
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      :				int          = logging.get_logger(__name__)

_UpperCAmelCase      :				Tuple          = torch.device("cpu")


def    A (      )     ->						Dict:
  '''simple docstring'''

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


def    A (      lowercase				)     ->						List[Any]:
  '''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    A (      lowercase					, lowercase					, lowercase				)     ->						str:
  '''simple docstring'''

  UpperCamelCase						=  dct.pop(lowercase				)
  UpperCamelCase						=  val


def    A (      lowercase				)     ->						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    A (      lowercase					, lowercase					, lowercase				)     ->						Optional[Any]:
  '''simple docstring'''

  UpperCamelCase						=  SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  UpperCamelCase						=  1_000
  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, 1_000]				)
  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__":
     _UpperCAmelCase      :				int          = 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      :				List[Any]          = parser.parse_args()
     convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset


@pytest.mark.parametrize('dataset_size'					, [None, 400 * 2**20, 600 * 2**20]				)
@pytest.mark.parametrize('input_in_memory_max_size'					, ['default', 0, 100 * 2**20, 900 * 2**20]				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		if input_in_memory_max_size != "default":
				monkeypatch.setattr(datasets.config					, 'IN_MEMORY_MAX_SIZE'					, lowercase				)
		UpperCamelCase						=  datasets.config.IN_MEMORY_MAX_SIZE
		if input_in_memory_max_size == "default":
				assert in_memory_max_size == 0
		else:
				assert in_memory_max_size == input_in_memory_max_size
		if dataset_size and in_memory_max_size:
				UpperCamelCase						=  dataset_size < in_memory_max_size
		else:
				UpperCamelCase						=  False
		UpperCamelCase						=  is_small_dataset(lowercase				)
		assert result == expected
