


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()
SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							torch.device('cpu')




def  a   ():
   __a					=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
   __a					=  Image.open(requests.get(lowerCAmelCase__		,				stream=lowerCAmelCase__   ).raw   )
   return im




def  a   (lowerCAmelCase__   ):
   if swiftformer_name == "swiftformer_xs":
      return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]   )

   elif swiftformer_name == "swiftformer_s":
      return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]   )

   elif swiftformer_name == "swiftformer_l1":
      return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]   )

   elif swiftformer_name == "swiftformer_l3":
      return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  dct.pop(lowerCAmelCase__   )
   __a					=  val




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




@torch.no_grad()
def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   __a					=  1_000
   __a					=  """huggingface/label-files"""
   __a					=  """imagenet-1k-id2label.json"""
   __a					=  json.load(open(hf_hub_download(lowerCAmelCase__		,				lowerCAmelCase__		,				repo_type="""dataset"""   )		,				"""r"""   )   )
   __a					=  {int(lowerCAmelCase__   ): v for k, v in idalabel.items()}
   __a					=  idalabel
   __a					=  {v: k for k, v in idalabel.items()}

   # size of the architecture
   if swiftformer_name == "swiftformer_xs":
      __a					=  [3, 3, 6, 4]
      __a					=  [48, 56, 112, 220]

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

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

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

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

   __a					=  create_rename_keys(lowerCAmelCase__   )
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   )

   # load HuggingFace model
   __a					=  SwiftFormerForImageClassification(lowerCAmelCase__   ).eval()
   hf_model.load_state_dict(lowerCAmelCase__   )

   # prepare test inputs
   __a					=  prepare_img()
   __a					=  ViTImageProcessor.from_pretrained("""preprocessor_config"""   )
   __a					=  processor(images=lowerCAmelCase__		,				return_tensors="""pt"""   )

   # compare outputs from both models
   __a					=  get_expected_output(lowerCAmelCase__   )
   __a					=  hf_model(inputs["""pixel_values"""]   ).logits

   assert hf_logits.shape == torch.Size([1, 1_000]   )
   assert torch.allclose(hf_logits[0, 0:5]		,				lowerCAmelCase__		,				atol=1E-3   )

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE							    =							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.')

  SCREAMING_SNAKE_CASE							    =							parser.parse_args()
  convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)








'''simple docstring'''





from __future__ import annotations

import time

import numpy as np

SCREAMING_SNAKE_CASE__          =  [8, 5, 9, 7]
SCREAMING_SNAKE_CASE__          =  [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
SCREAMING_SNAKE_CASE__          =  [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]


class 					snake_case			:



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,)	->				None:
           lowercase__									=    claim_vector
           lowercase__									=    allocated_resources_table
           lowercase__									=    maximum_claim_table



       def 				_a   (					self   )	->				list[int]:
           return [
               sum(p_item[i] for p_item in self.__allocated_resources_table   )
               for i in range(len(self.__allocated_resources_table[0]   )   )
           ]



       def 				_a   (					self   )	->				list[int]:
           return np.array(self.__claim_vector   ) - np.array(
               self.__processes_resource_summation()   )



       def 				_a   (					self   )	->				list[list[int]]:
           return [
               list(np.array(self.__maximum_claim_table[i]   ) - np.array(UpperCAmelCase_   )   )
               for i, allocated_resource in enumerate(self.__allocated_resources_table   )
           ]



       def 				_a   (					self   )	->				dict[int, list[int]]:
           return {self.__need().index(UpperCAmelCase_   ): i for i in self.__need()}



       def 				_a   (					self  ,**UpperCAmelCase_   )	->				None:
           lowercase__									=    self.__need()
           lowercase__									=    self.__allocated_resources_table
           lowercase__									=    self.__available_resources()
           lowercase__									=    self.__need_index_manager()
           for kw, val in kwargs.items():
               if kw and val is True:
                   self.__pretty_data()
           print("_" * 50 + "\n"   )
           while need_list:
               lowercase__									=    False
               for each_need in need_list:
                   lowercase__									=    True
                   for index, need in enumerate(UpperCAmelCase_   ):
                       if need > available_resources[index]:
                           lowercase__									=    False
                           break
                   if execution:
                       lowercase__									=    True
                       # get the original index of the process from ind_ctrl db
                       for original_need_index, need_clone in need_index_manager.items():
                           if each_need == need_clone:
                               lowercase__									=    original_need_index
                       print(F'''Process {process_number + 1} is executing.'''   )
                       # remove the process run from stack
                       need_list.remove(UpperCAmelCase_   )
                       # update available/freed resources stack
                       lowercase__									=    np.array(UpperCAmelCase_   ) + np.array(
                           alloc_resources_table[process_number]   )
                       print(
                           "Updated available resource stack for processes: "
                           + " ".join([str(UpperCAmelCase_   ) for x in available_resources]   )   )
                       break
               if safe:
                   print("The process is in a safe state.\n"   )
               else:
                   print("System in unsafe state. Aborting...\n"   )
                   break







       def 				_a   (					self   )	->				List[Any]:
           print(" " * 9 + "Allocated Resource Table"   )
           for item in self.__allocated_resources_table:
               print(
                   F'''P{self.__allocated_resources_table.index(UpperCAmelCase_   ) + 1}'''
                   + " ".join(F'''{it:>8}''' for it in item   )
                   + "\n"   )
           print(" " * 9 + "System Resource Table"   )
           for item in self.__maximum_claim_table:
               print(
                   F'''P{self.__maximum_claim_table.index(UpperCAmelCase_   ) + 1}'''
                   + " ".join(F'''{it:>8}''' for it in item   )
                   + "\n"   )
           print(
               "Current Usage by Active Processes: "
               + " ".join(str(UpperCAmelCase_   ) for x in self.__claim_vector   )   )
           print(
               "Initial Available Resources:       "
               + " ".join(str(UpperCAmelCase_   ) for x in self.__available_resources()   )   )
           time.sleep(1   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




