
import copy

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


lowercase_ 				=			logging.get_logger(__name__)

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





class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''deta'''
     lowerCAmelCase_							   =			{
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }



     def __init__(				self							:							List[str]     ,						_A							:							Dict=None     ,						_A							:							Optional[int]=900     ,						_A							:							Dict=2048     ,						_A							:							int=6     ,						_A							:							Dict=2048     ,						_A							:							List[Any]=8     ,						_A							:							Union[str, Any]=6     ,						_A							:							Tuple=1024     ,						_A							:							Tuple=8     ,						_A							:							Dict=0.0     ,						_A							:							List[Any]=True     ,						_A							:							List[Any]="relu"     ,						_A							:							Tuple=256     ,						_A							:							Union[str, Any]=0.1     ,						_A							:							Optional[Any]=0.0     ,						_A							:							Dict=0.0     ,						_A							:							int=0.02     ,						_A							:							int=1.0     ,						_A							:							Optional[Any]=True     ,						_A							:							Any=False     ,						_A							:							Optional[int]="sine"     ,						_A							:							Optional[int]=5     ,						_A							:							List[Any]=4     ,						_A							:							Tuple=4     ,						_A							:							Dict=True     ,						_A							:							Tuple=300     ,						_A							:							Union[str, Any]=True     ,						_A							:							Optional[Any]=True     ,						_A							:							Optional[Any]=1     ,						_A							:							Dict=5     ,						_A							:							Optional[int]=2     ,						_A							:							Tuple=1     ,						_A							:							Union[str, Any]=1     ,						_A							:							Union[str, Any]=5     ,						_A							:							Tuple=2     ,						_A							:							str=0.1     ,						_A							:							Optional[int]=0.25     ,						**_A							:							Optional[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      :	Dict					=		CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4'''] )
       else:
         if isinstance(_A     ,						_A ):
           __SCREAMING_SNAKE_CASE      :	int					=		backbone_config.pop('''model_type''' )
           __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		CONFIG_MAPPING[backbone_model_type]
           __SCREAMING_SNAKE_CASE      :	Any					=		config_class.from_dict(_A )

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



     @property
     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       return self.encoder_attention_heads



     @property
     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       return self.d_model




     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




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






"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	str     =      '''speech_to_text'''
 UpperCAmelCase       :	List[Any]     =      ['''past_key_values''']
 UpperCAmelCase       :	Tuple     =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

 def __init__(					self			:			int       ,				_UpperCAmelCase			:			Union[str, Any]=10_000       ,				_UpperCAmelCase			:			Tuple=12       ,				_UpperCAmelCase			:			int=2_048       ,				_UpperCAmelCase			:			Optional[Any]=4       ,				_UpperCAmelCase			:			List[str]=6       ,				_UpperCAmelCase			:			Tuple=2_048       ,				_UpperCAmelCase			:			str=4       ,				_UpperCAmelCase			:			int=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			Optional[int]=True       ,				_UpperCAmelCase			:			Optional[Any]=True       ,				_UpperCAmelCase			:			Union[str, Any]="relu"       ,				_UpperCAmelCase			:			List[Any]=256       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Any=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			str=0.02       ,				_UpperCAmelCase			:			Any=2       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[str]=1       ,				_UpperCAmelCase			:			Tuple=0       ,				_UpperCAmelCase			:			Tuple=2       ,				_UpperCAmelCase			:			List[str]=6_000       ,				_UpperCAmelCase			:			Optional[Any]=1_024       ,				_UpperCAmelCase			:			Optional[Any]=2       ,				_UpperCAmelCase			:			Any=(5, 5)       ,				_UpperCAmelCase			:			int=1_024       ,				_UpperCAmelCase			:			str=80       ,				_UpperCAmelCase			:			Any=1       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  vocab_size
       _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     =  encoder_layerdrop
       _A     =  decoder_layerdrop
       _A     =  use_cache
       _A     =  encoder_layers
       _A     =  scale_embedding  # scale factor will be sqrt(d_model) if True
       _A     =  max_source_positions
       _A     =  max_target_positions
       _A     =  num_conv_layers
       _A     =  list(_UpperCAmelCase						)
       _A     =  conv_channels
       _A     =  input_feat_per_channel
       _A     =  input_channels

       if len(self.conv_kernel_sizes						) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                 F'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes						)}`, '''
                 F'''`config.num_conv_layers = {self.num_conv_layers}`.'''						)

       super().__init__(
           pad_token_id=_UpperCAmelCase       ,				bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				is_encoder_decoder=_UpperCAmelCase       ,				decoder_start_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase       ,				)

