


'''simple docstring'''

from math import asin, atan, cos, radians, sin, sqrt, tan

a_						:       Optional[int]      			=				6_3_7_8_1_3_7.0
a_						:       Union[str, Any]      			=				6_3_5_6_7_5_2.3_1_4_2_4_5
a_						:       Dict      			=				6378137





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		float	):
 lowerCamelCase_  				=						(AXIS_A - AXIS_B) / AXIS_A
 lowerCamelCase_  				=						atan((1 - flattening) * tan(radians(UpperCAmelCase_	)	)	)
 lowerCamelCase_  				=						atan((1 - flattening) * tan(radians(UpperCAmelCase_	)	)	)
 lowerCamelCase_  				=						radians(UpperCAmelCase_	)
 lowerCamelCase_  				=						radians(UpperCAmelCase_	)
 # Equation
 lowerCamelCase_  				=						sin((phi_a - phi_a) / 2	)
 lowerCamelCase_  				=						sin((lambda_a - lambda_a) / 2	)
 # Square both values
 sin_sq_phi *= sin_sq_phi
 sin_sq_lambda *= sin_sq_lambda
 lowerCamelCase_  				=						sqrt(sin_sq_phi + (cos(UpperCAmelCase_	) * cos(UpperCAmelCase_	) * sin_sq_lambda)	)
 return 2 * RADIUS * asin(UpperCAmelCase_	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()







'''simple docstring'''

import functools
import operator

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


a_						:       List[str]      			=				logging.get_logger(__name__)

a_						:       Dict      			=				{
    """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   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						"unispeech"






  def __init__(					self    ,						UpperCamelCase=32    ,						UpperCamelCase=768    ,						UpperCamelCase=12    ,						UpperCamelCase=12    ,						UpperCamelCase=3072    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.02    ,						UpperCamelCase=1e-5    ,						UpperCamelCase="group"    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=(512, 512, 512, 512, 512, 512, 512)    ,						UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)    ,						UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)    ,						UpperCamelCase=False    ,						UpperCamelCase=128    ,						UpperCamelCase=16    ,						UpperCamelCase=False    ,						UpperCamelCase=True    ,						UpperCamelCase=0.05    ,						UpperCamelCase=10    ,						UpperCamelCase=2    ,						UpperCamelCase=0.0    ,						UpperCamelCase=10    ,						UpperCamelCase=0    ,						UpperCamelCase=320    ,						UpperCamelCase=2    ,						UpperCamelCase=0.1    ,						UpperCamelCase=100    ,						UpperCamelCase=256    ,						UpperCamelCase=256    ,						UpperCamelCase=0.1    ,						UpperCamelCase="mean"    ,						UpperCamelCase=False    ,						UpperCamelCase=False    ,						UpperCamelCase=256    ,						UpperCamelCase=80    ,						UpperCamelCase=0    ,						UpperCamelCase=1    ,						UpperCamelCase=2    ,						UpperCamelCase=0.5    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(**UpperCamelCase    ,						pad_token_id=UpperCamelCase    ,						bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase  )
   lowerCamelCase_  				=						hidden_size
   lowerCamelCase_  				=						feat_extract_norm
   lowerCamelCase_  				=						feat_extract_activation
   lowerCamelCase_  				=						list(UpperCamelCase  )
   lowerCamelCase_  				=						list(UpperCamelCase  )
   lowerCamelCase_  				=						list(UpperCamelCase  )
   lowerCamelCase_  				=						conv_bias
   lowerCamelCase_  				=						num_conv_pos_embeddings
   lowerCamelCase_  				=						num_conv_pos_embedding_groups
   lowerCamelCase_  				=						len(self.conv_dim  )
   lowerCamelCase_  				=						num_hidden_layers
   lowerCamelCase_  				=						intermediate_size
   lowerCamelCase_  				=						hidden_act
   lowerCamelCase_  				=						num_attention_heads
   lowerCamelCase_  				=						hidden_dropout
   lowerCamelCase_  				=						attention_dropout
   lowerCamelCase_  				=						activation_dropout
   lowerCamelCase_  				=						feat_proj_dropout
   lowerCamelCase_  				=						final_dropout
   lowerCamelCase_  				=						layerdrop
   lowerCamelCase_  				=						layer_norm_eps
   lowerCamelCase_  				=						initializer_range
   lowerCamelCase_  				=						num_ctc_classes
   lowerCamelCase_  				=						vocab_size
   lowerCamelCase_  				=						do_stable_layer_norm
   lowerCamelCase_  				=						use_weighted_layer_sum
   lowerCamelCase_  				=						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
   lowerCamelCase_  				=						apply_spec_augment
   lowerCamelCase_  				=						mask_time_prob
   lowerCamelCase_  				=						mask_time_length
   lowerCamelCase_  				=						mask_time_min_masks
   lowerCamelCase_  				=						mask_feature_prob
   lowerCamelCase_  				=						mask_feature_length
   lowerCamelCase_  				=						mask_feature_min_masks

   # parameters for pretraining with codevector quantized representations
   lowerCamelCase_  				=						num_codevectors_per_group
   lowerCamelCase_  				=						num_codevector_groups
   lowerCamelCase_  				=						contrastive_logits_temperature
   lowerCamelCase_  				=						feat_quantizer_dropout
   lowerCamelCase_  				=						num_negatives
   lowerCamelCase_  				=						codevector_dim
   lowerCamelCase_  				=						proj_codevector_dim
   lowerCamelCase_  				=						diversity_loss_weight

   # ctc loss
   lowerCamelCase_  				=						ctc_loss_reduction
   lowerCamelCase_  				=						ctc_zero_infinity

   # pretraining loss
   lowerCamelCase_  				=						replace_prob







  @property
  def 						snake_case	(					self  ):







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



