



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       __UpperCamelCase      (    _lowerCAmelCase						)			->    Optional[int]:



    """simple docstring"""






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

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

    if model_size == "tiny":
        A					:			List[Any]		=  96
        A					:			Dict		=  (2, 2, 6, 2)
        A					:			List[Any]		=  (3, 6, 12, 24)
    elif model_size == "small":
        A					:			Optional[Any]		=  96
        A					:			Union[str, Any]		=  (2, 2, 18, 2)
        A					:			str		=  (3, 6, 12, 24)
    elif model_size == "base":
        A					:			List[Any]		=  128
        A					:			int		=  (2, 2, 18, 2)
        A					:			Dict		=  (4, 8, 16, 32)
    else:
        A					:			Optional[int]		=  192
        A					:			Any		=  (2, 2, 18, 2)
        A					:			str		=  (6, 12, 24, 48)

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

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        A					:			int		=  2_1841
        A					:			int		=  '''huggingface/label-files'''
        A					:			Tuple		=  '''imagenet-22k-id2label.json'''
        A					:			str		=  json.load(open(hf_hub_download(UpperCAmelCase_ ,	UpperCAmelCase_ ,	repo_type="""dataset"""						) ,	"""r"""						)						)
        A					:			List[str]		=  {int(UpperCAmelCase_						): v for k, v in idalabel.items()}
        A					:			List[Any]		=  idalabel
        A					:			Dict		=  {v: k for k, v in idalabel.items()}

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

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

    return config




def       __UpperCamelCase      (    _lowerCAmelCase						)			->    Any:



    """simple docstring"""






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

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

    return name




def       __UpperCamelCase      (    _lowerCAmelCase ,	_lowerCAmelCase						)			->    List[Any]:



    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        A					:			Optional[int]		=  orig_state_dict.pop(UpperCAmelCase_						)

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

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

    return orig_state_dict




def       __UpperCamelCase      (    _lowerCAmelCase ,	_lowerCAmelCase						)			->    Optional[Any]:



    """simple docstring"""






    A					:			List[Any]		=  timm.create_model(UpperCAmelCase_ ,	pretrained=UpperCAmelCase_						)
    timm_model.eval()

    A					:			Any		=  get_swinva_config(UpperCAmelCase_						)
    A					:			List[Any]		=  SwinvaForImageClassification(UpperCAmelCase_						)
    model.eval()

    A					:			Any		=  convert_state_dict(timm_model.state_dict() ,	UpperCAmelCase_						)
    model.load_state_dict(UpperCAmelCase_						)

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

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

    A					:			List[Any]		=  timm_model(inputs["""pixel_values"""]						)
    A					:			Tuple		=  model(**UpperCAmelCase_						).logits

    assert torch.allclose(UpperCAmelCase_ ,	UpperCAmelCase_ ,	atol=1e-3						)

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

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

    model.push_to_hub(
        repo_path_or_name=Path(UpperCAmelCase_ ,	UpperCAmelCase_						) ,	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_:Any 			=		parser.parse_args()
     convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)






from __future__ import annotations

from typing import Any







class 						_snake_case    ( _snake_case				):
 pass







class 						_snake_case    :




 def __init__(						self   ,    _lowerCamelCase       ):
       a		:Any									=			data
       a		:Node | None									=			None




 def __iter__(						self       ):
       a		:Dict									=			self
       a		:Optional[int]									=			[]
       while node:
             if node in visited:
                   raise ContainsLoopError
             visited.append(_lowerCamelCase       )
             yield node.data
             a		:str									=			node.next_node




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       try:
             list(self       )
             return False
       except ContainsLoopError:
             return True


if __name__ == "__main__":
     snake_case							:       int										=	Node(1)
     snake_case							:       int										=	Node(2)
     snake_case							:       Optional[int]										=	Node(3)
     snake_case							:       List[str]										=	Node(4)
     print(root_node.has_loop)  # False
     snake_case							:       Optional[Any]										=	root_node.next_node
     print(root_node.has_loop)  # True

     snake_case							:       Dict										=	Node(5)
     snake_case							:       List[Any]										=	Node(6)
     snake_case							:       Optional[int]										=	Node(5)
     snake_case							:       Union[str, Any]										=	Node(6)
     print(root_node.has_loop)  # False

     snake_case							:       Tuple										=	Node(1)
     print(root_node.has_loop)  # False





