






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()
UpperCAmelCase_ :						str      =      logging.get_logger(__name__)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]					)		->			List[str]:






	"""simple docstring"""



	UpperCamelCase     :Tuple          =	"""huggingface/label-files"""
	UpperCamelCase     :Optional[Any]          =	"""imagenet-1k-id2label.json"""
	UpperCamelCase     :Optional[int]          =	json.load(open(hf_hub_download(lowerCAmelCase__	,			lowerCAmelCase__	,			repo_type="""dataset"""					)	,			"""r"""					)					)
	UpperCamelCase     :str          =	{int(lowerCAmelCase__					): v for k, v in idalabel.items()}
	UpperCamelCase     :Any          =	{v: k for k, v in idalabel.items()}

	UpperCamelCase     :List[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"
	UpperCamelCase     :List[str]          =	BitConfig(
	    conv_layer=lowerCAmelCase__	,			num_labels=1000	,			idalabel=lowerCAmelCase__	,			labelaid=lowerCAmelCase__	,			)

	return config

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any					)		->			Optional[Any]:






	"""simple docstring"""



	if "stem.conv" in name:
		UpperCamelCase     :Union[str, Any]          =	name.replace("""stem.conv"""	,			"""bit.embedder.convolution"""					)
	if "blocks" in name:
		UpperCamelCase     :List[str]          =	name.replace("""blocks"""	,			"""layers"""					)
	if "head.fc" in name:
		UpperCamelCase     :Tuple          =	name.replace("""head.fc"""	,			"""classifier.1"""					)
	if name.startswith("""norm"""					):
		UpperCamelCase     :List[str]          =	"""bit.""" + name
	if "bit" not in name and "classifier" not in name:
		UpperCamelCase     :str          =	"""bit.encoder.""" + name

	return name

def    SCREAMING_SNAKE_CASE_							(							)		->			Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	UpperCamelCase     :Dict          =	Image.open(requests.get(lowerCAmelCase__	,			stream=lowerCAmelCase__					).raw					)
	return im

@torch.no_grad()
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[Any]	,			__magic_name__					:  Tuple	,			__magic_name__					:  Optional[int]=False					)		->			Optional[Any]:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	get_config(lowerCAmelCase__					)

	# load original model from timm
	UpperCamelCase     :Optional[Any]          =	create_model(lowerCAmelCase__	,			pretrained=lowerCAmelCase__					)
	timm_model.eval()

	# load state_dict of original model
	UpperCamelCase     :str          =	timm_model.state_dict()
	for key in state_dict.copy().keys():
		UpperCamelCase     :Any          =	state_dict.pop(lowerCAmelCase__					)
		UpperCamelCase     :Tuple          =	val.squeeze() if """head""" in key else val

	# load HuggingFace model
	UpperCamelCase     :Optional[int]          =	BitForImageClassification(lowerCAmelCase__					)
	model.eval()
	model.load_state_dict(lowerCAmelCase__					)

	# create image processor
	UpperCamelCase     :Union[str, Any]          =	create_transform(**resolve_data_config({}	,			model=lowerCAmelCase__					)					)
	UpperCamelCase     :Union[str, Any]          =	transform.transforms

	UpperCamelCase     :Dict          =	{
	    """bilinear""": PILImageResampling.BILINEAR,
	    """bicubic""": PILImageResampling.BICUBIC,
	    """nearest""": PILImageResampling.NEAREST,
	}

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

	UpperCamelCase     :Optional[int]          =	prepare_img()
	UpperCamelCase     :Any          =	transform(lowerCAmelCase__					).unsqueeze(0					)
	UpperCamelCase     :Optional[Any]          =	processor(lowerCAmelCase__	,			return_tensors="""pt"""					).pixel_values

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

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

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

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

	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__":
	UpperCAmelCase_ :						List[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.''',
	)

	UpperCAmelCase_ :						List[str]      =      parser.parse_args()
	convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''

					if edge <= 0 or not isinstance(lowerCAmelCase__ ,			lowerCAmelCase__		):
										raise ValueError('''Length must be a positive.'''		)
					return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''

					if edge <= 0 or not isinstance(lowerCAmelCase__ ,			lowerCAmelCase__		):
										raise ValueError('''Length must be a positive.'''		)
					return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




