





"""simple docstring"""
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


__UpperCamelCase     :						Any						       =  logging.get_logger(__name__)

__UpperCamelCase     :						Optional[Any]						       =  {
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}







class       a							(   a__  ):
 snake_case__             =       '''dpt'''



 def __init__(     self				,       _snake_case=7_68				,       _snake_case=12				,       _snake_case=12				,       _snake_case=30_72				,       _snake_case="gelu"				,       _snake_case=0.0				,       _snake_case=0.0				,       _snake_case=0.02				,       _snake_case=1E-12				,       _snake_case=3_84				,       _snake_case=16				,       _snake_case=3				,       _snake_case=False				,       _snake_case=True				,       _snake_case=[2, 5, 8, 11]				,       _snake_case="project"				,       _snake_case=[4, 2, 1, 0.5]				,       _snake_case=[96, 1_92, 3_84, 7_68]				,       _snake_case=2_56				,       _snake_case=-1				,       _snake_case=False				,       _snake_case=True				,       _snake_case=0.4				,       _snake_case=2_55				,       _snake_case=0.1				,       _snake_case=[1, 10_24, 24, 24]				,       _snake_case=[0, 1]				,       _snake_case=None				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(**_snake_case     )

  lowerCAmelCase    				= hidden_size
  lowerCAmelCase    				= is_hybrid

  if self.is_hybrid:
   if backbone_config is None:
    logger.info('Initializing the config with a `BiT` backbone.'     )
    lowerCAmelCase    				= {
        'global_padding': 'same',
        'layer_type': 'bottleneck',
        'depths': [3, 4, 9],
        'out_features': ['stage1', 'stage2', 'stage3'],
        'embedding_dynamic_padding': True,
    }
    lowerCAmelCase    				= BitConfig(**_snake_case     )
   elif isinstance(_snake_case				,       _snake_case     ):
    logger.info('Initializing the config with a `BiT` backbone.'     )
    lowerCAmelCase    				= BitConfig(**_snake_case     )
   elif isinstance(_snake_case				,       _snake_case     ):
    lowerCAmelCase    				= backbone_config
   else:
    raise ValueError(
        F'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'     )

   lowerCAmelCase    				= backbone_featmap_shape
   lowerCAmelCase    				= neck_ignore_stages

   if readout_type != "project":
    raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.'     )
  else:
   lowerCAmelCase    				= None
   lowerCAmelCase    				= None
   lowerCAmelCase    				= []

  lowerCAmelCase    				= num_hidden_layers
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= intermediate_size
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= layer_norm_eps
  lowerCAmelCase    				= image_size
  lowerCAmelCase    				= patch_size
  lowerCAmelCase    				= num_channels
  lowerCAmelCase    				= qkv_bias
  lowerCAmelCase    				= backbone_out_indices
  if readout_type not in ["ignore", "add", "project"]:
   raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']'     )
  lowerCAmelCase    				= readout_type
  lowerCAmelCase    				= reassemble_factors
  lowerCAmelCase    				= neck_hidden_sizes
  lowerCAmelCase    				= fusion_hidden_size
  lowerCAmelCase    				= head_in_index
  lowerCAmelCase    				= use_batch_norm_in_fusion_residual
  # auxiliary head attributes (semantic segmentation)
  lowerCAmelCase    				= use_auxiliary_head
  lowerCAmelCase    				= auxiliary_loss_weight
  lowerCAmelCase    				= semantic_loss_ignore_index
  lowerCAmelCase    				= semantic_classifier_dropout



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= copy.deepcopy(self.__dict__     )

  if output["backbone_config"] is not None:
   lowerCAmelCase    				= self.backbone_config.to_dict()

  lowerCAmelCase    				= self.__class__.model_type
  return output












"""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     :						str						       =  logging.getLogger(__name__)

def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= 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    				= parser.parse_args()

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

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

 lowerCAmelCase    				= []
 lowerCAmelCase    				= 0
 lowerCAmelCase    				= 1_0000
 lowerCAmelCase    				= time.time()
 for text in data:
  lowerCAmelCase    				= F'{bos} {text.strip()} {sep}'
  lowerCAmelCase    				= tokenizer.encode(_UpperCAmelCase  ,     add_special_tokens=_UpperCAmelCase			)
  rslt.append(_UpperCAmelCase			)

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

 lowerCAmelCase    				= F'{args.dump_file}.{args.tokenizer_name}.pickle'
 lowerCAmelCase    				= tokenizer.vocab_size
 if vocab_size < (1 << 16):
  lowerCAmelCase    				= [np.uintaa(_UpperCAmelCase			) for d in rslt]
 else:
  lowerCAmelCase    				= [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()





