





'''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()
lowercase__       : Optional[int]         =       logging.get_logger(__name__)






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






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	List[Any]:
     __A		:					Optional[int]						=			['head.weight', 'head.bias']
     for k in ignore_keys:
          state_dict.pop(__snake_case					,				__snake_case     )






def 			_lowerCAmelCase     (    __snake_case    :					Optional[int]					,				__snake_case    :					Optional[Any]					,				__snake_case    :					Union[str, Any]     )	->	Dict:
     __A		:					int						=			dct.pop(__snake_case     )
     __A		:					Optional[Any]						=			val






def 			_lowerCAmelCase     (    )	->	str:
     __A		:					Optional[Any]						=			'http://images.cocodataset.org/val2017/000000039769.jpg'
     __A		:					List[Any]						=			Image.open(requests.get(__snake_case					,				stream=__snake_case     ).raw     )
     return im






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					List[Any]					,				__snake_case    :					Tuple     )	->	List[str]:
     __A		:					str						=			ViTConfig()
     __A		:					Optional[int]						=			False
     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     if vit_name[-5:] == "in21k":
          __A		:					Tuple						=			True
          __A		:					Union[str, Any]						=			int(vit_name[-12:-10]     )
          __A		:					Tuple						=			int(vit_name[-9:-6]     )
     else:
          __A		:					Optional[int]						=			10_00
          __A		:					Tuple						=			'huggingface/label-files'
          __A		:					Dict						=			'imagenet-1k-id2label.json'
          __A		:					Tuple						=			json.load(open(hf_hub_download(__snake_case					,				__snake_case					,				repo_type='dataset'     )					,				'r'     )     )
          __A		:					List[str]						=			{int(__snake_case     ): v for k, v in idalabel.items()}
          __A		:					str						=			idalabel
          __A		:					str						=			{v: k for k, v in idalabel.items()}
          __A		:					List[Any]						=			int(vit_name[-6:-4]     )
          __A		:					Any						=			int(vit_name[-3:]     )
     # size of the architecture
     if "deit" in vit_name:
          if vit_name[9:].startswith('tiny'     ):
               __A		:					Any						=			1_92
               __A		:					Tuple						=			7_68
               __A		:					Any						=			12
               __A		:					Dict						=			3
          elif vit_name[9:].startswith('small'     ):
               __A		:					Any						=			3_84
               __A		:					List[Any]						=			15_36
               __A		:					int						=			12
               __A		:					Union[str, Any]						=			6
          else:
               pass
     else:
          if vit_name[4:].startswith('small'     ):
               __A		:					str						=			7_68
               __A		:					List[str]						=			23_04
               __A		:					Any						=			8
               __A		:					str						=			8
          elif vit_name[4:].startswith('base'     ):
               pass
          elif vit_name[4:].startswith('large'     ):
               __A		:					Any						=			10_24
               __A		:					List[Any]						=			40_96
               __A		:					Optional[int]						=			24
               __A		:					str						=			16
          elif vit_name[4:].startswith('huge'     ):
               __A		:					str						=			12_80
               __A		:					Any						=			51_20
               __A		:					Union[str, Any]						=			32
               __A		:					List[Any]						=			16

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

     # load state_dict of original model, remove and rename some keys
     __A		:					str						=			timm_model.state_dict()
     if base_model:
          remove_classification_head_(__snake_case     )
     __A		:					Tuple						=			create_rename_keys(__snake_case					,				__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 vit_name[-5:] == "in21k":
          __A		:					str						=			ViTModel(__snake_case     ).eval()
     else:
          __A		:					Optional[int]						=			ViTForImageClassification(__snake_case     ).eval()
     model.load_state_dict(__snake_case     )

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

     if base_model:
          __A		:					str						=			timm_model.forward_features(__snake_case     )
          assert timm_pooled_output.shape == outputs.pooler_output.shape
          assert torch.allclose(__snake_case					,				outputs.pooler_output					,				atol=1e-3     )
     else:
          __A		:					int						=			timm_model(__snake_case     )
          assert timm_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 {vit_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__":
  lowercase__       : Optional[int]         =       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.'''
  )

  lowercase__       : Tuple         =       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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "shi-labs/dinat-mini-in1k-224": "https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}







class        UpperCAmelCase_			(			_a						,						_a      ):
    """simple docstring"""







    lowercase   =						"dinat"

    lowercase   =						{
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }




    def __init__(       self   :	Optional[int]					,	snake_case_   :	Optional[Any]=4					,	snake_case_   :	List[str]=3					,	snake_case_   :	Tuple=64					,	snake_case_   :	List[Any]=[3, 4, 6, 5]					,	snake_case_   :	List[Any]=[2, 4, 8, 16]					,	snake_case_   :	List[str]=7					,	snake_case_   :	List[str]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]					,	snake_case_   :	List[Any]=3.0					,	snake_case_   :	Dict=True					,	snake_case_   :	int=0.0					,	snake_case_   :	Optional[int]=0.0					,	snake_case_   :	str=0.1					,	snake_case_   :	Optional[Any]="gelu"					,	snake_case_   :	Union[str, Any]=0.02					,	snake_case_   :	Optional[int]=1E-5					,	snake_case_   :	str=0.0					,	snake_case_   :	Dict=None					,	snake_case_   :	Dict=None					,	**snake_case_   :	Dict					,	):
          super().__init__(**snake_case_		)

          snake_case__    :       Optional[Any]       		= patch_size
          snake_case__    :       Union[str, Any]       		= num_channels
          snake_case__    :       int       		= embed_dim
          snake_case__    :       Dict       		= depths
          snake_case__    :       Dict       		= len(snake_case_		)
          snake_case__    :       Optional[int]       		= num_heads
          snake_case__    :       Any       		= kernel_size
          snake_case__    :       str       		= dilations
          snake_case__    :       Any       		= mlp_ratio
          snake_case__    :       int       		= qkv_bias
          snake_case__    :       int       		= hidden_dropout_prob
          snake_case__    :       int       		= attention_probs_dropout_prob
          snake_case__    :       Optional[Any]       		= drop_path_rate
          snake_case__    :       int       		= hidden_act
          snake_case__    :       Tuple       		= layer_norm_eps
          snake_case__    :       Any       		= initializer_range
          # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          snake_case__    :       Optional[Any]       		= int(embed_dim * 2 ** (len(snake_case_		) - 1)		)
          snake_case__    :       int       		= layer_scale_init_value
          snake_case__    :       Dict       		= ["""stem"""] + [f"stage{idx}" for idx in range(1					,	len(snake_case_		) + 1		)]
          snake_case__		,							snake_case__    :       Tuple       		= get_aligned_output_features_output_indices(
              out_features=snake_case_					,	out_indices=snake_case_					,	stage_names=self.stage_names		)
