


"""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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__				:List[str]			   =	logging.get_logger(__name__)





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





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Optional[Any]:
			_UpperCamelCase					:Dict					     =	dct.pop(lowerCAmelCase_		)
			_UpperCamelCase					:Optional[Any]					     =	val





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





@torch.no_grad()
def 				A_       ( snake_case__       ,					snake_case__		)					->  Dict:
			_UpperCamelCase					:Tuple					     =	DeiTConfig()
			# all deit models have fine-tuned heads
			_UpperCamelCase					:Optional[Any]					     =	False
			# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
			_UpperCamelCase					:Optional[Any]					     =	10_00
			_UpperCamelCase					:List[Any]					     =	'''huggingface/label-files'''
			_UpperCamelCase					:Optional[int]					     =	'''imagenet-1k-id2label.json'''
			_UpperCamelCase					:Optional[Any]					     =	json.load(open(hf_hub_download(lowerCAmelCase_       ,					lowerCAmelCase_       ,					repo_type='''dataset'''		)       ,					'''r'''		)		)
			_UpperCamelCase					:int					     =	{int(lowerCAmelCase_		): v for k, v in idalabel.items()}
			_UpperCamelCase					:List[str]					     =	idalabel
			_UpperCamelCase					:List[str]					     =	{v: k for k, v in idalabel.items()}
			_UpperCamelCase					:Union[str, Any]					     =	int(deit_name[-6:-4]		)
			_UpperCamelCase					:str					     =	int(deit_name[-3:]		)
			# size of the architecture
			if deit_name[9:].startswith('''tiny'''		):
						_UpperCamelCase					:Optional[int]					     =	1_92
						_UpperCamelCase					:Any					     =	7_68
						_UpperCamelCase					:List[Any]					     =	12
						_UpperCamelCase					:int					     =	3
			elif deit_name[9:].startswith('''small'''		):
						_UpperCamelCase					:Any					     =	3_84
						_UpperCamelCase					:Any					     =	15_36
						_UpperCamelCase					:Optional[int]					     =	12
						_UpperCamelCase					:Tuple					     =	6
			if deit_name[9:].startswith('''base'''		):
						pass
			elif deit_name[4:].startswith('''large'''		):
						_UpperCamelCase					:str					     =	10_24
						_UpperCamelCase					:Union[str, Any]					     =	40_96
						_UpperCamelCase					:Optional[Any]					     =	24
						_UpperCamelCase					:Any					     =	16

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

			# load state_dict of original model, remove and rename some keys
			_UpperCamelCase					:List[str]					     =	timm_model.state_dict()
			_UpperCamelCase					:Tuple					     =	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
			_UpperCamelCase					:str					     =	DeiTForImageClassificationWithTeacher(lowerCAmelCase_		).eval()
			model.load_state_dict(lowerCAmelCase_		)

			# Check outputs on an image, prepared by DeiTImageProcessor
			_UpperCamelCase					:Dict					     =	int(
			    (2_56 / 2_24) * config.image_size		)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
			_UpperCamelCase					:Union[str, Any]					     =	DeiTImageProcessor(size=lowerCAmelCase_       ,					crop_size=config.image_size		)
			_UpperCamelCase					:List[Any]					     =	image_processor(images=prepare_img()       ,					return_tensors='''pt'''		)
			_UpperCamelCase					:Any					     =	encoding['''pixel_values''']
			_UpperCamelCase					:str					     =	model(lowerCAmelCase_		)

			_UpperCamelCase					:Tuple					     =	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 {deit_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__":
					UpperCamelCase__				:List[str]			   =	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."""
					)

					UpperCamelCase__				:Optional[int]			   =	parser.parse_args()
					convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






"""simple docstring"""






from abc import ABC, abstractmethod
from argparse import ArgumentParser



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   @staticmethod
   @abstractmethod
   def    __UpperCAmelCase    (       __lowerCamelCase    :	ArgumentParser				):



     """simple docstring"""
     raise NotImplementedError()





   @abstractmethod
   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     raise NotImplementedError()





