



'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_A				:    Tuple     						=logging.get_logger(__name__)







def 						__UpperCamelCase				(							_lowercase					) ->			int:
	_lowercase					:       Optional[Any]       =		'huggingface/label-files'
	_lowercase					:       Tuple       =		'imagenet-1k-id2label.json'
	_lowercase					:       int       =		json.load(open(hf_hub_download(_lowercase,				_lowercase,				repo_type='dataset'					),				'r'					)					)
	_lowercase					:       Dict       =		{int(_lowercase					): v for k, v in idalabel.items()}
	_lowercase					:       int       =		{v: k for k, v in idalabel.items()}

	_lowercase					:       str       =		'std_conv' if 'bit' in model_name else False

	# note that when using BiT as backbone for ViT-hybrid checkpoints,
	# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
	# config.conv_layer = "std_conv_same"
	_lowercase					:       Dict       =		BitConfig(
	    conv_layer=_lowercase,				num_labels=1000,				idalabel=_lowercase,				labelaid=_lowercase,				)

	return config







def 						__UpperCamelCase				(							_lowercase					) ->			Tuple:
	if "stem.conv" in name:
		_lowercase					:       Optional[Any]       =		name.replace('stem.conv',				'bit.embedder.convolution'					)
	if "blocks" in name:
		_lowercase					:       int       =		name.replace('blocks',				'layers'					)
	if "head.fc" in name:
		_lowercase					:       Dict       =		name.replace('head.fc',				'classifier.1'					)
	if name.startswith('norm'					):
		_lowercase					:       Union[str, Any]       =		'bit.' + name
	if "bit" not in name and "classifier" not in name:
		_lowercase					:       int       =		'bit.encoder.' + name

	return name







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







@torch.no_grad()
def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase=False					) ->			List[Any]:
	_lowercase					:       str       =		get_config(_lowercase					)

	# load original model from timm
	_lowercase					:       List[str]       =		create_model(_lowercase,				pretrained=_lowercase					)
	timm_model.eval()

	# load state_dict of original model
	_lowercase					:       Union[str, Any]       =		timm_model.state_dict()
	for key in state_dict.copy().keys():
		_lowercase					:       Union[str, Any]       =		state_dict.pop(_lowercase					)
		_lowercase					:       Union[str, Any]       =		val.squeeze() if 'head' in key else val

	# load HuggingFace model
	_lowercase					:       Any       =		BitForImageClassification(_lowercase					)
	model.eval()
	model.load_state_dict(_lowercase					)

	# create image processor
	_lowercase					:       int       =		create_transform(**resolve_data_config({},				model=_lowercase					)					)
	_lowercase					:       Optional[Any]       =		transform.transforms

	_lowercase					:       Optional[int]       =		{
	    'bilinear': PILImageResampling.BILINEAR,
	    'bicubic': PILImageResampling.BICUBIC,
	    'nearest': PILImageResampling.NEAREST,
	}

	_lowercase					:       Union[str, Any]       =		BitImageProcessor(
	    do_resize=_lowercase,				size={'shortest_edge': timm_transforms[0].size},				resample=pillow_resamplings[timm_transforms[0].interpolation.value],				do_center_crop=_lowercase,				crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]},				do_normalize=_lowercase,				image_mean=timm_transforms[-1].mean.tolist(),				image_std=timm_transforms[-1].std.tolist(),				)

	_lowercase					:       Any       =		prepare_img()
	_lowercase					:       str       =		transform(_lowercase					).unsqueeze(0					)
	_lowercase					:       Union[str, Any]       =		processor(_lowercase,				return_tensors='pt'					).pixel_values

	# verify pixel values
	assert torch.allclose(_lowercase,				_lowercase					)

	# verify logits
	with torch.no_grad():
		_lowercase					:       List[Any]       =		model(_lowercase					)
		_lowercase					:       Union[str, Any]       =		outputs.logits

	print('Logits:',				logits[0, :3]					)
	print('Predicted class:',				model.config.idalabel[logits.argmax(-1					).item()]					)
	_lowercase					:       str       =		timm_model(_lowercase					)
	assert timm_logits.shape == outputs.logits.shape
	assert torch.allclose(_lowercase,				outputs.logits,				atol=1E-3					)
	print('Looks ok!'					)

	if pytorch_dump_folder_path is not None:
		Path(_lowercase					).mkdir(exist_ok=_lowercase					)
		print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''					)
		model.save_pretrained(_lowercase					)
		processor.save_pretrained(_lowercase					)

	if push_to_hub:
		print(f'''Pushing model {model_name} and processor to the hub'''					)
		model.push_to_hub(f'''ybelkada/{model_name}'''					)
		processor.push_to_hub(f'''ybelkada/{model_name}'''					)


if __name__ == "__main__":
						_A				:    Any     						=argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--model_name''',
						    default='''resnetv2_50x1_bitm''',
						    type=str,
						    help='''Name of the BiT 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.'''
						)
						parser.add_argument(
						    '''--push_to_hub''',
						    action='''store_true''',
						    help='''Whether to push the model to the hub.''',
						)

						_A				:    str     						=parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''

_A				:    Dict     						='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

_A				:    Dict     						=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
_A				:    Dict     						={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}
