





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()
UpperCAmelCase_     							= logging.get_logger(__name__)




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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int      )      ->	List[str]:
    UpperCamelCase__							:				int          =      ['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(UpperCAmelCase_		,   UpperCAmelCase_      )




def 		lowerCAmelCase_    (  __UpperCAmelCase:			List[str]		,   __UpperCAmelCase:			Union[str, Any]		,   __UpperCAmelCase:			int      )      ->	List[Any]:
    UpperCamelCase__							:				int          =      dct.pop(UpperCAmelCase_      )
    UpperCamelCase__							:				List[str]          =      val




def 		lowerCAmelCase_    (  )      ->	List[Any]:
    UpperCamelCase__							:				Union[str, Any]          =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:				int          =      Image.open(requests.get(UpperCAmelCase_		,   stream=UpperCAmelCase_      ).raw      )
    return im




@torch.no_grad()
def 		lowerCAmelCase_    (  __UpperCAmelCase:			Any		,   __UpperCAmelCase:			Optional[int]      )      ->	Optional[Any]:
    UpperCamelCase__							:				Tuple          =      ViTConfig()
    UpperCamelCase__							:				str          =      False
    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    if vit_name[-5:] == "in21k":
        UpperCamelCase__							:				Tuple          =      True
        UpperCamelCase__							:				List[Any]          =      int(vit_name[-12:-10]      )
        UpperCamelCase__							:				Dict          =      int(vit_name[-9:-6]      )
    else:
        UpperCamelCase__							:				Dict          =      1000
        UpperCamelCase__							:				Dict          =      '''huggingface/label-files'''
        UpperCamelCase__							:				Any          =      '''imagenet-1k-id2label.json'''
        UpperCamelCase__							:				Optional[Any]          =      json.load(open(hf_hub_download(UpperCAmelCase_		,   UpperCAmelCase_		,   repo_type='''dataset'''      )		,   '''r'''      )      )
        UpperCamelCase__							:				List[Any]          =      {int(UpperCAmelCase_      ): v for k, v in idalabel.items()}
        UpperCamelCase__							:				Tuple          =      idalabel
        UpperCamelCase__							:				Dict          =      {v: k for k, v in idalabel.items()}
        UpperCamelCase__							:				Optional[int]          =      int(vit_name[-6:-4]      )
        UpperCamelCase__							:				Dict          =      int(vit_name[-3:]      )
    # size of the architecture
    if "deit" in vit_name:
        if vit_name[9:].startswith('''tiny'''      ):
            UpperCamelCase__							:				List[Any]          =      192
            UpperCamelCase__							:				List[Any]          =      768
            UpperCamelCase__							:				Dict          =      12
            UpperCamelCase__							:				Dict          =      3
        elif vit_name[9:].startswith('''small'''      ):
            UpperCamelCase__							:				List[str]          =      384
            UpperCamelCase__							:				Union[str, Any]          =      1536
            UpperCamelCase__							:				Optional[int]          =      12
            UpperCamelCase__							:				Union[str, Any]          =      6
        else:
            pass
    else:
        if vit_name[4:].startswith('''small'''      ):
            UpperCamelCase__							:				Tuple          =      768
            UpperCamelCase__							:				List[Any]          =      2304
            UpperCamelCase__							:				Optional[Any]          =      8
            UpperCamelCase__							:				Optional[Any]          =      8
        elif vit_name[4:].startswith('''base'''      ):
            pass
        elif vit_name[4:].startswith('''large'''      ):
            UpperCamelCase__							:				Optional[int]          =      1024
            UpperCamelCase__							:				List[str]          =      4096
            UpperCamelCase__							:				Optional[Any]          =      24
            UpperCamelCase__							:				Optional[Any]          =      16
        elif vit_name[4:].startswith('''huge'''      ):
            UpperCamelCase__							:				Optional[int]          =      1280
            UpperCamelCase__							:				Union[str, Any]          =      5120
            UpperCamelCase__							:				Optional[Any]          =      32
            UpperCamelCase__							:				str          =      16

             # load original model from timm
    UpperCamelCase__							:				Optional[Any]          =      timm.create_model(UpperCAmelCase_		,   pretrained=UpperCAmelCase_      )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    UpperCamelCase__							:				Tuple          =      timm_model.state_dict()
    if base_model:
        remove_classification_head_(UpperCAmelCase_      )
    UpperCamelCase__							:				List[str]          =      create_rename_keys(UpperCAmelCase_		,   UpperCAmelCase_      )
    for src, dest in rename_keys:
        rename_key(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_      )
    read_in_q_k_v(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_      )

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        UpperCamelCase__							:				Optional[Any]          =      ViTModel(UpperCAmelCase_      ).eval()
    else:
        UpperCamelCase__							:				str          =      ViTForImageClassification(UpperCAmelCase_      ).eval()
    model.load_state_dict(UpperCAmelCase_      )

    # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
    if "deit" in vit_name:
        UpperCamelCase__							:				Tuple          =      DeiTImageProcessor(size=config.image_size      )
    else:
        UpperCamelCase__							:				Union[str, Any]          =      ViTImageProcessor(size=config.image_size      )
    UpperCamelCase__							:				int          =      image_processor(images=prepare_img()		,   return_tensors='''pt'''      )
    UpperCamelCase__							:				Dict          =      encoding['''pixel_values''']
    UpperCamelCase__							:				Optional[Any]          =      model(UpperCAmelCase_      )

    if base_model:
        UpperCamelCase__							:				List[str]          =      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:
        UpperCamelCase__							:				Union[str, Any]          =      timm_model(UpperCAmelCase_      )
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(UpperCAmelCase_		,   outputs.logits		,   atol=1e-3      )

    Path(UpperCAmelCase_      ).mkdir(exist_ok=UpperCAmelCase_      )
    print(f"Saving model {vit_name} to {pytorch_dump_folder_path}"      )
    model.save_pretrained(UpperCAmelCase_      )
    print(f"Saving image processor to {pytorch_dump_folder_path}"      )
    image_processor.save_pretrained(UpperCAmelCase_      )


if __name__ == "__main__":
  UpperCAmelCase_     							= 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.'
  )

  UpperCAmelCase_     							= parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)





'''simple docstring'''

a_  :				str						= """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

a_  :				Any						= [{"""type""": """code""", """content""": INSTALL_CONTENT}]
a_  :				int						= {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}



