

import copy

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


snake_case_        =			logging.get_logger(__name__)

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


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[str]														=					"""deta"""
    __lowerCamelCase						:	List[Any]														=					{
        """hidden_size""": """d_model""",
        """num_attention_heads""": """encoder_attention_heads""",
    }





    def __init__(				self						,       a=None						,       a=900						,       a=2048						,       a=6						,       a=2048						,       a=8						,       a=6						,       a=1024						,       a=8						,       a=0.0						,       a=True						,       a="relu"						,       a=256						,       a=0.1						,       a=0.0						,       a=0.0						,       a=0.02						,       a=1.0						,       a=True						,       a=False						,       a="sine"						,       a=5						,       a=4						,       a=4						,       a=True						,       a=300						,       a=True						,       a=True						,       a=1						,       a=5						,       a=2						,       a=1						,       a=1						,       a=5						,       a=2						,       a=0.1						,       a=0.25						,       **a						,       ):
         if backbone_config is None:
              logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.')
              lowercase__   :       Optional[Any]				=	CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'])
         else:
              if isinstance(a						,       a):
                   lowercase__   :       str				=	backbone_config.pop('model_type')
                   lowercase__   :       str				=	CONFIG_MAPPING[backbone_model_type]
                   lowercase__   :       Tuple				=	config_class.from_dict(a)

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





    @property
    def 				snake_case_    (				self):
         return self.encoder_attention_heads





    @property
    def 				snake_case_    (				self):
         return self.d_model





    def 				snake_case_    (				self):
         lowercase__   :       Optional[int]				=	copy.deepcopy(self.__dict__)
         lowercase__   :       Union[str, Any]				=	self.backbone_config.to_dict()
         lowercase__   :       List[str]				=	self.__class__.model_type
         return output




from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    def __init__(				self						,       a						,       a = None						,       a = None						,       a = True						,       a = None						,       a = False						,       a = None						,       a = True						,       a = "arrow"						,       **a						,       ):
         super().__init__(
             split=a						,       features=a						,       cache_dir=a						,       keep_in_memory=a						,       streaming=a						,       **a						,       )
         lowercase__   :       Optional[int]				=	load_from_cache_file
         lowercase__   :       Optional[int]				=	file_format
         lowercase__   :       int				=	Spark(
             df=a						,       features=a						,       cache_dir=a						,       working_dir=a						,       **a						,       )





    def 				snake_case_    (				self):
         if self.streaming:
              return self.builder.as_streaming_dataset(split=self.split)
         lowercase__   :       Dict				=	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
         self.builder.download_and_prepare(
             download_mode=a						,       file_format=self._file_format						,       )
         return self.builder.as_dataset(split=self.split)

