




"""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()
lowercase__     						=						logging.get_logger(__name__)







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

				_lowerCamelCase     :			Optional[Any]    =    '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"
				_lowerCamelCase     :			List[Any]    =    BitConfig(
				    conv_layer=lowercase__							,    num_labels=1000							,    idalabel=lowercase__							,    labelaid=lowercase__							,    )

				return config







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

				return name







def      _snake_case			(				):
				_lowerCamelCase     :			int    =    'http://images.cocodataset.org/val2017/000000039769.jpg'
				_lowerCamelCase     :			int    =    Image.open(requests.get(lowercase__							,    stream=lowercase__		).raw		)
				return im







@torch.no_grad()
def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__=False		):
				_lowerCamelCase     :			Tuple    =    get_config(lowercase__		)

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

				# load state_dict of original model
				_lowerCamelCase     :			Dict    =    timm_model.state_dict()
				for key in state_dict.copy().keys():
								_lowerCamelCase     :			Dict    =    state_dict.pop(lowercase__		)
								_lowerCamelCase     :			str    =    val.squeeze() if 'head' in key else val

				# load HuggingFace model
				_lowerCamelCase     :			List[Any]    =    BitForImageClassification(lowercase__		)
				model.eval()
				model.load_state_dict(lowercase__		)

				# create image processor
				_lowerCamelCase     :			Dict    =    create_transform(**resolve_data_config({}							,    model=lowercase__		)		)
				_lowerCamelCase     :			str    =    transform.transforms

				_lowerCamelCase     :			int    =    {
				    'bilinear': PILImageResampling.BILINEAR,
				    'bicubic': PILImageResampling.BICUBIC,
				    'nearest': PILImageResampling.NEAREST,
				}

				_lowerCamelCase     :			int    =    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()							,    )

				_lowerCamelCase     :			Optional[Any]    =    prepare_img()
				_lowerCamelCase     :			Optional[Any]    =    transform(lowercase__		).unsqueeze(0		)
				_lowerCamelCase     :			Optional[Any]    =    processor(lowercase__							,    return_tensors='pt'		).pixel_values

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

				# verify logits
				with torch.no_grad():
								_lowerCamelCase     :			List[str]    =    model(lowercase__		)
								_lowerCamelCase     :			Dict    =    outputs.logits

				print('Logits:'							,    logits[0, :3]		)
				print('Predicted class:'							,    model.config.idalabel[logits.argmax(-1		).item()]		)
				_lowerCamelCase     :			List[Any]    =    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__":
			lowercase__     						=						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.""",
			)

			lowercase__     						=						parser.parse_args()
			convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""






lowercase__     						=						{
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
lowercase__     						=						{
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			List[Any]    =    from_type.lower().strip('s'		)
				_lowerCamelCase     :			List[Any]    =    to_type.lower().strip('s'		)

				_lowerCamelCase     :			Optional[int]    =    UNIT_SYMBOL.get(lowercase__							,    lowercase__		)
				_lowerCamelCase     :			Any    =    UNIT_SYMBOL.get(lowercase__							,    lowercase__		)

				if from_sanitized not in METRIC_CONVERSION:
								_lowerCamelCase     :			Tuple    =    (
								    f'''Invalid \'from_type\' value: {from_type!r}.\n'''
								    f'''Conversion abbreviations are: {', '.join(lowercase__		)}'''
								)
								raise ValueError(lowercase__		)
				if to_sanitized not in METRIC_CONVERSION:
								_lowerCamelCase     :			Any    =    (
								    f'''Invalid \'to_type\' value: {to_type!r}.\n'''
								    f'''Conversion abbreviations are: {', '.join(lowercase__		)}'''
								)
								raise ValueError(lowercase__		)
				_lowerCamelCase     :			List[Any]    =    METRIC_CONVERSION[from_sanitized]
				_lowerCamelCase     :			int    =    METRIC_CONVERSION[to_sanitized]
				_lowerCamelCase     :			List[str]    =    1

				if from_exponent > to_exponent:
								_lowerCamelCase     :			List[str]    =    from_exponent - to_exponent
				else:
								_lowerCamelCase     :			List[Any]    =    -(to_exponent - from_exponent)

				return value * pow(10							,    lowercase__		)


if __name__ == "__main__":
			from doctest import testmod

			testmod()