


import functools
import operator

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


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'wav2vec2'




						def __init__(	self						:   Tuple   ,     __A						:   Optional[int]=32   ,     __A						:   List[str]=768   ,     __A						:   Tuple=12   ,     __A						:   str=12   ,     __A						:   Any=3072   ,     __A						:   Tuple="gelu"   ,     __A						:   Tuple=0.1   ,     __A						:   Any=0.1   ,     __A						:   List[str]=0.1   ,     __A						:   List[Any]=0.0   ,     __A						:   str=0.0   ,     __A						:   str=0.1   ,     __A						:   List[Any]=0.1   ,     __A						:   Dict=0.02   ,     __A						:   int=1E-5   ,     __A						:   Dict="group"   ,     __A						:   Tuple="gelu"   ,     __A						:   Optional[int]=(512, 512, 512, 512, 512, 512, 512)   ,     __A						:   Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)   ,     __A						:   int=(10, 3, 3, 3, 3, 2, 2)   ,     __A						:   List[Any]=False   ,     __A						:   Tuple=128   ,     __A						:   Any=16   ,     __A						:   List[str]=False   ,     __A						:   List[str]=True   ,     __A						:   Dict=0.05   ,     __A						:   Union[str, Any]=10   ,     __A						:   int=2   ,     __A						:   List[Any]=0.0   ,     __A						:   List[Any]=10   ,     __A						:   List[str]=0   ,     __A						:   Optional[Any]=320   ,     __A						:   Optional[int]=2   ,     __A						:   List[Any]=0.1   ,     __A						:   int=100   ,     __A						:   int=256   ,     __A						:   str=256   ,     __A						:   Dict=0.1   ,     __A						:   Union[str, Any]="sum"   ,     __A						:   Optional[int]=False   ,     __A						:   Optional[int]=False   ,     __A						:   Dict=256   ,     __A						:   List[Any]=(512, 512, 512, 512, 1500)   ,     __A						:   int=(5, 3, 3, 1, 1)   ,     __A						:   Optional[Any]=(1, 2, 3, 1, 1)   ,     __A						:   Dict=512   ,     __A						:   Any=0   ,     __A						:   Union[str, Any]=1   ,     __A						:   List[str]=2   ,     __A						:   List[str]=False   ,     __A						:   int=3   ,     __A						:   Dict=2   ,     __A						:   Optional[Any]=3   ,     __A						:   Optional[int]=None   ,     __A						:   Tuple=None   ,     **__A						:   Tuple   ,     )      ->Dict:






									"""simple docstring"""






									super().__init__(**__UpperCamelCase   ,     pad_token_id=__UpperCamelCase   ,     bos_token_id=__UpperCamelCase   ,     eos_token_id=__UpperCamelCase						)
									a__ :Any						  =    hidden_size
									a__ :int						  =    feat_extract_norm
									a__ :Optional[Any]						  =    feat_extract_activation
									a__ :Union[str, Any]						  =    list(__UpperCamelCase						)
									a__ :Tuple						  =    list(__UpperCamelCase						)
									a__ :Any						  =    list(__UpperCamelCase						)
									a__ :Dict						  =    conv_bias
									a__ :str						  =    num_conv_pos_embeddings
									a__ :Optional[Any]						  =    num_conv_pos_embedding_groups
									a__ :str						  =    len(self.conv_dim						)
									a__ :Tuple						  =    num_hidden_layers
									a__ :List[str]						  =    intermediate_size
									a__ :Any						  =    hidden_act
									a__ :Union[str, Any]						  =    num_attention_heads
									a__ :Any						  =    hidden_dropout
									a__ :Tuple						  =    attention_dropout
									a__ :Union[str, Any]						  =    activation_dropout
									a__ :int						  =    feat_proj_dropout
									a__ :List[str]						  =    final_dropout
									a__ :int						  =    layerdrop
									a__ :str						  =    layer_norm_eps
									a__ :int						  =    initializer_range
									a__ :Optional[int]						  =    vocab_size
									a__ :Dict						  =    do_stable_layer_norm
									a__ :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
									a__ :Dict						  =    apply_spec_augment
									a__ :Any						  =    mask_time_prob
									a__ :Union[str, Any]						  =    mask_time_length
									a__ :Tuple						  =    mask_time_min_masks
									a__ :List[str]						  =    mask_feature_prob
									a__ :Tuple						  =    mask_feature_length
									a__ :Dict						  =    mask_feature_min_masks

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

									# ctc loss
									a__ :Dict						  =    ctc_loss_reduction
									a__ :Dict						  =    ctc_zero_infinity

									# adapter
									a__ :Optional[Any]						  =    add_adapter
									a__ :List[str]						  =    adapter_kernel_size
									a__ :int						  =    adapter_stride
									a__ :str						  =    num_adapter_layers
									a__ :List[Any]						  =    output_hidden_size or hidden_size
									a__ :int						  =    adapter_attn_dim

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

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






						@property
						def  _snake_case					(	self						:   Dict						)      ->Optional[int]:






									"""simple docstring"""






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






def 				lowerCamelCase__	(      a	:   int = 1_000_000				)    ->	int:





			"""simple docstring"""
			a__ :int						  =    [i - 1 for i in range(limit + 1				)]

			for i in range(2	,  limit + 1				):
						if phi[i] == i - 1:
									for j in range(2 * i	,  limit + 1	,  a				):
												phi[j] -= phi[j] // i

			return sum(phi[2 : limit + 1]				)


if __name__ == "__main__":
		print(solution())


