


'''simple docstring'''



def      UpperCAmelCase__   (      UpperCAmelCase_       :							str				)	->						bool:
     __lowerCamelCase  :      Dict														=						[int(UpperCAmelCase_				) for i in ip_va_address.split('.'				) if i.isdigit()]
     return len(UpperCAmelCase_				) == 4 and all(0 <= int(UpperCAmelCase_				) <= 2_54 for octet in octets				)


if __name__ == "__main__":
      A__						:   Optional[Any]				=    input().strip()
      A__						:   Tuple				=    """valid""" if is_ip_va_address_valid(ip) else """invalid"""
      print(f'''{ip} is a {valid_or_invalid} IP v4 address.''')









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 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






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

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

    if model_size == "tiny":
        UpperCamelCase_			  =						9_6
        UpperCamelCase_			  =						(2, 2, 6, 2)
        UpperCamelCase_			  =						(3, 6, 1_2, 2_4)
    elif model_size == "small":
        UpperCamelCase_			  =						9_6
        UpperCamelCase_			  =						(2, 2, 1_8, 2)
        UpperCamelCase_			  =						(3, 6, 1_2, 2_4)
    elif model_size == "base":
        UpperCamelCase_			  =						1_2_8
        UpperCamelCase_			  =						(2, 2, 1_8, 2)
        UpperCamelCase_			  =						(4, 8, 1_6, 3_2)
    else:
        UpperCamelCase_			  =						1_9_2
        UpperCamelCase_			  =						(2, 2, 1_8, 2)
        UpperCamelCase_			  =						(6, 1_2, 2_4, 4_8)

    if "to" in swinva_name:
        UpperCamelCase_			  =						(1_2, 1_2, 1_2, 6)

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        UpperCamelCase_			  =						2_1_8_4_1
        UpperCamelCase_			  =						"huggingface/label-files"
        UpperCamelCase_			  =						"imagenet-22k-id2label.json"
        UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
        UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
        UpperCamelCase_			  =						idalabel
        UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    else:
        UpperCamelCase_			  =						1_0_0_0
        UpperCamelCase_			  =						"huggingface/label-files"
        UpperCamelCase_			  =						"imagenet-1k-id2label.json"
        UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
        UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
        UpperCamelCase_			  =						idalabel
        UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    UpperCamelCase_			  =						img_size
    UpperCamelCase_			  =						num_classes
    UpperCamelCase_			  =						embed_dim
    UpperCamelCase_			  =						depths
    UpperCamelCase_			  =						num_heads
    UpperCamelCase_			  =						window_size

    return config




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[Any]:



    """simple docstring"""






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

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

    return name




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






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

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

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

    return orig_state_dict




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Any:



    """simple docstring"""






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

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

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

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

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

    UpperCamelCase_			  =						timm_model(inputs["pixel_values"]			)
    UpperCamelCase_			  =						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		: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."""
 )

 SCREAMING_SNAKE_CASE		:int						=     parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
