=      {int(__lowerCAmelCase   ): v for k, v in idalabel.items()}
				_UpperCAmelCase		:       Any								=      idalabel
				_UpperCAmelCase		:       List[str]								=      {v: k for k, v in idalabel.items()}

		else:
				_UpperCAmelCase		:       int								=      1_000
				_UpperCAmelCase		:       Dict								=      "huggingface/label-files"
				_UpperCAmelCase		:       List[Any]								=      "imagenet-1k-id2label.json"
				_UpperCAmelCase		:       Union[str, Any]								=      json.load(open(hf_hub_download(__lowerCAmelCase      , __lowerCAmelCase      , repo_type="dataset"   )      , "r"   )   )
				_UpperCAmelCase		:       str								=      {int(__lowerCAmelCase   ): v for k, v in idalabel.items()}
				_UpperCAmelCase		:       Dict								=      idalabel
				_UpperCAmelCase		:       Dict								=      {v: k for k, v in idalabel.items()}

		_UpperCAmelCase		:       Union[str, Any]								=      img_size
		_UpperCAmelCase		:       int								=      num_classes
		_UpperCAmelCase		:       List[Any]								=      embed_dim
		_UpperCAmelCase		:       Dict								=      depths
		_UpperCAmelCase		:       List[Any]								=      num_heads
		_UpperCAmelCase		:       str								=      window_size

		return config






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

		if "head" in name:
				_UpperCAmelCase		:       Optional[int]								=      name.replace("head"      , "classifier"   )
		else:
				_UpperCAmelCase		:       str								=      "swinv2." + name

		return name






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		for key in orig_state_dict.copy().keys():
				_UpperCAmelCase		:       List[str]								=      orig_state_dict.pop(__lowerCAmelCase   )

				if "mask" in key:
						continue
				elif "qkv" in key:
						_UpperCAmelCase		:       Union[str, Any]								=      key.split("."   )
						_UpperCAmelCase		:       List[str]								=      int(key_split[1]   )
						_UpperCAmelCase		:       Tuple								=      int(key_split[3]   )
						_UpperCAmelCase		:       Dict								=      model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

		return orig_state_dict






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       Optional[int]								=      timm.create_model(__lowerCAmelCase      , pretrained=__lowerCAmelCase   )
		timm_model.eval()

		_UpperCAmelCase		:       Dict								=      get_swinva_config(__lowerCAmelCase   )
		_UpperCAmelCase		:       Tuple								=      SwinvaForImageClassification(__lowerCAmelCase   )
		model.eval()

		_UpperCAmelCase		:       List[Any]								=      convert_state_dict(timm_model.state_dict()      , __lowerCAmelCase   )
		model.load_state_dict(__lowerCAmelCase   )

		_UpperCAmelCase		:       Union[str, Any]								=      "http://images.cocodataset.org/val2017/000000039769.jpg"

		_UpperCAmelCase		:       Optional[Any]								=      AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"      , "-"   )   )   )
		_UpperCAmelCase		:       Optional[int]								=      Image.open(requests.get(__lowerCAmelCase      , stream=__lowerCAmelCase   ).raw   )
		_UpperCAmelCase		:       List[Any]								=      image_processor(images=__lowerCAmelCase      , return_tensors="pt"   )

		_UpperCAmelCase		:       List[str]								=      timm_model(inputs["pixel_values"]   )
		_UpperCAmelCase		:       Dict								=      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__":
						lowerCamelCase__    	=     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.'
						)

						lowerCamelCase__    	=     parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)







'''simple docstring'''


from math import factorial






class 	lowerCAmelCase__			:




			def __init__(		self  :     int	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     str )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      real
					if isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Any								=      [1] * rank
					else:
							_UpperCAmelCase		:       Dict								=      rank




			def __repr__(		self  :     str )   ->List[str]:



					'''simple docstring'''







					return (
					    F"""{self.real}+"""
					    F"""{'+'.join(str(lowerCamelCase__ )+'E'+str(n+1 )for n,dual in enumerate(self.duals ) )}"""
					)




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      self.duals.copy()
					while cur[-1] == 0:
							cur.pop(-1 )
					return Dual(self.real	,  lowerCamelCase__ )




			def __add__(		self  :     Dict	,  lowerCamelCase__  :     List[Any] )   ->Any:



					'''simple docstring'''







					if not isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							return Dual(self.real + other	,  self.duals )
					_UpperCAmelCase		:       Optional[int]								=      self.duals.copy()
					_UpperCAmelCase		:       Optional[int]								=      other.duals.copy()
					if len(lowerCamelCase__ ) > len(lowerCamelCase__ ):
							o_dual.extend([1] * (len(lowerCamelCase__ ) - len(lowerCamelCase__ )) )
					elif len(lowerCamelCase__ ) < len(lowerCamelCase__ ):
							s_dual.extend([1] * (len(lowerCamelCase__ ) - len(lowerCamelCase__ )) )
					_UpperCAmelCase		:       Union[str, Any]								=      []
					for i in range(len(lowerCamelCase__ ) ):
							new_duals.append(s_dual[i] + o_dual[i] )
					return Dual(self.real + other.real	,  lowerCamelCase__ )

			lowerCAmelCase :	Tuple          =     __add__




			def __sub__(		self  :     List[Any]	,  lowerCamelCase__  :     Union[str, Any] )   ->Dict:



					'''simple docstring'''







					return self + other * -1




			def __mul__(		self  :     List[str]	,  lowerCamelCase__  :     Optional[Any] )   ->Union[str, Any]:



					'''simple docstring'''







					if not isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Optional[int]								=      []
							for i in self.duals:
									new_duals.append(i * other )
							return Dual(self.real * other	,  lowerCamelCase__ )
					_UpperCAmelCase		:       Union[str, Any]								=      [0] * (len(self.duals ) + len(other.duals ) + 1)
					for i, item in enumerate(self.duals ):
							for j, jtem in enumerate(other.duals ):
									new_duals[i + j + 1] += item * jtem
					for k in range(len(self.duals ) ):
							new_duals[k] += self.duals[k] * other.real
					for index in range(len(other.duals ) ):
							new_duals[index] += other.duals[index] * self.real
					return Dual(self.real * other.real	,  lowerCamelCase__ )

			lowerCAmelCase :	Union[str, Any]          =     __mul__




			def __truediv__(		self  :     Optional[Any]	,  lowerCamelCase__  :     List[Any] )   ->Union[str, Any]:



					'''simple docstring'''







					if not isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Union[str, Any]								=      []
							for i in self.duals:
									new_duals.append(i / other )
							return Dual(self.real / other	,  lowerCamelCase__ )
					raise ValueError




			def __floordiv__(		self  :     str	,  lowerCamelCase__  :     str )   ->List[str]:



					'''simple docstring'''







					if not isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Tuple								=      []
							for i in self.duals:
									new_duals.append(i // other )
							return Dual(self.real // other	,  lowerCamelCase__ )
					raise ValueError






			def __pow__(		self  :     Tuple	,  lowerCamelCase__  :     Optional[Any] )   ->Optional[int]:



					'''simple docstring'''







					if n < 0 or isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							raise ValueError("power must be a positive integer" )
					if n == 0:
							return 1
					if n == 1:
							return self
					_UpperCAmelCase		:       str								=      self
					for _ in range(n - 1 ):
							x *= self
					return x






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		if not callable(__lowerCAmelCase   ):
				raise ValueError("differentiate() requires a function as input for func"   )
		if not isinstance(__lowerCAmelCase      , (float, int)   ):
				raise ValueError("differentiate() requires a float as input for position"   )
		if not isinstance(__lowerCAmelCase      , __lowerCAmelCase   ):
				raise ValueError("differentiate() requires an int as input for order"   )
		_UpperCAmelCase		:       int								=      Dual(__lowerCAmelCase      , 1   )
		_UpperCAmelCase		:       Optional[int]								=      func(__lowerCAmelCase   )
		if order == 0:
				return result.real
		return result.duals[order - 1] * factorial(__lowerCAmelCase   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()






						def  __lowerCAmelCase (__lowerCAmelCase   ):
								return y**2 * y**4





						print(differentiate(f, 9, 2))



