





'''simple docstring'''







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


lowerCAmelCase_					:    str      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Tuple      	=			{
    'facebook/s2t-small-librispeech-asr': (
        'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='speech_to_text'
				__a            =['past_key_values']
				__a            ={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




				def __init__(  self     :					Optional[Any]					,				__a     :					Dict=1_00_00					,				__a     :					Any=12					,				__a     :					Optional[Any]=20_48					,				__a     :					List[str]=4					,				__a     :					Any=6					,				__a     :					str=20_48					,				__a     :					Optional[Any]=4					,				__a     :					Tuple=0.0					,				__a     :					str=0.0					,				__a     :					int=True					,				__a     :					Union[str, Any]=True					,				__a     :					Any="relu"					,				__a     :					Optional[int]=2_56					,				__a     :					Optional[Any]=0.1					,				__a     :					Optional[int]=0.0					,				__a     :					List[Any]=0.0					,				__a     :					Dict=0.02					,				__a     :					Any=2					,				__a     :					Union[str, Any]=True					,				__a     :					Optional[Any]=1					,				__a     :					Dict=0					,				__a     :					Optional[int]=2					,				__a     :					Dict=60_00					,				__a     :					Dict=10_24					,				__a     :					Optional[int]=2					,				__a     :					int=(5, 5)					,				__a     :					int=10_24					,				__a     :					str=80					,				__a     :					Any=1					,				**__a     :					Dict					,				):
								_a								= vocab_size
								_a								= d_model
								_a								= encoder_ffn_dim
								_a								= encoder_layers
								_a								= encoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= activation_function
								_a								= init_std
								_a								= encoder_layerdrop
								_a								= decoder_layerdrop
								_a								= use_cache
								_a								= encoder_layers
								_a								= scale_embedding  # scale factor will be sqrt(d_model) if True
								_a								= max_source_positions
								_a								= max_target_positions
								_a								= num_conv_layers
								_a								= list(__a					)
								_a								= conv_channels
								_a								= input_feat_per_channel
								_a								= input_channels

								if len(self.conv_kernel_sizes					) != self.num_conv_layers:
												raise ValueError(
												    "Configuration for convolutional module is incorrect. "
												    "It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` "
												    f'but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes					)}`, '
												    f'`config.num_conv_layers = {self.num_conv_layers}`.'					)

								super().__init__(
								    pad_token_id=__a					,				bos_token_id=__a					,				eos_token_id=__a					,				is_encoder_decoder=__a					,				decoder_start_token_id=__a					,				**__a					,				)





"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt

def       _snake_case						(						UpperCamelCase						:		int = 1000000			,		UpperCamelCase						:		int = 10						):
 UpperCAmelCase	:					defaultdict	=	defaultdict(UpperCamelCase						)

 for outer_width in range(3			,		(t_limit // 4) + 2						):
  if outer_width * outer_width > t_limit:
   UpperCAmelCase	:					str	=	max(
       ceil(sqrt(outer_width * outer_width - t_limit						)						)			,		1						)
  else:
   UpperCAmelCase	:					Optional[Any]	=	1

  hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

  for hole_width in range(UpperCamelCase			,		outer_width - 1			,		2						):
   count[outer_width * outer_width - hole_width * hole_width] += 1

 return sum(1 for n in count.values() if 1 <= n <= 10						)


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