


import copy

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


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    "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 						__SCREAMING_SNAKE_CASE					(      _UpperCAmelCase ):
     UpperCAmelCase 					=				"""dpt"""






     def __init__(			self     ,							__UpperCamelCase=768     ,							__UpperCamelCase=12     ,							__UpperCamelCase=12     ,							__UpperCamelCase=3_072     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=1e-12     ,							__UpperCamelCase=384     ,							__UpperCamelCase=16     ,							__UpperCamelCase=3     ,							__UpperCamelCase=False     ,							__UpperCamelCase=True     ,							__UpperCamelCase=[2, 5, 8, 11]     ,							__UpperCamelCase="project"     ,							__UpperCamelCase=[4, 2, 1, 0.5]     ,							__UpperCamelCase=[96, 192, 384, 768]     ,							__UpperCamelCase=256     ,							__UpperCamelCase=-1     ,							__UpperCamelCase=False     ,							__UpperCamelCase=True     ,							__UpperCamelCase=0.4     ,							__UpperCamelCase=255     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=[1, 1_024, 24, 24]     ,							__UpperCamelCase=[0, 1]     ,							__UpperCamelCase=None     ,							**__UpperCamelCase     ,							)       ->    int:
          super().__init__(**lowercase__		)

          _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(**lowercase__		)
               elif isinstance(lowercase__     ,							lowercase__		):
                    logger.info("Initializing the config with a `BiT` backbone."		)
                    _a						=		BitConfig(**lowercase__		)
               elif isinstance(lowercase__     ,							lowercase__		):
                    _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  a_					(			self		)       ->    Optional[Any]:
          _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





'''simple docstring'''

import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def __UpperCamelCase   (			__lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: Dict=1024				)      ->     str:







     '''simple docstring'''






     _a			,			_a						=		[], []

     _a						=		list(zip(__lowerCamelCase      ,  __lowerCamelCase				)				)
     _a			,			_a						=		sorted_examples[0]

     def is_too_big(__lowerCamelCase		: Union[str, Any]				):
          return tok(__lowerCamelCase      ,  return_tensors="pt"				).input_ids.shape[1] > max_tokens

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

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






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: Path      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: Optional[int]				)      ->     Optional[int]:







     '''simple docstring'''






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






def __UpperCamelCase   (			)      ->     Any:







     '''simple docstring'''






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


if __name__ == "__main__":
   packer_cli()

