



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

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


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



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		str ,				SCREAMING_SNAKE_CASE__				:		Union[str, Any]=False )     ->   Dict:
 _snake_case			:     Union[str, 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'''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"""),
      ] )

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  _snake_case			:     Dict 		=    [(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 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Optional[int] ,				SCREAMING_SNAKE_CASE__				:		Tuple ,				SCREAMING_SNAKE_CASE__				:		List[Any]=False )     ->   Optional[Any]:
 for i in range(config.num_hidden_layers ):
  if base_model:
   _snake_case			:     Optional[int] 		=    ''
  else:
   _snake_case			:     str 		=    'vit.'
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  _snake_case			:     Union[str, Any] 		=    state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
  _snake_case			:     int 		=    state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
  # next, add query, keys and values (in that order) to the state dict
  _snake_case			:     Dict 		=    in_proj_weight[
      : config.hidden_size, :
  ]
  _snake_case			:     str 		=    in_proj_bias[: config.hidden_size]
  _snake_case			:     int 		=    in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  _snake_case			:     int 		=    in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  _snake_case			:     Tuple 		=    in_proj_weight[
      -config.hidden_size :, :
  ]
  _snake_case			:     Optional[int] 		=    in_proj_bias[-config.hidden_size :]



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Union[str, Any] )     ->   Optional[Any]:
 _snake_case			:     Dict 		=    ['head.weight', 'head.bias']
 for k in ignore_keys:
  state_dict.pop(__snake_case ,				__snake_case )



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Optional[int] ,				SCREAMING_SNAKE_CASE__				:		List[str] ,				SCREAMING_SNAKE_CASE__				:		Tuple )     ->   str:
 _snake_case			:     Dict 		=    dct.pop(__snake_case )
 _snake_case			:     Any 		=    val



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



@torch.no_grad()
def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Any ,				SCREAMING_SNAKE_CASE__				:		int ,				SCREAMING_SNAKE_CASE__				:		Optional[Any]=True )     ->   List[str]:
 _snake_case			:     List[str] 		=    ViTConfig()
 # patch_size
 if model_name[-1] == "8":
  _snake_case			:     Optional[int] 		=    8
 # set labels if required
 if not base_model:
  _snake_case			:     Optional[Any] 		=    1_000
  _snake_case			:     Union[str, Any] 		=    'huggingface/label-files'
  _snake_case			:     Union[str, Any] 		=    'imagenet-1k-id2label.json'
  _snake_case			:     List[Any] 		=    json.load(open(hf_hub_download(__snake_case ,				__snake_case ,				repo_type="""dataset""" ) ,				"""r""" ) )
  _snake_case			:     int 		=    {int(__snake_case ): v for k, v in idalabel.items()}
  _snake_case			:     Optional[int] 		=    idalabel
  _snake_case			:     Dict 		=    {v: k for k, v in idalabel.items()}
 # size of the architecture
 if model_name in ["dino_vits8", "dino_vits16"]:
  _snake_case			:     List[Any] 		=    384
  _snake_case			:     str 		=    1_536
  _snake_case			:     Optional[Any] 		=    12
  _snake_case			:     List[str] 		=    6

 # load original model from torch hub
 _snake_case			:     List[Any] 		=    torch.hub.load("""facebookresearch/dino:main""" ,				__snake_case )
 original_model.eval()

 # load state_dict of original model, remove and rename some keys
 _snake_case			:     Tuple 		=    original_model.state_dict()
 if base_model:
  remove_classification_head_(__snake_case )
 _snake_case			:     Any 		=    create_rename_keys(__snake_case ,				base_model=__snake_case )
 for src, dest in rename_keys:
  rename_key(__snake_case ,				__snake_case ,				__snake_case )
 read_in_q_k_v(__snake_case ,				__snake_case ,				__snake_case )

 # load HuggingFace model
 if base_model:
  _snake_case			:     Optional[int] 		=    ViTModel(__snake_case ,				add_pooling_layer=__snake_case ).eval()
 else:
  _snake_case			:     Any 		=    ViTForImageClassification(__snake_case ).eval()
 model.load_state_dict(__snake_case )

 # Check outputs on an image, prepared by ViTImageProcessor
 _snake_case			:     int 		=    ViTImageProcessor()
 _snake_case			:     str 		=    image_processor(images=prepare_img() ,				return_tensors="""pt""" )
 _snake_case			:     Any 		=    encoding['pixel_values']
 _snake_case			:     Dict 		=    model(__snake_case )

 if base_model:
  _snake_case			:     str 		=    original_model(__snake_case )
  assert torch.allclose(__snake_case ,				outputs.last_hidden_state[:, 0, :] ,				atol=1e-1 )
 else:
  _snake_case			:     Dict 		=    original_model(__snake_case )
  assert logits.shape == outputs.logits.shape
  assert torch.allclose(__snake_case ,				outputs.logits ,				atol=1e-3 )

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


if __name__ == "__main__":
  a__			=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""dino_vitb16""",
      type=str,
      help="""Name of the model trained with DINO 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(
      """--base_model""",
      action="""store_true""",
      help="""Whether to only convert the base model (no projection head weights).""",
  )

  parser.set_defaults(base_model=True)
  a__			=		parser.parse_args()
  convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)







'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__    :      int						=    logging.get_logger(__name__)

lowercase__    :      int						=    {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''roberta-prelayernorm'''


     def __init__(						self						,	_UpperCAmelCase=5_0265						,	_UpperCAmelCase=768						,	_UpperCAmelCase=12						,	_UpperCAmelCase=12						,	_UpperCAmelCase=3072						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1e-1_2						,	_UpperCAmelCase=1						,	_UpperCAmelCase=0						,	_UpperCAmelCase=2						,	_UpperCAmelCase="absolute"						,	_UpperCAmelCase=True						,	_UpperCAmelCase=None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,	bos_token_id=_UpperCAmelCase						,	eos_token_id=_UpperCAmelCase						,	**_UpperCAmelCase)

          __A						: Optional[int]      =       vocab_size
          __A						: List[Any]      =       hidden_size
          __A						: Optional[int]      =       num_hidden_layers
          __A						: Optional[Any]      =       num_attention_heads
          __A						: List[str]      =       hidden_act
          __A						: Dict      =       intermediate_size
          __A						: Optional[int]      =       hidden_dropout_prob
          __A						: Optional[Any]      =       attention_probs_dropout_prob
          __A						: Tuple      =       max_position_embeddings
          __A						: Union[str, Any]      =       type_vocab_size
          __A						: Any      =       initializer_range
          __A						: str      =       layer_norm_eps
          __A						: int      =       position_embedding_type
          __A						: Optional[Any]      =       use_cache
          __A						: Any      =       classifier_dropout




class  SCREAMING_SNAKE_CASE    (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						: Dict      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						: Union[str, Any]      =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])