





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()
_a 						=      logging.get_logger(__name__)





def lowerCAmelCase__(__snake_case       ,__snake_case=False							)				->      str:







	'''simple docstring'''







	lowerCamelCase__								=		[]
	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"
		lowerCamelCase__								=		[(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__(__snake_case       ,__snake_case       ,__snake_case=False							)				->      Dict:







	'''simple docstring'''







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





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case							)				->      Optional[int]:







	'''simple docstring'''







	lowerCamelCase__								=		dct.pop(_lowerCAmelCase							)
	lowerCamelCase__								=		val





def lowerCAmelCase__()				->      str:







	'''simple docstring'''







	lowerCamelCase__								=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	lowerCamelCase__								=		Image.open(requests.get(_lowerCAmelCase       ,stream=_lowerCAmelCase							).raw							)
	return im





@torch.no_grad()
def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      List[Any]:







	'''simple docstring'''







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

	# load original model from timm
	lowerCamelCase__								=		timm.create_model(_lowerCAmelCase       ,pretrained=_lowerCAmelCase							)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	lowerCamelCase__								=		timm_model.state_dict()
	lowerCamelCase__								=		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
	lowerCamelCase__								=		DeiTForImageClassificationWithTeacher(_lowerCAmelCase							).eval()
	model.load_state_dict(_lowerCAmelCase							)

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

	lowerCamelCase__								=		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__":
			_a 						=      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."
			)

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








'''simple docstring'''






from __future__ import annotations


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						str  )      ->   bool:
							UpperCAmelCase :       str 							=		get_failure_array(_lowerCAmelCase  )

							# 2) Step through text searching for pattern
							UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		0, 0  # index into text, pattern
							while i < len(_lowerCAmelCase  ):
														if pattern[j] == text[i]:
																					if j == (len(_lowerCAmelCase  ) - 1):
																												return True
																					j += 1

														# if this is a prefix in our pattern
														# just go back far enough to continue
														elif j > 0:
																					UpperCAmelCase :       Optional[Any] 							=		failure[j - 1]
																					continue
														i += 1
							return False


def  snake_case_     (					_lowerCAmelCase  :						str  )      ->   list[int]:
							UpperCAmelCase :       Optional[Any] 							=		[0]
							UpperCAmelCase :       str 							=		0
							UpperCAmelCase :       List[str] 							=		1
							while j < len(_lowerCAmelCase  ):
														if pattern[i] == pattern[j]:
																					i += 1
														elif i > 0:
																					UpperCAmelCase :       Union[str, Any] 							=		failure[i - 1]
																					continue
														j += 1
														failure.append(_lowerCAmelCase  )
							return failure


if __name__ == "__main__":
						# Test 1)
						UpperCamelCase__:   str								=			"abc1abc12"
						UpperCamelCase__:   str								=			"alskfjaldsabc1abc1abc12k23adsfabcabc"
						UpperCamelCase__:   Any								=			"alskfjaldsk23adsfabcabc"
						assert kmp(pattern, texta) and not kmp(pattern, texta)

						# Test 2)
						UpperCamelCase__:   Tuple								=			"ABABX"
						UpperCamelCase__:   Union[str, Any]								=			"ABABZABABYABABX"
						assert kmp(pattern, text)

						# Test 3)
						UpperCamelCase__:   Any								=			"AAAB"
						UpperCamelCase__:   str								=			"ABAAAAAB"
						assert kmp(pattern, text)

						# Test 4)
						UpperCamelCase__:   int								=			"abcdabcy"
						UpperCamelCase__:   Any								=			"abcxabcdabxabcdabcdabcy"
						assert kmp(pattern, text)

						# Test 5)
						UpperCamelCase__:   List[str]								=			"aabaabaaa"
						assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]





