






import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)




class 				__A     (			unittest.TestCase ):





				"""simple docstring"""







				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[str]       		=['a', 'b', 'c']

									# Defaults to last layer if both are None
									__UpperCamelCase		,				__UpperCamelCase      :					List[str]       		=get_aligned_output_features_output_indices(lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__    )
									self.assertEqual(lowerCamelCase__							,  ['c']    )
									self.assertEqual(lowerCamelCase__							,  [2]    )

									# Out indices set to match out features
									__UpperCamelCase		,				__UpperCamelCase      :					List[Any]       		=get_aligned_output_features_output_indices(['a', 'c']							,  lowerCamelCase__							,  lowerCamelCase__    )
									self.assertEqual(lowerCamelCase__							,  ['a', 'c']    )
									self.assertEqual(lowerCamelCase__							,  [0, 2]    )

									# Out features set to match out indices
									__UpperCamelCase		,				__UpperCamelCase      :					int       		=get_aligned_output_features_output_indices(lowerCamelCase__							,  [0, 2]							,  lowerCamelCase__    )
									self.assertEqual(lowerCamelCase__							,  ['a', 'c']    )
									self.assertEqual(lowerCamelCase__							,  [0, 2]    )

									# Out features selected from negative indices
									__UpperCamelCase		,				__UpperCamelCase      :					Dict       		=get_aligned_output_features_output_indices(lowerCamelCase__							,  [-3, -1]							,  lowerCamelCase__    )
									self.assertEqual(lowerCamelCase__							,  ['a', 'c']    )
									self.assertEqual(lowerCamelCase__							,  [-3, -1]    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									with self.assertRaises(lowerCamelCase__    ):
														verify_out_features_out_indices(['a', 'b']							,  (0, 1)							,  lowerCamelCase__    )

									# Out features must be a list
									with self.assertRaises(lowerCamelCase__    ):
														verify_out_features_out_indices(('a', 'b')							,  (0, 1)							,  ['a', 'b']    )

									# Out features must be a subset of stage names
									with self.assertRaises(lowerCamelCase__    ):
														verify_out_features_out_indices(['a', 'b']							,  (0, 1)							,  ['a']    )

									# Out indices must be a list or tuple
									with self.assertRaises(lowerCamelCase__    ):
														verify_out_features_out_indices(lowerCamelCase__							,  0							,  ['a', 'b']    )

									# Out indices must be a subset of stage names
									with self.assertRaises(lowerCamelCase__    ):
														verify_out_features_out_indices(lowerCamelCase__							,  (0, 1)							,  ['a']    )

									# Out features and out indices must be the same length
									with self.assertRaises(lowerCamelCase__    ):
														verify_out_features_out_indices(['a', 'b']							,  (0,)							,  ['a', 'b', 'c']    )

									# Out features should match out indices
									with self.assertRaises(lowerCamelCase__    ):
														verify_out_features_out_indices(['a', 'b']							,  (0, 2)							,  ['a', 'b', 'c']    )

									# Out features and out indices should be in order
									with self.assertRaises(lowerCamelCase__    ):
														verify_out_features_out_indices(['b', 'a']							,  (0, 1)							,  ['a', 'b']    )

									# Check passes with valid inputs
									verify_out_features_out_indices(['a', 'b', 'd']							,  (0, 1, -1)							,  ['a', 'b', 'c', 'd']    )







				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=BackboneMixin()

									__UpperCamelCase      :					Optional[Any]       		=['a', 'b', 'c']
									__UpperCamelCase      :					Dict       		=['a', 'c']
									__UpperCamelCase      :					Union[str, Any]       		=[0, 2]

									# Check that the output features and indices are set correctly
									self.assertEqual(backbone.out_features							,  ['a', 'c']    )
									self.assertEqual(backbone.out_indices							,  [0, 2]    )

									# Check out features and indices are updated correctly
									__UpperCamelCase      :					Tuple       		=['a', 'b']
									self.assertEqual(backbone.out_features							,  ['a', 'b']    )
									self.assertEqual(backbone.out_indices							,  [0, 1]    )

									__UpperCamelCase      :					Tuple       		=[-3, -1]
									self.assertEqual(backbone.out_features							,  ['a', 'c']    )
									self.assertEqual(backbone.out_indices							,  [-3, -1]    )









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

_snake_case          =   torch.device('cpu')

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a  :       int             =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  _a  :       Dict             =	Image.open(requests.get(UpperCamelCase__     ,   stream=UpperCamelCase__				).raw				)
  return im

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''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 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Any             =	dct.pop(UpperCamelCase__				)
  _a  :       Dict             =	val

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Tuple             =	[]
  for k in state_dict.keys():
    _a  :       Any             =	k
    if ".pwconv" in k:
      _a  :       int             =	k_new.replace(""".pwconv"""     ,   """.point_wise_conv"""				)
    if ".dwconv" in k:
      _a  :       List[str]             =	k_new.replace(""".dwconv"""     ,   """.depth_wise_conv"""				)
    if ".Proj." in k:
      _a  :       Optional[int]             =	k_new.replace(""".Proj."""     ,   """.proj."""				)
    if "patch_embed" in k_new:
      _a  :       Tuple             =	k_new.replace("""patch_embed"""     ,   """swiftformer.patch_embed.patch_embedding"""				)
    if "network" in k_new:
      _a  :       int             =	k_new.split("""."""				)
      if ls[2].isdigit():
        _a  :       Union[str, Any]             =	"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]				)
      else:
        _a  :       Tuple             =	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  :       Tuple             =	SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  _a  :       Optional[int]             =	1_0_0_0
  _a  :       Optional[Any]             =	"""huggingface/label-files"""
  _a  :       Optional[Any]             =	"""imagenet-1k-id2label.json"""
  _a  :       List[str]             =	json.load(open(hf_hub_download(UpperCamelCase__     ,   UpperCamelCase__     ,   repo_type="""dataset"""				)     ,   """r"""				)				)
  _a  :       Optional[Any]             =	{int(UpperCamelCase__				): v for k, v in idalabel.items()}
  _a  :       Dict             =	idalabel
  _a  :       Optional[int]             =	{v: k for k, v in idalabel.items()}

  # size of the architecture
  if swiftformer_name == "swiftformer_xs":
    _a  :       Any             =	[3, 3, 6, 4]
    _a  :       int             =	[4_8, 5_6, 1_1_2, 2_2_0]

  elif swiftformer_name == "swiftformer_s":
    _a  :       Any             =	[3, 3, 9, 6]
    _a  :       List[str]             =	[4_8, 6_4, 1_6_8, 2_2_4]

  elif swiftformer_name == "swiftformer_l1":
    _a  :       List[Any]             =	[4, 3, 1_0, 5]
    _a  :       Optional[int]             =	[4_8, 9_6, 1_9_2, 3_8_4]

  elif swiftformer_name == "swiftformer_l3":
    _a  :       List[Any]             =	[4, 4, 1_2, 6]
    _a  :       Optional[Any]             =	[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  :       Tuple             =	torch.hub.load_state_dict_from_url(UpperCamelCase__     ,   map_location="""cpu"""     ,   check_hash=UpperCamelCase__				)
    else:
      _a  :       Dict             =	torch.load(UpperCamelCase__     ,   map_location="""cpu"""				)
  _a  :       int             =	checkpoint

  _a  :       Optional[Any]             =	create_rename_keys(UpperCamelCase__				)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)

  # load HuggingFace model
  _a  :       Any             =	SwiftFormerForImageClassification(UpperCamelCase__				).eval()
  hf_model.load_state_dict(UpperCamelCase__				)

  # prepare test inputs
  _a  :       Any             =	prepare_img()
  _a  :       Union[str, Any]             =	ViTImageProcessor.from_pretrained("""preprocessor_config"""				)
  _a  :       Optional[int]             =	processor(images=UpperCamelCase__     ,   return_tensors="""pt"""				)

  # compare outputs from both models
  _a  :       Dict             =	get_expected_output(UpperCamelCase__				)
  _a  :       int             =	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]     ,   UpperCamelCase__     ,   atol=1e-3				)

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


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

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

