


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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase		   =			logging.get_logger(__name__)




def       a_ (       SCREAMING_SNAKE_CASE__   : Optional[Any]						,     SCREAMING_SNAKE_CASE__   : Any=False	):
       '''simple docstring'''





       _lowerCamelCase			:						Any            =[]
       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'''deit.encoder.layer.{i}.layernorm_before.weight''')	)
              rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')	)
              rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')	)
              rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')	)
              rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')	)
              rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')	)
              rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')	)
              rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')	)
              rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')	)
              rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')	)

       # projection layer + position embeddings
       rename_keys.extend(
           [
               ('cls_token', 'deit.embeddings.cls_token'),
               ('dist_token', 'deit.embeddings.distillation_token'),
               ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
               ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
               ('pos_embed', 'deit.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 "deit" from all keys that start with "deit"
              _lowerCamelCase			:						Optional[Any]            =[(pair[0], pair[1][4:]) if pair[1].startswith('deit'	) else pair for pair in rename_keys]
       else:
              # layernorm + classification heads
              rename_keys.extend(
                  [
                      ('norm.weight', 'deit.layernorm.weight'),
                      ('norm.bias', 'deit.layernorm.bias'),
                      ('head.weight', 'cls_classifier.weight'),
                      ('head.bias', 'cls_classifier.bias'),
                      ('head_dist.weight', 'distillation_classifier.weight'),
                      ('head_dist.bias', 'distillation_classifier.bias'),
                  ]	)

       return rename_keys




def       a_ (       SCREAMING_SNAKE_CASE__   : Tuple						,     SCREAMING_SNAKE_CASE__   : Union[str, Any]						,     SCREAMING_SNAKE_CASE__   : Any=False	):
       '''simple docstring'''





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




def       a_ (       SCREAMING_SNAKE_CASE__   : List[str]						,     SCREAMING_SNAKE_CASE__   : Dict						,     SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						Tuple            =dct.pop(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						Optional[int]            =val




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						Dict            ='http://images.cocodataset.org/val2017/000000039769.jpg'
       _lowerCamelCase			:						str            =Image.open(requests.get(SCREAMING_SNAKE_CASE__						,     stream=SCREAMING_SNAKE_CASE__	).raw	)
       return im




@torch.no_grad()
def       a_ (       SCREAMING_SNAKE_CASE__   : List[str]						,     SCREAMING_SNAKE_CASE__   : Union[str, Any]	):
       '''simple docstring'''





       _lowerCamelCase			:						Dict            =DeiTConfig()
       # all deit models have fine-tuned heads
       _lowerCamelCase			:						Tuple            =False
       # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
       _lowerCamelCase			:						Tuple            =1_000
       _lowerCamelCase			:						List[str]            ='huggingface/label-files'
       _lowerCamelCase			:						str            ='imagenet-1k-id2label.json'
       _lowerCamelCase			:						Tuple            =json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     repo_type='dataset'	)						,     'r'	)	)
       _lowerCamelCase			:						Dict            ={int(SCREAMING_SNAKE_CASE__	): v for k, v in idalabel.items()}
       _lowerCamelCase			:						List[Any]            =idalabel
       _lowerCamelCase			:						int            ={v: k for k, v in idalabel.items()}
       _lowerCamelCase			:						Any            =int(deit_name[-6:-4]	)
       _lowerCamelCase			:						int            =int(deit_name[-3:]	)
       # size of the architecture
       if deit_name[9:].startswith('tiny'	):
              _lowerCamelCase			:						List[str]            =192
              _lowerCamelCase			:						str            =768
              _lowerCamelCase			:						Any            =12
              _lowerCamelCase			:						List[Any]            =3
       elif deit_name[9:].startswith('small'	):
              _lowerCamelCase			:						List[Any]            =384
              _lowerCamelCase			:						List[Any]            =1_536
              _lowerCamelCase			:						str            =12
              _lowerCamelCase			:						int            =6
       if deit_name[9:].startswith('base'	):
              pass
       elif deit_name[4:].startswith('large'	):
              _lowerCamelCase			:						Optional[Any]            =1_024
              _lowerCamelCase			:						Optional[int]            =4_096
              _lowerCamelCase			:						Any            =24
              _lowerCamelCase			:						List[str]            =16

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

       # load state_dict of original model, remove and rename some keys
       _lowerCamelCase			:						Tuple            =timm_model.state_dict()
       _lowerCamelCase			:						Optional[int]            =create_rename_keys(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
       for src, dest in rename_keys:
              rename_key(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
       read_in_q_k_v(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)

       # load HuggingFace model
       _lowerCamelCase			:						Optional[int]            =DeiTForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE__	).eval()
       model.load_state_dict(SCREAMING_SNAKE_CASE__	)

       # Check outputs on an image, prepared by DeiTImageProcessor
       _lowerCamelCase			:						Union[str, Any]            =int(
           (256 / 224) * config.image_size	)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
       _lowerCamelCase			:						List[Any]            =DeiTImageProcessor(size=SCREAMING_SNAKE_CASE__						,     crop_size=config.image_size	)
       _lowerCamelCase			:						Union[str, Any]            =image_processor(images=prepare_img()						,     return_tensors='pt'	)
       _lowerCamelCase			:						Any            =encoding['pixel_values']
       _lowerCamelCase			:						Optional[int]            =model(SCREAMING_SNAKE_CASE__	)

       _lowerCamelCase			:						Any            =timm_model(SCREAMING_SNAKE_CASE__	)
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(SCREAMING_SNAKE_CASE__						,     outputs.logits						,     atol=1e-3	)

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


if __name__ == "__main__":
 lowerCamelCase		   =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--deit_name',
     default='vit_deit_base_distilled_patch16_224',
     type=str,
     help='Name of the DeiT 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.'
 )

 lowerCamelCase		   =			parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






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


lowerCamelCase		   =			logging.get_logger(__name__)

lowerCamelCase		   =			{
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    Union[str, Any] ='vit_msn'






      def __init__(   self		:	Union[str, Any]     ,  lowercase_		:	List[str]=768     ,  lowercase_		:	Optional[int]=12     ,  lowercase_		:	List[str]=12     ,  lowercase_		:	List[Any]=3072     ,  lowercase_		:	int="gelu"     ,  lowercase_		:	List[Any]=0.0     ,  lowercase_		:	Optional[int]=0.0     ,  lowercase_		:	Dict=0.02     ,  lowercase_		:	Any=1E-06     ,  lowercase_		:	Union[str, Any]=224     ,  lowercase_		:	Optional[int]=16     ,  lowercase_		:	List[Any]=3     ,  lowercase_		:	Any=True     ,  **lowercase_		:	Tuple     ,  )   ->		Dict:
             """simple docstring"""




             super().__init__(**lowercase_					)

             _lowerCamelCase			:						Dict            =hidden_size
             _lowerCamelCase			:						Any            =num_hidden_layers
             _lowerCamelCase			:						Union[str, Any]            =num_attention_heads
             _lowerCamelCase			:						List[Any]            =intermediate_size
             _lowerCamelCase			:						Tuple            =hidden_act
             _lowerCamelCase			:						Any            =hidden_dropout_prob
             _lowerCamelCase			:						Dict            =attention_probs_dropout_prob
             _lowerCamelCase			:						Any            =initializer_range
             _lowerCamelCase			:						List[Any]            =layer_norm_eps
             _lowerCamelCase			:						Optional[int]            =image_size
             _lowerCamelCase			:						Optional[int]            =patch_size
             _lowerCamelCase			:						List[Any]            =num_channels
             _lowerCamelCase			:						Optional[Any]            =qkv_bias


