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


UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					{
    '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							(				UpperCAmelCase_     ):
   __UpperCAmelCase : List[str]      =			'beit'


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						str=8_1_9_2       ,  __UpperCAmelCase     :						List[Any]=7_6_8       ,  __UpperCAmelCase     :						str=1_2       ,  __UpperCAmelCase     :						Tuple=1_2       ,  __UpperCAmelCase     :						Optional[int]=3_0_7_2       ,  __UpperCAmelCase     :						List[Any]="gelu"       ,  __UpperCAmelCase     :						Tuple=0.0       ,  __UpperCAmelCase     :						str=0.0       ,  __UpperCAmelCase     :						Optional[Any]=0.02       ,  __UpperCAmelCase     :						List[str]=1E-12       ,  __UpperCAmelCase     :						Union[str, Any]=2_2_4       ,  __UpperCAmelCase     :						Optional[Any]=1_6       ,  __UpperCAmelCase     :						Dict=3       ,  __UpperCAmelCase     :						Tuple=False       ,  __UpperCAmelCase     :						Any=False       ,  __UpperCAmelCase     :						Union[str, Any]=False       ,  __UpperCAmelCase     :						Optional[int]=False       ,  __UpperCAmelCase     :						Optional[Any]=0.1       ,  __UpperCAmelCase     :						int=0.1       ,  __UpperCAmelCase     :						List[str]=True       ,  __UpperCAmelCase     :						Any=[3, 5, 7, 1_1]       ,  __UpperCAmelCase     :						str=[1, 2, 3, 6]       ,  __UpperCAmelCase     :						Any=True       ,  __UpperCAmelCase     :						Any=0.4       ,  __UpperCAmelCase     :						Dict=2_5_6       ,  __UpperCAmelCase     :						List[str]=1       ,  __UpperCAmelCase     :						Any=False       ,  __UpperCAmelCase     :						Tuple=2_5_5       ,  **__UpperCAmelCase     :						Union[str, Any]       ,  )						->				Union[str, Any]:
         """simple docstring"""



         super().__init__(**__UpperCAmelCase		)

         UpperCAmelCase__								=  vocab_size
         UpperCAmelCase__								=  hidden_size
         UpperCAmelCase__								=  num_hidden_layers
         UpperCAmelCase__								=  num_attention_heads
         UpperCAmelCase__								=  intermediate_size
         UpperCAmelCase__								=  hidden_act
         UpperCAmelCase__								=  hidden_dropout_prob
         UpperCAmelCase__								=  attention_probs_dropout_prob
         UpperCAmelCase__								=  initializer_range
         UpperCAmelCase__								=  layer_norm_eps

         UpperCAmelCase__								=  image_size
         UpperCAmelCase__								=  patch_size
         UpperCAmelCase__								=  num_channels
         UpperCAmelCase__								=  use_mask_token
         UpperCAmelCase__								=  use_absolute_position_embeddings
         UpperCAmelCase__								=  use_relative_position_bias
         UpperCAmelCase__								=  use_shared_relative_position_bias
         UpperCAmelCase__								=  layer_scale_init_value
         UpperCAmelCase__								=  drop_path_rate
         UpperCAmelCase__								=  use_mean_pooling
         # decode head attributes (semantic segmentation)
         UpperCAmelCase__								=  out_indices
         UpperCAmelCase__								=  pool_scales
         # auxiliary head attributes (semantic segmentation)
         UpperCAmelCase__								=  use_auxiliary_head
         UpperCAmelCase__								=  auxiliary_loss_weight
         UpperCAmelCase__								=  auxiliary_channels
         UpperCAmelCase__								=  auxiliary_num_convs
         UpperCAmelCase__								=  auxiliary_concat_input
         UpperCAmelCase__								=  semantic_loss_ignore_index





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[Any]      =			version.parse('1.11'     )


   @property
   def 			lowercase_      (self     :						List[Any]		)						->				Mapping[str, Mapping[int, str]]:
         """simple docstring"""



         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]		)





   @property
   def 			lowercase_      (self     :						List[Any]		)						->				float:
         """simple docstring"""



         return 1E-4




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__											=					logging.getLogger(__name__)




def    lowerCAmelCase_ (     )     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  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."							)
      UpperCAmelCase__								=  parser.parse_args()

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

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

      UpperCAmelCase__								=  []
      UpperCAmelCase__								=  0
      UpperCAmelCase__								=  10_000
      UpperCAmelCase__								=  time.time()
      for text in data:
            UpperCAmelCase__								=  f"""{bos} {text.strip()} {sep}"""
            UpperCAmelCase__								=  tokenizer.encode(__A,  add_special_tokens=__A							)
            rslt.append(__A							)

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

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



