


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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__a     =			logging.get_logger(__name__)
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_=False   )						->int:
   UpperCAmelCase							=     []

   # fmt: off
   # stem:
   rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""")   )
   rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""")   )

   rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""")   )
   rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""")   )

   # backbone
   rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""")   )
   rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""")   )
   rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""")   )

   for stage_idx in range(len(config.backbone_config.depths   )   ):
      for layer_idx in range(config.backbone_config.depths[stage_idx]   ):
         rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")   )
         rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")   )
         rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")   )
         rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")   )
         rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")   )
         rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")   )
         rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")   )
         rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")   )
         rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")   )

      rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")   )
      rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")   )
      rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")   )

   # transformer encoder
   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"""vit.encoder.layer.{i}.layernorm_before.weight""")   )
      rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")   )
      rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")   )
      rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")   )
      rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")   )
      rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")   )
      rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")   )
      rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")   )
      rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")   )
      rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")   )

   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 "vit" from all keys that start with "vit"
      UpperCAmelCase							=     [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""   ) else pair for pair in rename_keys]
   else:
      # layernorm + classification head
      rename_keys.extend(
          [
              ("""norm.weight""", """vit.layernorm.weight"""),
              ("""norm.bias""", """vit.layernorm.bias"""),
              ("""head.weight""", """classifier.weight"""),
              ("""head.bias""", """classifier.bias"""),
          ]   )
   # fmt: on

   return rename_keys
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=False   )						->Any:
   for i in range(config.num_hidden_layers   ):
      if base_model:
         UpperCAmelCase							=     """"""
      else:
         UpperCAmelCase							=     """vit."""
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      UpperCAmelCase							=     state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""   )
      UpperCAmelCase							=     state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""   )
      # next, add query, keys and values (in that order) to the state dict
      UpperCAmelCase							=     in_proj_weight[
          : config.hidden_size, :
      ]
      UpperCAmelCase							=     in_proj_bias[: config.hidden_size]
      UpperCAmelCase							=     in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      UpperCAmelCase							=     in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      UpperCAmelCase							=     in_proj_weight[
          -config.hidden_size :, :
      ]
      UpperCAmelCase							=     in_proj_bias[-config.hidden_size :]
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Dict:
   UpperCAmelCase							=     ["""head.weight""", """head.bias"""]
   for k in ignore_keys:
      state_dict.pop(lowerCAmelCase_  ,						lowerCAmelCase_   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Union[str, Any]:
   UpperCAmelCase							=     dct.pop(lowerCAmelCase_   )
   UpperCAmelCase							=     val
def   _UpperCamelCase				(						)						->Any:
   UpperCAmelCase							=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
   UpperCAmelCase							=     Image.open(requests.get(lowerCAmelCase_  ,						stream=lowerCAmelCase_   ).raw   )
   return im
@torch.no_grad()
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=False   )						->Optional[Any]:
   UpperCAmelCase							=     BitConfig(
       global_padding="""same"""  ,						layer_type="""bottleneck"""  ,						depths=(3, 4, 9)  ,						out_features=["""stage3"""]  ,						embedding_dynamic_padding=lowerCAmelCase_  ,						)
   UpperCAmelCase							=     ViTHybridConfig(backbone_config=lowerCAmelCase_  ,						image_size=3_8_4  ,						num_labels=1_0_0_0   )
   UpperCAmelCase							=     False

   # load original model from timm
   UpperCAmelCase							=     timm.create_model(lowerCAmelCase_  ,						pretrained=lowerCAmelCase_   )
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   UpperCAmelCase							=     timm_model.state_dict()
   if base_model:
      remove_classification_head_(lowerCAmelCase_   )
   UpperCAmelCase							=     create_rename_keys(lowerCAmelCase_  ,						lowerCAmelCase_   )
   for src, dest in rename_keys:
      rename_key(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
   read_in_q_k_v(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )

   UpperCAmelCase							=     """huggingface/label-files"""
   UpperCAmelCase							=     """imagenet-1k-id2label.json"""
   UpperCAmelCase							=     json.load(open(hf_hub_download(lowerCAmelCase_  ,						lowerCAmelCase_  ,						repo_type="""dataset"""   )  ,						"""r"""   )   )
   UpperCAmelCase							=     {int(lowerCAmelCase_   ): v for k, v in idalabel.items()}
   UpperCAmelCase							=     idalabel
   UpperCAmelCase							=     {v: k for k, v in idalabel.items()}

   # load HuggingFace model
   if vit_name[-5:] == "in21k":
      UpperCAmelCase							=     ViTHybridModel(lowerCAmelCase_   ).eval()
   else:
      UpperCAmelCase							=     ViTHybridForImageClassification(lowerCAmelCase_   ).eval()
   model.load_state_dict(lowerCAmelCase_   )

   # create image processor
   UpperCAmelCase							=     create_transform(**resolve_data_config({}  ,						model=lowerCAmelCase_   )   )
   UpperCAmelCase							=     transform.transforms

   UpperCAmelCase							=     {
       """bilinear""": PILImageResampling.BILINEAR,
       """bicubic""": PILImageResampling.BICUBIC,
       """nearest""": PILImageResampling.NEAREST,
   }

   UpperCAmelCase							=     ViTHybridImageProcessor(
       do_resize=lowerCAmelCase_  ,						size={"""shortest_edge""": timm_transforms[0].size}  ,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]  ,						do_center_crop=lowerCAmelCase_  ,						crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}  ,						do_normalize=lowerCAmelCase_  ,						image_mean=timm_transforms[-1].mean.tolist()  ,						image_std=timm_transforms[-1].std.tolist()  ,						)

   UpperCAmelCase							=     prepare_img()
   UpperCAmelCase							=     transform(lowerCAmelCase_   ).unsqueeze(0   )
   UpperCAmelCase							=     processor(lowerCAmelCase_  ,						return_tensors="""pt"""   ).pixel_values

   # verify pixel values
   assert torch.allclose(lowerCAmelCase_  ,						lowerCAmelCase_   )

   # verify logits
   with torch.no_grad():
      UpperCAmelCase							=     model(lowerCAmelCase_   )
      UpperCAmelCase							=     outputs.logits

   print("""Predicted class:"""  ,						logits.argmax(-1   ).item()   )
   if base_model:
      UpperCAmelCase							=     timm_model.forward_features(lowerCAmelCase_   )
      assert timm_pooled_output.shape == outputs.pooler_output.shape
      assert torch.allclose(lowerCAmelCase_  ,						outputs.pooler_output  ,						atol=1e-3   )
   else:
      UpperCAmelCase							=     timm_model(lowerCAmelCase_   )
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(lowerCAmelCase_  ,						outputs.logits  ,						atol=1e-3   )
   print("""Looks ok!"""   )

   if pytorch_dump_folder_path is not None:
      Path(lowerCAmelCase_   ).mkdir(exist_ok=lowerCAmelCase_   )
      print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""   )
      model.save_pretrained(lowerCAmelCase_   )
      print(F"""Saving processor to {pytorch_dump_folder_path}"""   )
      processor.save_pretrained(lowerCAmelCase_   )

   if push_to_hub:
      print(F"""Pushing model and processor to the hub {vit_name}"""   )
      model.push_to_hub(F"""ybelkada/{vit_name}"""   )
      processor.push_to_hub(F"""ybelkada/{vit_name}"""   )


if __name__ == "__main__":
 __a     =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--vit_name""",
     default="""vit_base_r50_s16_384""",
     type=str,
     help="""Name of the hybrid ViT 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."""
 )
 parser.add_argument(
     """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
 )

 __a     =			parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)







__a     =			[
    (1000, """M"""),
    (900, """CM"""),
    (500, """D"""),
    (400, """CD"""),
    (100, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]
def   _UpperCamelCase				(						lowerCAmelCase_   )						->int:
   UpperCAmelCase							=     {"""I""": 1, """V""": 5, """X""": 1_0, """L""": 5_0, """C""": 1_0_0, """D""": 5_0_0, """M""": 1_0_0_0}
   UpperCAmelCase							=     0
   UpperCAmelCase							=     0
   while place < len(lowerCAmelCase_   ):
      if (place + 1 < len(lowerCAmelCase_   )) and (vals[roman[place]] < vals[roman[place + 1]]):
         total += vals[roman[place + 1]] - vals[roman[place]]
         place += 2
      else:
         total += vals[roman[place]]
         place += 1
   return total
def   _UpperCamelCase				(						lowerCAmelCase_   )						->str:
   UpperCAmelCase							=     []
   for arabic, roman in ROMAN:
      ((UpperCAmelCase)				,  (UpperCAmelCase))							=     divmod(lowerCAmelCase_  ,						lowerCAmelCase_   )
      result.append(roman * factor   )
      if number == 0:
         break
   return "".join(lowerCAmelCase_   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



