

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    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     int:
	__lowerCamelCase				:				str					       = 'huggingface/label-files'
	__lowerCamelCase				:				Tuple					       = 'imagenet-1k-id2label.json'
	__lowerCamelCase				:				Dict					       = json.load(open(hf_hub_download(snake_case__	, snake_case__	, repo_type='dataset'			)	, 'r'			)			)
	__lowerCamelCase				:				Tuple					       = {int(snake_case__			): v for k, v in idalabel.items()}
	__lowerCamelCase				:				Any					       = {v: k for k, v in idalabel.items()}

	__lowerCamelCase				:				List[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=snake_case__	, num_labels=1_0_0_0	, idalabel=snake_case__	, labelaid=snake_case__	, )

	return config






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Optional[int]:
	if "stem.conv" in name:
		__lowerCamelCase				:				List[str]					       = name.replace('stem.conv'	, 'bit.embedder.convolution'			)
	if "blocks" in name:
		__lowerCamelCase				:				List[Any]					       = name.replace('blocks'	, 'layers'			)
	if "head.fc" in name:
		__lowerCamelCase				:				Optional[int]					       = name.replace('head.fc'	, 'classifier.1'			)
	if name.startswith('norm'			):
		__lowerCamelCase				:				Optional[Any]					       = 'bit.' + name
	if "bit" not in name and "classifier" not in name:
		__lowerCamelCase				:				Tuple					       = 'bit.encoder.' + name

	return name






def    SCREAMING_SNAKE_CASE__       (  )  ->     Optional[Any]:
	__lowerCamelCase				:				str					       = 'http://images.cocodataset.org/val2017/000000039769.jpg'
	__lowerCamelCase				:				Dict					       = Image.open(requests.get(snake_case__	, stream=snake_case__			).raw			)
	return im






@torch.no_grad()
def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=False			)  ->     List[Any]:
	__lowerCamelCase				:				str					       = get_config(snake_case__			)

	# load original model from timm
	__lowerCamelCase				:				List[Any]					       = create_model(snake_case__	, pretrained=snake_case__			)
	timm_model.eval()

	# load state_dict of original model
	__lowerCamelCase				:				Optional[Any]					       = timm_model.state_dict()
	for key in state_dict.copy().keys():
		__lowerCamelCase				:				Dict					       = state_dict.pop(snake_case__			)
		__lowerCamelCase				:				List[Any]					       = val.squeeze() if 'head' in key else val

	# load HuggingFace model
	__lowerCamelCase				:				Dict					       = BitForImageClassification(snake_case__			)
	model.eval()
	model.load_state_dict(snake_case__			)

	# create image processor
	__lowerCamelCase				:				str					       = create_transform(**resolve_data_config({}	, model=snake_case__			)			)
	__lowerCamelCase				:				Optional[Any]					       = transform.transforms

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

	__lowerCamelCase				:				str					       = 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()	, )

	__lowerCamelCase				:				str					       = prepare_img()
	__lowerCamelCase				:				Tuple					       = transform(snake_case__			).unsqueeze(0			)
	__lowerCamelCase				:				List[str]					       = processor(snake_case__	, return_tensors='pt'			).pixel_values

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

	# verify logits
	with torch.no_grad():
		__lowerCamelCase				:				int					       = model(snake_case__			)
		__lowerCamelCase				:				Optional[Any]					       = outputs.logits

	print('Logits:'	, logits[0, :3]			)
	print('Predicted class:'	, model.config.idalabel[logits.argmax(-1			).item()]			)
	__lowerCamelCase				:				int					       = 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)









import argparse
import json

from tqdm import tqdm
def 		UpperCamelCase	(   )	->     Optional[int]:
   UpperCamelCase    :  List[Any]							=	argparse.ArgumentParser()

   # Required parameters
   parser.add_argument(
       '--src_path'       ,     type=snake_case__       ,     default='biencoder-nq-dev.json'       ,     help='Path to raw DPR training data'       ,     )
   parser.add_argument(
       '--evaluation_set'       ,     type=snake_case__       ,     help='where to store parsed evaluation_set file'       ,     )
   parser.add_argument(
       '--gold_data_path'       ,     type=snake_case__       ,     help='where to store parsed gold_data_path file'       ,     )
   UpperCamelCase    :  int							=	parser.parse_args()

   with open(args.src_path       ,     'r'	) as src_file, open(args.evaluation_set       ,     'w'	) as eval_file, open(
       args.gold_data_path       ,     'w'	) as gold_file:
      UpperCamelCase    :  int							=	json.load(snake_case__	)
      for dpr_record in tqdm(snake_case__	):
         UpperCamelCase    :  Union[str, Any]							=	dpr_record['question']
         UpperCamelCase    :  Dict							=	[context['title'] for context in dpr_record['positive_ctxs']]
         eval_file.write(question + '\n'	)
         gold_file.write('\t'.join(snake_case__	) + '\n'	)


if __name__ == "__main__":
    main()



