
'''simple docstring'''

import functools
import operator

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


UpperCamelCase_     			=       logging.get_logger(__name__)

UpperCamelCase_     			=       {
    """facebook/hubert-base-ls960""": """https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json""",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}


class   a_  (_a    ):
       __lowerCAmelCase    :				Union[str, Any]    =      """hubert"""




       def __init__(     self   ,   snake_case_=3_2   ,   snake_case_=7_6_8   ,   snake_case_=1_2   ,   snake_case_=1_2   ,   snake_case_=3_0_7_2   ,   snake_case_="gelu"   ,   snake_case_=0.1   ,   snake_case_=0.1   ,   snake_case_=0.1   ,   snake_case_=True   ,   snake_case_=0.0   ,   snake_case_=0.1   ,   snake_case_=0.1   ,   snake_case_=0.02   ,   snake_case_=1E-5   ,   snake_case_="group"   ,   snake_case_="gelu"   ,   snake_case_=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)   ,   snake_case_=(5, 2, 2, 2, 2, 2, 2)   ,   snake_case_=(1_0, 3, 3, 3, 3, 2, 2)   ,   snake_case_=False   ,   snake_case_=1_2_8   ,   snake_case_=1_6   ,   snake_case_=False   ,   snake_case_=True   ,   snake_case_=0.05   ,   snake_case_=1_0   ,   snake_case_=2   ,   snake_case_=0.0   ,   snake_case_=1_0   ,   snake_case_=0   ,   snake_case_="sum"   ,   snake_case_=False   ,   snake_case_=False   ,   snake_case_=2_5_6   ,   snake_case_=0   ,   snake_case_=1   ,   snake_case_=2   ,   **snake_case_   ,   ):
         super().__init__(**snake_case_   ,   pad_token_id=snake_case_   ,   bos_token_id=snake_case_   ,   eos_token_id=snake_case_  )
         _lowerCAmelCase       :  Optional[int]   				=					hidden_size
         _lowerCAmelCase       :  Optional[int]   				=					feat_extract_norm
         _lowerCAmelCase       :  int   				=					feat_extract_activation
         _lowerCAmelCase       :  List[Any]   				=					list(snake_case_  )
         _lowerCAmelCase       :  Optional[int]   				=					list(snake_case_  )
         _lowerCAmelCase       :  str   				=					list(snake_case_  )
         _lowerCAmelCase       :  Any   				=					conv_bias
         _lowerCAmelCase       :  Optional[int]   				=					num_conv_pos_embeddings
         _lowerCAmelCase       :  Optional[Any]   				=					num_conv_pos_embedding_groups
         _lowerCAmelCase       :  str   				=					len(self.conv_dim  )
         _lowerCAmelCase       :  Union[str, Any]   				=					num_hidden_layers
         _lowerCAmelCase       :  int   				=					intermediate_size
         _lowerCAmelCase       :  Dict   				=					hidden_act
         _lowerCAmelCase       :  Any   				=					num_attention_heads
         _lowerCAmelCase       :  List[Any]   				=					hidden_dropout
         _lowerCAmelCase       :  Dict   				=					attention_dropout
         _lowerCAmelCase       :  int   				=					activation_dropout
         _lowerCAmelCase       :  List[str]   				=					feat_proj_layer_norm
         _lowerCAmelCase       :  Dict   				=					feat_proj_dropout
         _lowerCAmelCase       :  List[str]   				=					final_dropout
         _lowerCAmelCase       :  Union[str, Any]   				=					layerdrop
         _lowerCAmelCase       :  Optional[Any]   				=					layer_norm_eps
         _lowerCAmelCase       :  Union[str, Any]   				=					initializer_range
         _lowerCAmelCase       :  Union[str, Any]   				=					vocab_size
         _lowerCAmelCase       :  Dict   				=					do_stable_layer_norm
         _lowerCAmelCase       :  Union[str, Any]   				=					use_weighted_layer_sum
         _lowerCAmelCase       :  List[Any]   				=					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       :  int   				=					apply_spec_augment
         _lowerCAmelCase       :  int   				=					mask_time_prob
         _lowerCAmelCase       :  Union[str, Any]   				=					mask_time_length
         _lowerCAmelCase       :  Optional[int]   				=					mask_time_min_masks
         _lowerCAmelCase       :  Dict   				=					mask_feature_prob
         _lowerCAmelCase       :  Dict   				=					mask_feature_length
         _lowerCAmelCase       :  List[str]   				=					mask_feature_min_masks

         # ctc loss
         _lowerCAmelCase       :  Optional[Any]   				=					ctc_loss_reduction
         _lowerCAmelCase       :  Any   				=					ctc_zero_infinity







       @property
       def 							__UpperCamelCase       (     self  ):
         return functools.reduce(operator.mul   ,   self.conv_stride   ,   1  )




'''simple docstring'''

import random


class   a_  :




       def __init__(     self   ,   snake_case_   ,   snake_case_  ):
         _lowerCAmelCase       :  List[str]   				=					x
         _lowerCAmelCase       :  List[str]   				=					y




       def 							__UpperCamelCase       (     self  ):
         return (self.x**2 + self.y**2) <= 1







       @classmethod
       def 							__UpperCamelCase       (     cls  ):
         return cls(x=random.random()   ,   y=random.random()  )



def 	_UpperCAmelCase				(  _lowerCamelCase						:       int		)    ->						float:
  if number_of_simulations < 1:
    raise ValueError("""At least one simulation is necessary to estimate PI."""		)

  _lowerCAmelCase       :  Tuple   				=					0
  for _ in range(_lowerCamelCase		):
    _lowerCAmelCase       :  Union[str, Any]   				=					Point.random_unit_square()

    if random_point.is_in_unit_circle():
      number_in_unit_circle += 1

  return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
     # import doctest

     # doctest.testmod()
     from math import pi

     UpperCamelCase_     			=       """Please enter the desired number of Monte Carlo simulations: """
     UpperCamelCase_     			=       estimate_pi(int(input(prompt).strip()))
     print(F'An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}')


