






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()
snake_case__   :						List[str]													=   logging.get_logger(__name__)

snake_case__   :						str													=   torch.device("""cpu""")






def      _snake_case     ():
   UpperCamelCase_						=  'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase_						=  Image.open(requests.get(__lowercase     ,   stream=__lowercase).raw)
   return im






def      _snake_case     (__lowercase):
   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      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  dct.pop(__lowercase)
   UpperCamelCase_						=  val






def      _snake_case     (__lowercase):
   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      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   UpperCamelCase_						=  1000
   UpperCamelCase_						=  'huggingface/label-files'
   UpperCamelCase_						=  'imagenet-1k-id2label.json'
   UpperCamelCase_						=  json.load(open(hf_hub_download(__lowercase     ,   __lowercase     ,   repo_type='dataset')     ,   'r'))
   UpperCamelCase_						=  {int(__lowercase): 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, 112, 220]

   elif swiftformer_name == "swiftformer_s":
      UpperCamelCase_						=  [3, 3, 9, 6]
      UpperCamelCase_						=  [48, 64, 168, 224]

   elif swiftformer_name == "swiftformer_l1":
      UpperCamelCase_						=  [4, 3, 10, 5]
      UpperCamelCase_						=  [48, 96, 192, 384]

   elif swiftformer_name == "swiftformer_l3":
      UpperCamelCase_						=  [4, 4, 12, 6]
      UpperCamelCase_						=  [64, 128, 320, 512]

   # 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(__lowercase     ,   map_location='cpu'     ,   check_hash=__lowercase)
      else:
         UpperCamelCase_						=  torch.load(__lowercase     ,   map_location='cpu')
   UpperCamelCase_						=  checkpoint

   UpperCamelCase_						=  create_rename_keys(__lowercase)
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(__lowercase     ,   __lowercase     ,   __lowercase)

   # load HuggingFace model
   UpperCamelCase_						=  SwiftFormerForImageClassification(__lowercase).eval()
   hf_model.load_state_dict(__lowercase)

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

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

   assert hf_logits.shape == torch.Size([1, 1000])
   assert torch.allclose(hf_logits[0, 0:5]     ,   __lowercase     ,   atol=1e-3)

   Path(__lowercase).mkdir(exist_ok=__lowercase)
   print(f"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}""")
   hf_model.save_pretrained(__lowercase)


if __name__ == "__main__":
 snake_case__   :						Tuple													=   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.""")

 snake_case__   :						Tuple													=   parser.parse_args()
 convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)













from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

snake_case__   :						List[str]													=   TypeVar("""T""")






def      _snake_case     (__lowercase):
   return (position - 1) // 2






def      _snake_case     (__lowercase):
   return (2 * position) + 1






def      _snake_case     (__lowercase):
   return (2 * position) + 2


class     _a							(	Generic[T]     ):






  """simple docstring"""





  def __init__(  self			)      ->			None:
     UpperCamelCase_						=  []
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  0




  def __len__(  self			)      ->			int:
     return self.elements




  def __repr__(  self			)      ->			str:
     return str(self.heap			)




  def  _UpperCAmelCase  (  self			)      ->			bool:
     # Check if the priority queue is empty
     return self.elements == 0




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Add an element with given priority to the queue
     self.heap.append((elem, weight)			)
     UpperCamelCase_						=  self.elements
     self.elements += 1
     self._bubble_up(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			T:
     # Remove and return the element with lowest weight (highest priority)
     if self.elements > 1:
        self._swap_nodes(0		,		self.elements - 1			)
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap.pop()
     del self.position_map[elem]
     self.elements -= 1
     if self.elements > 0:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[0]
        self._bubble_down(_UpperCAmelCase			)
     return elem




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Update the weight of the given key
     UpperCamelCase_						=  self.position_map[elem]
     UpperCamelCase_						=  (elem, weight)
     if position > 0:
        UpperCamelCase_						=  get_parent_position(_UpperCAmelCase			)
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[parent_position]
        if parent_weight > weight:
           self._bubble_up(_UpperCAmelCase			)
        else:
           self._bubble_down(_UpperCAmelCase			)
     else:
        self._bubble_down(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Place a node at the proper position (upward movement) [to be used internally
     # only]
     UpperCamelCase_						=  self.position_map[elem]
     if curr_pos == 0:
        return None
     UpperCamelCase_						=  get_parent_position(_UpperCAmelCase			)
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[curr_pos]
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[parent_position]
     if parent_weight > weight:
        self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
        return self._bubble_up(_UpperCAmelCase			)
     return None




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Place a node at the proper position (downward movement) [to be used
     # internally only]
     UpperCamelCase_						=  self.position_map[elem]
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[curr_pos]
     UpperCamelCase_						=  get_child_left_position(_UpperCAmelCase			)
     UpperCamelCase_						=  get_child_right_position(_UpperCAmelCase			)
     if child_left_position < self.elements and child_right_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_left_position]
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_right_position]
        if child_right_weight < child_left_weight and child_right_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     if child_left_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_left_position]
        if child_left_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     else:
        return None
     if child_right_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_right_position]
        if child_right_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     return None




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Swap the nodes at the given positions
     UpperCamelCase_						=  self.heap[nodea_pos][0]
     UpperCamelCase_						=  self.heap[nodea_pos][0]
     UpperCamelCase_		,   UpperCamelCase_						=  (
         self.heap[nodea_pos],
         self.heap[nodea_pos],
     )
     UpperCamelCase_						=  nodea_pos
     UpperCamelCase_						=  nodea_pos




class     _a							(	Generic[T]     ):






  """simple docstring"""





  def __init__(  self			)      ->			None:
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  0




  def __repr__(  self			)      ->			str:
     return str(self.connections			)




  def __len__(  self			)      ->			int:
     return self.nodes




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Add a node in the graph if it is not in the graph
     if node not in self.connections:
        UpperCamelCase_						=  {}
        self.nodes += 1




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Add an edge between 2 nodes in the graph
     self.add_node(_UpperCAmelCase			)
     self.add_node(_UpperCAmelCase			)
     UpperCamelCase_						=  weight
     UpperCamelCase_						=  weight






def      _snake_case     (__lowercase     ,   ):
   UpperCamelCase_						=  {node: maxsize for node in graph.connections}
   UpperCamelCase_						=  {node: None for node in graph.connections}

   UpperCamelCase_						=  MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(__lowercase     ,   __lowercase)

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   UpperCamelCase_						=  priority_queue.extract_min()
   UpperCamelCase_						=  0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         UpperCamelCase_						=  dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(__lowercase     ,   dist[neighbour])
         UpperCamelCase_						=  node

    # running prim's algorithm
   while not priority_queue.is_empty():
      UpperCamelCase_						=  priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            UpperCamelCase_						=  dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(__lowercase     ,   dist[neighbour])
            UpperCamelCase_						=  node
   return dist, parent





