




"""simple docstring"""



import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
SCREAMING_SNAKE_CASE    =	logging.getLogger(__name__)

if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	argparse.ArgumentParser(
          description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
      )
      parser.add_argument(
          "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
      )
      parser.add_argument(
          "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
      )
      parser.add_argument("--vocab_size", default=3_0522, type=int)
      SCREAMING_SNAKE_CASE    =	parser.parse_args()

      logger.info(f'Loading data from {args.data_file}')
      with open(args.data_file, "rb") as fp:
            SCREAMING_SNAKE_CASE    =	pickle.load(fp)

      logger.info("Counting occurrences for MLM.")
      SCREAMING_SNAKE_CASE    =	Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      SCREAMING_SNAKE_CASE    =	[0] * args.vocab_size
      for k, v in counter.items():
            SCREAMING_SNAKE_CASE    =	v

      logger.info(f'Dump to {args.token_counts_dump}')
      with open(args.token_counts_dump, "wb") as handle:
            pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)











"""simple docstring"""



import copy

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


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "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      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''dpt'''




    def __init__(   self						:	List[Any]     ,			snake_case_						:	Union[str, Any]=768     ,			snake_case_						:	Tuple=12     ,			snake_case_						:	Tuple=12     ,			snake_case_						:	List[Any]=3_072     ,			snake_case_						:	Dict="gelu"     ,			snake_case_						:	Tuple=0.0     ,			snake_case_						:	int=0.0     ,			snake_case_						:	Optional[int]=0.02     ,			snake_case_						:	Union[str, Any]=1e-12     ,			snake_case_						:	Tuple=384     ,			snake_case_						:	Tuple=16     ,			snake_case_						:	Optional[Any]=3     ,			snake_case_						:	Dict=False     ,			snake_case_						:	Any=True     ,			snake_case_						:	Any=[2, 5, 8, 11]     ,			snake_case_						:	Union[str, Any]="project"     ,			snake_case_						:	Union[str, Any]=[4, 2, 1, 0.5]     ,			snake_case_						:	List[str]=[96, 192, 384, 768]     ,			snake_case_						:	int=256     ,			snake_case_						:	Tuple=-1     ,			snake_case_						:	List[str]=False     ,			snake_case_						:	int=True     ,			snake_case_						:	List[Any]=0.4     ,			snake_case_						:	Optional[Any]=255     ,			snake_case_						:	List[str]=0.1     ,			snake_case_						:	List[str]=[1, 1_024, 24, 24]     ,			snake_case_						:	Union[str, Any]=[0, 1]     ,			snake_case_						:	Any=None     ,			**snake_case_						:	Optional[Any]     ,			)				->							Optional[int]:




     '''simple docstring'''

     super().__init__(**snake_case_				)

     A__	=    hidden_size
     A__	=    is_hybrid

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

      A__	=    backbone_featmap_shape
      A__	=    neck_ignore_stages

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

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




    def 							__magic_name__							(   self						:	List[str]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    copy.deepcopy(self.__dict__				)

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

     A__	=    self.__class__.model_type
     return output





