
"""simple docstring"""







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
)
lowerCamelCase__           =	logging.getLogger(__name__)
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Union[str, Any]		    =   argparse.ArgumentParser(
	    description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'	)
	parser.add_argument('--file_path' ,						type=_UpperCamelCase ,						default='data/dump.txt' ,						help='The path to the data.'	)
	parser.add_argument('--tokenizer_type' ,						type=_UpperCamelCase ,						default='bert' ,						choices=['bert', 'roberta', 'gpt2']	)
	parser.add_argument('--tokenizer_name' ,						type=_UpperCamelCase ,						default='bert-base-uncased' ,						help='The tokenizer to use.'	)
	parser.add_argument('--dump_file' ,						type=_UpperCamelCase ,						default='data/dump' ,						help='The dump file prefix.'	)
	__lowerCAmelCase    :		Optional[Any]		    =   parser.parse_args()

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

	logger.info('Start encoding'	)
	logger.info(F"{len(_UpperCamelCase	)} examples to process."	)

	__lowerCAmelCase    :		str		    =   []
	__lowerCAmelCase    :		Tuple		    =   0
	__lowerCAmelCase    :		Tuple		    =   1_0000
	__lowerCAmelCase    :		Any		    =   time.time()
	for text in data:
		__lowerCAmelCase    :		int		    =   F"{bos} {text.strip()} {sep}"
		__lowerCAmelCase    :		str		    =   tokenizer.encode(_UpperCamelCase ,						add_special_tokens=_UpperCamelCase	)
		rslt.append(_UpperCamelCase	)

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

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


if __name__ == "__main__":
							main()

"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			'beit'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=81_92 ,	_SCREAMING_SNAKE_CASE=7_68 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=30_72 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=1E-12 ,	_SCREAMING_SNAKE_CASE=2_24 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=[3, 5, 7, 11] ,	_SCREAMING_SNAKE_CASE=[1, 2, 3, 6] ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=0.4 ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=2_55 ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   vocab_size
			__lowerCAmelCase    :		Union[str, Any]		    =   hidden_size
			__lowerCAmelCase    :		Dict		    =   num_hidden_layers
			__lowerCAmelCase    :		Any		    =   num_attention_heads
			__lowerCAmelCase    :		Union[str, Any]		    =   intermediate_size
			__lowerCAmelCase    :		Union[str, Any]		    =   hidden_act
			__lowerCAmelCase    :		List[Any]		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Dict		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		Optional[Any]		    =   initializer_range
			__lowerCAmelCase    :		Optional[Any]		    =   layer_norm_eps

			__lowerCAmelCase    :		Any		    =   image_size
			__lowerCAmelCase    :		Union[str, Any]		    =   patch_size
			__lowerCAmelCase    :		List[Any]		    =   num_channels
			__lowerCAmelCase    :		List[Any]		    =   use_mask_token
			__lowerCAmelCase    :		Any		    =   use_absolute_position_embeddings
			__lowerCAmelCase    :		int		    =   use_relative_position_bias
			__lowerCAmelCase    :		List[str]		    =   use_shared_relative_position_bias
			__lowerCAmelCase    :		Tuple		    =   layer_scale_init_value
			__lowerCAmelCase    :		int		    =   drop_path_rate
			__lowerCAmelCase    :		Dict		    =   use_mean_pooling
			# decode head attributes (semantic segmentation)
			__lowerCAmelCase    :		Optional[Any]		    =   out_indices
			__lowerCAmelCase    :		Optional[Any]		    =   pool_scales
			# auxiliary head attributes (semantic segmentation)
			__lowerCAmelCase    :		Union[str, Any]		    =   use_auxiliary_head
			__lowerCAmelCase    :		Optional[int]		    =   auxiliary_loss_weight
			__lowerCAmelCase    :		Optional[Any]		    =   auxiliary_channels
			__lowerCAmelCase    :		List[str]		    =   auxiliary_num_convs
			__lowerCAmelCase    :		List[str]		    =   auxiliary_concat_input
			__lowerCAmelCase    :		Optional[int]		    =   semantic_loss_ignore_index




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			version.parse('1.11')

		@property
		def 							__lowerCamelCase							(   self				):
			return OrderedDict(
			    [
			        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
			    ]				)







		@property
		def 							__lowerCamelCase							(   self				):
			return 1E-4