


"""simple docstring"""



from __future__ import annotations

from collections import namedtuple







def     __lowerCAmelCase		(		lowercase    :    float		,				lowercase    :    float		,				lowercase    :    float					)      ->		tuple:


    """simple docstring"""





    snake_case       : Dict    =       namedtuple("result"		,				"name value"					)
    if (voltage, current, power).count(0					) != 1:
        raise ValueError("Only one argument must be 0"					)
    elif power < 0:
        raise ValueError(
            "Power cannot be negative in any electrical/electronics system"					)
    elif voltage == 0:
        return result("voltage"		,				power / current					)
    elif current == 0:
        return result("current"		,				power / voltage					)
    elif power == 0:
        return result("power"		,				float(round(abs(voltage * current					)		,				2					)					)					)
    else:
        raise ValueError("Exactly one argument must be 0"					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()









import functools
import operator

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


snake_case__				:	Union[str, Any]					       =					logging.get_logger(__name__)

snake_case__				:	Tuple					       =					{
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"unispeech-sat"



       def __init__(						self				:				List[str]		,				__a				:				Dict=32		,				__a				:				int=768		,				__a				:				int=12		,				__a				:				Tuple=12		,				__a				:				Optional[int]=3_072		,				__a				:				int="gelu"		,				__a				:				Optional[int]=0.1		,				__a				:				Union[str, Any]=0.1		,				__a				:				Any=0.1		,				__a				:				str=0.0		,				__a				:				List[str]=0.0		,				__a				:				int=0.1		,				__a				:				List[Any]=0.1		,				__a				:				Tuple=0.02		,				__a				:				str=1e-5		,				__a				:				Optional[int]="group"		,				__a				:				Any="gelu"		,				__a				:				Optional[Any]=(512, 512, 512, 512, 512, 512, 512)		,				__a				:				Optional[Any]=(5, 2, 2, 2, 2, 2, 2)		,				__a				:				List[Any]=(10, 3, 3, 3, 3, 2, 2)		,				__a				:				Optional[Any]=False		,				__a				:				Optional[int]=128		,				__a				:				Dict=16		,				__a				:				str=False		,				__a				:				List[Any]=True		,				__a				:				Tuple=0.05		,				__a				:				str=10		,				__a				:				Any=2		,				__a				:				Any=0.0		,				__a				:				List[Any]=10		,				__a				:				str=0		,				__a				:				str=320		,				__a				:				int=2		,				__a				:				Optional[Any]=0.1		,				__a				:				Optional[int]=100		,				__a				:				Any=256		,				__a				:				Optional[int]=256		,				__a				:				int=0.1		,				__a				:				Tuple="mean"		,				__a				:				List[str]=False		,				__a				:				Optional[Any]=False		,				__a				:				List[Any]=256		,				__a				:				Dict=(512, 512, 512, 512, 1_500)		,				__a				:				int=(5, 3, 3, 1, 1)		,				__a				:				str=(1, 2, 3, 1, 1)		,				__a				:				str=512		,				__a				:				str=0		,				__a				:				List[Any]=1		,				__a				:				Optional[Any]=2		,				__a				:				Optional[int]=504		,				**__a				:				Tuple		,				)					->Dict:
          super().__init__(**__a		,				pad_token_id=__a		,				bos_token_id=__a		,				eos_token_id=__a							)
          lowerCamelCase_						:   Any		= hidden_size
          lowerCamelCase_						:   Optional[int]		= feat_extract_norm
          lowerCamelCase_						:   Dict		= feat_extract_activation
          lowerCamelCase_						:   List[Any]		= list(__a							)
          lowerCamelCase_						:   Any		= list(__a							)
          lowerCamelCase_						:   List[Any]		= list(__a							)
          lowerCamelCase_						:   str		= conv_bias
          lowerCamelCase_						:   int		= num_conv_pos_embeddings
          lowerCamelCase_						:   List[Any]		= num_conv_pos_embedding_groups
          lowerCamelCase_						:   str		= len(self.conv_dim							)
          lowerCamelCase_						:   Any		= num_hidden_layers
          lowerCamelCase_						:   List[str]		= intermediate_size
          lowerCamelCase_						:   Tuple		= hidden_act
          lowerCamelCase_						:   List[str]		= num_attention_heads
          lowerCamelCase_						:   Any		= hidden_dropout
          lowerCamelCase_						:   Optional[int]		= attention_dropout
          lowerCamelCase_						:   Tuple		= activation_dropout
          lowerCamelCase_						:   str		= feat_proj_dropout
          lowerCamelCase_						:   Any		= final_dropout
          lowerCamelCase_						:   Optional[Any]		= layerdrop
          lowerCamelCase_						:   str		= layer_norm_eps
          lowerCamelCase_						:   str		= initializer_range
          lowerCamelCase_						:   Optional[Any]		= vocab_size
          lowerCamelCase_						:   Optional[int]		= num_clusters
          lowerCamelCase_						:   List[Any]		= do_stable_layer_norm
          lowerCamelCase_						:   Tuple		= use_weighted_layer_sum

          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
          lowerCamelCase_						:   List[str]		= apply_spec_augment
          lowerCamelCase_						:   str		= mask_time_prob
          lowerCamelCase_						:   List[Any]		= mask_time_length
          lowerCamelCase_						:   Optional[int]		= mask_time_min_masks
          lowerCamelCase_						:   List[Any]		= mask_feature_prob
          lowerCamelCase_						:   Optional[int]		= mask_feature_length
          lowerCamelCase_						:   Dict		= mask_feature_min_masks

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

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

          # SequenceClassification-specific parameter. Feel free to ignore for other classes.
          lowerCamelCase_						:   Dict		= classifier_proj_size

          # XVector-specific parameters. Feel free to ignore for other classes.
          lowerCamelCase_						:   List[Any]		= list(__a							)
          lowerCamelCase_						:   Any		= list(__a							)
          lowerCamelCase_						:   List[Any]		= list(__a							)
          lowerCamelCase_						:   Optional[int]		= xvector_output_dim




       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->Optional[Any]:
          return functools.reduce(operator.mul		,				self.conv_stride		,				1							)





