






"""simple docstring"""







from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ = 1 / sqrt(2)):
					a__          =  tau * frequency / samplerate
					a__          =  sin(lowerCamelCase_)
					a__          =  cos(lowerCamelCase_)
					a__          =  _sin / (2 * q_factor)

					a__          =  (1 - _cos) / 2
					a__          =  1 - _cos

					a__          =  1 + alpha
					a__          =  -2 * _cos
					a__          =  1 - alpha

					a__          =  IIRFilter(2)
					filt.set_coefficients([aa, aa, aa] ,     [ba, ba, ba])
					return filt





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ = 1 / sqrt(2)):
					a__          =  tau * frequency / samplerate
					a__          =  sin(lowerCamelCase_)
					a__          =  cos(lowerCamelCase_)
					a__          =  _sin / (2 * q_factor)

					a__          =  (1 + _cos) / 2
					a__          =  -1 - _cos

					a__          =  1 + alpha
					a__          =  -2 * _cos
					a__          =  1 - alpha

					a__          =  IIRFilter(2)
					filt.set_coefficients([aa, aa, aa] ,     [ba, ba, ba])
					return filt





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ = 1 / sqrt(2)):
					a__          =  tau * frequency / samplerate
					a__          =  sin(lowerCamelCase_)
					a__          =  cos(lowerCamelCase_)
					a__          =  _sin / (2 * q_factor)

					a__          =  _sin / 2
					a__          =  0
					a__          =  -ba

					a__          =  1 + alpha
					a__          =  -2 * _cos
					a__          =  1 - alpha

					a__          =  IIRFilter(2)
					filt.set_coefficients([aa, aa, aa] ,     [ba, ba, ba])
					return filt





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ = 1 / sqrt(2)):
					a__          =  tau * frequency / samplerate
					a__          =  sin(lowerCamelCase_)
					a__          =  cos(lowerCamelCase_)
					a__          =  _sin / (2 * q_factor)

					a__          =  1 - alpha
					a__          =  -2 * _cos
					a__          =  1 + alpha

					a__          =  IIRFilter(2)
					filt.set_coefficients([ba, ba, ba] ,     [ba, ba, ba])
					return filt





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ = 1 / sqrt(2) ,     ):
					a__          =  tau * frequency / samplerate
					a__          =  sin(lowerCamelCase_)
					a__          =  cos(lowerCamelCase_)
					a__          =  _sin / (2 * q_factor)
					a__          =  10 ** (gain_db / 40)

					a__          =  1 + alpha * big_a
					a__          =  -2 * _cos
					a__          =  1 - alpha * big_a
					a__          =  1 + alpha / big_a
					a__          =  -2 * _cos
					a__          =  1 - alpha / big_a

					a__          =  IIRFilter(2)
					filt.set_coefficients([aa, aa, aa] ,     [ba, ba, ba])
					return filt





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ = 1 / sqrt(2) ,     ):
					a__          =  tau * frequency / samplerate
					a__          =  sin(lowerCamelCase_)
					a__          =  cos(lowerCamelCase_)
					a__          =  _sin / (2 * q_factor)
					a__          =  10 ** (gain_db / 40)
					a__          =  (big_a + 1) - (big_a - 1) * _cos
					a__          =  (big_a + 1) + (big_a - 1) * _cos
					a__          =  (big_a - 1) - (big_a + 1) * _cos
					a__          =  (big_a - 1) + (big_a + 1) * _cos
					a__          =  2 * sqrt(lowerCamelCase_) * alpha

					a__          =  big_a * (pmc + aaa)
					a__          =  2 * big_a * mpc
					a__          =  big_a * (pmc - aaa)
					a__          =  ppmc + aaa
					a__          =  -2 * pmpc
					a__          =  ppmc - aaa

					a__          =  IIRFilter(2)
					filt.set_coefficients([aa, aa, aa] ,     [ba, ba, ba])
					return filt





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ = 1 / sqrt(2) ,     ):
					a__          =  tau * frequency / samplerate
					a__          =  sin(lowerCamelCase_)
					a__          =  cos(lowerCamelCase_)
					a__          =  _sin / (2 * q_factor)
					a__          =  10 ** (gain_db / 40)
					a__          =  (big_a + 1) - (big_a - 1) * _cos
					a__          =  (big_a + 1) + (big_a - 1) * _cos
					a__          =  (big_a - 1) - (big_a + 1) * _cos
					a__          =  (big_a - 1) + (big_a + 1) * _cos
					a__          =  2 * sqrt(lowerCamelCase_) * alpha

					a__          =  big_a * (ppmc + aaa)
					a__          =  -2 * big_a * pmpc
					a__          =  big_a * (ppmc - aaa)
					a__          =  pmc + aaa
					a__          =  2 * mpc
					a__          =  pmc - aaa

					a__          =  IIRFilter(2)
					filt.set_coefficients([aa, aa, aa] ,     [ba, ba, ba])
					return filt










"""simple docstring"""







import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
	pass

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


__a       :	List[Any]             = logging.get_logger(__name__)

__a       :	Tuple             = {
    'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='align_text_model'




							def __init__(   self:     Tuple				,		__A:     Optional[int]=30522				,		__A:     Tuple=768				,		__A:     Any=12				,		__A:     Any=12				,		__A:     Dict=3072				,		__A:     Tuple="gelu"				,		__A:     Union[str, Any]=0.1				,		__A:     List[str]=0.1				,		__A:     Optional[int]=512				,		__A:     Tuple=2				,		__A:     str=0.0_2				,		__A:     int=1e-12				,		__A:     Optional[int]=0				,		__A:     Optional[int]="absolute"				,		__A:     List[Any]=True				,		**__A:     Tuple				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  vocab_size
												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  hidden_act
												a__          =  intermediate_size
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  max_position_embeddings
												a__          =  type_vocab_size
												a__          =  initializer_range
												a__          =  layer_norm_eps
												a__          =  position_embedding_type
												a__          =  use_cache
												a__          =  pad_token_id





							@classmethod
							def  lowercase		(   cls:     Any				,		__A:     Union[str, os.PathLike]				,		**__A:     Tuple    ):

												'''simple docstring'''

												cls._set_token_in_kwargs(__A    )

												a__       ,a__          =  cls.get_config_dict(__A				,		**__A    )

												# get the text config dict if we are loading from AlignConfig
												if config_dict.get('''model_type'''    ) == "align":
																	a__          =  config_dict['''text_config''']

												if "model_type" in config_dict and hasattr(cls				,		'''model_type'''    ) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

												return cls.from_dict(__A				,		**__A    )







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='align_vision_model'




							def __init__(   self:     Dict				,		__A:     int = 3				,		__A:     int = 600				,		__A:     float = 2.0				,		__A:     float = 3.1				,		__A:     int = 8				,		__A:     List[int] = [3, 3, 5, 3, 5, 5, 3]				,		__A:     List[int] = [32, 16, 24, 40, 80, 112, 192]				,		__A:     List[int] = [16, 24, 40, 80, 112, 192, 320]				,		__A:     List[int] = []				,		__A:     List[int] = [1, 2, 2, 2, 1, 2, 1]				,		__A:     List[int] = [1, 2, 2, 3, 3, 4, 1]				,		__A:     List[int] = [1, 6, 6, 6, 6, 6, 6]				,		__A:     float = 0.2_5				,		__A:     str = "swish"				,		__A:     int = 2560				,		__A:     str = "mean"				,		__A:     float = 0.0_2				,		__A:     float = 0.0_0_1				,		__A:     float = 0.9_9				,		__A:     float = 0.2				,		**__A:     List[Any]				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  num_channels
												a__          =  image_size
												a__          =  width_coefficient
												a__          =  depth_coefficient
												a__          =  depth_divisor
												a__          =  kernel_sizes
												a__          =  in_channels
												a__          =  out_channels
												a__          =  depthwise_padding
												a__          =  strides
												a__          =  num_block_repeats
												a__          =  expand_ratios
												a__          =  squeeze_expansion_ratio
												a__          =  hidden_act
												a__          =  hidden_dim
												a__          =  pooling_type
												a__          =  initializer_range
												a__          =  batch_norm_eps
												a__          =  batch_norm_momentum
												a__          =  drop_connect_rate
												a__          =  sum(__A    ) * 4





							@classmethod
							def  lowercase		(   cls:     Dict				,		__A:     Union[str, os.PathLike]				,		**__A:     List[Any]    ):

												'''simple docstring'''

												cls._set_token_in_kwargs(__A    )

												a__       ,a__          =  cls.get_config_dict(__A				,		**__A    )

												# get the vision config dict if we are loading from AlignConfig
												if config_dict.get('''model_type'''    ) == "align":
																	a__          =  config_dict['''vision_config''']

												if "model_type" in config_dict and hasattr(cls				,		'''model_type'''    ) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

												return cls.from_dict(__A				,		**__A    )







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='align'
							_SCREAMING_SNAKE_CASE    =True




							def __init__(   self:     Optional[int]				,		__A:     Optional[int]=None				,		__A:     Dict=None				,		__A:     List[str]=640				,		__A:     Optional[int]=1.0				,		__A:     str=0.0_2				,		**__A:     List[str]				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												if text_config is None:
																	a__          =  {}
																	logger.info('''text_config is None. Initializing the AlignTextConfig with default values.'''    )

												if vision_config is None:
																	a__          =  {}
																	logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.'''    )

												a__          =  AlignTextConfig(**__A    )
												a__          =  AlignVisionConfig(**__A    )

												a__          =  projection_dim
												a__          =  temperature_init_value
												a__          =  initializer_range




							@classmethod
							def  lowercase		(   cls:     Dict				,		__A:     AlignTextConfig				,		__A:     AlignVisionConfig				,		**__A:     str    ):

												'''simple docstring'''

												return cls(text_config=text_config.to_dict()				,		vision_config=vision_config.to_dict()				,		**__A    )





							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  copy.deepcopy(self.__dict__    )
												a__          =  self.text_config.to_dict()
												a__          =  self.vision_config.to_dict()
												a__          =  self.__class__.model_type
												return output


