






"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__=False				):



  '''simple docstring'''
  _a  :       int             =	[]
  for i in range(config.num_hidden_layers				):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""deit.encoder.layer.{i}.layernorm_before.weight""")				)
    rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")				)
    rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")				)
    rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")				)
    rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")				)
    rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")				)
    rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")				)
    rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")				)
    rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")				)
    rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")				)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ("""cls_token""", """deit.embeddings.cls_token"""),
          ("""dist_token""", """deit.embeddings.distillation_token"""),
          ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
          ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
          ("""pos_embed""", """deit.embeddings.position_embeddings"""),
      ]				)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ("""norm.weight""", """layernorm.weight"""),
            ("""norm.bias""", """layernorm.bias"""),
            ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
            ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
        ]				)

    # if just the base model, we should remove "deit" from all keys that start with "deit"
    _a  :       List[Any]             =	[(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""				) else pair for pair in rename_keys]
  else:
    # layernorm + classification heads
    rename_keys.extend(
        [
            ("""norm.weight""", """deit.layernorm.weight"""),
            ("""norm.bias""", """deit.layernorm.bias"""),
            ("""head.weight""", """cls_classifier.weight"""),
            ("""head.bias""", """cls_classifier.bias"""),
            ("""head_dist.weight""", """distillation_classifier.weight"""),
            ("""head_dist.bias""", """distillation_classifier.bias"""),
        ]				)

  return rename_keys

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=False				):



  '''simple docstring'''
  for i in range(config.num_hidden_layers				):
    if base_model:
      _a  :       Optional[Any]             =	""""""
    else:
      _a  :       Any             =	"""deit."""
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _a  :       Tuple             =	state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""				)
    _a  :       str             =	state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""				)
    # next, add query, keys and values (in that order) to the state dict
    _a  :       Dict             =	in_proj_weight[
        : config.hidden_size, :
    ]
    _a  :       List[Any]             =	in_proj_bias[: config.hidden_size]
    _a  :       int             =	in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _a  :       Tuple             =	in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _a  :       Tuple             =	in_proj_weight[
        -config.hidden_size :, :
    ]
    _a  :       Any             =	in_proj_bias[-config.hidden_size :]

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



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

def 			lowerCAmelCase__     (     ):



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

@torch.no_grad()
def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Union[str, Any]             =	DeiTConfig()
  # all deit models have fine-tuned heads
  _a  :       Optional[int]             =	False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  _a  :       Tuple             =	1_0_0_0
  _a  :       Dict             =	"""huggingface/label-files"""
  _a  :       Union[str, Any]             =	"""imagenet-1k-id2label.json"""
  _a  :       Any             =	json.load(open(hf_hub_download(UpperCamelCase__     ,   UpperCamelCase__     ,   repo_type="""dataset"""				)     ,   """r"""				)				)
  _a  :       Any             =	{int(UpperCamelCase__				): v for k, v in idalabel.items()}
  _a  :       Tuple             =	idalabel
  _a  :       List[Any]             =	{v: k for k, v in idalabel.items()}
  _a  :       Union[str, Any]             =	int(deit_name[-6:-4]				)
  _a  :       List[str]             =	int(deit_name[-3:]				)
  # size of the architecture
  if deit_name[9:].startswith("""tiny"""				):
    _a  :       Dict             =	1_9_2
    _a  :       Dict             =	7_6_8
    _a  :       Dict             =	1_2
    _a  :       List[str]             =	3
  elif deit_name[9:].startswith("""small"""				):
    _a  :       Optional[Any]             =	3_8_4
    _a  :       Dict             =	1_5_3_6
    _a  :       List[str]             =	1_2
    _a  :       List[Any]             =	6
  if deit_name[9:].startswith("""base"""				):
    pass
  elif deit_name[4:].startswith("""large"""				):
    _a  :       Optional[Any]             =	1_0_2_4
    _a  :       List[str]             =	4_0_9_6
    _a  :       Optional[Any]             =	2_4
    _a  :       Union[str, Any]             =	1_6

  # load original model from timm
  _a  :       Optional[int]             =	timm.create_model(UpperCamelCase__     ,   pretrained=UpperCamelCase__				)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  _a  :       Optional[int]             =	timm_model.state_dict()
  _a  :       str             =	create_rename_keys(UpperCamelCase__     ,   UpperCamelCase__				)
  for src, dest in rename_keys:
    rename_key(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)
  read_in_q_k_v(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)

  # load HuggingFace model
  _a  :       List[Any]             =	DeiTForImageClassificationWithTeacher(UpperCamelCase__				).eval()
  model.load_state_dict(UpperCamelCase__				)

  # Check outputs on an image, prepared by DeiTImageProcessor
  _a  :       List[Any]             =	int(
      (2_5_6 / 2_2_4) * config.image_size				)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
  _a  :       Any             =	DeiTImageProcessor(size=UpperCamelCase__     ,   crop_size=config.image_size				)
  _a  :       List[str]             =	image_processor(images=prepare_img()     ,   return_tensors="""pt"""				)
  _a  :       List[str]             =	encoding["""pixel_values"""]
  _a  :       str             =	model(UpperCamelCase__				)

  _a  :       int             =	timm_model(UpperCamelCase__				)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(UpperCamelCase__     ,   outputs.logits     ,   atol=1e-3				)

  Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
  print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""				)
  model.save_pretrained(UpperCamelCase__				)
  print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
  image_processor.save_pretrained(UpperCamelCase__				)


if __name__ == "__main__":
 _snake_case          =   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--deit_name',
     default='vit_deit_base_distilled_patch16_224',
     type=str,
     help='Name of the DeiT timm model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )

 _snake_case          =   parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)









"""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)

