
"""simple docstring"""

import copy

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


UpperCamelCase			:       List[Any]									=				logging.get_logger(__name__)

UpperCamelCase			:       Optional[Any]									=				{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"deta"
     lowercase		     =		{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }





     def __init__(			self			,  __UpperCAmelCase=None			,  __UpperCAmelCase=900			,  __UpperCAmelCase=2048			,  __UpperCAmelCase=6			,  __UpperCAmelCase=2048			,  __UpperCAmelCase=8			,  __UpperCAmelCase=6			,  __UpperCAmelCase=1024			,  __UpperCAmelCase=8			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=True			,  __UpperCAmelCase="relu"			,  __UpperCAmelCase=256			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=1.0			,  __UpperCAmelCase=True			,  __UpperCAmelCase=False			,  __UpperCAmelCase="sine"			,  __UpperCAmelCase=5			,  __UpperCAmelCase=4			,  __UpperCAmelCase=4			,  __UpperCAmelCase=True			,  __UpperCAmelCase=300			,  __UpperCAmelCase=True			,  __UpperCAmelCase=True			,  __UpperCAmelCase=1			,  __UpperCAmelCase=5			,  __UpperCAmelCase=2			,  __UpperCAmelCase=1			,  __UpperCAmelCase=1			,  __UpperCAmelCase=5			,  __UpperCAmelCase=2			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.2_5			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      if backbone_config is None:
       logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'   )
       __UpperCamelCase       						= CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4']   )
      else:
       if isinstance(__UpperCAmelCase			,  __UpperCAmelCase   ):
        __UpperCamelCase       						= backbone_config.pop('model_type'   )
        __UpperCamelCase       						= CONFIG_MAPPING[backbone_model_type]
        __UpperCamelCase       						= config_class.from_dict(__UpperCAmelCase   )

      __UpperCamelCase       						= backbone_config
      __UpperCamelCase       						= num_queries
      __UpperCamelCase       						= max_position_embeddings
      __UpperCamelCase       						= d_model
      __UpperCamelCase       						= encoder_ffn_dim
      __UpperCamelCase       						= encoder_layers
      __UpperCamelCase       						= encoder_attention_heads
      __UpperCamelCase       						= decoder_ffn_dim
      __UpperCamelCase       						= decoder_layers
      __UpperCamelCase       						= decoder_attention_heads
      __UpperCamelCase       						= dropout
      __UpperCamelCase       						= attention_dropout
      __UpperCamelCase       						= activation_dropout
      __UpperCamelCase       						= activation_function
      __UpperCamelCase       						= init_std
      __UpperCamelCase       						= init_xavier_std
      __UpperCamelCase       						= encoder_layerdrop
      __UpperCamelCase       						= auxiliary_loss
      __UpperCamelCase       						= position_embedding_type
      # deformable attributes
      __UpperCamelCase       						= num_feature_levels
      __UpperCamelCase       						= encoder_n_points
      __UpperCamelCase       						= decoder_n_points
      __UpperCamelCase       						= two_stage
      __UpperCamelCase       						= two_stage_num_proposals
      __UpperCamelCase       						= with_box_refine
      __UpperCamelCase       						= assign_first_stage
      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
      __UpperCamelCase       						= class_cost
      __UpperCamelCase       						= bbox_cost
      __UpperCamelCase       						= giou_cost
      # Loss coefficients
      __UpperCamelCase       						= mask_loss_coefficient
      __UpperCamelCase       						= dice_loss_coefficient
      __UpperCamelCase       						= bbox_loss_coefficient
      __UpperCamelCase       						= giou_loss_coefficient
      __UpperCamelCase       						= eos_coefficient
      __UpperCamelCase       						= focal_alpha
      super().__init__(is_encoder_decoder=__UpperCAmelCase			,  **__UpperCAmelCase   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.encoder_attention_heads





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.d_model







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= copy.deepcopy(self.__dict__   )
      __UpperCamelCase       						= self.backbone_config.to_dict()
      __UpperCamelCase       						= self.__class__.model_type
      return output





"""simple docstring"""

import re

from filelock import FileLock


try:
      import nltk

      UpperCamelCase			:       int									=				True
except (ImportError, ModuleNotFoundError):
      UpperCamelCase			:       List[str]									=				False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)






def 							A		(       snake_case   :str	)   ->			str:
 re.sub('<n>'       ,      ''       ,      snake_case	)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(snake_case	)	)



