


"""simple docstring"""
import math

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


__SCREAMING_SNAKE_CASE							:      Any						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      str						=      {
    'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						Optional[Any]							 =      """data2vec-audio"""



							def __init__(			self							: int						,    UpperCAmelCase_							: List[str]=32						,    UpperCAmelCase_							: Optional[int]=768						,    UpperCAmelCase_							: List[Any]=12						,    UpperCAmelCase_							: Tuple=12						,    UpperCAmelCase_							: str=3_072						,    UpperCAmelCase_							: List[str]="gelu"						,    UpperCAmelCase_							: Optional[int]=0.1						,    UpperCAmelCase_							: Optional[Any]=0.1						,    UpperCAmelCase_							: Optional[int]=0.1						,    UpperCAmelCase_							: List[str]=0.0						,    UpperCAmelCase_							: int=0.1						,    UpperCAmelCase_							: Union[str, Any]=0.1						,    UpperCAmelCase_							: Any=0.02						,    UpperCAmelCase_							: int=1E-5						,    UpperCAmelCase_							: str="gelu"						,    UpperCAmelCase_							: str=(512, 512, 512, 512, 512, 512, 512)						,    UpperCAmelCase_							: Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)						,    UpperCAmelCase_							: Tuple=(10, 3, 3, 3, 3, 2, 2)						,    UpperCAmelCase_							: Tuple=False						,    UpperCAmelCase_							: List[Any]=16						,    UpperCAmelCase_							: str=19						,    UpperCAmelCase_							: Any=5						,    UpperCAmelCase_							: int=0.05						,    UpperCAmelCase_							: Tuple=10						,    UpperCAmelCase_							: Optional[Any]=2						,    UpperCAmelCase_							: Optional[int]=0.0						,    UpperCAmelCase_							: Tuple=10						,    UpperCAmelCase_							: Union[str, Any]=0						,    UpperCAmelCase_							: Optional[Any]="sum"						,    UpperCAmelCase_							: str=False						,    UpperCAmelCase_							: Optional[int]=False						,    UpperCAmelCase_							: List[str]=256						,    UpperCAmelCase_							: Optional[int]=(512, 512, 512, 512, 1_500)						,    UpperCAmelCase_							: str=(5, 3, 3, 1, 1)						,    UpperCAmelCase_							: Tuple=(1, 2, 3, 1, 1)						,    UpperCAmelCase_							: Union[str, Any]=512						,    UpperCAmelCase_							: Dict=0						,    UpperCAmelCase_							: Dict=1						,    UpperCAmelCase_							: Any=2						,    UpperCAmelCase_							: Any=False						,    UpperCAmelCase_							: List[str]=3						,    UpperCAmelCase_							: Tuple=2						,    UpperCAmelCase_							: List[str]=3						,    UpperCAmelCase_							: str=None						,    **UpperCAmelCase_							: int						,    )   ->Optional[Any]:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_						,    pad_token_id=UpperCAmelCase_						,    bos_token_id=UpperCAmelCase_						,    eos_token_id=UpperCAmelCase_    )
														snake_case_          =						hidden_size
														snake_case_          =						feat_extract_activation
														snake_case_          =						list(UpperCAmelCase_    )
														snake_case_          =						list(UpperCAmelCase_    )
														snake_case_          =						list(UpperCAmelCase_    )
														snake_case_          =						conv_bias
														snake_case_          =						num_conv_pos_embeddings
														snake_case_          =						num_conv_pos_embedding_groups
														snake_case_          =						conv_pos_kernel_size
														snake_case_          =						len(self.conv_dim    )
														snake_case_          =						num_hidden_layers
														snake_case_          =						intermediate_size
														snake_case_          =						hidden_act
														snake_case_          =						num_attention_heads
														snake_case_          =						hidden_dropout
														snake_case_          =						attention_dropout
														snake_case_          =						activation_dropout
														snake_case_          =						feat_proj_dropout
														snake_case_          =						final_dropout
														snake_case_          =						layerdrop
														snake_case_          =						layer_norm_eps
														snake_case_          =						initializer_range
														snake_case_          =						vocab_size
														snake_case_          =						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
														snake_case_          =						mask_time_prob
														snake_case_          =						mask_time_length
														snake_case_          =						mask_time_min_masks
														snake_case_          =						mask_feature_prob
														snake_case_          =						mask_feature_length
														snake_case_          =						mask_feature_min_masks

														# ctc loss
														snake_case_          =						ctc_loss_reduction
														snake_case_          =						ctc_zero_infinity

														# adapter
														snake_case_          =						add_adapter
														snake_case_          =						adapter_kernel_size
														snake_case_          =						adapter_stride
														snake_case_          =						num_adapter_layers
														snake_case_          =						output_hidden_size or hidden_size

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														snake_case_          =						classifier_proj_size

														# XVector-specific parameters. Feel free to ignore for other classes.
														snake_case_          =						list(UpperCAmelCase_    )
														snake_case_          =						list(UpperCAmelCase_    )
														snake_case_          =						list(UpperCAmelCase_    )
														snake_case_          =						xvector_output_dim



							@property
							def 						lowerCAmelCase    (			self							: List[str]    )   ->List[str]:



														"""simple docstring"""



														return math.prod(self.conv_stride    )







"""simple docstring"""
from collections import defaultdict
from math import gcd







def   _a			( _SCREAMING_SNAKE_CASE = 1_500_000			)      ->			int:
							snake_case_          =						defaultdict(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						2
							while 2 * euclid_m * (euclid_m + 1) <= limit:
														for euclid_n in range((euclid_m % 2) + 1			,	_SCREAMING_SNAKE_CASE			,	2			):
																					if gcd(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			) > 1:
																												continue
																					snake_case_          =						2 * euclid_m * (euclid_m + euclid_n)
																					for perimeter in range(_SCREAMING_SNAKE_CASE			,	limit + 1			,	_SCREAMING_SNAKE_CASE			):
																												frequencies[perimeter] += 1
														euclid_m += 1
							return sum(1 for frequency in frequencies.values() if frequency == 1			)


if __name__ == "__main__":
				print(f"""{solution() = }""")



