






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 	UpperCamelCase	(							):




     '''simple docstring'''






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

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

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

     __snake_case      :List[Any] 							=    []
     __snake_case      :List[Any] 							=    0
     __snake_case      :Any 							=    1_0000
     __snake_case      :int 							=    time.time()
     for text in data:
          __snake_case      :int 							=    f'''{bos} {text.strip()} {sep}'''
          __snake_case      :Tuple 							=    tokenizer.encode(snake_case__						,add_special_tokens=snake_case__	)
          rslt.append(snake_case__	)

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

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


if __name__ == "__main__":
     main()










from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {
    """microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}




class       snake_case__ (				lowercase_						,     lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					List[Any]								=			"focalnet"



       def __init__(				self      ,    a__=2_24      ,    a__=4      ,    a__=3      ,    a__=96      ,    a__=False      ,    a__=[1_92, 3_84, 7_68, 7_68]      ,    a__=[2, 2, 6, 2]      ,    a__=[2, 2, 2, 2]      ,    a__=[3, 3, 3, 3]      ,    a__="gelu"      ,    a__=4.0      ,    a__=0.0      ,    a__=0.1      ,    a__=False      ,    a__=1e-4      ,    a__=False      ,    a__=False      ,    a__=False      ,    a__=0.02      ,    a__=1e-5      ,    a__=32      ,    a__=None      ,    a__=None      ,    **a__      ,    )			->   Tuple:






            '''simple docstring'''





            super().__init__(**a__     )

            __snake_case      :Optional[Any] 							=    image_size
            __snake_case      :Dict 							=    patch_size
            __snake_case      :Tuple 							=    num_channels
            __snake_case      :List[str] 							=    embed_dim
            __snake_case      :Any 							=    use_conv_embed
            __snake_case      :List[Any] 							=    hidden_sizes
            __snake_case      :str 							=    depths
            __snake_case      :List[Any] 							=    focal_levels
            __snake_case      :int 							=    focal_windows
            __snake_case      :Any 							=    hidden_act
            __snake_case      :List[str] 							=    mlp_ratio
            __snake_case      :str 							=    hidden_dropout_prob
            __snake_case      :List[str] 							=    drop_path_rate
            __snake_case      :int 							=    use_layerscale
            __snake_case      :int 							=    layerscale_value
            __snake_case      :Tuple 							=    use_post_layernorm
            __snake_case      :Dict 							=    use_post_layernorm_in_modulation
            __snake_case      :Union[str, Any] 							=    normalize_modulator
            __snake_case      :List[str] 							=    initializer_range
            __snake_case      :Optional[Any] 							=    layer_norm_eps
            __snake_case      :Tuple 							=    encoder_stride
            __snake_case      :List[str] 							=    ["""stem"""] + [F'''stage{idx}''' for idx in range(1      ,    len(self.depths     ) + 1     )]
            __snake_case							,			__snake_case      :str 							=    get_aligned_output_features_output_indices(
                out_features=a__      ,    out_indices=a__      ,    stage_names=self.stage_names     )


