import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
__UpperCamelCase	: Optional[Any]    		=  logging.getLogger(__name__)
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							argparse.ArgumentParser(
						    description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''		)
						parser.add_argument('''--file_path'''	,						type=lowerCAmelCase__	,						default='''data/dump.txt'''	,						help='''The path to the data.'''		)
						parser.add_argument('''--tokenizer_type'''	,						type=lowerCAmelCase__	,						default='''bert'''	,						choices=['''bert''', '''roberta''', '''gpt2''']		)
						parser.add_argument('''--tokenizer_name'''	,						type=lowerCAmelCase__	,						default='''bert-base-uncased'''	,						help='''The tokenizer to use.'''		)
						parser.add_argument('''--dump_file'''	,						type=lowerCAmelCase__	,						default='''data/dump'''	,						help='''The dump file prefix.'''		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							parser.parse_args()

						logger.info(f"""Loading Tokenizer ({args.tokenizer_name})"""		)
						if args.tokenizer_type == "bert":
												SCREAMING_SNAKE_CASE							:					Any           =							BertTokenizer.from_pretrained(args.tokenizer_name		)
												SCREAMING_SNAKE_CASE							:					int           =							tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
												SCREAMING_SNAKE_CASE							:					Any           =							tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
						elif args.tokenizer_type == "roberta":
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							RobertaTokenizer.from_pretrained(args.tokenizer_name		)
												SCREAMING_SNAKE_CASE							:					Any           =							tokenizer.special_tokens_map['''cls_token''']  # `<s>`
												SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer.special_tokens_map['''sep_token''']  # `</s>`
						elif args.tokenizer_type == "gpt2":
												SCREAMING_SNAKE_CASE							:					List[str]           =							GPTaTokenizer.from_pretrained(args.tokenizer_name		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

						logger.info(f"""Loading text from {args.file_path}"""		)
						with open(args.file_path	,						'''r'''	,						encoding='''utf8'''		) as fp:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							fp.readlines()

						logger.info('''Start encoding'''		)
						logger.info(f"""{len(lowerCAmelCase__		)} examples to process."""		)

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[]
						SCREAMING_SNAKE_CASE							:					List[str]           =							0
						SCREAMING_SNAKE_CASE							:					List[str]           =							10_000
						SCREAMING_SNAKE_CASE							:					int           =							time.time()
						for text in data:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							f"""{bos} {text.strip()} {sep}"""
												SCREAMING_SNAKE_CASE							:					Any           =							tokenizer.encode(lowerCAmelCase__	,						add_special_tokens=lowerCAmelCase__		)
												rslt.append(lowerCAmelCase__		)

												iter += 1
												if iter % interval == 0:
																		SCREAMING_SNAKE_CASE							:					int           =							time.time()
																		logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""		)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							time.time()
						logger.info('''Finished binarization'''		)
						logger.info(f"""{len(lowerCAmelCase__		)} examples processed."""		)

						SCREAMING_SNAKE_CASE							:					int           =							f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
						SCREAMING_SNAKE_CASE							:					str           =							tokenizer.vocab_size
						if vocab_size < (1 << 16):
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[np.uintaa(lowerCAmelCase__		) for d in rslt]
						else:
												SCREAMING_SNAKE_CASE							:					int           =							[np.intaa(lowerCAmelCase__		) for d in rslt]
						random.shuffle(rslt_		)
						logger.info(f"""Dump to {dp_file}"""		)
						with open(lowerCAmelCase__	,						'''wb'''		) as handle:
												pickle.dump(rslt_	,						lowerCAmelCase__	,						protocol=pickle.HIGHEST_PROTOCOL		)


if __name__ == "__main__":
		main()






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()
__UpperCamelCase	: str    		=  logging.get_logger(__name__)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							'''huggingface/label-files'''
						SCREAMING_SNAKE_CASE							:					Any           =							'''imagenet-1k-id2label.json'''
						SCREAMING_SNAKE_CASE							:					Any           =							json.load(open(hf_hub_download(_lowercase	,						_lowercase	,						repo_type='''dataset'''		)	,						'''r'''		)		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{int(_lowercase		): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE							:					Dict           =							{v: k for k, v in idalabel.items()}

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

						return config
def    A			(					_lowercase		):
						if "stem.conv" in name:
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							name.replace('''stem.conv'''	,						'''bit.embedder.convolution'''		)
						if "blocks" in name:
												SCREAMING_SNAKE_CASE							:					Tuple           =							name.replace('''blocks'''	,						'''layers'''		)
						if "head.fc" in name:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							name.replace('''head.fc'''	,						'''classifier.1'''		)
						if name.startswith('''norm'''		):
												SCREAMING_SNAKE_CASE							:					str           =							'''bit.''' + name
						if "bit" not in name and "classifier" not in name:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''bit.encoder.''' + name

						return name
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Any           =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						SCREAMING_SNAKE_CASE							:					List[str]           =							Image.open(requests.get(_lowercase	,						stream=_lowercase		).raw		)
						return im
@torch.no_grad()
def    A			(					_lowercase	,						_lowercase	,						_lowercase=False		):
						SCREAMING_SNAKE_CASE							:					List[Any]           =							get_config(_lowercase		)

						# load original model from timm
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							create_model(_lowercase	,						pretrained=_lowercase		)
						timm_model.eval()

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

						# load HuggingFace model
						SCREAMING_SNAKE_CASE							:					str           =							BitForImageClassification(_lowercase		)
						model.eval()
						model.load_state_dict(_lowercase		)

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

						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{
						    '''bilinear''': PILImageResampling.BILINEAR,
						    '''bicubic''': PILImageResampling.BICUBIC,
						    '''nearest''': PILImageResampling.NEAREST,
						}

						SCREAMING_SNAKE_CASE							:					Tuple           =							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()	,						)

						SCREAMING_SNAKE_CASE							:					Any           =							prepare_img()
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							transform(_lowercase		).unsqueeze(0		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							processor(_lowercase	,						return_tensors='''pt'''		).pixel_values

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

						# verify logits
						with torch.no_grad():
												SCREAMING_SNAKE_CASE							:					Dict           =							model(_lowercase		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							outputs.logits

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

					__UpperCamelCase	: Optional[int]    		=  parser.parse_args()
					convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





