






"""simple docstring"""
import functools
import operator

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


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{
    "microsoft/unispeech-large-1500h-cv": (
        "https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Dict    =				"""unispeech"""





	def __init__(self     ,     lowerCAmelCase_=32     ,     lowerCAmelCase_=768     ,     lowerCAmelCase_=12     ,     lowerCAmelCase_=12     ,     lowerCAmelCase_=3072     ,     lowerCAmelCase_="gelu"     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=1e-5     ,     lowerCAmelCase_="group"     ,     lowerCAmelCase_="gelu"     ,     lowerCAmelCase_=(512, 512, 512, 512, 512, 512, 512)     ,     lowerCAmelCase_=(5, 2, 2, 2, 2, 2, 2)     ,     lowerCAmelCase_=(10, 3, 3, 3, 3, 2, 2)     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=128     ,     lowerCAmelCase_=16     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=0.05     ,     lowerCAmelCase_=10     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=10     ,     lowerCAmelCase_=0     ,     lowerCAmelCase_=320     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=100     ,     lowerCAmelCase_=256     ,     lowerCAmelCase_=256     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_="mean"     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=256     ,     lowerCAmelCase_=80     ,     lowerCAmelCase_=0     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=0.5     ,     **lowerCAmelCase_     ,     ):
		super().__init__(**lowerCAmelCase_     ,     pad_token_id=lowerCAmelCase_     ,     bos_token_id=lowerCAmelCase_     ,     eos_token_id=lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   hidden_size
		A_		:						List[str]	    =   feat_extract_norm
		A_		:						Tuple	    =   feat_extract_activation
		A_		:						Any	    =   list(lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   list(lowerCAmelCase_							)
		A_		:						Dict	    =   list(lowerCAmelCase_							)
		A_		:						str	    =   conv_bias
		A_		:						Tuple	    =   num_conv_pos_embeddings
		A_		:						Tuple	    =   num_conv_pos_embedding_groups
		A_		:						Union[str, Any]	    =   len(self.conv_dim							)
		A_		:						int	    =   num_hidden_layers
		A_		:						Optional[int]	    =   intermediate_size
		A_		:						Union[str, Any]	    =   hidden_act
		A_		:						int	    =   num_attention_heads
		A_		:						List[str]	    =   hidden_dropout
		A_		:						Optional[Any]	    =   attention_dropout
		A_		:						Tuple	    =   activation_dropout
		A_		:						Any	    =   feat_proj_dropout
		A_		:						Optional[int]	    =   final_dropout
		A_		:						List[Any]	    =   layerdrop
		A_		:						List[str]	    =   layer_norm_eps
		A_		:						Dict	    =   initializer_range
		A_		:						Tuple	    =   num_ctc_classes
		A_		:						Union[str, Any]	    =   vocab_size
		A_		:						Tuple	    =   do_stable_layer_norm
		A_		:						Union[str, Any]	    =   use_weighted_layer_sum
		A_		:						Optional[int]	    =   classifier_proj_size

		if (
		    (len(self.conv_stride							) != self.num_feat_extract_layers)
		    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
		    or (len(self.conv_dim							) != self.num_feat_extract_layers)
		):
			raise ValueError(
			    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
			    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
			    f""" {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,"""
			    f""" `len(config.conv_kernel) = {len(self.conv_kernel							)}`."""							)

		# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
		A_		:						Dict	    =   apply_spec_augment
		A_		:						Dict	    =   mask_time_prob
		A_		:						List[Any]	    =   mask_time_length
		A_		:						Dict	    =   mask_time_min_masks
		A_		:						Any	    =   mask_feature_prob
		A_		:						Tuple	    =   mask_feature_length
		A_		:						Dict	    =   mask_feature_min_masks

		# parameters for pretraining with codevector quantized representations
		A_		:						Dict	    =   num_codevectors_per_group
		A_		:						List[str]	    =   num_codevector_groups
		A_		:						Tuple	    =   contrastive_logits_temperature
		A_		:						Union[str, Any]	    =   feat_quantizer_dropout
		A_		:						List[Any]	    =   num_negatives
		A_		:						Optional[int]	    =   codevector_dim
		A_		:						Optional[Any]	    =   proj_codevector_dim
		A_		:						str	    =   diversity_loss_weight

		# ctc loss
		A_		:						Optional[int]	    =   ctc_loss_reduction
		A_		:						Tuple	    =   ctc_zero_infinity

		# pretraining loss
		A_		:						str	    =   replace_prob





	@property
	def    lowerCamelCase(self							):
		return functools.reduce(operator.mul     ,     self.conv_stride     ,     1							)









"""simple docstring"""
import copy

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


_lowerCAmelCase         =							logging.get_logger(__name__)

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



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				List[str]    =				"""deta"""
	_A       :				Tuple    =				{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}





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

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





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





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





	def    lowerCamelCase(self							):
		A_		:						str	    =   copy.deepcopy(self.__dict__							)
		A_		:						int	    =   self.backbone_config.to_dict()
		A_		:						Dict	    =   self.__class__.model_type
		return output

