





from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
      from ..models.auto import AutoTokenizer

class 	UpperCamelCase__				:



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					Tuple )  ->	List[Any]:




           '''simple docstring'''


           raise NotImplementedError()






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	str:




           '''simple docstring'''


           raise NotImplementedError()

class 	UpperCamelCase__				(   __lowercase     ):



     '''simple docstring'''

     def __init__(							self		:					Any		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					Optional[Any] = False		,**lowerCamelCase__		:					Tuple )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		tokenizer
           SCREAMING_SNAKE_CASE	=		skip_prompt
           SCREAMING_SNAKE_CASE	=		decode_kwargs

           # variables used in the streaming process
           SCREAMING_SNAKE_CASE	=		[]
           SCREAMING_SNAKE_CASE	=		0
           SCREAMING_SNAKE_CASE	=		True






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					str )  ->	int:




           '''simple docstring'''


           if len(value.shape ) > 1 and value.shape[0] > 1:
                 raise ValueError("""TextStreamer only supports batch size 1""" )
           elif len(value.shape ) > 1:
                 SCREAMING_SNAKE_CASE	=		value[0]

           if self.skip_prompt and self.next_tokens_are_prompt:
                 SCREAMING_SNAKE_CASE	=		False
                 return

           # Add the new token to the cache and decodes the entire thing.
           self.token_cache.extend(value.tolist() )
           SCREAMING_SNAKE_CASE	=		self.tokenizer.decode(self.token_cache		,**self.decode_kwargs )

           # After the symbol for a new line, we flush the cache.
           if text.endswith("""\n""" ):
                 SCREAMING_SNAKE_CASE	=		text[self.print_len :]
                 SCREAMING_SNAKE_CASE	=		[]
                 SCREAMING_SNAKE_CASE	=		0
           # If the last token is a CJK character, we print the characters.
           elif len(__A ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
                 SCREAMING_SNAKE_CASE	=		text[self.print_len :]
                 self.print_len += len(__A )
           # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
           # which may change with the subsequent token -- there are probably smarter ways to do this!)
           else:
                 SCREAMING_SNAKE_CASE	=		text[self.print_len : text.rfind(""" """ ) + 1]
                 self.print_len += len(__A )

           self.on_finalized_text(__A )






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	int:




           '''simple docstring'''


           if len(self.token_cache ) > 0:
                 SCREAMING_SNAKE_CASE	=		self.tokenizer.decode(self.token_cache		,**self.decode_kwargs )
                 SCREAMING_SNAKE_CASE	=		text[self.print_len :]
                 SCREAMING_SNAKE_CASE	=		[]
                 SCREAMING_SNAKE_CASE	=		0
           else:
                 SCREAMING_SNAKE_CASE	=		""""""

           SCREAMING_SNAKE_CASE	=		True
           self.on_finalized_text(__A		,stream_end=__A )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Optional[int] = False )  ->	Tuple:




           '''simple docstring'''


           print(__A		,flush=__A		,end="""""" if not stream_end else None )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					int )  ->	str:




           '''simple docstring'''


           if (
               (cp >= 0X4_e00 and cp <= 0X9_fff)
               or (cp >= 0X3_400 and cp <= 0X4_dbf)  #
               or (cp >= 0X20_000 and cp <= 0X2a_6df)  #
               or (cp >= 0X2a_700 and cp <= 0X2b_73f)  #
               or (cp >= 0X2b_740 and cp <= 0X2b_81f)  #
               or (cp >= 0X2b_820 and cp <= 0X2c_eaf)  #
               or (cp >= 0Xf_900 and cp <= 0Xf_aff)
               or (cp >= 0X2f_800 and cp <= 0X2f_a1f)  #
           ):  #
                 return True

           return False

class 	UpperCamelCase__				(   __lowercase     ):



     '''simple docstring'''

     def __init__(							self		:					List[str]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					Union[str, Any] = False		,lowerCamelCase__		:					Optional[int] = None		,**lowerCamelCase__		:					Union[str, Any] )  ->	List[Any]:




           '''simple docstring'''


           super().__init__(__A		,__A		,**__A )
           SCREAMING_SNAKE_CASE	=		Queue()
           SCREAMING_SNAKE_CASE	=		None
           SCREAMING_SNAKE_CASE	=		timeout






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Dict = False )  ->	Any:




           '''simple docstring'''


           self.text_queue.put(__A		,timeout=self.timeout )
           if stream_end:
                 self.text_queue.put(self.stop_signal		,timeout=self.timeout )






     def __iter__(							self		:					Dict )  ->	Optional[int]:




           '''simple docstring'''


           return self






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.text_queue.get(timeout=self.timeout )
           if value == self.stop_signal:
                 raise StopIteration()
           else:
                 return value









import copy

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


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_		     = {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			str				=    "deta"
     __snake_case					:			int				=    {
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }






     def __init__(							self		:					List[Any]		,lowerCamelCase__		:					Any=None		,lowerCamelCase__		:					Optional[int]=900		,lowerCamelCase__		:					Optional[Any]=2048		,lowerCamelCase__		:					Optional[Any]=6		,lowerCamelCase__		:					Dict=2048		,lowerCamelCase__		:					Optional[int]=8		,lowerCamelCase__		:					List[str]=6		,lowerCamelCase__		:					Union[str, Any]=1024		,lowerCamelCase__		:					str=8		,lowerCamelCase__		:					Union[str, Any]=0.0		,lowerCamelCase__		:					Dict=True		,lowerCamelCase__		:					Dict="relu"		,lowerCamelCase__		:					int=256		,lowerCamelCase__		:					Optional[Any]=0.1		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					Dict=0.02		,lowerCamelCase__		:					List[Any]=1.0		,lowerCamelCase__		:					List[str]=True		,lowerCamelCase__		:					int=False		,lowerCamelCase__		:					int="sine"		,lowerCamelCase__		:					Any=5		,lowerCamelCase__		:					Optional[int]=4		,lowerCamelCase__		:					Optional[int]=4		,lowerCamelCase__		:					List[Any]=True		,lowerCamelCase__		:					Optional[int]=300		,lowerCamelCase__		:					Optional[Any]=True		,lowerCamelCase__		:					Optional[Any]=True		,lowerCamelCase__		:					Optional[int]=1		,lowerCamelCase__		:					Tuple=5		,lowerCamelCase__		:					Tuple=2		,lowerCamelCase__		:					Dict=1		,lowerCamelCase__		:					str=1		,lowerCamelCase__		:					List[Any]=5		,lowerCamelCase__		:					Tuple=2		,lowerCamelCase__		:					Union[str, Any]=0.1		,lowerCamelCase__		:					List[str]=0.25		,**lowerCamelCase__		:					int		,)  ->	List[Any]:




           '''simple docstring'''


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

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






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	int:




           '''simple docstring'''


           return self.encoder_attention_heads






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	int:




           '''simple docstring'''


           return self.d_model






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Optional[int]:




           '''simple docstring'''


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


