
'''simple docstring'''




import math
from typing import Optional

import numpy as np

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


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

lowercase     :							List[Any]       						=     {
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''encodec'''





     def __init__(				self		,     SCREAMING_SNAKE_CASE=[1.5, 3.0, 6.0, 12.0, 24.0]		,     SCREAMING_SNAKE_CASE=24000		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=128		,     SCREAMING_SNAKE_CASE=32		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=[8, 5, 4, 2]		,     SCREAMING_SNAKE_CASE="weight_norm"		,     SCREAMING_SNAKE_CASE=7		,     SCREAMING_SNAKE_CASE=7		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE="reflect"		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=1.0		,     SCREAMING_SNAKE_CASE=1024		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=True		,     **SCREAMING_SNAKE_CASE		,     )					->							int:






         """simple docstring"""






         A			:  List[Any]   =		target_bandwidths
         A			:  Optional[int]   =		sampling_rate
         A			:  Any   =		audio_channels
         A			:  Union[str, Any]   =		normalize
         A			:  List[str]   =		chunk_length_s
         A			:  str   =		overlap
         A			:  Tuple   =		hidden_size
         A			:  Optional[int]   =		num_filters
         A			:  Tuple   =		num_residual_layers
         A			:  List[Any]   =		upsampling_ratios
         A			:  Optional[int]   =		norm_type
         A			:  int   =		kernel_size
         A			:  Optional[Any]   =		last_kernel_size
         A			:  List[str]   =		residual_kernel_size
         A			:  Any   =		dilation_growth_rate
         A			:  Optional[Any]   =		use_causal_conv
         A			:  List[Any]   =		pad_mode
         A			:  Any   =		compress
         A			:  List[Any]   =		num_lstm_layers
         A			:  List[Any]   =		trim_right_ratio
         A			:  str   =		codebook_size
         A			:  List[Any]   =		codebook_dim if codebook_dim is not None else hidden_size
         A			:  Optional[int]   =		use_conv_shortcut

         if self.norm_type not in ["weight_norm", "time_group_norm"]:
             raise ValueError(
                 F'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'      )

         super().__init__(**SCREAMING_SNAKE_CASE      )





     @property
     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         if self.chunk_length_s is None:
             return None
         else:
             return int(self.chunk_length_s * self.sampling_rate      )





     @property
     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         if self.chunk_length_s is None or self.overlap is None:
             return None
         else:
             return max(1		,     int((1.0 - self.overlap) * self.chunk_length      )      )





     @property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Any   =		np.prod(self.upsampling_ratios      )
         return math.ceil(self.sampling_rate / hop_length      )





     @property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)      )






'''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




