




"""simple docstring"""

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


_lowerCamelCase  :			Dict        =       logging.get_logger(__name__)

_lowerCamelCase  :			Optional[int]        =       {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Any         =   """pegasus"""
				__lowerCAmelCase    :    List[Any]         =   ["""past_key_values"""]
				__lowerCAmelCase    :    Dict         =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







				def __init__(							self   :					Optional[int]      ,     _lowerCamelCase   :					str=5_02_65      ,     _lowerCamelCase   :					List[str]=10_24      ,     _lowerCamelCase   :					Optional[int]=12      ,     _lowerCamelCase   :					Tuple=40_96      ,     _lowerCamelCase   :					List[str]=16      ,     _lowerCamelCase   :					str=12      ,     _lowerCamelCase   :					Tuple=40_96      ,     _lowerCamelCase   :					str=16      ,     _lowerCamelCase   :					List[str]=0.0      ,     _lowerCamelCase   :					str=0.0      ,     _lowerCamelCase   :					int=True      ,     _lowerCamelCase   :					Any=True      ,     _lowerCamelCase   :					int="gelu"      ,     _lowerCamelCase   :					int=10_24      ,     _lowerCamelCase   :					str=0.1      ,     _lowerCamelCase   :					Tuple=0.0      ,     _lowerCamelCase   :					str=0.0      ,     _lowerCamelCase   :					List[str]=0.02      ,     _lowerCamelCase   :					List[str]=0      ,     _lowerCamelCase   :					List[Any]=False      ,     _lowerCamelCase   :					Tuple=0      ,     _lowerCamelCase   :					Dict=1      ,     _lowerCamelCase   :					Any=1      ,     **_lowerCamelCase   :					Dict      ,     ):



					"""simple docstring"""

					A_  :				List[str]	   =		vocab_size
					A_  :				List[str]	   =		max_position_embeddings
					A_  :				List[str]	   =		d_model
					A_  :				List[Any]	   =		encoder_ffn_dim
					A_  :				int	   =		encoder_layers
					A_  :				List[str]	   =		encoder_attention_heads
					A_  :				Any	   =		decoder_ffn_dim
					A_  :				Union[str, Any]	   =		decoder_layers
					A_  :				Tuple	   =		decoder_attention_heads
					A_  :				Optional[Any]	   =		dropout
					A_  :				Tuple	   =		attention_dropout
					A_  :				str	   =		activation_dropout
					A_  :				Dict	   =		activation_function
					A_  :				Optional[Any]	   =		init_std
					A_  :				List[Any]	   =		encoder_layerdrop
					A_  :				Dict	   =		decoder_layerdrop
					A_  :				Optional[int]	   =		use_cache
					A_  :				List[Any]	   =		encoder_layers
					A_  :				List[Any]	   =		scale_embedding  # scale factor will be sqrt(d_model) if True
					super().__init__(
					    pad_token_id=_lowerCamelCase      ,     eos_token_id=_lowerCamelCase      ,     is_encoder_decoder=_lowerCamelCase      ,     decoder_start_token_id=_lowerCamelCase      ,     forced_eos_token_id=_lowerCamelCase      ,     **_lowerCamelCase      ,     )







				@property
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					return self.encoder_attention_heads







				@property
				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					return self.d_model











"""simple docstring"""

import random

from .binary_exp_mod import bin_exp_mod

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase=1000	):




	"""simple docstring"""






	if n < 2:
		return False

	if n % 2 == 0:
		return n == 2

	# this means n is odd
	A_  :				List[Any]	   =		n - 1
	A_  :				Dict	   =		0
	while d % 2 == 0:
		d /= 2
		exp += 1

	# n - 1=d*(2**exp)
	A_  :				Optional[Any]	   =		0
	while count < prec:
		A_  :				Optional[int]	   =		random.randint(2  ,      n - 1	)
		A_  :				Any	   =		bin_exp_mod(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
		if b != 1:
			A_  :				Dict	   =		True
			for _ in range(_UpperCAmelCase	):
				if b == n - 1:
					A_  :				Optional[int]	   =		False
					break
				A_  :				Any	   =		b * b
				b %= n
			if flag:
				return False
			count += 1
	return True


if __name__ == "__main__":
		_lowerCamelCase  :			List[str]        =       abs(int(input('Enter bound : ').strip()))
		print('Here\'s the list of primes:')
		print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))





