





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()
_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)


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


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	snake_case_		:			Any											=	dct.pop(__a   )
	snake_case_		:			Optional[int]											=	val


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


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

	# load original model from timm
	snake_case_		:			Any											=	timm.create_model(__a   , pretrained=__a   )
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	snake_case_		:			str											=	timm_model.state_dict()
	snake_case_		:			List[str]											=	create_rename_keys(__a   , __a   )
	for src, dest in rename_keys:
		rename_key(__a   , __a   , __a   )
	read_in_q_k_v(__a   , __a   , __a   )

	# load HuggingFace model
	snake_case_		:			Union[str, Any]											=	DeiTForImageClassificationWithTeacher(__a   ).eval()
	model.load_state_dict(__a   )

	# Check outputs on an image, prepared by DeiTImageProcessor
	snake_case_		:			List[str]											=	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
	snake_case_		:			Any											=	DeiTImageProcessor(size=__a   , crop_size=config.image_size   )
	snake_case_		:			List[Any]											=	image_processor(images=prepare_img()   , return_tensors='pt'   )
	snake_case_		:			Union[str, Any]											=	encoding['pixel_values']
	snake_case_		:			str											=	model(__a   )

	snake_case_		:			int											=	timm_model(__a   )
	assert timm_logits.shape == outputs.logits.shape
	assert torch.allclose(__a   , outputs.logits   , atol=1E-3   )

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


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		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."""
						)

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








def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	if not isinstance(__a   , __a   ):
		raise ValueError('Input must be an integer'   )
	if input_num <= 0:
		raise ValueError('Input must be positive'   )
	return sum(
	    divisor for divisor in range(1   , input_num // 2 + 1   ) if input_num % divisor == 0   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

