


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_		(		_A			:    int						):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  SwinvaConfig()
      lowerCamelCase__		:				Optional[int]    =  swinva_name.split("_"						)

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

      if model_size == "tiny":
            lowerCamelCase__		:				Union[str, Any]    =  96
            lowerCamelCase__		:				Union[str, Any]    =  (2, 2, 6, 2)
            lowerCamelCase__		:				int    =  (3, 6, 12, 24)
      elif model_size == "small":
            lowerCamelCase__		:				Optional[int]    =  96
            lowerCamelCase__		:				int    =  (2, 2, 18, 2)
            lowerCamelCase__		:				Optional[Any]    =  (3, 6, 12, 24)
      elif model_size == "base":
            lowerCamelCase__		:				Tuple    =  128
            lowerCamelCase__		:				Any    =  (2, 2, 18, 2)
            lowerCamelCase__		:				List[Any]    =  (4, 8, 16, 32)
      else:
            lowerCamelCase__		:				Tuple    =  192
            lowerCamelCase__		:				Optional[Any]    =  (2, 2, 18, 2)
            lowerCamelCase__		:				List[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__		:				Optional[int]    =  21841
            lowerCamelCase__		:				List[str]    =  "huggingface/label-files"
            lowerCamelCase__		:				int    =  "imagenet-22k-id2label.json"
            lowerCamelCase__		:				Dict    =  json.load(open(hf_hub_download(__a    ,					__a    ,					repo_type="dataset"						)    ,					"r"						)						)
            lowerCamelCase__		:				Dict    =  {int(__a						): v for k, v in idalabel.items()}
            lowerCamelCase__		:				Tuple    =  idalabel
            lowerCamelCase__		:				int    =  {v: k for k, v in idalabel.items()}

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

      lowerCamelCase__		:				Tuple    =  img_size
      lowerCamelCase__		:				Tuple    =  num_classes
      lowerCamelCase__		:				List[str]    =  embed_dim
      lowerCamelCase__		:				int    =  depths
      lowerCamelCase__		:				List[Any]    =  num_heads
      lowerCamelCase__		:				Dict    =  window_size

      return config

def       lowercase_		(		_A			:    List[str]						):

      """simple docstring"""




      if "patch_embed.proj" in name:
            lowerCamelCase__		:				str    =  name.replace("patch_embed.proj"    ,					"embeddings.patch_embeddings.projection"						)
      if "patch_embed.norm" in name:
            lowerCamelCase__		:				List[Any]    =  name.replace("patch_embed.norm"    ,					"embeddings.norm"						)
      if "layers" in name:
            lowerCamelCase__		:				List[Any]    =  "encoder." + name
      if "attn.proj" in name:
            lowerCamelCase__		:				str    =  name.replace("attn.proj"    ,					"attention.output.dense"						)
      if "attn" in name:
            lowerCamelCase__		:				Tuple    =  name.replace("attn"    ,					"attention.self"						)
      if "norm1" in name:
            lowerCamelCase__		:				Tuple    =  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__		:				Optional[int]    =  name.replace("mlp.fc2"    ,					"output.dense"						)
      if "q_bias" in name:
            lowerCamelCase__		:				Dict    =  name.replace("q_bias"    ,					"query.bias"						)
      if "k_bias" in name:
            lowerCamelCase__		:				Optional[Any]    =  name.replace("k_bias"    ,					"key.bias"						)
      if "v_bias" in name:
            lowerCamelCase__		:				Optional[int]    =  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__		:				List[Any]    =  "layernorm.weight"
      if name == "norm.bias":
            lowerCamelCase__		:				Optional[Any]    =  "layernorm.bias"

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

      return name

def       lowercase_		(		_A			:    Tuple    ,					_A			:    List[str]						):

      """simple docstring"""




      for key in orig_state_dict.copy().keys():
            lowerCamelCase__		:				List[str]    =  orig_state_dict.pop(__a						)

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

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

      return orig_state_dict

def       lowercase_		(		_A			:    str    ,					_A			:    Optional[Any]						):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  timm.create_model(__a    ,					pretrained=__a						)
      timm_model.eval()

      lowerCamelCase__		:				int    =  get_swinva_config(__a						)
      lowerCamelCase__		:				Optional[Any]    =  SwinvaForImageClassification(__a						)
      model.eval()

      lowerCamelCase__		:				List[Any]    =  convert_state_dict(timm_model.state_dict()    ,					__a						)
      model.load_state_dict(__a						)

      lowerCamelCase__		:				Optional[int]    =  "http://images.cocodataset.org/val2017/000000039769.jpg"

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

      lowerCamelCase__		:				Tuple    =  timm_model(inputs["pixel_values"]						)
      lowerCamelCase__		:				Any    =  model(**__a						).logits

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

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

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

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


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

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









def    __lowerCamelCase					(							__a   :str    )		->				list:






		"""simple docstring"""

		A__        =   [0] * len(__a    )

		for i in range(1    ,     len(__a    )    ):
				# use last results for better performance - dynamic programming
				A__        =   prefix_result[i - 1]
				while j > 0 and input_string[i] != input_string[j]:
						A__        =   prefix_result[j - 1]

				if input_string[i] == input_string[j]:
						j += 1
				A__        =   j

		return prefix_result







def    __lowerCamelCase					(							__a   :str    )		->				int:






		"""simple docstring"""

		return max(prefix_function(__a    )    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()



