






"""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()
lowercase_				     =     logging.get_logger(__name__)

lowercase_				     =     torch.device('cpu')



def 					lowerCAmelCase				(    ):




  """simple docstring"""







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



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  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 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= dct.pop(snake_case__ )
  __A									= val



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __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 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  __A									= 1_0_0_0
  __A									= '''huggingface/label-files'''
  __A									= '''imagenet-1k-id2label.json'''
  __A									= json.load(open(hf_hub_download(snake_case__		,       snake_case__		,       repo_type='''dataset''' )		,       '''r''' ) )
  __A									= {int(snake_case__ ): 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									= [4_8, 5_6, 1_1_2, 2_2_0]

  elif swiftformer_name == "swiftformer_s":
    __A									= [3, 3, 9, 6]
    __A									= [4_8, 6_4, 1_6_8, 2_2_4]

  elif swiftformer_name == "swiftformer_l1":
    __A									= [4, 3, 1_0, 5]
    __A									= [4_8, 9_6, 1_9_2, 3_8_4]

  elif swiftformer_name == "swiftformer_l3":
    __A									= [4, 4, 1_2, 6]
    __A									= [6_4, 1_2_8, 3_2_0, 5_1_2]

  # 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(snake_case__		,       map_location='''cpu'''		,       check_hash=snake_case__ )
    else:
      __A									= torch.load(snake_case__		,       map_location='''cpu''' )
  __A									= checkpoint

  __A									= create_rename_keys(snake_case__ )
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(snake_case__		,       snake_case__		,       snake_case__ )

  # load HuggingFace model
  __A									= SwiftFormerForImageClassification(snake_case__ ).eval()
  hf_model.load_state_dict(snake_case__ )

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

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

  assert hf_logits.shape == torch.Size([1, 1_0_0_0] )
  assert torch.allclose(hf_logits[0, 0:5]		,       snake_case__		,       atol=1e-3 )

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


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

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










"""simple docstring"""




import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def     a__ (				)						->					Union[str, Any]:
			lowerCamelCase			      =					ArgumentParser(
			    description=(
			        """PyTorch TPU distributed training launch """
			        """helper utility that will spawn up """
			        """multiple distributed processes"""
			    )				)

			# Optional arguments for the launch helper
			parser.add_argument("""--num_cores"""       , type=snake_case__       , default=1       , help="""Number of TPU cores to use (1 or 8)."""				)

			# positional
			parser.add_argument(
			    """training_script"""       , type=snake_case__       , help=(
			        """The full path to the single TPU training """
			        """program/script to be launched in parallel, """
			        """followed by all the arguments for the """
			        """training script"""
			    )       , )

			# rest from the training program
			parser.add_argument("""training_script_args"""       , nargs=snake_case__				)

			return parser.parse_args()



def     a__ (				)						->					List[str]:
			lowerCamelCase			      =					parse_args()

			# Import training_script as a module.
			lowerCamelCase			      =					Path(args.training_script				)
			sys.path.append(str(script_fpath.parent.resolve()				)				)
			lowerCamelCase			      =					script_fpath.stem
			lowerCamelCase			      =					importlib.import_module(snake_case__				)

			# Patch sys.argv
			lowerCamelCase			      =					[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores				)]
			xmp.spawn(mod._mp_fn       , args=()       , nprocs=args.num_cores				)


if __name__ == "__main__":
					main()



