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





def 						__lowerCamelCase				(UpperCAmelCase__  :				Optional[Any]	):
					SCREAMING_SNAKE_CASE           =							"""huggingface/label-files"""
					SCREAMING_SNAKE_CASE           =							"""imagenet-1k-id2label.json"""
					SCREAMING_SNAKE_CASE           =							json.load(open(hf_hub_download(snake_case__   ,			snake_case__   ,			repo_type="dataset"	)   ,			"r"	)	)
					SCREAMING_SNAKE_CASE           =							{int(snake_case__	): v for k, v in idalabel.items()}
					SCREAMING_SNAKE_CASE           =							{v: k for k, v in idalabel.items()}

					SCREAMING_SNAKE_CASE           =							"""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           =							BitConfig(
					    conv_layer=snake_case__   ,			num_labels=1_0_0_0   ,			idalabel=snake_case__   ,			labelaid=snake_case__   ,			)

					return config





def 						__lowerCamelCase				(UpperCAmelCase__  :				Union[str, Any]	):






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

					return name





def 						__lowerCamelCase				():
					SCREAMING_SNAKE_CASE           =							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
					SCREAMING_SNAKE_CASE           =							Image.open(requests.get(snake_case__   ,			stream=snake_case__	).raw	)
					return im





@torch.no_grad()
def 						__lowerCamelCase				(UpperCAmelCase__  :				Dict   ,			UpperCAmelCase__  :				Dict   ,			UpperCAmelCase__  :				Tuple=False	):
					SCREAMING_SNAKE_CASE           =							get_config(snake_case__	)

					# load original model from timm
					SCREAMING_SNAKE_CASE           =							create_model(snake_case__   ,			pretrained=snake_case__	)
					timm_model.eval()

					# load state_dict of original model
					SCREAMING_SNAKE_CASE           =							timm_model.state_dict()
					for key in state_dict.copy().keys():
										SCREAMING_SNAKE_CASE           =							state_dict.pop(snake_case__	)
										SCREAMING_SNAKE_CASE           =							val.squeeze() if """head""" in key else val

					# load HuggingFace model
					SCREAMING_SNAKE_CASE           =							BitForImageClassification(snake_case__	)
					model.eval()
					model.load_state_dict(snake_case__	)

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

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

					SCREAMING_SNAKE_CASE           =							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()   ,			)

					SCREAMING_SNAKE_CASE           =							prepare_img()
					SCREAMING_SNAKE_CASE           =							transform(snake_case__	).unsqueeze(0	)
					SCREAMING_SNAKE_CASE           =							processor(snake_case__   ,			return_tensors="pt"	).pixel_values

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

					# verify logits
					with torch.no_grad():
										SCREAMING_SNAKE_CASE           =							model(snake_case__	)
										SCREAMING_SNAKE_CASE           =							outputs.logits

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

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












def 	UpperCamelCase	(							snake_case__  : int	):




     '''simple docstring'''






     __snake_case      :List[Any] 							=    0
     while num > 0:
          digit_sum += num % 10
          num //= 10
     return digit_sum






def 	UpperCamelCase	(							snake_case__  : int = 100	):




     '''simple docstring'''






     __snake_case      :Tuple 							=    1
     __snake_case      :Dict 							=    2

     for i in range(2						,max_n + 1	):
          __snake_case      :Optional[int] 							=    pre_numerator
          __snake_case      :List[Any] 							=    2 * i // 3 if i % 3 == 0 else 1
          __snake_case      :Dict 							=    cur_numerator
          __snake_case      :Optional[Any] 							=    e_cont * pre_numerator + temp

     return sum_digits(snake_case__	)


if __name__ == "__main__":
     print(f'''{solution() = }''')


