


'''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()
lowerCAmelCase				:str        =      logging.get_logger(__name__)





def lowerCamelCase      (      lowerCAmelCase    :     Any		,		lowerCAmelCase    :     int=False		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					[]
	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"
		__magic_name__	:   List[str]					     =					[(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      (      lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     List[str]=False		):





	"""simple docstring"""







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





def lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		):





	"""simple docstring"""







	__magic_name__	:   Any					     =					['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(lowerCAmelCase		,		lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     int		,		lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	__magic_name__	:   int					     =					dct.pop(lowerCAmelCase		)
	__magic_name__	:   Optional[Any]					     =					val





def lowerCamelCase      (      ):





	"""simple docstring"""







	__magic_name__	:   List[Any]					     =					'http://images.cocodataset.org/val2017/000000039769.jpg'
	__magic_name__	:   Dict					     =					Image.open(requests.get(lowerCAmelCase		,		stream=lowerCAmelCase		).raw		)
	return im





@torch.no_grad()
def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Optional[Any]		):





	"""simple docstring"""







	__magic_name__	:   Union[str, Any]					     =					ViTConfig()
	__magic_name__	:   Dict					     =					False
	# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
	if vit_name[-5:] == "in21k":
		__magic_name__	:   Optional[int]					     =					True
		__magic_name__	:   Tuple					     =					int(vit_name[-12:-10]		)
		__magic_name__	:   Tuple					     =					int(vit_name[-9:-6]		)
	else:
		__magic_name__	:   Optional[Any]					     =					1000
		__magic_name__	:   List[Any]					     =					'huggingface/label-files'
		__magic_name__	:   str					     =					'imagenet-1k-id2label.json'
		__magic_name__	:   Optional[Any]					     =					json.load(open(hf_hub_download(lowerCAmelCase		,		lowerCAmelCase		,		repo_type='dataset'		)		,		'r'		)		)
		__magic_name__	:   int					     =					{int(lowerCAmelCase		): v for k, v in idalabel.items()}
		__magic_name__	:   Optional[Any]					     =					idalabel
		__magic_name__	:   List[str]					     =					{v: k for k, v in idalabel.items()}
		__magic_name__	:   int					     =					int(vit_name[-6:-4]		)
		__magic_name__	:   Tuple					     =					int(vit_name[-3:]		)
	# size of the architecture
	if "deit" in vit_name:
		if vit_name[9:].startswith('tiny'		):
			__magic_name__	:   List[Any]					     =					192
			__magic_name__	:   str					     =					768
			__magic_name__	:   Any					     =					12
			__magic_name__	:   Optional[int]					     =					3
		elif vit_name[9:].startswith('small'		):
			__magic_name__	:   Any					     =					384
			__magic_name__	:   int					     =					1536
			__magic_name__	:   Tuple					     =					12
			__magic_name__	:   Union[str, Any]					     =					6
		else:
			pass
	else:
		if vit_name[4:].startswith('small'		):
			__magic_name__	:   Tuple					     =					768
			__magic_name__	:   Union[str, Any]					     =					2304
			__magic_name__	:   List[Any]					     =					8
			__magic_name__	:   Optional[int]					     =					8
		elif vit_name[4:].startswith('base'		):
			pass
		elif vit_name[4:].startswith('large'		):
			__magic_name__	:   str					     =					1024
			__magic_name__	:   Optional[Any]					     =					4096
			__magic_name__	:   Tuple					     =					24
			__magic_name__	:   str					     =					16
		elif vit_name[4:].startswith('huge'		):
			__magic_name__	:   int					     =					1280
			__magic_name__	:   int					     =					5120
			__magic_name__	:   Optional[int]					     =					32
			__magic_name__	:   Tuple					     =					16

    # load original model from timm
	__magic_name__	:   Dict					     =					timm.create_model(lowerCAmelCase		,		pretrained=lowerCAmelCase		)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	__magic_name__	:   int					     =					timm_model.state_dict()
	if base_model:
		remove_classification_head_(lowerCAmelCase		)
	__magic_name__	:   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
	if vit_name[-5:] == "in21k":
		__magic_name__	:   List[str]					     =					ViTModel(lowerCAmelCase		).eval()
	else:
		__magic_name__	:   Union[str, Any]					     =					ViTForImageClassification(lowerCAmelCase		).eval()
	model.load_state_dict(lowerCAmelCase		)

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

	if base_model:
		__magic_name__	:   str					     =					timm_model.forward_features(lowerCAmelCase		)
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(lowerCAmelCase		,		outputs.pooler_output		,		atol=1e-3		)
	else:
		__magic_name__	:   Union[str, Any]					     =					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 {vit_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__":
							lowerCAmelCase				: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.'''
							)

							lowerCAmelCase				:Union[str, Any]        =      parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	if not isinstance(lowerCAmelCase		,		lowerCAmelCase		):
		__magic_name__	:   int					     =					f'Input value of [number={number}] must be an integer'
		raise TypeError(lowerCAmelCase		)
	if number < 0:
		return False
	__magic_name__	:   Tuple					     =					number * number
	while number > 0:
		if number % 10 != number_square % 10:
			return False
		number //= 10
		number_square //= 10
	return True


if __name__ == "__main__":
							import doctest

							doctest.testmod()