





'''simple docstring'''




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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_A:					str							=				logging.get_logger(__name__)







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase=False    )->     int:
 __UpperCAmelCase					=   []
 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')    )

 # projection layer + position embeddings
 rename_keys.extend(
     [
         ('cls_token', 'vit.embeddings.cls_token'),
         ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
         ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
         ('pos_embed', 'vit.embeddings.position_embeddings'),
     ]    )

 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'),
      ]    )

 return rename_keys







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase=False    )->     str:
 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 _lowerCAmelCase     (      _lowerCAmelCase    )->     Optional[Any]:
 __UpperCAmelCase					=   ['head.weight', 'head.bias']
 for k in ignore_keys:
  state_dict.pop(_lowerCAmelCase       ,    _lowerCAmelCase    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[str]:
 __UpperCAmelCase					=   dct.pop(_lowerCAmelCase    )
 __UpperCAmelCase					=   val







def _lowerCAmelCase     (      )->     str:
 __UpperCAmelCase					=   'http://images.cocodataset.org/val2017/000000039769.jpg'
 __UpperCAmelCase					=   Image.open(requests.get(_lowerCAmelCase       ,    stream=_lowerCAmelCase    ).raw    )
 return im







@torch.no_grad()
def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     int:
 __UpperCAmelCase					=   ViTConfig()
 __UpperCAmelCase					=   False
 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 if vit_name[-5:] == "in21k":
  __UpperCAmelCase					=   True
  __UpperCAmelCase					=   int(vit_name[-12:-10]    )
  __UpperCAmelCase					=   int(vit_name[-9:-6]    )
 else:
  __UpperCAmelCase					=   10_00
  __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()}
  __UpperCAmelCase					=   int(vit_name[-6:-4]    )
  __UpperCAmelCase					=   int(vit_name[-3:]    )
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith('tiny'    ):
   __UpperCAmelCase					=   1_92
   __UpperCAmelCase					=   7_68
   __UpperCAmelCase					=   12
   __UpperCAmelCase					=   3
  elif vit_name[9:].startswith('small'    ):
   __UpperCAmelCase					=   3_84
   __UpperCAmelCase					=   15_36
   __UpperCAmelCase					=   12
   __UpperCAmelCase					=   6
  else:
   pass
 else:
  if vit_name[4:].startswith('small'    ):
   __UpperCAmelCase					=   7_68
   __UpperCAmelCase					=   23_04
   __UpperCAmelCase					=   8
   __UpperCAmelCase					=   8
  elif vit_name[4:].startswith('base'    ):
   pass
  elif vit_name[4:].startswith('large'    ):
   __UpperCAmelCase					=   10_24
   __UpperCAmelCase					=   40_96
   __UpperCAmelCase					=   24
   __UpperCAmelCase					=   16
  elif vit_name[4:].startswith('huge'    ):
   __UpperCAmelCase					=   12_80
   __UpperCAmelCase					=   51_20
   __UpperCAmelCase					=   32
   __UpperCAmelCase					=   16

    # 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    )

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  __UpperCAmelCase					=   ViTModel(_lowerCAmelCase    ).eval()
 else:
  __UpperCAmelCase					=   ViTForImageClassification(_lowerCAmelCase    ).eval()
 model.load_state_dict(_lowerCAmelCase    )

 # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
 if "deit" in vit_name:
  __UpperCAmelCase					=   DeiTImageProcessor(size=config.image_size    )
 else:
  __UpperCAmelCase					=   ViTImageProcessor(size=config.image_size    )
 __UpperCAmelCase					=   image_processor(images=prepare_img()       ,    return_tensors='pt'    )
 __UpperCAmelCase					=   encoding['pixel_values']
 __UpperCAmelCase					=   model(_lowerCAmelCase    )

 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    )

 Path(_lowerCAmelCase    ).mkdir(exist_ok=_lowerCAmelCase    )
 print(F'Saving model {vit_name} to {pytorch_dump_folder_path}'    )
 model.save_pretrained(_lowerCAmelCase    )
 print(F'Saving image processor to {pytorch_dump_folder_path}'    )
 image_processor.save_pretrained(_lowerCAmelCase    )


if __name__ == "__main__":
    _A:					Union[str, Any]							=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--vit_name""",
        default="""vit_base_patch16_224""",
        type=str,
        help="""Name of the 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."""
    )

    _A:					Union[str, Any]							=				parser.parse_args()
    convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)












'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A:					Tuple							=				logging.get_logger(__name__)






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			List[Any]						   =       """timm_backbone"""



    def __init__(  self     ,			__A=None     ,			__A=3     ,			__A=True     ,			__A=True     ,			__A=None     ,			**__A     ,			):
     super().__init__(**__A					)
     __UpperCAmelCase					=   backbone
     __UpperCAmelCase					=   num_channels
     __UpperCAmelCase					=   features_only
     __UpperCAmelCase					=   use_pretrained_backbone
     __UpperCAmelCase					=   True
     __UpperCAmelCase					=   out_indices if out_indices is not None else (-1,)





