

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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Tuple	)				->    str:
				_UpperCAmelCase    :						Dict							=      SwinvaConfig()
				_UpperCAmelCase    :						List[str]							=      swinva_name.split("_"	)

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

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

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

				if ("22k" in swinva_name) and ("to" not in swinva_name):
								_UpperCAmelCase    :						List[Any]							=      2_1841
								_UpperCAmelCase    :						Any							=      "huggingface/label-files"
								_UpperCAmelCase    :						int							=      "imagenet-22k-id2label.json"
								_UpperCAmelCase    :						List[Any]							=      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    :						Optional[Any]							=      idalabel
								_UpperCAmelCase    :						Any							=      {v: k for k, v in idalabel.items()}

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

				_UpperCAmelCase    :						List[str]							=      img_size
				_UpperCAmelCase    :						Dict							=      num_classes
				_UpperCAmelCase    :						str							=      embed_dim
				_UpperCAmelCase    :						Optional[int]							=      depths
				_UpperCAmelCase    :						int							=      num_heads
				_UpperCAmelCase    :						Tuple							=      window_size

				return config

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

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

				return name

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Tuple					, lowerCAmelCase:					Optional[Any]	)				->    str:
				for key in orig_state_dict.copy().keys():
								_UpperCAmelCase    :						int							=      orig_state_dict.pop(lowerCAmelCase	)

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

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

				return orig_state_dict

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					Optional[Any]	)				->    Optional[int]:
				_UpperCAmelCase    :						str							=      timm.create_model(lowerCAmelCase					, pretrained=lowerCAmelCase	)
				timm_model.eval()

				_UpperCAmelCase    :						Optional[Any]							=      get_swinva_config(lowerCAmelCase	)
				_UpperCAmelCase    :						Dict							=      SwinvaForImageClassification(lowerCAmelCase	)
				model.eval()

				_UpperCAmelCase    :						int							=      convert_state_dict(timm_model.state_dict()					, lowerCAmelCase	)
				model.load_state_dict(lowerCAmelCase	)

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

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

				_UpperCAmelCase    :						Optional[Any]							=      timm_model(inputs["pixel_values"]	)
				_UpperCAmelCase    :						List[str]							=      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__":
					SCREAMING_SNAKE_CASE_            = 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.'
					)

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







from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					float | Decimal					, lowerCAmelCase:					float = 10**-10	)				->    float:
				_UpperCAmelCase    :						Optional[int]							=      a
				while True:
								_UpperCAmelCase    :						Tuple							=      Decimal(lowerCAmelCase	) - (
								    Decimal(eval(lowerCAmelCase	)	) / Decimal(eval(str(diff(lowerCAmelCase	)	)	)	)  # noqa: S307
								)
								# This number dictates the accuracy of the answer
								if abs(eval(lowerCAmelCase	)	) < precision:  # noqa: S307
												return float(lowerCAmelCase	)


# Let's Execute
if __name__ == "__main__":
					# Find root of trigonometric function
					# Find value of pi
					print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
					# Find root of polynomial
					print(F'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
					# Find Square Root of 5
					print(F'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
					# Exponential Roots
					print(F'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')


