



"""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							(    __snake_case      )		->  Dict:
			__lowerCAmelCase :    Dict				=			SwinvaConfig()
			__lowerCAmelCase :    List[str]				=			swinva_name.split("_"      )

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

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

			if "to" in swinva_name:
						__lowerCAmelCase :    Dict				=			(12, 12, 12, 6)

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

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

			__lowerCAmelCase :    str				=			img_size
			__lowerCAmelCase :    str				=			num_classes
			__lowerCAmelCase :    List[str]				=			embed_dim
			__lowerCAmelCase :    List[str]				=			depths
			__lowerCAmelCase :    Any				=			num_heads
			__lowerCAmelCase :    str				=			window_size

			return config


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

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

			return name


def      _lowercase							(    __snake_case   ,__snake_case      )		->  Optional[Any]:
			for key in orig_state_dict.copy().keys():
						__lowerCAmelCase :    str				=			orig_state_dict.pop(__snake_case      )

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

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

			return orig_state_dict


def      _lowercase							(    __snake_case   ,__snake_case      )		->  Dict:
			__lowerCAmelCase :    Tuple				=			timm.create_model(__snake_case   ,pretrained=__snake_case      )
			timm_model.eval()

			__lowerCAmelCase :    int				=			get_swinva_config(__snake_case      )
			__lowerCAmelCase :    Optional[Any]				=			SwinvaForImageClassification(__snake_case      )
			model.eval()

			__lowerCAmelCase :    Dict				=			convert_state_dict(timm_model.state_dict()   ,__snake_case      )
			model.load_state_dict(__snake_case      )

			__lowerCAmelCase :    Optional[Any]				=			"http://images.cocodataset.org/val2017/000000039769.jpg"

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

			__lowerCAmelCase :    List[Any]				=			timm_model(inputs["pixel_values"]      )
			__lowerCAmelCase :    Dict				=			model(**__snake_case      ).logits

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

			print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""      )
			model.save_pretrained(__snake_case      )

			print(F"""Saving image processor to {pytorch_dump_folder_path}"""      )
			image_processor.save_pretrained(__snake_case      )

			model.push_to_hub(
			    repo_path_or_name=Path(__snake_case   ,__snake_case      )   ,organization="nandwalritik"   ,commit_message="Add model"   ,)


if __name__ == "__main__":
	__snake_case				:  Tuple =       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				:  Tuple =       parser.parse_args()
	convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)




"""simple docstring"""






def      _lowercase							(    __snake_case      )		->  float:
			return 10 - x * x


def      _lowercase							(    __snake_case   ,__snake_case      )		->  float:
			# Bolzano theory in order to find if there is a root between a and b
			if equation(__snake_case      ) * equation(__snake_case      ) >= 0:
						raise ValueError("Wrong space!"      )

			__lowerCAmelCase :    List[Any]				=			a
			while (b - a) >= 0.01:
						# Find middle point
						__lowerCAmelCase :    str				=			(a + b) / 2
						# Check if middle point is root
						if equation(__snake_case      ) == 0.0:
									break
						# Decide the side to repeat the steps
						if equation(__snake_case      ) * equation(__snake_case      ) < 0:
									__lowerCAmelCase :    List[Any]				=			c
						else:
									__lowerCAmelCase :    List[str]				=			c
			return c


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	print(bisection(-2, 5))
	print(bisection(0, 6))