





'''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 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				SwinvaConfig()
  _snake_case     		=				swinva_name.split("_"       )

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

  if model_size == "tiny":
    _snake_case     		=				96
    _snake_case     		=				(2, 2, 6, 2)
    _snake_case     		=				(3, 6, 12, 24)
  elif model_size == "small":
    _snake_case     		=				96
    _snake_case     		=				(2, 2, 18, 2)
    _snake_case     		=				(3, 6, 12, 24)
  elif model_size == "base":
    _snake_case     		=				1_28
    _snake_case     		=				(2, 2, 18, 2)
    _snake_case     		=				(4, 8, 16, 32)
  else:
    _snake_case     		=				1_92
    _snake_case     		=				(2, 2, 18, 2)
    _snake_case     		=				(6, 12, 24, 48)

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

  if ("22k" in swinva_name) and ("to" not in swinva_name):
    _snake_case     		=				2_18_41
    _snake_case     		=				"huggingface/label-files"
    _snake_case     		=				"imagenet-22k-id2label.json"
    _snake_case     		=				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	repo_type="dataset"       )					,	"r"       )       )
    _snake_case     		=				{int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}
    _snake_case     		=				idalabel
    _snake_case     		=				{v: k for k, v in idalabel.items()}

  else:
    _snake_case     		=				10_00
    _snake_case     		=				"huggingface/label-files"
    _snake_case     		=				"imagenet-1k-id2label.json"
    _snake_case     		=				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	repo_type="dataset"       )					,	"r"       )       )
    _snake_case     		=				{int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}
    _snake_case     		=				idalabel
    _snake_case     		=				{v: k for k, v in idalabel.items()}

  _snake_case     		=				img_size
  _snake_case     		=				num_classes
  _snake_case     		=				embed_dim
  _snake_case     		=				depths
  _snake_case     		=				num_heads
  _snake_case     		=				window_size

  return config




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

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

  return name




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  for key in orig_state_dict.copy().keys():
    _snake_case     		=				orig_state_dict.pop(SCREAMING_SNAKE_CASE__       )

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

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

  return orig_state_dict




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				timm.create_model(SCREAMING_SNAKE_CASE__					,	pretrained=SCREAMING_SNAKE_CASE__       )
  timm_model.eval()

  _snake_case     		=				get_swinva_config(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				SwinvaForImageClassification(SCREAMING_SNAKE_CASE__       )
  model.eval()

  _snake_case     		=				convert_state_dict(timm_model.state_dict()					,	SCREAMING_SNAKE_CASE__       )
  model.load_state_dict(SCREAMING_SNAKE_CASE__       )

  _snake_case     		=				"http://images.cocodataset.org/val2017/000000039769.jpg"

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

  _snake_case     		=				timm_model(inputs["pixel_values"]       )
  _snake_case     		=				model(**SCREAMING_SNAKE_CASE__       ).logits

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

  print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''       )
  model.save_pretrained(SCREAMING_SNAKE_CASE__       )

  print(f'''Saving image processor to {pytorch_dump_folder_path}'''       )
  image_processor.save_pretrained(SCREAMING_SNAKE_CASE__       )

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


if __name__ == "__main__":
 __magic_name__   :						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."""
 )

 __magic_name__   :						str    =			parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)









'''simple docstring'''



import cva
import numpy as np


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase	):

       if k in (0.04, 0.06):
         _snake_case     		=				k
         _snake_case     		=				window_size
       else:
         raise ValueError("invalid k value"	)

     def __str__(					self	):
       return str(self.k	)






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				cva.imread(lowerCamelCase						,   0	)
       _snake_case		,		_snake_case     		=				img.shape
       _snake_case     		=				[]
       _snake_case     		=				img.copy()
       _snake_case     		=				cva.cvtColor(lowerCamelCase						,   cva.COLOR_GRAY2RGB	)
       _snake_case		,		_snake_case     		=				np.gradient(lowerCamelCase	)
       _snake_case     		=				dx**2
       _snake_case     		=				dy**2
       _snake_case     		=				dx * dy
       _snake_case     		=				0.04
       _snake_case     		=				self.window_size // 2
       for y in range(lowerCamelCase						,   h - offset	):
         for x in range(lowerCamelCase						,   w - offset	):
           _snake_case     		=				ixx[
               y - offset : y + offset + 1, x - offset : x + offset + 1
           ].sum()
           _snake_case     		=				iyy[
               y - offset : y + offset + 1, x - offset : x + offset + 1
           ].sum()
           _snake_case     		=				ixy[
               y - offset : y + offset + 1, x - offset : x + offset + 1
           ].sum()

           _snake_case     		=				(wxx * wyy) - (wxy**2)
           _snake_case     		=				wxx + wyy
           _snake_case     		=				det - k * (trace**2)
           # Can change the value
           if r > 0.5:
             corner_list.append([x, y, r]	)
             color_img.itemset((y, x, 0)						,   0	)
             color_img.itemset((y, x, 1)						,   0	)
             color_img.itemset((y, x, 2)						,   255	)
       return color_img, corner_list


if __name__ == "__main__":
 __magic_name__   :						Tuple    =			HarrisCorner(0.04, 3)
 __magic_name__   ,    __magic_name__   :						Any    =			edge_detect.detect("""path_to_image""")
 cva.imwrite("""detect.png""", color_img)


