






"""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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase    :   Union[str, Any]   			=						logging.get_logger(__name__)




def       snake_case  (A_				:Optional[int]				):
  a					:      Tuple    =       YolosConfig()

  # size of the architecture
  if "yolos_ti" in yolos_name:
    a					:      Dict    =       1_9_2
    a					:      Optional[Any]    =       7_6_8
    a					:      Optional[int]    =       1_2
    a					:      Any    =       3
    a					:      List[Any]    =       [8_0_0, 1_3_3_3]
    a					:      Tuple    =       False
  elif yolos_name == "yolos_s_dWr":
    a					:      Dict    =       3_3_0
    a					:      Any    =       1_4
    a					:      Tuple    =       6
    a					:      Any    =       1_3_2_0
  elif "yolos_s" in yolos_name:
    a					:      Any    =       3_8_4
    a					:      int    =       1_5_3_6
    a					:      List[Any]    =       1_2
    a					:      Tuple    =       6
  elif "yolos_b" in yolos_name:
    a					:      Tuple    =       [8_0_0, 1_3_4_4]

  a					:      Dict    =       9_1
  a					:      Any    =       'huggingface/label-files'
  a					:      List[str]    =       'coco-detection-id2label.json'
  a					:      Union[str, Any]    =       json.load(open(hf_hub_download(_A     ,      _A     ,      repo_type='dataset'				)     ,      'r'				)				)
  a					:      List[str]    =       {int(_A				): v for k, v in idalabel.items()}
  a					:      Tuple    =       idalabel
  a					:      Optional[Any]    =       {v: k for k, v in idalabel.items()}

  return config




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




def       snake_case  (A_				:List[str]				):
  if "backbone" in name:
    a					:      Tuple    =       name.replace('backbone'     ,      'vit'				)
  if "cls_token" in name:
    a					:      List[str]    =       name.replace('cls_token'     ,      'embeddings.cls_token'				)
  if "det_token" in name:
    a					:      List[str]    =       name.replace('det_token'     ,      'embeddings.detection_tokens'				)
  if "mid_pos_embed" in name:
    a					:      Dict    =       name.replace('mid_pos_embed'     ,      'encoder.mid_position_embeddings'				)
  if "pos_embed" in name:
    a					:      Union[str, Any]    =       name.replace('pos_embed'     ,      'embeddings.position_embeddings'				)
  if "patch_embed.proj" in name:
    a					:      Optional[int]    =       name.replace('patch_embed.proj'     ,      'embeddings.patch_embeddings.projection'				)
  if "blocks" in name:
    a					:      Dict    =       name.replace('blocks'     ,      'encoder.layer'				)
  if "attn.proj" in name:
    a					:      Dict    =       name.replace('attn.proj'     ,      'attention.output.dense'				)
  if "attn" in name:
    a					:      Any    =       name.replace('attn'     ,      'attention.self'				)
  if "norm1" in name:
    a					:      Dict    =       name.replace('norm1'     ,      'layernorm_before'				)
  if "norm2" in name:
    a					:      int    =       name.replace('norm2'     ,      'layernorm_after'				)
  if "mlp.fc1" in name:
    a					:      List[Any]    =       name.replace('mlp.fc1'     ,      'intermediate.dense'				)
  if "mlp.fc2" in name:
    a					:      Dict    =       name.replace('mlp.fc2'     ,      'output.dense'				)
  if "class_embed" in name:
    a					:      List[str]    =       name.replace('class_embed'     ,      'class_labels_classifier'				)
  if "bbox_embed" in name:
    a					:      List[Any]    =       name.replace('bbox_embed'     ,      'bbox_predictor'				)
  if "vit.norm" in name:
    a					:      List[str]    =       name.replace('vit.norm'     ,      'vit.layernorm'				)

  return name




def       snake_case  (A_				:Any     ,      A_				:int				):
  for key in orig_state_dict.copy().keys():
    a					:      Dict    =       orig_state_dict.pop(_A				)

    if "qkv" in key:
      a					:      Tuple    =       key.split('.'				)
      a					:      Any    =       int(key_split[2]				)
      a					:      Union[str, Any]    =       model.vit.encoder.layer[layer_num].attention.attention.all_head_size
      if "weight" in key:
        a					:      List[Any]    =       val[:dim, :]
        a					:      List[Any]    =       val[
            dim : dim * 2, :
        ]
        a					:      int    =       val[-dim:, :]
      else:
        a					:      Any    =       val[:dim]
        a					:      Optional[int]    =       val[dim : dim * 2]
        a					:      Dict    =       val[-dim:]
    else:
      a					:      Any    =       val

  return orig_state_dict




def       snake_case  ():
  a					:      Union[str, Any]    =       'http://images.cocodataset.org/val2017/000000039769.jpg'
  a					:      List[Any]    =       Image.open(requests.get(_A     ,      stream=_A				).raw				)
  return im




@torch.no_grad()
def       snake_case  (A_				:Any     ,      A_				:Optional[int]     ,      A_				:int     ,      A_				:Optional[Any] = False				):
  a					:      int    =       get_yolos_config(_A				)

  # load original state_dict
  a					:      Dict    =       torch.load(_A     ,      map_location='cpu'				)['model']

  # load 🤗 model
  a					:      str    =       YolosForObjectDetection(_A				)
  model.eval()
  a					:      Any    =       convert_state_dict(_A     ,      _A				)
  model.load_state_dict(_A				)

  # Check outputs on an image, prepared by YolosImageProcessor
  a					:      Dict    =       8_0_0 if yolos_name != 'yolos_ti' else 5_1_2
  a					:      str    =       YolosImageProcessor(format='coco_detection'     ,      size=_A				)
  a					:      List[str]    =       image_processor(images=prepare_img()     ,      return_tensors='pt'				)
  a					:      List[Any]    =       model(**_A				)
  a					:      List[Any]    =       outputs.logits, outputs.pred_boxes

  a					:      List[str]    =       None, None
  if yolos_name == "yolos_ti":
    a					:      List[str]    =       torch.tensor(
        [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]				)
    a					:      Optional[int]    =       torch.tensor(
        [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]				)
  elif yolos_name == "yolos_s_200_pre":
    a					:      List[str]    =       torch.tensor(
        [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]				)
    a					:      Tuple    =       torch.tensor(
        [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]				)
  elif yolos_name == "yolos_s_300_pre":
    a					:      Tuple    =       torch.tensor(
        [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]				)
    a					:      str    =       torch.tensor(
        [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]				)
  elif yolos_name == "yolos_s_dWr":
    a					:      str    =       torch.tensor(
        [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]				)
    a					:      Dict    =       torch.tensor(
        [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]				)
  elif yolos_name == "yolos_base":
    a					:      int    =       torch.tensor(
        [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]				)
    a					:      str    =       torch.tensor(
        [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]				)
  else:
    raise ValueError(f'''Unknown yolos_name: {yolos_name}'''				)

  assert torch.allclose(logits[0, :3, :3]     ,      _A     ,      atol=1E-4				)
  assert torch.allclose(pred_boxes[0, :3, :3]     ,      _A     ,      atol=1E-4				)

  Path(_A				).mkdir(exist_ok=_A				)
  print(f'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''				)
  model.save_pretrained(_A				)
  print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
  image_processor.save_pretrained(_A				)

  if push_to_hub:
    a					:      List[Any]    =       {
        'yolos_ti': 'yolos-tiny',
        'yolos_s_200_pre': 'yolos-small',
        'yolos_s_300_pre': 'yolos-small-300',
        'yolos_s_dWr': 'yolos-small-dwr',
        'yolos_base': 'yolos-base',
    }

    print('Pushing to the hub...'				)
    a					:      List[str]    =       model_mapping[yolos_name]
    image_processor.push_to_hub(_A     ,      organization='hustvl'				)
    model.push_to_hub(_A     ,      organization='hustvl'				)


if __name__ == "__main__":
   _UpperCamelCase    :   Tuple   			=						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--yolos_name',
       default='yolos_s_200_pre',
       type=str,
       help=(
           'Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','
           ' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'
       ),
   )
   parser.add_argument(
       '--checkpoint_path', default=None, type=str, help='Path to the original state dict (.pth file).'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
   )

   _UpperCamelCase    :   Dict   			=						parser.parse_args()
   convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)












"""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       snake_case  (A_				:Dict				):







  '''simple docstring'''

  a					:      str    =       []
  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       snake_case  (A_				:Any     ,      A_				:List[Any]				):







  '''simple docstring'''

  a					:      Union[str, Any]    =       []
  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       snake_case  (A_				:Dict				):







  '''simple docstring'''

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




def       snake_case  ():







  '''simple docstring'''

  a					:      int    =       []
  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       snake_case  (A_				:int     ,      A_				:Optional[int]     ,      A_				:Dict     ,      A_				:Dict				):







  '''simple docstring'''

  a					:      Optional[Any]    =       'imagenet-1k-id2label.json'
  a					:      Dict    =       1_0_0_0

  a					:      Tuple    =       'huggingface/label-files'
  a					:      List[Any]    =       num_labels
  a					:      List[str]    =       json.load(open(cached_download(hf_hub_url(A_     ,      A_     ,      repo_type='dataset'				)				)     ,      'r'				)				)
  a					:      int    =       {int(A_				): v for k, v in idalabel.items()}

  a					:      str    =       idalabel
  a					:      Optional[int]    =       {v: k for k, v in idalabel.items()}

  a					:      Tuple    =       CvtConfig(num_labels=A_     ,      idalabel=A_     ,      labelaid=A_				)

  # For depth size 13 (13 = 1+2+10)
  if cvt_model.rsplit('/'     ,      1				)[-1][4:6] == "13":
    a					:      int    =       [1, 2, 1_0]

  # For depth size 21 (21 = 1+4+16)
  elif cvt_model.rsplit('/'     ,      1				)[-1][4:6] == "21":
    a					:      List[Any]    =       [1, 4, 1_6]

  # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
  else:
    a					:      Optional[int]    =       [2, 2, 2_0]
    a					:      Any    =       [3, 1_2, 1_6]
    a					:      str    =       [1_9_2, 7_6_8, 1_0_2_4]

  a					:      List[Any]    =       CvtForImageClassification(A_				)
  a					:      Optional[int]    =       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				)
  a					:      Union[str, Any]    =       image_size
  a					:      Optional[Any]    =       torch.load(A_     ,      map_location=torch.device('cpu'				)				)

  a					:      int    =       OrderedDict()
  a					:      Any    =       []

  for idx in range(len(config.depth				)				):
    if config.cls_token[idx]:
      a					:      Dict    =       list_of_state_dict + cls_token(A_				)
    a					:      Any    =       list_of_state_dict + embeddings(A_				)
    for cnt in range(config.depth[idx]				):
      a					:      Dict    =       list_of_state_dict + attention(A_     ,      A_				)

  a					:      Any    =       list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(A_				)
  for i in range(len(A_				)				):
    a					:      List[Any]    =       original_weights[list_of_state_dict[i][1]]

  model.load_state_dict(A_				)
  model.save_pretrained(A_				)
  image_processor.save_pretrained(A_				)


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

if __name__ == "__main__":
   _UpperCamelCase    :   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=384,
       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    :   int   			=						parser.parse_args()
   convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




