






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()
__magic_name__:					Optional[Any]							=     logging.get_logger(__name__)

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       Dict       						= """huggingface/label-files"""
						__magic_name__      :       Dict       						= """imagenet-1k-id2label.json"""
						__magic_name__      :       List[Any]       						= json.load(open(hf_hub_download(_A,   _A,   repo_type="""dataset"""			),   """r"""			)			)
						__magic_name__      :       Any       						= {int(_A			): v for k, v in idalabel.items()}
						__magic_name__      :       int       						= {v: k for k, v in idalabel.items()}

						__magic_name__      :       Union[str, 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"
						__magic_name__      :       Any       						= BitConfig(
						    conv_layer=_A,   num_labels=1000,   idalabel=_A,   labelaid=_A,   )

						return config

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						if "stem.conv" in name:
												__magic_name__      :       Union[str, Any]       						= name.replace("""stem.conv""",   """bit.embedder.convolution"""			)
						if "blocks" in name:
												__magic_name__      :       str       						= name.replace("""blocks""",   """layers"""			)
						if "head.fc" in name:
												__magic_name__      :       Optional[int]       						= name.replace("""head.fc""",   """classifier.1"""			)
						if name.startswith("""norm"""			):
												__magic_name__      :       Optional[Any]       						= """bit.""" + name
						if "bit" not in name and "classifier" not in name:
												__magic_name__      :       Any       						= """bit.encoder.""" + name

						return name

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       Dict       						= """http://images.cocodataset.org/val2017/000000039769.jpg"""
						__magic_name__      :       Dict       						= Image.open(requests.get(_A,   stream=_A			).raw			)
						return im

@torch.no_grad()
def    UpperCamelCase						(	_A,   _A,   _A=False			):






						"""simple docstring"""







						__magic_name__      :       Union[str, Any]       						= get_config(_A			)

						# load original model from timm
						__magic_name__      :       int       						= create_model(_A,   pretrained=_A			)
						timm_model.eval()

						# load state_dict of original model
						__magic_name__      :       List[str]       						= timm_model.state_dict()
						for key in state_dict.copy().keys():
												__magic_name__      :       int       						= state_dict.pop(_A			)
												__magic_name__      :       Dict       						= val.squeeze() if """head""" in key else val

						# load HuggingFace model
						__magic_name__      :       Optional[Any]       						= BitForImageClassification(_A			)
						model.eval()
						model.load_state_dict(_A			)

						# create image processor
						__magic_name__      :       int       						= create_transform(**resolve_data_config({},   model=_A			)			)
						__magic_name__      :       Any       						= transform.transforms

						__magic_name__      :       Optional[int]       						= {
						    """bilinear""": PILImageResampling.BILINEAR,
						    """bicubic""": PILImageResampling.BICUBIC,
						    """nearest""": PILImageResampling.NEAREST,
						}

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

						__magic_name__      :       Union[str, Any]       						= prepare_img()
						__magic_name__      :       Optional[int]       						= transform(_A			).unsqueeze(0			)
						__magic_name__      :       Tuple       						= processor(_A,   return_tensors="""pt"""			).pixel_values

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

						# verify logits
						with torch.no_grad():
												__magic_name__      :       Tuple       						= model(_A			)
												__magic_name__      :       Any       						= outputs.logits

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

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

						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__":
		__magic_name__:					str							=     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.",
		)

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








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
)
__magic_name__:					str							=     logging.getLogger(__name__)

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       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=_A,   default="""data/dump.txt""",   help="""The path to the data."""			)
						parser.add_argument("""--tokenizer_type""",   type=_A,   default="""bert""",   choices=["""bert""", """roberta""", """gpt2"""]			)
						parser.add_argument("""--tokenizer_name""",   type=_A,   default="""bert-base-uncased""",   help="""The tokenizer to use."""			)
						parser.add_argument("""--dump_file""",   type=_A,   default="""data/dump""",   help="""The dump file prefix."""			)
						__magic_name__      :       Dict       						= parser.parse_args()

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

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

						__magic_name__      :       List[Any]       						= []
						__magic_name__      :       str       						= 0
						__magic_name__      :       str       						= 10000
						__magic_name__      :       Dict       						= time.time()
						for text in data:
												__magic_name__      :       Tuple       						= f'{bos} {text.strip()} {sep}'
												__magic_name__      :       Optional[int]       						= tokenizer.encode(_A,   add_special_tokens=_A			)
												rslt.append(_A			)

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

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


if __name__ == "__main__":
		main()
