from collections import namedtuple

lowerCAmelCase_     =	namedtuple('''from_to''', '''from_ to''')

lowerCAmelCase_     =	{
    '''cubicmeter''': from_to(1, 1),
    '''litre''': from_to(0.0_01, 1000),
    '''kilolitre''': from_to(1, 1),
    '''gallon''': from_to(0.0_04_54, 264.172),
    '''cubicyard''': from_to(0.7_64_55, 1.3_07_95),
    '''cubicfoot''': from_to(0.0_28, 35.31_47),
    '''cup''': from_to(0.0_00_23_65_88, 4226.75),
}



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    float:
							if from_type not in METRIC_CONVERSION:
														raise ValueError(
														    F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
														    + """, """.join(__SCREAMING_SNAKE_CASE  )  )
							if to_type not in METRIC_CONVERSION:
														raise ValueError(
														    F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
														    + """, """.join(__SCREAMING_SNAKE_CASE  )  )
							return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
			import doctest

			doctest.testmod()



"""simple docstring"""





import functools
import operator

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


lowerCAmelCase_     =	logging.get_logger(__name__)

lowerCAmelCase_     =	{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"sew"





				def __init__(      self							:							List[Any]      ,    _A							:							Tuple=3_2      ,    _A							:							str=7_6_8      ,    _A							:							Dict=1_2      ,    _A							:							Tuple=1_2      ,    _A							:							Optional[Any]=3_0_7_2      ,    _A							:							List[str]=2      ,    _A							:							Dict="gelu"      ,    _A							:							Union[str, Any]=0.1      ,    _A							:							Optional[int]=0.1      ,    _A							:							Optional[int]=0.1      ,    _A							:							Optional[int]=0.0      ,    _A							:							str=0.1      ,    _A							:							Tuple=0.1      ,    _A							:							Optional[int]=0.02      ,    _A							:							Dict=1e-5      ,    _A							:							str="group"      ,    _A							:							Tuple="gelu"      ,    _A							:							Union[str, Any]=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)      ,    _A							:							Optional[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)      ,    _A							:							Any=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)      ,    _A							:							Tuple=False      ,    _A							:							Tuple=1_2_8      ,    _A							:							int=1_6      ,    _A							:							Union[str, Any]=True      ,    _A							:							Optional[Any]=0.05      ,    _A							:							List[Any]=1_0      ,    _A							:							Union[str, Any]=2      ,    _A							:							Tuple=0.0      ,    _A							:							Union[str, Any]=1_0      ,    _A							:							Optional[int]=0      ,    _A							:							Union[str, Any]="mean"      ,    _A							:							Optional[int]=False      ,    _A							:							List[Any]=False      ,    _A							:							int=2_5_6      ,    _A							:							str=0      ,    _A							:							Optional[int]=1      ,    _A							:							List[Any]=2      ,    **_A							:							Dict      ,    ):



											"""simple docstring"""







											super().__init__(**_A      ,    pad_token_id=_A      ,    bos_token_id=_A      ,    eos_token_id=_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    feat_extract_norm
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    feat_extract_activation
											_SCREAMING_SNAKE_CASE		:						Dict			  =    list(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    list(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    list(_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    conv_bias
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    num_conv_pos_embeddings
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    num_conv_pos_embedding_groups
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    len(self.conv_dim)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    intermediate_size
											_SCREAMING_SNAKE_CASE		:						str			  =    squeeze_factor
											_SCREAMING_SNAKE_CASE		:						Dict			  =    hidden_act
											_SCREAMING_SNAKE_CASE		:						str			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						Dict			  =    hidden_dropout
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    attention_dropout
											_SCREAMING_SNAKE_CASE		:						int			  =    activation_dropout
											_SCREAMING_SNAKE_CASE		:						Any			  =    feat_proj_dropout
											_SCREAMING_SNAKE_CASE		:						str			  =    final_dropout
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    layerdrop
											_SCREAMING_SNAKE_CASE		:						Any			  =    layer_norm_eps
											_SCREAMING_SNAKE_CASE		:						int			  =    initializer_range
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    vocab_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)`,"""
																		    f"""but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)"""
																		    f"""= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

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

											# ctc loss
											_SCREAMING_SNAKE_CASE		:						int			  =    ctc_loss_reduction
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    ctc_zero_infinity

											# sequence classification
											_SCREAMING_SNAKE_CASE		:						Dict			  =    use_weighted_layer_sum
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    classifier_proj_size





				@property
				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											return functools.reduce(operator.mul      ,    self.conv_stride      ,    1)


