

'''simple docstring'''




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCAmelCase         =						logging.get_logger(__name__)

_lowerCAmelCase         =						{
    """SenseTime/deformable-detr""": """https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json""",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}






class   _SCREAMING_SNAKE_CASE   (   lowerCAmelCase_     ):
 __SCREAMING_SNAKE_CASE					:Tuple      	=					"""deformable_detr"""
 __SCREAMING_SNAKE_CASE					:int      	=					{
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }



 def __init__(   self		:   Tuple			,				a__		:   Any=True			,				a__		:   str=None			,				a__		:   str=3			,				a__		:   str=300			,				a__		:   Dict=1024			,				a__		:   Union[str, Any]=6			,				a__		:   Tuple=1024			,				a__		:   str=8			,				a__		:   Optional[Any]=6			,				a__		:   Union[str, Any]=1024			,				a__		:   Optional[Any]=8			,				a__		:   Optional[int]=0.0			,				a__		:   int=True			,				a__		:   List[Any]="relu"			,				a__		:   Union[str, Any]=256			,				a__		:   Dict=0.1			,				a__		:   Optional[int]=0.0			,				a__		:   Optional[int]=0.0			,				a__		:   List[Any]=0.02			,				a__		:   List[str]=1.0			,				a__		:   Any=True			,				a__		:   Dict=False			,				a__		:   str="sine"			,				a__		:   Dict="resnet50"			,				a__		:   Union[str, Any]=True			,				a__		:   List[str]=False			,				a__		:   Tuple=4			,				a__		:   int=4			,				a__		:   Optional[Any]=4			,				a__		:   Dict=False			,				a__		:   Optional[int]=300			,				a__		:   List[str]=False			,				a__		:   Union[str, Any]=1			,				a__		:   Tuple=5			,				a__		:   List[str]=2			,				a__		:   Any=1			,				a__		:   List[Any]=1			,				a__		:   Tuple=5			,				a__		:   Optional[Any]=2			,				a__		:   Optional[Any]=0.1			,				a__		:   Optional[int]=0.25			,				a__		:   int=False			,				**a__		:   Union[str, Any]			,				):

      if backbone_config is not None and use_timm_backbone:
           raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''      )

      if not use_timm_backbone:
           if backbone_config is None:
                logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''      )
                __magic_name__						       =    CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']      )
           elif isinstance(__SCREAMING_SNAKE_CASE			,				__SCREAMING_SNAKE_CASE      ):
                __magic_name__						       =    backbone_config.get('''model_type'''      )
                __magic_name__						       =    CONFIG_MAPPING[backbone_model_type]
                __magic_name__						       =    config_class.from_dict(__SCREAMING_SNAKE_CASE      )
      __magic_name__						       =    use_timm_backbone
      __magic_name__						       =    backbone_config
      __magic_name__						       =    num_channels
      __magic_name__						       =    num_queries
      __magic_name__						       =    max_position_embeddings
      __magic_name__						       =    d_model
      __magic_name__						       =    encoder_ffn_dim
      __magic_name__						       =    encoder_layers
      __magic_name__						       =    encoder_attention_heads
      __magic_name__						       =    decoder_ffn_dim
      __magic_name__						       =    decoder_layers
      __magic_name__						       =    decoder_attention_heads
      __magic_name__						       =    dropout
      __magic_name__						       =    attention_dropout
      __magic_name__						       =    activation_dropout
      __magic_name__						       =    activation_function
      __magic_name__						       =    init_std
      __magic_name__						       =    init_xavier_std
      __magic_name__						       =    encoder_layerdrop
      __magic_name__						       =    auxiliary_loss
      __magic_name__						       =    position_embedding_type
      __magic_name__						       =    backbone
      __magic_name__						       =    use_pretrained_backbone
      __magic_name__						       =    dilation
      # deformable attributes
      __magic_name__						       =    num_feature_levels
      __magic_name__						       =    encoder_n_points
      __magic_name__						       =    decoder_n_points
      __magic_name__						       =    two_stage
      __magic_name__						       =    two_stage_num_proposals
      __magic_name__						       =    with_box_refine
      if two_stage is True and with_box_refine is False:
           raise ValueError('''If two_stage is True, with_box_refine must be True.'''      )
      # Hungarian matcher
      __magic_name__						       =    class_cost
      __magic_name__						       =    bbox_cost
      __magic_name__						       =    giou_cost
      # Loss coefficients
      __magic_name__						       =    mask_loss_coefficient
      __magic_name__						       =    dice_loss_coefficient
      __magic_name__						       =    bbox_loss_coefficient
      __magic_name__						       =    giou_loss_coefficient
      __magic_name__						       =    eos_coefficient
      __magic_name__						       =    focal_alpha
      __magic_name__						       =    disable_custom_kernels
      super().__init__(is_encoder_decoder=__SCREAMING_SNAKE_CASE			,				**__SCREAMING_SNAKE_CASE      )



 @property
 def 			snake_case__  (   self		:   Union[str, Any]      ):
      return self.encoder_attention_heads



 @property
 def 			snake_case__  (   self		:   Union[str, Any]      ):
      return self.d_model



 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    copy.deepcopy(self.__dict__      )
      if self.backbone_config is not None:
           __magic_name__						       =    self.backbone_config.to_dict()
      __magic_name__						       =    self.__class__.model_type
      return output









'''simple docstring'''




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def 						UpperCamelCase	(			a		,						a		,						a		,						a=1024			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    [], []

     __magic_name__						       =    list(zip(a		,						a			)			)
     __magic_name__       ,  __magic_name__						       =    sorted_examples[0]

     def is_too_big(a			):
          return tok(a		,						return_tensors='''pt'''			).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]			):
          __magic_name__						       =    new_src + ''' ''' + src
          __magic_name__						       =    new_tgt + ''' ''' + tgt
          if is_too_big(a			) or is_too_big(a			):  # cant fit, finalize example
               finished_src.append(a			)
               finished_tgt.append(a			)
               __magic_name__       ,  __magic_name__						       =    src, tgt
          else:  # can fit, keep adding
               __magic_name__       ,  __magic_name__						       =    cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(a			)
          finished_tgt.append(a			)
     return finished_src, finished_tgt




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     Any:






     '''simple docstring'''
     __magic_name__						       =    Path(a			)
     save_path.mkdir(exist_ok=a			)
     for split in ["train"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__       ,  __magic_name__						       =    pack_examples(a		,						a		,						a		,						a			)
          print(F'''packed {split} split from {len(a			)} examples -> {len(a			)}.'''			)
          Path(save_path / F'''{split}.source'''			).open('''w'''			).write('''\n'''.join(a			)			)
          Path(save_path / F'''{split}.target'''			).open('''w'''			).write('''\n'''.join(a			)			)
     for split in ["val", "test"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          shutil.copyfile(a		,						save_path / F'''{split}.source'''			)
          shutil.copyfile(a		,						save_path / F'''{split}.target'''			)




def 						UpperCamelCase	(			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    argparse.ArgumentParser()
     parser.add_argument('''--tok_name'''		,						type=a		,						help='''like facebook/bart-large-cnn,t5-base, etc.'''			)
     parser.add_argument('''--max_seq_len'''		,						type=a		,						default=128			)
     parser.add_argument('''--data_dir'''		,						type=a			)
     parser.add_argument('''--save_path'''		,						type=a			)
     __magic_name__						       =    parser.parse_args()
     __magic_name__						       =    AutoTokenizer.from_pretrained(args.tok_name			)
     return pack_data_dir(a		,						Path(args.data_dir			)		,						args.max_seq_len		,						args.save_path			)


if __name__ == "__main__":
      packer_cli()






