


"""simple docstring"""






import math
from collections.abc import Generator
def    A_ ( _lowerCAmelCase  :       int							):






     """simple docstring"""

     _a     						=					(i for i in range(1,  (max_n + 1)							))
     for i in (n for n in numbers if n > 1):
          for j in range(2,  _A							):
               if (i % j) == 0:
                    break
          else:
               yield i
def    A_ ( _lowerCAmelCase  :       int							):






     """simple docstring"""

     _a     						=					(i for i in range(1,  (max_n + 1)							))
     for i in (n for n in numbers if n > 1):
          # only need to check for factors up to sqrt(i)
          _a     						=					int(math.sqrt(_A							)							) + 1
          for j in range(2,  _A							):
               if (i % j) == 0:
                    break
          else:
               yield i
def    A_ ( _lowerCAmelCase  :       int							):






     """simple docstring"""

     _a     						=					(i for i in range(1,  (max_n + 1),  2							))
     # It's useless to test even numbers as they will not be prime
     if max_n > 2:
          yield 2  # Because 2 will not be tested, it's necessary to yield it now
     for i in (n for n in numbers if n > 1):
          _a     						=					int(math.sqrt(_A							)							) + 1
          for j in range(3,  _A,  2							):
               # As we removed the even numbers, we don't need them now
               if (i % j) == 0:
                    break
          else:
               yield i
def    A_ ( ):






     """simple docstring"""

     from timeit import timeit

     _a     						=					'''from __main__ import slow_primes, primes, fast_primes'''
     print(timeit('''slow_primes(1_000_000_000_000)''',  setup=_A,  number=1_00_00_00							)							)
     print(timeit('''primes(1_000_000_000_000)''',  setup=_A,  number=1_00_00_00							)							)
     print(timeit('''fast_primes(1_000_000_000_000)''',  setup=_A,  number=1_00_00_00							)							)


if __name__ == "__main__":
   __snake_case												=							int(input('''Calculate primes up to:\n>> ''').strip())
   for ret in primes(number):
      print(ret)
   benchmark()


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A	:   List[Any]	=					logging.get_logger(__name__)

__A	:   Tuple	=					{
    'google/electra-small-generator': 'https://huggingface.co/google/electra-small-generator/resolve/main/config.json',
    'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/config.json',
    'google/electra-large-generator': 'https://huggingface.co/google/electra-large-generator/resolve/main/config.json',
    'google/electra-small-discriminator': (
        'https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'
    ),
    'google/electra-base-discriminator': (
        'https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'
    ),
    'google/electra-large-discriminator': (
        'https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'
    ),
}


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):
						_UpperCamelCase:Tuple	=		"electra"







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE=3_0522     ,						_SCREAMING_SNAKE_CASE=128     ,						_SCREAMING_SNAKE_CASE=256     ,						_SCREAMING_SNAKE_CASE=12     ,						_SCREAMING_SNAKE_CASE=4     ,						_SCREAMING_SNAKE_CASE=1024     ,						_SCREAMING_SNAKE_CASE="gelu"     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=512     ,						_SCREAMING_SNAKE_CASE=2     ,						_SCREAMING_SNAKE_CASE=0.0_2     ,						_SCREAMING_SNAKE_CASE=1E-12     ,						_SCREAMING_SNAKE_CASE="first"     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE="gelu"     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=0     ,						_SCREAMING_SNAKE_CASE="absolute"     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=None     ,						**_SCREAMING_SNAKE_CASE     ,						)->							Optional[int]:
													super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=vocab_size
													lowerCamelCase_								=embedding_size
													lowerCamelCase_								=hidden_size
													lowerCamelCase_								=num_hidden_layers
													lowerCamelCase_								=num_attention_heads
													lowerCamelCase_								=intermediate_size
													lowerCamelCase_								=hidden_act
													lowerCamelCase_								=hidden_dropout_prob
													lowerCamelCase_								=attention_probs_dropout_prob
													lowerCamelCase_								=max_position_embeddings
													lowerCamelCase_								=type_vocab_size
													lowerCamelCase_								=initializer_range
													lowerCamelCase_								=layer_norm_eps

													lowerCamelCase_								=summary_type
													lowerCamelCase_								=summary_use_proj
													lowerCamelCase_								=summary_activation
													lowerCamelCase_								=summary_last_dropout
													lowerCamelCase_								=position_embedding_type
													lowerCamelCase_								=use_cache
													lowerCamelCase_								=classifier_dropout


class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):







						@property
						def        _snake_case				(			self							)->							Mapping[str, Mapping[int, str]]:
													if self.task == "multiple-choice":
																				lowerCamelCase_								={0: """batch""", 1: """choice""", 2: """sequence"""}
													else:
																				lowerCamelCase_								={0: """batch""", 1: """sequence"""}
													return OrderedDict(
													    [
													        ("""input_ids""", dynamic_axis),
													        ("""attention_mask""", dynamic_axis),
													        ("""token_type_ids""", dynamic_axis),
													    ]							)
