






"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A__					: int=	logging.get_logger(__name__)

A__					: Dict=	torch.device("""cpu""")







def   lowerCAmelCase_( )					->	List[str]:
    """simple docstring"""

    UpperCamelCase__         =    'http://images.cocodataset.org/val2017/000000039769.jpg'
    UpperCamelCase__         =    Image.open(requests.get(SCREAMING_SNAKE_CASE ,	stream=SCREAMING_SNAKE_CASE					).raw					)
    return im







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Dict:
    """simple docstring"""

    if swiftformer_name == "swiftformer_xs":
        return torch.tensor([-2.17_03E00, 2.11_07E00, -2.08_11E00, 8.86_85E-01, 2.43_60E-01]					)

    elif swiftformer_name == "swiftformer_s":
        return torch.tensor([3.96_36E-01, 2.34_78E-01, -1.69_63E00, -1.73_81E00, -8.63_37E-01]					)

    elif swiftformer_name == "swiftformer_l1":
        return torch.tensor([-4.27_68E-01, -4.74_29E-01, -1.08_97E00, -1.02_48E00, 3.55_23E-02]					)

    elif swiftformer_name == "swiftformer_l3":
        return torch.tensor([-2.53_30E-01, 2.42_11E-01, -6.01_85E-01, -8.27_89E-01, -6.04_46E-02]					)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[str]:
    """simple docstring"""

    UpperCamelCase__         =    dct.pop(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    val







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Tuple:
    """simple docstring"""

    UpperCamelCase__         =    []
    for k in state_dict.keys():
        UpperCamelCase__         =    k
        if ".pwconv" in k:
            UpperCamelCase__         =    k_new.replace('.pwconv' ,	'.point_wise_conv'					)
        if ".dwconv" in k:
            UpperCamelCase__         =    k_new.replace('.dwconv' ,	'.depth_wise_conv'					)
        if ".Proj." in k:
            UpperCamelCase__         =    k_new.replace('.Proj.' ,	'.proj.'					)
        if "patch_embed" in k_new:
            UpperCamelCase__         =    k_new.replace('patch_embed' ,	'swiftformer.patch_embed.patch_embedding'					)
        if "network" in k_new:
            UpperCamelCase__         =    k_new.split('.'					)
            if ls[2].isdigit():
                UpperCamelCase__         =    'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]					)
            else:
                UpperCamelCase__         =    k_new.replace('network' ,	'swiftformer.encoder.network'					)
        rename_keys.append((k, k_new)					)
    return rename_keys







@torch.no_grad()
def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Optional[int]:
    """simple docstring"""

    UpperCamelCase__         =    SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    UpperCamelCase__         =    10_00
    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()}

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        UpperCamelCase__         =    [3, 3, 6, 4]
        UpperCamelCase__         =    [48, 56, 1_12, 2_20]

    elif swiftformer_name == "swiftformer_s":
        UpperCamelCase__         =    [3, 3, 9, 6]
        UpperCamelCase__         =    [48, 64, 1_68, 2_24]

    elif swiftformer_name == "swiftformer_l1":
        UpperCamelCase__         =    [4, 3, 10, 5]
        UpperCamelCase__         =    [48, 96, 1_92, 3_84]

    elif swiftformer_name == "swiftformer_l3":
        UpperCamelCase__         =    [4, 4, 12, 6]
        UpperCamelCase__         =    [64, 1_28, 3_20, 5_12]

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith('https'					):
            UpperCamelCase__         =    torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE ,	map_location='cpu' ,	check_hash=SCREAMING_SNAKE_CASE					)
        else:
            UpperCamelCase__         =    torch.load(SCREAMING_SNAKE_CASE ,	map_location='cpu'					)
    UpperCamelCase__         =    checkpoint

    UpperCamelCase__         =    create_rename_keys(SCREAMING_SNAKE_CASE					)
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)

    # load HuggingFace model
    UpperCamelCase__         =    SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE					).eval()
    hf_model.load_state_dict(SCREAMING_SNAKE_CASE					)

    # prepare test inputs
    UpperCamelCase__         =    prepare_img()
    UpperCamelCase__         =    ViTImageProcessor.from_pretrained('preprocessor_config'					)
    UpperCamelCase__         =    processor(images=SCREAMING_SNAKE_CASE ,	return_tensors='pt'					)

    # compare outputs from both models
    UpperCamelCase__         =    get_expected_output(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    hf_model(inputs['pixel_values']					).logits

    assert hf_logits.shape == torch.Size([1, 10_00]					)
    assert torch.allclose(hf_logits[0, 0:5] ,	SCREAMING_SNAKE_CASE ,	atol=1E-3					)

    Path(SCREAMING_SNAKE_CASE					).mkdir(exist_ok=SCREAMING_SNAKE_CASE					)
    print(F'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'					)
    hf_model.save_pretrained(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  A__					: Any=	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swiftformer_name""",
      default="""swiftformer_xs""",
      choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
      type=str,
      help="""Name of the SwiftFormer model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default="""./converted_outputs/""",
      type=str,
      help="""Path to the output PyTorch model directory.""",
  )
  parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

  A__					: List[Any]=	parser.parse_args()
  convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)












"""simple docstring"""





from __future__ import annotations

from typing import Generic, TypeVar

A__					: Any=	TypeVar("""T""")
class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    data
       UpperCamelCase__         =    self
       UpperCamelCase__         =    0







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # map from node name to the node object
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # create a new set with x as its member
       UpperCamelCase__         =    DisjointSetTreeNode(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       DisjointSetTreeNode[T]:
       # find the set x belongs to (with path-compression)
       UpperCamelCase__         =    self.map[data]
       if elem_ref != elem_ref.parent:
           UpperCamelCase__         =    self.find_set(elem_ref.parent.data      )
       return elem_ref.parent

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # helper function for union operation
       if nodea.rank > nodea.rank:
           UpperCamelCase__         =    nodea
       else:
           UpperCamelCase__         =    nodea
           if nodea.rank == nodea.rank:
               nodea.rank += 1







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # merge 2 disjoint sets
       self.link(self.find_set(snake_case_      ) ,				self.find_set(snake_case_      )      )







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # connections: map from the node to the neighbouring nodes (with weights)
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # add a node ONLY if its not present in the graph
       if node not in self.connections:
           UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       None:
       # add an edge with the given weight
       self.add_node(snake_case_      )
       self.add_node(snake_case_      )
       UpperCamelCase__         =    weight
       UpperCamelCase__         =    weight







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       GraphUndirectedWeighted[T]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    set()
       for start in self.connections:
           for end in self.connections[start]:
               if (start, end) not in seen:
                   seen.add((end, start)      )
                   edges.append((start, end, self.connections[start][end])      )
       edges.sort(key=lambda snake_case_      : x[2]      )

       # creating the disjoint set
       UpperCamelCase__         =    DisjointSetTree[T]()
       for node in self.connections:
           disjoint_set.make_set(snake_case_      )

       # MST generation
       UpperCamelCase__         =    0
       UpperCamelCase__         =    0
       UpperCamelCase__         =    GraphUndirectedWeighted[T]()
       while num_edges < len(self.connections      ) - 1:
           UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    edges[index]
           index += 1
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           if parent_u != parent_v:
               num_edges += 1
               graph.add_edge(snake_case_ ,				snake_case_ ,				snake_case_      )
               disjoint_set.union(snake_case_ ,				snake_case_      )
       return graph




