


'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_ :     List[Any]								=      logging.get_logger(__name__)

UpperCAmelCase_ :     Dict								=      {
    """google/vivit-b-16x2-kinetics400""": (
        """https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}

class 						_lowerCamelCase					(    _lowerCAmelCase				):




 '''simple docstring'''
 __lowercase				:		Dict    							=						'vivit'


 def __init__(      self      ,      __lowercase=224      ,      __lowercase=32      ,      __lowercase=[2, 16, 16]      ,      __lowercase=3      ,      __lowercase=768      ,      __lowercase=12      ,      __lowercase=12      ,      __lowercase=3_072      ,      __lowercase="gelu_fast"      ,      __lowercase=0.0      ,      __lowercase=0.0      ,      __lowercase=0.0_2      ,      __lowercase=1E-06      ,      __lowercase=True      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		Tuple       =			hidden_size
  __A  :		Optional[Any]       =			num_hidden_layers
  __A  :		Optional[int]       =			num_attention_heads
  __A  :		Any       =			intermediate_size
  __A  :		int       =			hidden_act
  __A  :		Optional[int]       =			hidden_dropout_prob
  __A  :		Tuple       =			attention_probs_dropout_prob
  __A  :		Optional[Any]       =			initializer_range
  __A  :		str       =			layer_norm_eps

  __A  :		Union[str, Any]       =			image_size
  __A  :		Union[str, Any]       =			num_frames
  __A  :		Optional[int]       =			tubelet_size
  __A  :		int       =			num_channels
  __A  :		Any       =			qkv_bias

  super().__init__(**_lowerCAmelCase						)










'''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
)
UpperCAmelCase_ :     Any								=      logging.getLogger(__name__)


def 			_lowercase       (				):
 __A  :		Tuple       =			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.'			)
 __A  :		Tuple       =			parser.parse_args()

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

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

 __A  :		int       =			[]
 __A  :		List[str]       =			0
 __A  :		Tuple       =			10000
 __A  :		List[str]       =			time.time()
 for text in data:
  __A  :		int       =			f"""{bos} {text.strip()} {sep}"""
  __A  :		List[Any]       =			tokenizer.encode(UpperCamelCase__,				add_special_tokens=UpperCamelCase__			)
  rslt.append(UpperCamelCase__			)

  iter += 1
  if iter % interval == 0:
   __A  :		Any       =			time.time()
   logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""			)
   __A  :		Any       =			time.time()
 logger.info('Finished binarization'			)
 logger.info(f"""{len(UpperCamelCase__			)} examples processed."""			)

 __A  :		List[str]       =			f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
 __A  :		str       =			tokenizer.vocab_size
 if vocab_size < (1 << 16):
  __A  :		Optional[int]       =			[np.uintaa(UpperCamelCase__			) for d in rslt]
 else:
  __A  :		int       =			[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()






