



"""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    =			logging.get_logger(__name__)

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							A         =					'huggingface/label-files'
							A         =					'imagenet-1k-id2label.json'
							A         =					json.load(open(hf_hub_download(__snake_case				,      __snake_case				,      repo_type='dataset'			)				,      'r'			)			)
							A         =					{int(__snake_case			): v for k, v in idalabel.items()}
							A         =					{v: k for k, v in idalabel.items()}

							A         =					'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"
							A         =					BitConfig(
							    conv_layer=__snake_case				,      num_labels=1000				,      idalabel=__snake_case				,      labelaid=__snake_case				,      )

							return config

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Dict:





							if "stem.conv" in name:
														A         =					name.replace('stem.conv'				,      'bit.embedder.convolution'			)
							if "blocks" in name:
														A         =					name.replace('blocks'				,      'layers'			)
							if "head.fc" in name:
														A         =					name.replace('head.fc'				,      'classifier.1'			)
							if name.startswith('norm'			):
														A         =					'bit.' + name
							if "bit" not in name and "classifier" not in name:
														A         =					'bit.encoder.' + name

							return name

def   lowerCAmelCase__			(     ) ->							Dict:
							A         =					'http://images.cocodataset.org/val2017/000000039769.jpg'
							A         =					Image.open(requests.get(__snake_case				,      stream=__snake_case			).raw			)
							return im

@torch.no_grad()
def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__=False			) ->							Union[str, Any]:
							A         =					get_config(__snake_case			)

							# load original model from timm
							A         =					create_model(__snake_case				,      pretrained=__snake_case			)
							timm_model.eval()

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

							# load HuggingFace model
							A         =					BitForImageClassification(__snake_case			)
							model.eval()
							model.load_state_dict(__snake_case			)

							# create image processor
							A         =					create_transform(**resolve_data_config({}				,      model=__snake_case			)			)
							A         =					transform.transforms

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

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

							A         =					prepare_img()
							A         =					transform(__snake_case			).unsqueeze(0			)
							A         =					processor(__snake_case				,      return_tensors='pt'			).pixel_values

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

							# verify logits
							with torch.no_grad():
														A         =					model(__snake_case			)
														A         =					outputs.logits

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

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

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








"""simple docstring"""







def   lowerCAmelCase__			(     lowerCamelCase__			) ->							int:

							if not isinstance(lowerCamelCase__				,      lowerCamelCase__			):
														raise ValueError('multiplicative_persistence() only accepts integral values'			)
							if num < 0:
														raise ValueError('multiplicative_persistence() does not accept negative values'			)

							A         =					0
							A         =					str(lowerCamelCase__			)

							while len(lowerCamelCase__			) != 1:
														A         =					[int(lowerCamelCase__			) for i in num_string]

														A         =					1
														for i in range(0				,      len(lowerCamelCase__			)			):
																					total *= numbers[i]

														A         =					str(lowerCamelCase__			)

														steps += 1
							return steps

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							int:

							if not isinstance(lowerCamelCase__				,      lowerCamelCase__			):
														raise ValueError('additive_persistence() only accepts integral values'			)
							if num < 0:
														raise ValueError('additive_persistence() does not accept negative values'			)

							A         =					0
							A         =					str(lowerCamelCase__			)

							while len(lowerCamelCase__			) != 1:
														A         =					[int(lowerCamelCase__			) for i in num_string]

														A         =					0
														for i in range(0				,      len(lowerCamelCase__			)			):
																					total += numbers[i]

														A         =					str(lowerCamelCase__			)

														steps += 1
							return steps


if __name__ == "__main__":
							import doctest

							doctest.testmod()



