



'''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()
_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		torch.device('cpu')

def 			a_	(				) -> Union[str, Any]:
  __lowerCamelCase			:			Any       			=					'http://images.cocodataset.org/val2017/000000039769.jpg'
  __lowerCamelCase			:			Any       			=					Image.open(requests.get(_lowerCAmelCase			,stream=_lowerCAmelCase			).raw			)
  return im

def 			a_	(				_lowerCAmelCase			) -> List[str]:
  if swiftformer_name == "swiftformer_xs":
    return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]			)

  elif swiftformer_name == "swiftformer_s":
    return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]			)

  elif swiftformer_name == "swiftformer_l1":
    return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]			)

  elif swiftformer_name == "swiftformer_l3":
    return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]			)

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> Dict:
  __lowerCamelCase			:			Any       			=					dct.pop(_lowerCAmelCase			)
  __lowerCamelCase			:			Optional[int]       			=					val

def 			a_	(				_lowerCAmelCase			) -> int:
  __lowerCamelCase			:			Dict       			=					[]
  for k in state_dict.keys():
    __lowerCamelCase			:			Union[str, Any]       			=					k
    if ".pwconv" in k:
      __lowerCamelCase			:			Any       			=					k_new.replace('.pwconv'			,'.point_wise_conv'			)
    if ".dwconv" in k:
      __lowerCamelCase			:			Union[str, Any]       			=					k_new.replace('.dwconv'			,'.depth_wise_conv'			)
    if ".Proj." in k:
      __lowerCamelCase			:			Dict       			=					k_new.replace('.Proj.'			,'.proj.'			)
    if "patch_embed" in k_new:
      __lowerCamelCase			:			str       			=					k_new.replace('patch_embed'			,'swiftformer.patch_embed.patch_embedding'			)
    if "network" in k_new:
      __lowerCamelCase			:			Union[str, Any]       			=					k_new.split('.'			)
      if ls[2].isdigit():
        __lowerCamelCase			:			str       			=					'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]			)
      else:
        __lowerCamelCase			:			int       			=					k_new.replace('network'			,'swiftformer.encoder.network'			)
    rename_keys.append((k, k_new)			)
  return rename_keys

@torch.no_grad()
def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> Optional[int]:
  __lowerCamelCase			:			List[str]       			=					SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  __lowerCamelCase			:			List[str]       			=					1000
  __lowerCamelCase			:			int       			=					'huggingface/label-files'
  __lowerCamelCase			:			Optional[Any]       			=					'imagenet-1k-id2label.json'
  __lowerCamelCase			:			int       			=					json.load(open(hf_hub_download(_lowerCAmelCase			,_lowerCAmelCase			,repo_type='dataset'			)			,'r'			)			)
  __lowerCamelCase			:			Union[str, Any]       			=					{int(_lowerCAmelCase			): v for k, v in idalabel.items()}
  __lowerCamelCase			:			Optional[int]       			=					idalabel
  __lowerCamelCase			:			str       			=					{v: k for k, v in idalabel.items()}

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

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

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

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

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

  __lowerCamelCase			:			List[Any]       			=					create_rename_keys(_lowerCAmelCase			)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			)

  # load HuggingFace model
  __lowerCamelCase			:			Tuple       			=					SwiftFormerForImageClassification(_lowerCAmelCase			).eval()
  hf_model.load_state_dict(_lowerCAmelCase			)

  # prepare test inputs
  __lowerCamelCase			:			Dict       			=					prepare_img()
  __lowerCamelCase			:			Dict       			=					ViTImageProcessor.from_pretrained('preprocessor_config'			)
  __lowerCamelCase			:			List[Any]       			=					processor(images=_lowerCAmelCase			,return_tensors='pt'			)

  # compare outputs from both models
  __lowerCamelCase			:			Any       			=					get_expected_output(_lowerCAmelCase			)
  __lowerCamelCase			:			Optional[int]       			=					hf_model(inputs['pixel_values']			).logits

  assert hf_logits.shape == torch.Size([1, 1000]			)
  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__":
   _UpperCamelCase											=		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.')

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











'''simple docstring'''





def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> tuple[float, float]:

  # Check if the input is valid
  if not len(_lowerCAmelCase			) == len(_lowerCAmelCase			) == 3:
    raise ValueError('Please enter a valid equation.'			)
  if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
    raise ValueError('Both a & b of two equations can\'t be zero.'			)

  # Extract the coefficients
  __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Union[str, Any]       			=					equationa
  __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Any       			=					equationa

  # Calculate the determinants of the matrices
  __lowerCamelCase			:			Union[str, Any]       			=					aa * ba - aa * ba
  __lowerCamelCase			:			Dict       			=					ca * ba - ca * ba
  __lowerCamelCase			:			List[str]       			=					aa * ca - aa * ca

  # Check if the system of linear equations has a solution (using Cramer's rule)
  if determinant == 0:
    if determinant_x == determinant_y == 0:
      raise ValueError('Infinite solutions. (Consistent system)'			)
    else:
      raise ValueError('No solution. (Inconsistent system)'			)
  else:
    if determinant_x == determinant_y == 0:
      # Trivial solution (Inconsistent system)
      return (0.0, 0.0)
    else:
      __lowerCamelCase			:			Tuple       			=					determinant_x / determinant
      __lowerCamelCase			:			Union[str, Any]       			=					determinant_y / determinant
      # Non-Trivial Solution (Consistent system)
      return (x, y)






