
import functools
import operator

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


_UpperCAmelCase   :					str    =			logging.get_logger(__name__)

_UpperCAmelCase   :					Any    =			{
    "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 							lowercase   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			str      	=							"hubert"



		def __init__(					self							, A_=32							, A_=768							, A_=12							, A_=12							, A_=3_072							, A_="gelu"							, A_=0.1							, A_=0.1							, A_=0.1							, A_=True							, A_=0.0							, A_=0.1							, A_=0.1							, A_=0.02							, A_=1e-5							, A_="group"							, A_="gelu"							, A_=(512, 512, 512, 512, 512, 512, 512)							, A_=(5, 2, 2, 2, 2, 2, 2)							, A_=(10, 3, 3, 3, 3, 2, 2)							, A_=False							, A_=128							, A_=16							, A_=False							, A_=True							, A_=0.05							, A_=10							, A_=2							, A_=0.0							, A_=10							, A_=0							, A_="sum"							, A_=False							, A_=False							, A_=256							, A_=0							, A_=1							, A_=2							, **A_							, )    ->   Optional[Any]:
				"""simple docstring"""
				super().__init__(**A_							, pad_token_id=A_							, bos_token_id=A_							, eos_token_id=A_			)
				UpperCamelCase            =	hidden_size
				UpperCamelCase            =	feat_extract_norm
				UpperCamelCase            =	feat_extract_activation
				UpperCamelCase            =	list(A_			)
				UpperCamelCase            =	list(A_			)
				UpperCamelCase            =	list(A_			)
				UpperCamelCase            =	conv_bias
				UpperCamelCase            =	num_conv_pos_embeddings
				UpperCamelCase            =	num_conv_pos_embedding_groups
				UpperCamelCase            =	len(self.conv_dim			)
				UpperCamelCase            =	num_hidden_layers
				UpperCamelCase            =	intermediate_size
				UpperCamelCase            =	hidden_act
				UpperCamelCase            =	num_attention_heads
				UpperCamelCase            =	hidden_dropout
				UpperCamelCase            =	attention_dropout
				UpperCamelCase            =	activation_dropout
				UpperCamelCase            =	feat_proj_layer_norm
				UpperCamelCase            =	feat_proj_dropout
				UpperCamelCase            =	final_dropout
				UpperCamelCase            =	layerdrop
				UpperCamelCase            =	layer_norm_eps
				UpperCamelCase            =	initializer_range
				UpperCamelCase            =	vocab_size
				UpperCamelCase            =	do_stable_layer_norm
				UpperCamelCase            =	use_weighted_layer_sum
				UpperCamelCase            =	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
				UpperCamelCase            =	apply_spec_augment
				UpperCamelCase            =	mask_time_prob
				UpperCamelCase            =	mask_time_length
				UpperCamelCase            =	mask_time_min_masks
				UpperCamelCase            =	mask_feature_prob
				UpperCamelCase            =	mask_feature_length
				UpperCamelCase            =	mask_feature_min_masks

				# ctc loss
				UpperCamelCase            =	ctc_loss_reduction
				UpperCamelCase            =	ctc_zero_infinity



		@property
		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				return functools.reduce(operator.mul							, self.conv_stride							, 1			)


def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if not isinstance(lowercase       ,			lowercase					):
				UpperCamelCase            =	(
				    'operation can not be conducted on a object of type '
				    f'''{type(lowercase					).__name__}'''
				)
				raise TypeError(lowercase					)
		UpperCamelCase            =	''
		for _ in range(0       ,			32					):
				bit_string += str(number % 2					)
				UpperCamelCase            =	number >> 1
		return bit_string


def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if number < 0:
				raise ValueError('the value of input must be positive'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('Input value must be a \'int\' type'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''					)
		UpperCamelCase            =	0
		# iterator over [1 to 32],since we are dealing with 32 bit integer
		for _ in range(1       ,			33					):
				# left shift the bits by unity
				UpperCamelCase            =	result << 1
				# get the end bit
				UpperCamelCase            =	number % 2
				# right shift the bits by unity
				UpperCamelCase            =	number >> 1
				# add that bit to our ans
				UpperCamelCase            =	result | end_bit
		return get_reverse_bit_string(lowercase					)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
