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()
__lowerCamelCase    :				Any							     =	logging.get_logger(__name__)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: int=False			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   []

  # 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"
    SCREAMING_SNAKE_CASE__    							=   [(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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Any				,  __UpperCamelCase					: str				,  __UpperCamelCase					: Dict=False			)							->      int:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple			)							->      Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   ["""head.weight""", """head.bias"""]
  for k in ignore_keys:
    state_dict.pop(__UpperCamelCase				,  __UpperCamelCase			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Any				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: str			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   dct.pop(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   val



def 			__SCREAMING_SNAKE_CASE						(      )							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
  SCREAMING_SNAKE_CASE__    							=   Image.open(requests.get(__UpperCamelCase				,  stream=__UpperCamelCase			).raw			)
  return im



@torch.no_grad()
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: str				,  __UpperCamelCase					: Optional[int]=False			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   BitConfig(
      global_padding="""same"""				,  layer_type="""bottleneck"""				,  depths=(3, 4, 9)				,  out_features=["""stage3"""]				,  embedding_dynamic_padding=__UpperCamelCase				,  )
  SCREAMING_SNAKE_CASE__    							=   ViTHybridConfig(backbone_config=__UpperCamelCase				,  image_size=3_84				,  num_labels=10_00			)
  SCREAMING_SNAKE_CASE__    							=   False

  # load original model from timm
  SCREAMING_SNAKE_CASE__    							=   timm.create_model(__UpperCamelCase				,  pretrained=__UpperCamelCase			)
  timm_model.eval()

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

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

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

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

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

  SCREAMING_SNAKE_CASE__    							=   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()				,  )

  SCREAMING_SNAKE_CASE__    							=   prepare_img()
  SCREAMING_SNAKE_CASE__    							=   transform(__UpperCamelCase			).unsqueeze(0			)
  SCREAMING_SNAKE_CASE__    							=   processor(__UpperCamelCase				,  return_tensors="""pt"""			).pixel_values

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

  # verify logits
  with torch.no_grad():
    SCREAMING_SNAKE_CASE__    							=   model(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   outputs.logits

  print("""Predicted class:"""				,  logits.argmax(-1			).item()			)
  if base_model:
    SCREAMING_SNAKE_CASE__    							=   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:
    SCREAMING_SNAKE_CASE__    							=   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__":
   __lowerCamelCase    :				Optional[int]							     =	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.'''
   )

   __lowerCamelCase    :				List[str]							     =	parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)

from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__lowerCamelCase    :				List[str]							     =	TypeVar('''KEY''')
__lowerCamelCase    :				int							     =	TypeVar('''VAL''')







@dataclass(frozen=lowerCamelCase_     ,	slots=lowerCamelCase_	)
class __snake_case       (		Generic[KEY, VAL]	):
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42







class __snake_case       (		_Item	):




   def __init__(					self    :       List[Any]   ):



     """simple docstring"""






     super().__init__(_lowercase ,			_lowercase   )




   def __bool__(					self    :       List[str]   ):



     """simple docstring"""






     return False





__lowerCamelCase    :				str							     =	_DeletedItem()







class __snake_case       (		MutableMapping[KEY, VAL]	):




   def __init__(					self    :       Optional[int] ,			_lowercase    :       int = 8 ,			_lowercase    :       float = 0.75   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   initial_block_size
     SCREAMING_SNAKE_CASE__    							=   [None] * initial_block_size
     assert 0.0 < capacity_factor < 1.0
     SCREAMING_SNAKE_CASE__    							=   capacity_factor
     SCREAMING_SNAKE_CASE__    							=   0




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       KEY   ):



     """simple docstring"""






     return hash(_lowercase   ) % len(self._buckets   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       int   ):



     """simple docstring"""






     return (ind + 1) % len(self._buckets   )




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       int ,			_lowercase    :       KEY ,			_lowercase    :       VAL   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self._buckets[ind]
     if not stored:
       SCREAMING_SNAKE_CASE__    							=   _Item(_lowercase ,			_lowercase   )
       self._len += 1
       return True
     elif stored.key == key:
       SCREAMING_SNAKE_CASE__    							=   _Item(_lowercase ,			_lowercase   )
       return True
     else:
       return False




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   len(self._buckets   ) * self._capacity_factor
     return len(self   ) >= int(_lowercase   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     if len(self._buckets   ) <= self._initial_block_size:
       return False
     SCREAMING_SNAKE_CASE__    							=   len(self._buckets   ) * self._capacity_factor / 2
     return len(self   ) < limit




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self._buckets
     SCREAMING_SNAKE_CASE__    							=   [None] * new_size
     SCREAMING_SNAKE_CASE__    							=   0
     for item in old_buckets:
       if item:
         self._add_item(item.key ,			item.val   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     self._resize(len(self._buckets   ) * 2   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     self._resize(len(self._buckets   ) // 2   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       KEY   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self._get_bucket_index(_lowercase   )
     for _ in range(len(self._buckets   )   ):
       yield ind
       SCREAMING_SNAKE_CASE__    							=   self._get_next_ind(_lowercase   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       KEY ,			_lowercase    :       VAL   ):



     """simple docstring"""






     for ind in self._iterate_buckets(_lowercase   ):
       if self._try_set(_lowercase ,			_lowercase ,			_lowercase   ):
         break




   def __setitem__(					self    :       List[str] ,			_lowercase    :       KEY ,			_lowercase    :       VAL   ):



     """simple docstring"""






     if self._is_full():
       self._size_up()

     self._add_item(_lowercase ,			_lowercase   )




   def __delitem__(					self    :       Tuple ,			_lowercase    :       KEY   ):



     """simple docstring"""






     for ind in self._iterate_buckets(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   self._buckets[ind]
       if item is None:
         raise KeyError(_lowercase   )
       if item is _deleted:
         continue
       if item.key == key:
         SCREAMING_SNAKE_CASE__    							=   _deleted
         self._len -= 1
         break
     if self._is_sparse():
       self._size_down()




   def __getitem__(					self    :       Tuple ,			_lowercase    :       KEY   ):



     """simple docstring"""






     for ind in self._iterate_buckets(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   self._buckets[ind]
       if item is None:
         break
       if item is _deleted:
         continue
       if item.key == key:
         return item.val
     raise KeyError(_lowercase   )




   def __len__(					self    :       List[Any]   ):



     """simple docstring"""






     return self._len




   def __iter__(					self    :       List[str]   ):



     """simple docstring"""






     yield from (item.key for item in self._buckets if item)




   def __repr__(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """ ,""".join(
         f"""{item.key}: {item.val}""" for item in self._buckets if item   )
     return f"""HashMap({val_string})"""
