


'''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	:						Tuple       =	logging.get_logger(__name__)

__lowercase	:						List[Any]       =	torch.device('cpu')






def   lowerCamelCase							():
					__a			:       int					    =  'http://images.cocodataset.org/val2017/000000039769.jpg'
					__a			:       Tuple					    =  Image.open(requests.get(_SCREAMING_SNAKE_CASE						,      stream=_SCREAMING_SNAKE_CASE   ).raw   )
					return im






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					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							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       int					    =  dct.pop(_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  val






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					__a			:       Dict					    =  []
					for k in state_dict.keys():
										__a			:       List[Any]					    =  k
										if ".pwconv" in k:
															__a			:       List[Any]					    =  k_new.replace('.pwconv'						,      '.point_wise_conv'   )
										if ".dwconv" in k:
															__a			:       Dict					    =  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			:       List[Any]					    =  k_new.replace('patch_embed'						,      'swiftformer.patch_embed.patch_embedding'   )
										if "network" in k_new:
															__a			:       Union[str, Any]					    =  k_new.split('.'   )
															if ls[2].isdigit():
																				__a			:       Union[str, Any]					    =  'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]   )
															else:
																				__a			:       Union[str, Any]					    =  k_new.replace('network'						,      'swiftformer.encoder.network'   )
										rename_keys.append((k, k_new)   )
					return rename_keys






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       Union[str, Any]					    =  SwiftFormerConfig()

					# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
					__a			:       List[str]					    =  1_000
					__a			:       Tuple					    =  'huggingface/label-files'
					__a			:       str					    =  'imagenet-1k-id2label.json'
					__a			:       Dict					    =  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      repo_type='dataset'   )						,      'r'   )   )
					__a			:       Optional[int]					    =  {int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
					__a			:       Any					    =  idalabel
					__a			:       str					    =  {v: k for k, v in idalabel.items()}

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

					elif swiftformer_name == "swiftformer_s":
										__a			:       Dict					    =  [3, 3, 9, 6]
										__a			:       List[str]					    =  [48, 64, 168, 224]

					elif swiftformer_name == "swiftformer_l1":
										__a			:       Dict					    =  [4, 3, 10, 5]
										__a			:       Optional[int]					    =  [48, 96, 192, 384]

					elif swiftformer_name == "swiftformer_l3":
										__a			:       Tuple					    =  [4, 4, 12, 6]
										__a			:       Dict					    =  [64, 128, 320, 512]

					# load state_dict of original model, remove and rename some keys
					if original_ckpt:
										if original_ckpt.startswith('https'   ):
															__a			:       List[Any]					    =  torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE						,      map_location='cpu'						,      check_hash=_SCREAMING_SNAKE_CASE   )
										else:
															__a			:       Union[str, Any]					    =  torch.load(_SCREAMING_SNAKE_CASE						,      map_location='cpu'   )
					__a			:       Optional[Any]					    =  checkpoint

					__a			:       Dict					    =  create_rename_keys(_SCREAMING_SNAKE_CASE   )
					for rename_key_src, rename_key_dest in rename_keys:
										rename_key(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					# load HuggingFace model
					__a			:       Tuple					    =  SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE   ).eval()
					hf_model.load_state_dict(_SCREAMING_SNAKE_CASE   )

					# prepare test inputs
					__a			:       Tuple					    =  prepare_img()
					__a			:       str					    =  ViTImageProcessor.from_pretrained('preprocessor_config'   )
					__a			:       Tuple					    =  processor(images=_SCREAMING_SNAKE_CASE						,      return_tensors='pt'   )

					# compare outputs from both models
					__a			:       List[Any]					    =  get_expected_output(_SCREAMING_SNAKE_CASE   )
					__a			:       Dict					    =  hf_model(inputs['pixel_values']   ).logits

					assert hf_logits.shape == torch.Size([1, 1_000]   )
					assert torch.allclose(hf_logits[0, 0:5]						,      _SCREAMING_SNAKE_CASE						,      atol=1e-3   )

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


if __name__ == "__main__":
	__lowercase	:						Optional[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.')

	__lowercase	:						Tuple       =	parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)











"""simple docstring"""



import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]    ):





  '''simple docstring'''





  lowercase							    =       []
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
          f'stage{idx}.patch_embed.proj.weight',
      )    )
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
          f'stage{idx}.patch_embed.proj.bias',
      )    )
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
          f'stage{idx}.patch_embed.norm.weight',
      )    )
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
          f'stage{idx}.patch_embed.norm.bias',
      )    )
  return embed
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       []
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj.bias',
      )    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc1.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc1.bias')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc2.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc2.bias')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', f'stage{idx}.blocks.{cnt}.norm1.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', f'stage{idx}.blocks.{cnt}.norm1.bias')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', f'stage{idx}.blocks.{cnt}.norm2.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', f'stage{idx}.blocks.{cnt}.norm2.bias')    )
  return attention_weights
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       []
  token.append((f'cvt.encoder.stages.{idx}.cls_token', 'stage2.cls_token')    )
  return token
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       []
  head.append(('layernorm.weight', 'norm.weight')    )
  head.append(('layernorm.bias', 'norm.bias')    )
  head.append(('classifier.weight', 'head.weight')    )
  head.append(('classifier.bias', 'head.bias')    )
  return head
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]		,					__snake_case			:  Dict		,					__snake_case			:  List[str]		,					__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  lowercase							    =       'imagenet-1k-id2label.json'
  lowercase							    =       10_00

  lowercase							    =       'huggingface/label-files'
  lowercase							    =       num_labels
  lowercase							    =       json.load(open(cached_download(hf_hub_url(__snake_case		,					__snake_case		,					repo_type='dataset'    )    )		,					'r'    )    )
  lowercase							    =       {int(__snake_case    ): v for k, v in idalabel.items()}

  lowercase							    =       idalabel
  lowercase							    =       {v: k for k, v in idalabel.items()}

  lowercase							    =       lowercase							    =       CvtConfig(num_labels=__snake_case		,					idalabel=__snake_case		,					labelaid=__snake_case    )

  # For depth size 13 (13 = 1+2+10)
  if cvt_model.rsplit('/'		,					1    )[-1][4:6] == "13":
    lowercase							    =       [1, 2, 10]

  # For depth size 21 (21 = 1+4+16)
  elif cvt_model.rsplit('/'		,					1    )[-1][4:6] == "21":
    lowercase							    =       [1, 4, 16]

  # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
  else:
    lowercase							    =       [2, 2, 20]
    lowercase							    =       [3, 12, 16]
    lowercase							    =       [1_92, 7_68, 10_24]

  lowercase							    =       CvtForImageClassification(__snake_case    )
  lowercase							    =       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'    )
  lowercase							    =       image_size
  lowercase							    =       torch.load(__snake_case		,					map_location=torch.device('cpu'    )    )

  lowercase							    =       OrderedDict()
  lowercase							    =       []

  for idx in range(len(config.depth    )    ):
    if config.cls_token[idx]:
      lowercase							    =       list_of_state_dict + cls_token(__snake_case    )
    lowercase							    =       list_of_state_dict + embeddings(__snake_case    )
    for cnt in range(config.depth[idx]    ):
      lowercase							    =       list_of_state_dict + attention(__snake_case		,					__snake_case    )

  lowercase							    =       list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(__snake_case    )
  for i in range(len(__snake_case    )    ):
    lowercase							    =       original_weights[list_of_state_dict[i][1]]

  model.load_state_dict(__snake_case    )
  model.save_pretrained(__snake_case    )
  image_processor.save_pretrained(__snake_case    )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
    _UpperCamelCase		:  Optional[Any]						=		argparse.ArgumentParser()
    parser.add_argument(
        '--cvt_model',
        default='cvt-w24',
        type=str,
        help='Name of the cvt model you\'d like to convert.',
    )
    parser.add_argument(
        '--image_size',
        default=3_8_4,
        type=int,
        help='Input Image Size',
    )
    parser.add_argument(
        '--cvt_file_name',
        default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
        type=str,
        help='Input Image Size',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )

    _UpperCamelCase		:  Tuple						=		parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)


