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 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						SwinvaConfig()
    _lowerCamelCase     :		Tuple 						=						swinva_name.split("""_"""	)

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

    if model_size == "tiny":
        _lowerCamelCase     :		Optional[Any] 						=						96
        _lowerCamelCase     :		str 						=						(2, 2, 6, 2)
        _lowerCamelCase     :		Any 						=						(3, 6, 12, 24)
    elif model_size == "small":
        _lowerCamelCase     :		List[str] 						=						96
        _lowerCamelCase     :		List[Any] 						=						(2, 2, 18, 2)
        _lowerCamelCase     :		List[Any] 						=						(3, 6, 12, 24)
    elif model_size == "base":
        _lowerCamelCase     :		List[str] 						=						128
        _lowerCamelCase     :		int 						=						(2, 2, 18, 2)
        _lowerCamelCase     :		Optional[Any] 						=						(4, 8, 16, 32)
    else:
        _lowerCamelCase     :		List[Any] 						=						192
        _lowerCamelCase     :		Optional[Any] 						=						(2, 2, 18, 2)
        _lowerCamelCase     :		Dict 						=						(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     :		List[Any] 						=						21_841
        _lowerCamelCase     :		List[Any] 						=						'''huggingface/label-files'''
        _lowerCamelCase     :		Union[str, Any] 						=						'''imagenet-22k-id2label.json'''
        _lowerCamelCase     :		Dict 						=						json.load(open(hf_hub_download(a_   , a_   , repo_type="""dataset"""	)   , """r"""	)	)
        _lowerCamelCase     :		Optional[int] 						=						{int(a_	): 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     :		Any 						=						1_000
        _lowerCamelCase     :		Dict 						=						'''huggingface/label-files'''
        _lowerCamelCase     :		int 						=						'''imagenet-1k-id2label.json'''
        _lowerCamelCase     :		List[Any] 						=						json.load(open(hf_hub_download(a_   , a_   , repo_type="""dataset"""	)   , """r"""	)	)
        _lowerCamelCase     :		Tuple 						=						{int(a_	): v for k, v in idalabel.items()}
        _lowerCamelCase     :		Union[str, Any] 						=						idalabel
        _lowerCamelCase     :		Tuple 						=						{v: k for k, v in idalabel.items()}

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

    return config







def 						A__  (					__A	):







    '''simple docstring'''






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

    if "head" in name:
        _lowerCamelCase     :		Any 						=						name.replace("""head"""   , """classifier"""	)
    else:
        _lowerCamelCase     :		Tuple 						=						'''swinv2.''' + name

    return name







def 						A__  (					__A   , __A	):







    '''simple docstring'''






    for key in orig_state_dict.copy().keys():
        _lowerCamelCase     :		Union[str, Any] 						=						orig_state_dict.pop(a_	)

        if "mask" in key:
            continue
        elif "qkv" in key:
            _lowerCamelCase     :		str 						=						key.split("""."""	)
            _lowerCamelCase     :		Optional[int] 						=						int(key_split[1]	)
            _lowerCamelCase     :		str 						=						int(key_split[3]	)
            _lowerCamelCase     :		Optional[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     :		Optional[int] 						=						val[dim : dim * 2, :]
                _lowerCamelCase     :		List[Any] 						=						val[-dim:, :]
            else:
                _lowerCamelCase     :		int 						=						val[:dim]
                _lowerCamelCase     :		str 						=						val[
                    dim : dim * 2
                ]
                _lowerCamelCase     :		int 						=						val[-dim:]
        else:
            _lowerCamelCase     :		Tuple 						=						val

    return orig_state_dict







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						timm.create_model(a_   , pretrained=a_	)
    timm_model.eval()

    _lowerCamelCase     :		List[Any] 						=						get_swinva_config(a_	)
    _lowerCamelCase     :		int 						=						SwinvaForImageClassification(a_	)
    model.eval()

    _lowerCamelCase     :		Tuple 						=						convert_state_dict(timm_model.state_dict()   , a_	)
    model.load_state_dict(a_	)

    _lowerCamelCase     :		List[Any] 						=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''

    _lowerCamelCase     :		Dict 						=						AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""   , """-"""	)	)	)
    _lowerCamelCase     :		Tuple 						=						Image.open(requests.get(a_   , stream=a_	).raw	)
    _lowerCamelCase     :		Tuple 						=						image_processor(images=a_   , return_tensors="""pt"""	)

    _lowerCamelCase     :		Dict 						=						timm_model(inputs["""pixel_values"""]	)
    _lowerCamelCase     :		Dict 						=						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__":
    lowerCAmelCase			:							Dict     =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			:							List[Any]     =parser.parse_args()
    convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)





import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase			:							Dict     =logging.get_logger(__name__)

lowerCAmelCase			:							Dict     ={"vocab_file": "vocab.json"}

lowerCAmelCase			:							List[str]     ={
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowerCAmelCase			:							int     ={"mgp-str": 27}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



      def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int="[GO]"    ,  _UpperCamelCase     :				Any="[GO]"    ,  _UpperCamelCase     :				Optional[Any]="[s]"    ,  _UpperCamelCase     :				List[str]="[GO]"    ,  **_UpperCamelCase     :				Dict)				->Union[str, Any]:


          """simple docstring"""


          super().__init__(
              unk_token=_UpperCamelCase    ,  bos_token=_UpperCamelCase    ,  eos_token=_UpperCamelCase    ,  pad_token=_UpperCamelCase    ,  **_UpperCamelCase    ,  )

          with open(_UpperCamelCase    ,  encoding="""utf-8""") as vocab_handle:
              _lowerCamelCase     :		Optional[Any] 						=						json.load(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						{v: k for k, v in self.vocab.items()}



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Any:


          """simple docstring"""


          return len(self.vocab)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[Any]:


          """simple docstring"""


          return dict(self.vocab    ,  **self.added_tokens_encoder)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						[]
          for s in text:
              char_tokens.extend(_UpperCamelCase)
          return char_tokens



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				int)				->Optional[int]:


          """simple docstring"""


          return self.vocab.get(_UpperCamelCase    ,  self.vocab.get(self.unk_token))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[Any])				->Dict:


          """simple docstring"""


          return self.decoder.get(_UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[str] = None)				->Tuple[str]:


          """simple docstring"""


          if not os.path.isdir(_UpperCamelCase):
              logger.error("""Vocabulary path ({}) should be a directory""".format(_UpperCamelCase))
              return
          _lowerCamelCase     :		Tuple 						=						os.path.join(
              _UpperCamelCase    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

          with open(_UpperCamelCase    ,  """w"""    ,  encoding="""utf-8""") as f:
              f.write(json.dumps(self.vocab    ,  indent=2    ,  sort_keys=_UpperCamelCase    ,  ensure_ascii=_UpperCamelCase) + """\n""")

          return (vocab_file,)




