


from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
   from ..models.auto import AutoTokenizer

class     UpperCAmelCase__ :



     '''simple docstring'''




     def 	lowerCAmelCase_ (			self							:							List[Any]						,   UpperCamelCase							:							List[Any]		):





            """simple docstring"""



            raise NotImplementedError()




     def 	lowerCAmelCase_ (			self							:							Any		):





            """simple docstring"""



            raise NotImplementedError()




class     UpperCAmelCase__ (   __snake_case		):



     '''simple docstring'''




     def __init__(			self							:							Dict						,   UpperCamelCase							:							Any						,   UpperCamelCase							:							Dict = False						,   **UpperCamelCase							:							List[Any]		):





            """simple docstring"""



            _lowercase       :					int     							=      tokenizer
            _lowercase       :					Any     							=      skip_prompt
            _lowercase       :					Any     							=      decode_kwargs

            # variables used in the streaming process
            _lowercase       :					List[Any]     							=      []
            _lowercase       :					Tuple     							=      0
            _lowercase       :					Tuple     							=      True




     def 	lowerCAmelCase_ (			self							:							Optional[int]						,   UpperCamelCase							:							Optional[Any]		):





            """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:
                   _lowercase       :					Union[str, Any]     							=      value[0]

            if self.skip_prompt and self.next_tokens_are_prompt:
                   _lowercase       :					Optional[int]     							=      False
                   return

            # Add the new token to the cache and decodes the entire thing.
            self.token_cache.extend(value.tolist()		)
            _lowercase       :					Optional[Any]     							=      self.tokenizer.decode(self.token_cache						,   **self.decode_kwargs		)

            # After the symbol for a new line, we flush the cache.
            if text.endswith('''\n'''		):
                   _lowercase       :					Optional[Any]     							=      text[self.print_len :]
                   _lowercase       :					Any     							=      []
                   _lowercase       :					Optional[Any]     							=      0
            # If the last token is a CJK character, we print the characters.
            elif len(UpperCamelCase		) > 0 and self._is_chinese_char(ord(text[-1]		)		):
                   _lowercase       :					Optional[Any]     							=      text[self.print_len :]
                   self.print_len += len(UpperCamelCase		)
            # 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:
                   _lowercase       :					str     							=      text[self.print_len : text.rfind(''' '''		) + 1]
                   self.print_len += len(UpperCamelCase		)

            self.on_finalized_text(UpperCamelCase		)




     def 	lowerCAmelCase_ (			self							:							str		):





            """simple docstring"""



            if len(self.token_cache		) > 0:
                   _lowercase       :					Any     							=      self.tokenizer.decode(self.token_cache						,   **self.decode_kwargs		)
                   _lowercase       :					Dict     							=      text[self.print_len :]
                   _lowercase       :					str     							=      []
                   _lowercase       :					int     							=      0
            else:
                   _lowercase       :					Union[str, Any]     							=      ''''''

            _lowercase       :					Tuple     							=      True
            self.on_finalized_text(UpperCamelCase						,   stream_end=UpperCamelCase		)




     def 	lowerCAmelCase_ (			self							:							Optional[int]						,   UpperCamelCase							:							Optional[int]						,   UpperCamelCase							:							Optional[Any] = False		):





            """simple docstring"""



            print(UpperCamelCase						,   flush=UpperCamelCase						,   end='''''' if not stream_end else None		)




     def 	lowerCAmelCase_ (			self							:							List[str]						,   UpperCamelCase							:							Dict		):





            """simple docstring"""



            if (
                (cp >= 0x4E_00 and cp <= 0x9F_FF)
                or (cp >= 0x34_00 and cp <= 0x4D_BF)  #
                or (cp >= 0x2_00_00 and cp <= 0x2_A6_DF)  #
                or (cp >= 0x2_A7_00 and cp <= 0x2_B7_3F)  #
                or (cp >= 0x2_B7_40 and cp <= 0x2_B8_1F)  #
                or (cp >= 0x2_B8_20 and cp <= 0x2_CE_AF)  #
                or (cp >= 0xF9_00 and cp <= 0xFA_FF)
                or (cp >= 0x2_F8_00 and cp <= 0x2_FA_1F)  #
            ):  #
                   return True

            return False




class     UpperCAmelCase__ (   __snake_case		):



     '''simple docstring'''




     def __init__(			self							:							Tuple						,   UpperCamelCase							:							List[str]						,   UpperCamelCase							:							int = False						,   UpperCamelCase							:							List[Any] = None						,   **UpperCamelCase							:							Tuple		):





            """simple docstring"""



            super().__init__(UpperCamelCase						,   UpperCamelCase						,   **UpperCamelCase		)
            _lowercase       :					Tuple     							=      Queue()
            _lowercase       :					Optional[Any]     							=      None
            _lowercase       :					Any     							=      timeout




     def 	lowerCAmelCase_ (			self							:							Optional[Any]						,   UpperCamelCase							:							Optional[Any]						,   UpperCamelCase							:							Union[str, Any] = False		):





            """simple docstring"""



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




     def __iter__(			self							:							Tuple		):





            """simple docstring"""



            return self




     def 	lowerCAmelCase_ (			self							:							Tuple		):





            """simple docstring"""



            _lowercase       :					Optional[Any]     							=      self.text_queue.get(timeout=self.timeout		)
            if value == self.stop_signal:
                   raise StopIteration()
            else:
                   return value

'''simple docstring'''




import copy

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


lowercase     :							Optional[Any]       						=     logging.get_logger(__name__)

lowercase     :							int       						=     {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''deta'''
     __magic_name__          =						{
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }





     def __init__(				self		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=900		,     SCREAMING_SNAKE_CASE=2048		,     SCREAMING_SNAKE_CASE=6		,     SCREAMING_SNAKE_CASE=2048		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=6		,     SCREAMING_SNAKE_CASE=1024		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE="relu"		,     SCREAMING_SNAKE_CASE=256		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=1.0		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE="sine"		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=300		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.25		,     **SCREAMING_SNAKE_CASE		,     )					->							Any:






         """simple docstring"""






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

         A			:  str   =		backbone_config
         A			:  Optional[int]   =		num_queries
         A			:  Dict   =		max_position_embeddings
         A			:  Optional[Any]   =		d_model
         A			:  Optional[Any]   =		encoder_ffn_dim
         A			:  List[str]   =		encoder_layers
         A			:  Tuple   =		encoder_attention_heads
         A			:  Optional[Any]   =		decoder_ffn_dim
         A			:  Optional[int]   =		decoder_layers
         A			:  List[str]   =		decoder_attention_heads
         A			:  Union[str, Any]   =		dropout
         A			:  str   =		attention_dropout
         A			:  Any   =		activation_dropout
         A			:  Optional[int]   =		activation_function
         A			:  Tuple   =		init_std
         A			:  Any   =		init_xavier_std
         A			:  Optional[Any]   =		encoder_layerdrop
         A			:  int   =		auxiliary_loss
         A			:  Dict   =		position_embedding_type
         # deformable attributes
         A			:  str   =		num_feature_levels
         A			:  Optional[int]   =		encoder_n_points
         A			:  Any   =		decoder_n_points
         A			:  Tuple   =		two_stage
         A			:  Dict   =		two_stage_num_proposals
         A			:  List[str]   =		with_box_refine
         A			:  List[str]   =		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
         A			:  Dict   =		class_cost
         A			:  Optional[int]   =		bbox_cost
         A			:  Optional[Any]   =		giou_cost
         # Loss coefficients
         A			:  int   =		mask_loss_coefficient
         A			:  int   =		dice_loss_coefficient
         A			:  Tuple   =		bbox_loss_coefficient
         A			:  int   =		giou_loss_coefficient
         A			:  Dict   =		eos_coefficient
         A			:  Optional[Any]   =		focal_alpha
         super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      )





     @property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         return self.encoder_attention_heads





     @property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         return self.d_model





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Any   =		copy.deepcopy(self.__dict__      )
         A			:  Dict   =		self.backbone_config.to_dict()
         A			:  List[Any]   =		self.__class__.model_type
         return output




