


import copy
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
from ..auto import CONFIG_MAPPING


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}





class 						__SCREAMING_SNAKE_CASE					(      __A ):
     UpperCAmelCase 					=				'''conditional_detr'''
     UpperCAmelCase 					=				['''past_key_values''']
     UpperCAmelCase 					=				{
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }






     def __init__(			self     ,							__UpperCamelCase=True     ,							__UpperCamelCase=None     ,							__UpperCamelCase=3     ,							__UpperCamelCase=300     ,							__UpperCamelCase=6     ,							__UpperCamelCase=2_048     ,							__UpperCamelCase=8     ,							__UpperCamelCase=6     ,							__UpperCamelCase=2_048     ,							__UpperCamelCase=8     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=True     ,							__UpperCamelCase="relu"     ,							__UpperCamelCase=256     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=1.0     ,							__UpperCamelCase=False     ,							__UpperCamelCase="sine"     ,							__UpperCamelCase="resnet50"     ,							__UpperCamelCase=True     ,							__UpperCamelCase=False     ,							__UpperCamelCase=2     ,							__UpperCamelCase=5     ,							__UpperCamelCase=2     ,							__UpperCamelCase=1     ,							__UpperCamelCase=1     ,							__UpperCamelCase=2     ,							__UpperCamelCase=5     ,							__UpperCamelCase=2     ,							__UpperCamelCase=0.25     ,							**__UpperCamelCase     ,							)       ->    Optional[int]:





          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."		)
                    _a						=		CONFIG_MAPPING['resnet'](out_features=["stage4"]		)
               elif isinstance(__UpperCamelCase     ,							__UpperCamelCase		):
                    _a						=		backbone_config.get("model_type"		)
                    _a						=		CONFIG_MAPPING[backbone_model_type]
                    _a						=		config_class.from_dict(__UpperCamelCase		)

          _a						=		use_timm_backbone
          _a						=		backbone_config
          _a						=		num_channels
          _a						=		num_queries
          _a						=		d_model
          _a						=		encoder_ffn_dim
          _a						=		encoder_layers
          _a						=		encoder_attention_heads
          _a						=		decoder_ffn_dim
          _a						=		decoder_layers
          _a						=		decoder_attention_heads
          _a						=		dropout
          _a						=		attention_dropout
          _a						=		activation_dropout
          _a						=		activation_function
          _a						=		init_std
          _a						=		init_xavier_std
          _a						=		encoder_layerdrop
          _a						=		decoder_layerdrop
          _a						=		encoder_layers
          _a						=		auxiliary_loss
          _a						=		position_embedding_type
          _a						=		backbone
          _a						=		use_pretrained_backbone
          _a						=		dilation
          # Hungarian matcher
          _a						=		class_cost
          _a						=		bbox_cost
          _a						=		giou_cost
          # Loss coefficients
          _a						=		mask_loss_coefficient
          _a						=		dice_loss_coefficient
          _a						=		cls_loss_coefficient
          _a						=		bbox_loss_coefficient
          _a						=		giou_loss_coefficient
          _a						=		focal_alpha
          super().__init__(is_encoder_decoder=__UpperCamelCase     ,							**__UpperCamelCase		)






     @property
     def  a_					(			self		)       ->    Dict:
          return self.encoder_attention_heads






     @property
     def  a_					(			self		)       ->    Tuple:
          return self.d_model






     def  a_					(			self		)       ->    Tuple:
          _a						=		copy.deepcopy(self.__dict__		)
          if self.backbone_config is not None:
               _a						=		self.backbone_config.to_dict()
          _a						=		self.__class__.model_type
          return output







class 						__SCREAMING_SNAKE_CASE					(      __A ):
     UpperCAmelCase 					=				version.parse('''1.11''' )






     @property
     def  a_					(			self		)       ->    str:
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                  ("pixel_mask", {0: "batch"}),
              ]		)






     @property
     def  a_					(			self		)       ->    Optional[Any]:
          return 1e-5






     @property
     def  a_					(			self		)       ->    Optional[Any]:
          return 12





'''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()

