





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()
_snake_case  :    int						    =  logging.get_logger(__name__)

def   a_    ( lowerCAmelCase_      :			str,	lowerCAmelCase_      :			List[Any]=False	):
   __lowerCAmelCase								=	[]

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

def   a_    ( lowerCAmelCase_      :			int	):
   __lowerCAmelCase								=	['head.weight', 'head.bias']
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase_,	UpperCamelCase_	)

def   a_    ( lowerCAmelCase_      :			Union[str, Any],	lowerCAmelCase_      :			Any,	lowerCAmelCase_      :			Dict	):
   __lowerCAmelCase								=	dct.pop(UpperCamelCase_	)
   __lowerCAmelCase								=	val

def   a_    ( ):
   __lowerCAmelCase								=	'http://images.cocodataset.org/val2017/000000039769.jpg'
   __lowerCAmelCase								=	Image.open(requests.get(UpperCamelCase_,	stream=UpperCamelCase_	).raw	)
   return im

@torch.no_grad()
def   a_    ( lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			int=False	):
   __lowerCAmelCase								=	BitConfig(
       global_padding='same',	layer_type='bottleneck',	depths=(3, 4, 9),	out_features=['stage3'],	embedding_dynamic_padding=UpperCamelCase_,	)
   __lowerCAmelCase								=	ViTHybridConfig(backbone_config=UpperCamelCase_,	image_size=384,	num_labels=1000	)
   __lowerCAmelCase								=	False

   # load original model from timm
   __lowerCAmelCase								=	timm.create_model(UpperCamelCase_,	pretrained=UpperCamelCase_	)
   timm_model.eval()

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

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

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

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

   __lowerCAmelCase								=	{
       'bilinear': PILImageResampling.BILINEAR,
       'bicubic': PILImageResampling.BICUBIC,
       'nearest': PILImageResampling.NEAREST,
   }

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

   __lowerCAmelCase								=	prepare_img()
   __lowerCAmelCase								=	transform(UpperCamelCase_	).unsqueeze(0	)
   __lowerCAmelCase								=	processor(UpperCamelCase_,	return_tensors='pt'	).pixel_values

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

   # verify logits
   with torch.no_grad():
      __lowerCAmelCase								=	model(UpperCamelCase_	)
      __lowerCAmelCase								=	outputs.logits

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

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

   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__":
     _snake_case  :    List[Any]						    =  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.'
     )

     _snake_case  :    Optional[int]						    =  parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



"""simple docstring"""





def 						_lowerCAmelCase				(				UpperCamelCase_				):

		if isinstance(UpperCamelCase_  ,				UpperCamelCase_				):
				raise TypeError("""'float' object cannot be interpreted as an integer"""				)
		if isinstance(UpperCamelCase_  ,				UpperCamelCase_				):
				raise TypeError("""'str' object cannot be interpreted as an integer"""				)

		if num == 0:
				return "0b0"

		__SCREAMING_SNAKE_CASE            = False

		if num < 0:
				__SCREAMING_SNAKE_CASE            = True
				__SCREAMING_SNAKE_CASE            = -num

		__SCREAMING_SNAKE_CASE            = []
		while num > 0:
				binary.insert(0  ,				num % 2				)
				num >>= 1

		if negative:
				return "-0b" + "".join(str(UpperCamelCase_				) for e in binary				)

		return "0b" + "".join(str(UpperCamelCase_				) for e in binary				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




