



"""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     =							logging.get_logger(__name__)

def       _lowerCamelCase				(							UpperCAmelCase_ :       List[str],	UpperCAmelCase_ :       Union[str, Any]=False     )	-> str:






	"""simple docstring"""





	A__											=					[]
	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"
		A__											=					[(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       _lowerCamelCase				(							UpperCAmelCase_ :       Optional[int],	UpperCAmelCase_ :       Dict,	UpperCAmelCase_ :       Tuple=False     )	-> List[str]:






	"""simple docstring"""





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

def       _lowerCamelCase				(							UpperCAmelCase_ :       Union[str, Any],	UpperCAmelCase_ :       Optional[Any],	UpperCAmelCase_ :       int     )	-> Optional[int]:






	"""simple docstring"""





	A__											=					dct.pop(UpperCAmelCase_     )
	A__											=					val

def       _lowerCamelCase				(							)	-> List[str]:






	"""simple docstring"""





	A__											=					"http://images.cocodataset.org/val2017/000000039769.jpg"
	A__											=					Image.open(requests.get(UpperCAmelCase_,	stream=UpperCAmelCase_     ).raw     )
	return im

@torch.no_grad()
def       _lowerCamelCase				(							UpperCAmelCase_ :       Dict,	UpperCAmelCase_ :       Union[str, Any]     )	-> Optional[Any]:






	"""simple docstring"""





	A__											=					DeiTConfig()
	# all deit models have fine-tuned heads
	A__											=					False
	# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
	A__											=					1000
	A__											=					"huggingface/label-files"
	A__											=					"imagenet-1k-id2label.json"
	A__											=					json.load(open(hf_hub_download(UpperCAmelCase_,	UpperCAmelCase_,	repo_type="dataset"     ),	"r"     )     )
	A__											=					{int(UpperCAmelCase_     ): v for k, v in idalabel.items()}
	A__											=					idalabel
	A__											=					{v: k for k, v in idalabel.items()}
	A__											=					int(deit_name[-6:-4]     )
	A__											=					int(deit_name[-3:]     )
	# size of the architecture
	if deit_name[9:].startswith("tiny"     ):
		A__											=					192
		A__											=					768
		A__											=					12
		A__											=					3
	elif deit_name[9:].startswith("small"     ):
		A__											=					384
		A__											=					1536
		A__											=					12
		A__											=					6
	if deit_name[9:].startswith("base"     ):
		pass
	elif deit_name[4:].startswith("large"     ):
		A__											=					1024
		A__											=					4096
		A__											=					24
		A__											=					16

	# load original model from timm
	A__											=					timm.create_model(UpperCAmelCase_,	pretrained=UpperCAmelCase_     )
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	A__											=					timm_model.state_dict()
	A__											=					create_rename_keys(UpperCAmelCase_,	UpperCAmelCase_     )
	for src, dest in rename_keys:
		rename_key(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )
	read_in_q_k_v(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )

	# load HuggingFace model
	A__											=					DeiTForImageClassificationWithTeacher(UpperCAmelCase_     ).eval()
	model.load_state_dict(UpperCAmelCase_     )

	# Check outputs on an image, prepared by DeiTImageProcessor
	A__											=					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
	A__											=					DeiTImageProcessor(size=UpperCAmelCase_,	crop_size=config.image_size     )
	A__											=					image_processor(images=prepare_img(),	return_tensors="pt"     )
	A__											=					encoding["pixel_values"]
	A__											=					model(UpperCAmelCase_     )

	A__											=					timm_model(UpperCAmelCase_     )
	assert timm_logits.shape == outputs.logits.shape
	assert torch.allclose(UpperCAmelCase_,	outputs.logits,	atol=1e-3     )

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


if __name__ == "__main__":
			UpperCamelCase     =							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     =							parser.parse_args()
			convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






from functools import lru_cache

@lru_cache
def  __lowerCamelCase					( _lowercase			)       ->      int:
     if num < 0:
          raise ValueError('Number should not be negative.'			)

     return 1 if num in (0, 1) else num * factorial(num - 1			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


