


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


_lowercase       :      List[str]	=	logging.get_logger(__name__)

_lowercase       :      Optional[int]	=	{
    "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       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







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

				def __init__(						self				,   a__=1_0000				,   a__=12				,   a__=2048				,   a__=4				,   a__=6				,   a__=2048				,   a__=4				,   a__=0.0				,   a__=0.0				,   a__=True				,   a__=True				,   a__="relu"				,   a__=256				,   a__=0.1				,   a__=0.0				,   a__=0.0				,   a__=0.02				,   a__=2				,   a__=True				,   a__=1				,   a__=0				,   a__=2				,   a__=6000				,   a__=1024				,   a__=2				,   a__=(5, 5)				,   a__=1024				,   a__=80				,   a__=1				,   **a__				,   )	->				int:
					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__				,   )







import math





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   bool:





	"""simple docstring"""







	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5							,					int(math.sqrt(UpperCamelCase__						) + 1						)							,					6						):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True





def   _lowerCAmelCase				(   UpperCamelCase__:     float = 0.1						) ->   int:





	"""simple docstring"""







	A								=				3
	A								=				3

	while primes / (2 * j - 1) >= ratio:
		for i in range(j * j + j + 1							,					(j + 2) * (j + 2)							,					j + 1						):
			primes += is_prime(UpperCamelCase__						)
		j += 2
	return j


if __name__ == "__main__":
				import doctest

				doctest.testmod()



