



'''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()
UpperCamelCase__ :			List[Any]												=				logging.get_logger(__name__)


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[Any]      ):
		__SCREAMING_SNAKE_CASE :	Any					 =	"""huggingface/label-files"""
		__SCREAMING_SNAKE_CASE :	Dict					 =	"""imagenet-1k-id2label.json"""
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	json.load(open(hf_hub_download(_lowerCamelCase ,   _lowerCamelCase ,   repo_type="""dataset"""      ) ,   """r"""      )      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	{int(_lowerCamelCase      ): v for k, v in idalabel.items()}
		__SCREAMING_SNAKE_CASE :	List[str]					 =	{v: k for k, v in idalabel.items()}

		__SCREAMING_SNAKE_CASE :	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"
		__SCREAMING_SNAKE_CASE :	Tuple					 =	BitConfig(
		    conv_layer=_lowerCamelCase ,   num_labels=10_00 ,   idalabel=_lowerCamelCase ,   labelaid=_lowerCamelCase ,   )

		return config


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

		return name


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Dict					 =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	Image.open(requests.get(_lowerCamelCase ,   stream=_lowerCamelCase      ).raw      )
		return im


@torch.no_grad()
def        lowerCAmelCase_     (			_lowerCamelCase:     str ,   _lowerCamelCase:     Dict ,   _lowerCamelCase:     Optional[Any]=False      ):
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	get_config(_lowerCamelCase      )

		# load original model from timm
		__SCREAMING_SNAKE_CASE :	str					 =	create_model(_lowerCamelCase ,   pretrained=_lowerCamelCase      )
		timm_model.eval()

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

		# load HuggingFace model
		__SCREAMING_SNAKE_CASE :	str					 =	BitForImageClassification(_lowerCamelCase      )
		model.eval()
		model.load_state_dict(_lowerCamelCase      )

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

		__SCREAMING_SNAKE_CASE :	int					 =	{
		    """bilinear""": PILImageResampling.BILINEAR,
		    """bicubic""": PILImageResampling.BICUBIC,
		    """nearest""": PILImageResampling.NEAREST,
		}

		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	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() ,   )

		__SCREAMING_SNAKE_CASE :	Dict					 =	prepare_img()
		__SCREAMING_SNAKE_CASE :	Dict					 =	transform(_lowerCamelCase      ).unsqueeze(0      )
		__SCREAMING_SNAKE_CASE :	Any					 =	processor(_lowerCamelCase ,   return_tensors="""pt"""      ).pixel_values

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

		# verify logits
		with torch.no_grad():
				__SCREAMING_SNAKE_CASE :	str					 =	model(_lowerCamelCase      )
				__SCREAMING_SNAKE_CASE :	str					 =	outputs.logits

		print("""Logits:""" ,   logits[0, :3]      )
		print("""Predicted class:""" ,   model.config.idalabel[logits.argmax(-1      ).item()]      )
		__SCREAMING_SNAKE_CASE :	str					 =	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__ :			Optional[int]												=				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__ :			Optional[int]												=				parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''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 AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification


def        lowerCAmelCase_     (			_lowerCamelCase:     Tuple      ):
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	SwinvaConfig()
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	swinva_name.split("""_"""      )

		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	name_split[1]
		if "to" in name_split[3]:
				__SCREAMING_SNAKE_CASE :	Dict					 =	int(name_split[3][-3:]      )
		else:
				__SCREAMING_SNAKE_CASE :	str					 =	int(name_split[3]      )
		if "to" in name_split[2]:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	int(name_split[2][-2:]      )
		else:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	int(name_split[2][6:]      )

		if model_size == "tiny":
				__SCREAMING_SNAKE_CASE :	Dict					 =	96
				__SCREAMING_SNAKE_CASE :	List[str]					 =	(2, 2, 6, 2)
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	(3, 6, 12, 24)
		elif model_size == "small":
				__SCREAMING_SNAKE_CASE :	List[str]					 =	96
				__SCREAMING_SNAKE_CASE :	int					 =	(2, 2, 18, 2)
				__SCREAMING_SNAKE_CASE :	int					 =	(3, 6, 12, 24)
		elif model_size == "base":
				__SCREAMING_SNAKE_CASE :	int					 =	1_28
				__SCREAMING_SNAKE_CASE :	str					 =	(2, 2, 18, 2)
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	(4, 8, 16, 32)
		else:
				__SCREAMING_SNAKE_CASE :	List[str]					 =	1_92
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	(2, 2, 18, 2)
				__SCREAMING_SNAKE_CASE :	Dict					 =	(6, 12, 24, 48)

		if "to" in swinva_name:
				__SCREAMING_SNAKE_CASE :	int					 =	(12, 12, 12, 6)

		if ("22k" in swinva_name) and ("to" not in swinva_name):
				__SCREAMING_SNAKE_CASE :	int					 =	2_18_41
				__SCREAMING_SNAKE_CASE :	str					 =	"""huggingface/label-files"""
				__SCREAMING_SNAKE_CASE :	List[str]					 =	"""imagenet-22k-id2label.json"""
				__SCREAMING_SNAKE_CASE :	List[str]					 =	json.load(open(hf_hub_download(_lowerCamelCase ,   _lowerCamelCase ,   repo_type="""dataset"""      ) ,   """r"""      )      )
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	{int(_lowerCamelCase      ): v for k, v in idalabel.items()}
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	idalabel
				__SCREAMING_SNAKE_CASE :	str					 =	{v: k for k, v in idalabel.items()}

		else:
				__SCREAMING_SNAKE_CASE :	str					 =	10_00
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	"""huggingface/label-files"""
				__SCREAMING_SNAKE_CASE :	Any					 =	"""imagenet-1k-id2label.json"""
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	json.load(open(hf_hub_download(_lowerCamelCase ,   _lowerCamelCase ,   repo_type="""dataset"""      ) ,   """r"""      )      )
				__SCREAMING_SNAKE_CASE :	int					 =	{int(_lowerCamelCase      ): v for k, v in idalabel.items()}
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	idalabel
				__SCREAMING_SNAKE_CASE :	Dict					 =	{v: k for k, v in idalabel.items()}

		__SCREAMING_SNAKE_CASE :	Any					 =	img_size
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	num_classes
		__SCREAMING_SNAKE_CASE :	int					 =	embed_dim
		__SCREAMING_SNAKE_CASE :	Dict					 =	depths
		__SCREAMING_SNAKE_CASE :	str					 =	num_heads
		__SCREAMING_SNAKE_CASE :	int					 =	window_size

		return config


def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
		if "patch_embed.proj" in name:
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	name.replace("""patch_embed.proj""" ,   """embeddings.patch_embeddings.projection"""      )
		if "patch_embed.norm" in name:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	name.replace("""patch_embed.norm""" ,   """embeddings.norm"""      )
		if "layers" in name:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	"""encoder.""" + name
		if "attn.proj" in name:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	name.replace("""attn.proj""" ,   """attention.output.dense"""      )
		if "attn" in name:
				__SCREAMING_SNAKE_CASE :	Any					 =	name.replace("""attn""" ,   """attention.self"""      )
		if "norm1" in name:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	name.replace("""norm1""" ,   """layernorm_before"""      )
		if "norm2" in name:
				__SCREAMING_SNAKE_CASE :	Dict					 =	name.replace("""norm2""" ,   """layernorm_after"""      )
		if "mlp.fc1" in name:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	name.replace("""mlp.fc1""" ,   """intermediate.dense"""      )
		if "mlp.fc2" in name:
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	name.replace("""mlp.fc2""" ,   """output.dense"""      )
		if "q_bias" in name:
				__SCREAMING_SNAKE_CASE :	Tuple					 =	name.replace("""q_bias""" ,   """query.bias"""      )
		if "k_bias" in name:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	name.replace("""k_bias""" ,   """key.bias"""      )
		if "v_bias" in name:
				__SCREAMING_SNAKE_CASE :	List[str]					 =	name.replace("""v_bias""" ,   """value.bias"""      )
		if "cpb_mlp" in name:
				__SCREAMING_SNAKE_CASE :	str					 =	name.replace("""cpb_mlp""" ,   """continuous_position_bias_mlp"""      )
		if name == "norm.weight":
				__SCREAMING_SNAKE_CASE :	Tuple					 =	"""layernorm.weight"""
		if name == "norm.bias":
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	"""layernorm.bias"""

		if "head" in name:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	name.replace("""head""" ,   """classifier"""      )
		else:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	"""swinv2.""" + name

		return name


def        lowerCAmelCase_     (			_lowerCamelCase:     int ,   _lowerCamelCase:     Optional[Any]      ):
		for key in orig_state_dict.copy().keys():
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	orig_state_dict.pop(_lowerCamelCase      )

				if "mask" in key:
						continue
				elif "qkv" in key:
						__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	key.split("""."""      )
						__SCREAMING_SNAKE_CASE :	List[str]					 =	int(key_split[1]      )
						__SCREAMING_SNAKE_CASE :	Dict					 =	int(key_split[3]      )
						__SCREAMING_SNAKE_CASE :	str					 =	model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								__SCREAMING_SNAKE_CASE :	Optional[int]					 =	val[:dim, :]
								__SCREAMING_SNAKE_CASE :	str					 =	val[dim : dim * 2, :]
								__SCREAMING_SNAKE_CASE :	Dict					 =	val[-dim:, :]
						else:
								__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	val[:dim]
								__SCREAMING_SNAKE_CASE :	int					 =	val[
								    dim : dim * 2
								]
								__SCREAMING_SNAKE_CASE :	int					 =	val[-dim:]
				else:
						__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	val

		return orig_state_dict


def        lowerCAmelCase_     (			_lowerCamelCase:     Tuple ,   _lowerCamelCase:     int      ):
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	timm.create_model(_lowerCamelCase ,   pretrained=_lowerCamelCase      )
		timm_model.eval()

		__SCREAMING_SNAKE_CASE :	int					 =	get_swinva_config(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	SwinvaForImageClassification(_lowerCamelCase      )
		model.eval()

		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	convert_state_dict(timm_model.state_dict() ,   _lowerCamelCase      )
		model.load_state_dict(_lowerCamelCase      )

		__SCREAMING_SNAKE_CASE :	int					 =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""

		__SCREAMING_SNAKE_CASE :	List[Any]					 =	AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_""" ,   """-"""      )      )      )
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	Image.open(requests.get(_lowerCamelCase ,   stream=_lowerCamelCase      ).raw      )
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	image_processor(images=_lowerCamelCase ,   return_tensors="""pt"""      )

		__SCREAMING_SNAKE_CASE :	int					 =	timm_model(inputs["""pixel_values"""]      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	model(**_lowerCamelCase      ).logits

		assert torch.allclose(_lowerCamelCase ,   _lowerCamelCase ,   atol=1E-3      )

		print(F"Saving model {swinva_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      )

		model.push_to_hub(
		    repo_path_or_name=Path(_lowerCamelCase ,   _lowerCamelCase      ) ,   organization="""nandwalritik""" ,   commit_message="""Add model""" ,   )


if __name__ == "__main__":
						UpperCamelCase__ :			Union[str, Any]												=				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--swinv2_name''',
						    default='''swinv2_tiny_patch4_window8_256''',
						    type=str,
						    help='''Name of the Swinv2 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__ :			Optional[int]												=				parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)