





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 :str     =    logging.get_logger(__name__)







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

					_snake_case       :					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"
					_snake_case       :					Optional[int]				       =   BitConfig(
					    conv_layer=UpperCAmelCase  ,  num_labels=1_000  ,  idalabel=UpperCAmelCase  ,  labelaid=UpperCAmelCase  ,  )

					return config







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

					return name







def    A				(							):
					_snake_case       :					Optional[int]				       =   "http://images.cocodataset.org/val2017/000000039769.jpg"
					_snake_case       :					int				       =   Image.open(requests.get(UpperCAmelCase  ,  stream=UpperCAmelCase				).raw				)
					return im







@torch.no_grad()
def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=False				):
					_snake_case       :					Union[str, Any]				       =   get_config(UpperCAmelCase				)

					# load original model from timm
					_snake_case       :					List[str]				       =   create_model(UpperCAmelCase  ,  pretrained=UpperCAmelCase				)
					timm_model.eval()

					# load state_dict of original model
					_snake_case       :					Any				       =   timm_model.state_dict()
					for key in state_dict.copy().keys():
										_snake_case       :					Tuple				       =   state_dict.pop(UpperCAmelCase				)
										_snake_case       :					List[Any]				       =   val.squeeze() if "head" in key else val

					# load HuggingFace model
					_snake_case       :					List[str]				       =   BitForImageClassification(UpperCAmelCase				)
					model.eval()
					model.load_state_dict(UpperCAmelCase				)

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

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

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

					_snake_case       :					Any				       =   prepare_img()
					_snake_case       :					Any				       =   transform(UpperCAmelCase				).unsqueeze(0				)
					_snake_case       :					int				       =   processor(UpperCAmelCase  ,  return_tensors="pt"				).pixel_values

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

					# verify logits
					with torch.no_grad():
										_snake_case       :					Dict				       =   model(UpperCAmelCase				)
										_snake_case       :					Dict				       =   outputs.logits

					print("Logits:"  ,  logits[0, :3]				)
					print("Predicted class:"  ,  model.config.idalabel[logits.argmax(-1				).item()]				)
					_snake_case       :					Tuple				       =   timm_model(UpperCAmelCase				)
					assert timm_logits.shape == outputs.logits.shape
					assert torch.allclose(UpperCAmelCase  ,  outputs.logits  ,  atol=1e-3				)
					print("Looks ok!"				)

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

					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 :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.',
							)

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






import re







def    A				(							UpperCAmelCase				):
					_snake_case       :					Any				       =   re.compile(R"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$"				)
					if match := re.search(UpperCAmelCase  ,  UpperCAmelCase				):
										return match.string == phone
					return False


if __name__ == "__main__":
							print(indian_phone_validator('+918827897895'))