





'''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 	__a(SCREAMING_SNAKE_CASE_  :					Optional[Any]    ):



    '''simple docstring'''





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

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

    if model_size == "tiny":
        _lowerCAmelCase				=				96
        _lowerCAmelCase				=				(2, 2, 6, 2)
        _lowerCAmelCase				=				(3, 6, 12, 24)
    elif model_size == "small":
        _lowerCAmelCase				=				96
        _lowerCAmelCase				=				(2, 2, 18, 2)
        _lowerCAmelCase				=				(3, 6, 12, 24)
    elif model_size == "base":
        _lowerCAmelCase				=				128
        _lowerCAmelCase				=				(2, 2, 18, 2)
        _lowerCAmelCase				=				(4, 8, 16, 32)
    else:
        _lowerCAmelCase				=				192
        _lowerCAmelCase				=				(2, 2, 18, 2)
        _lowerCAmelCase				=				(6, 12, 24, 48)

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

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        _lowerCAmelCase				=				21841
        _lowerCAmelCase				=				"huggingface/label-files"
        _lowerCAmelCase				=				"imagenet-22k-id2label.json"
        _lowerCAmelCase				=				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       repo_type="dataset"    )		,       "r"    )    )
        _lowerCAmelCase				=				{int(SCREAMING_SNAKE_CASE_    ): v for k, v in idalabel.items()}
        _lowerCAmelCase				=				idalabel
        _lowerCAmelCase				=				{v: k for k, v in idalabel.items()}

    else:
        _lowerCAmelCase				=				1000
        _lowerCAmelCase				=				"huggingface/label-files"
        _lowerCAmelCase				=				"imagenet-1k-id2label.json"
        _lowerCAmelCase				=				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       repo_type="dataset"    )		,       "r"    )    )
        _lowerCAmelCase				=				{int(SCREAMING_SNAKE_CASE_    ): v for k, v in idalabel.items()}
        _lowerCAmelCase				=				idalabel
        _lowerCAmelCase				=				{v: k for k, v in idalabel.items()}

    _lowerCAmelCase				=				img_size
    _lowerCAmelCase				=				num_classes
    _lowerCAmelCase				=				embed_dim
    _lowerCAmelCase				=				depths
    _lowerCAmelCase				=				num_heads
    _lowerCAmelCase				=				window_size

    return config


def 	__a(SCREAMING_SNAKE_CASE_  :					Tuple    ):



    '''simple docstring'''





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

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

    return name


def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[Any]		,       SCREAMING_SNAKE_CASE_  :					List[str]    ):



    '''simple docstring'''





    for key in orig_state_dict.copy().keys():
        _lowerCAmelCase				=				orig_state_dict.pop(SCREAMING_SNAKE_CASE_    )

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

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

    return orig_state_dict


def 	__a(SCREAMING_SNAKE_CASE_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					List[Any]    ):



    '''simple docstring'''





    _lowerCAmelCase				=				timm.create_model(SCREAMING_SNAKE_CASE_		,       pretrained=SCREAMING_SNAKE_CASE_    )
    timm_model.eval()

    _lowerCAmelCase				=				get_swinva_config(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=				SwinvaForImageClassification(SCREAMING_SNAKE_CASE_    )
    model.eval()

    _lowerCAmelCase				=				convert_state_dict(timm_model.state_dict()		,       SCREAMING_SNAKE_CASE_    )
    model.load_state_dict(SCREAMING_SNAKE_CASE_    )

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

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

    _lowerCAmelCase				=				timm_model(inputs["pixel_values"]    )
    _lowerCAmelCase				=				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__":
       _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)









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        raise ValueError("check_bouncy() accepts only integer arguments"    )
    _lowerCAmelCase				=      str(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      "".join(sorted(SCREAMING_SNAKE_CASE_    )    )
    return sorted_str_n != str_n and sorted_str_n[::-1] != str_n


def 	__a(SCREAMING_SNAKE_CASE_  :					float = 99    ):



    '''simple docstring'''





    if not 0 < percent < 100:
        raise ValueError("solution() only accepts values from 0 to 100"    )
    _lowerCAmelCase				=      0
    _lowerCAmelCase				=      1

    while True:
        if check_bouncy(SCREAMING_SNAKE_CASE_    ):
            bouncy_num += 1
        if (bouncy_num / num) * 100 >= percent:
            return num
        num += 1


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       print(f'''{solution(99)}''')


