'''simple docstring'''





import random
def 						__A							(     UpperCAmelCase					)     ->			bool:




						'''simple docstring'''
						_UpperCamelCase			:			Tuple   			=						num - 1
						_UpperCamelCase			:			str   			=						0

						while s % 2 == 0:
												_UpperCamelCase			:			List[Any]   			=						s // 2
												t += 1

						for _ in range(5					):
												_UpperCamelCase			:			Tuple   			=						random.randrange(2     ,num - 1					)
												_UpperCamelCase			:			Tuple   			=						pow(UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)
												if v != 1:
																		_UpperCamelCase			:			str   			=						0
																		while v != (num - 1):
																								if i == t - 1:
																														return False
																								else:
																														_UpperCamelCase			:			List[str]   			=						i + 1
																														_UpperCamelCase			:			Optional[Any]   			=						(v**2) % num
						return True
def 						__A							(     UpperCAmelCase					)     ->			bool:




						'''simple docstring'''
						if num < 2:
												return False

						_UpperCamelCase			:			Union[str, Any]   			=						[
						    2,
						    3,
						    5,
						    7,
						    1_1,
						    1_3,
						    1_7,
						    1_9,
						    2_3,
						    2_9,
						    3_1,
						    3_7,
						    4_1,
						    4_3,
						    4_7,
						    5_3,
						    5_9,
						    6_1,
						    6_7,
						    7_1,
						    7_3,
						    7_9,
						    8_3,
						    8_9,
						    9_7,
						    1_0_1,
						    1_0_3,
						    1_0_7,
						    1_0_9,
						    1_1_3,
						    1_2_7,
						    1_3_1,
						    1_3_7,
						    1_3_9,
						    1_4_9,
						    1_5_1,
						    1_5_7,
						    1_6_3,
						    1_6_7,
						    1_7_3,
						    1_7_9,
						    1_8_1,
						    1_9_1,
						    1_9_3,
						    1_9_7,
						    1_9_9,
						    2_1_1,
						    2_2_3,
						    2_2_7,
						    2_2_9,
						    2_3_3,
						    2_3_9,
						    2_4_1,
						    2_5_1,
						    2_5_7,
						    2_6_3,
						    2_6_9,
						    2_7_1,
						    2_7_7,
						    2_8_1,
						    2_8_3,
						    2_9_3,
						    3_0_7,
						    3_1_1,
						    3_1_3,
						    3_1_7,
						    3_3_1,
						    3_3_7,
						    3_4_7,
						    3_4_9,
						    3_5_3,
						    3_5_9,
						    3_6_7,
						    3_7_3,
						    3_7_9,
						    3_8_3,
						    3_8_9,
						    3_9_7,
						    4_0_1,
						    4_0_9,
						    4_1_9,
						    4_2_1,
						    4_3_1,
						    4_3_3,
						    4_3_9,
						    4_4_3,
						    4_4_9,
						    4_5_7,
						    4_6_1,
						    4_6_3,
						    4_6_7,
						    4_7_9,
						    4_8_7,
						    4_9_1,
						    4_9_9,
						    5_0_3,
						    5_0_9,
						    5_2_1,
						    5_2_3,
						    5_4_1,
						    5_4_7,
						    5_5_7,
						    5_6_3,
						    5_6_9,
						    5_7_1,
						    5_7_7,
						    5_8_7,
						    5_9_3,
						    5_9_9,
						    6_0_1,
						    6_0_7,
						    6_1_3,
						    6_1_7,
						    6_1_9,
						    6_3_1,
						    6_4_1,
						    6_4_3,
						    6_4_7,
						    6_5_3,
						    6_5_9,
						    6_6_1,
						    6_7_3,
						    6_7_7,
						    6_8_3,
						    6_9_1,
						    7_0_1,
						    7_0_9,
						    7_1_9,
						    7_2_7,
						    7_3_3,
						    7_3_9,
						    7_4_3,
						    7_5_1,
						    7_5_7,
						    7_6_1,
						    7_6_9,
						    7_7_3,
						    7_8_7,
						    7_9_7,
						    8_0_9,
						    8_1_1,
						    8_2_1,
						    8_2_3,
						    8_2_7,
						    8_2_9,
						    8_3_9,
						    8_5_3,
						    8_5_7,
						    8_5_9,
						    8_6_3,
						    8_7_7,
						    8_8_1,
						    8_8_3,
						    8_8_7,
						    9_0_7,
						    9_1_1,
						    9_1_9,
						    9_2_9,
						    9_3_7,
						    9_4_1,
						    9_4_7,
						    9_5_3,
						    9_6_7,
						    9_7_1,
						    9_7_7,
						    9_8_3,
						    9_9_1,
						    9_9_7,
						]

						if num in low_primes:
												return True

						for prime in low_primes:
												if (num % prime) == 0:
																		return False

						return rabin_miller(UpperCAmelCase					)
def 						__A							(     UpperCAmelCase = 1_0_2_4					)     ->			int:




						'''simple docstring'''
						while True:
												_UpperCamelCase			:			List[str]   			=						random.randrange(2 ** (keysize - 1)     ,2 ** (keysize)					)
												if is_prime_low_num(UpperCAmelCase					):
																		return num


if __name__ == "__main__":
			lowerCAmelCase_	:		List[str]			=     generate_large_prime()
			print(("""Prime number:""", num))
			print(("""is_prime_low_num:""", is_prime_low_num(num)))



'''simple docstring'''





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCAmelCase_	:		int			=     logging.get_logger(__name__)

lowerCAmelCase_	:		Any			=     {
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							'''gptj'''
				UpperCAmelCase__							=							{
				    '''max_position_embeddings''': '''n_positions''',
				    '''hidden_size''': '''n_embd''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}





				def __init__(						self					:						Optional[Any]       ,		lowercase__					:						Union[str, Any]=50_400       ,		lowercase__					:						Union[str, Any]=2_048       ,		lowercase__					:						Tuple=4_096       ,		lowercase__					:						List[str]=28       ,		lowercase__					:						Optional[int]=16       ,		lowercase__					:						str=64       ,		lowercase__					:						Any=None       ,		lowercase__					:						Any="gelu_new"       ,		lowercase__					:						Union[str, Any]=0.0       ,		lowercase__					:						Optional[Any]=0.0       ,		lowercase__					:						Any=0.0       ,		lowercase__					:						Tuple=1e-5       ,		lowercase__					:						Any=0.0_2       ,		lowercase__					:						int=True       ,		lowercase__					:						int=50_256       ,		lowercase__					:						Any=50_256       ,		lowercase__					:						Tuple=False       ,		**lowercase__					:						str       ,		)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Dict   			=						vocab_size
										_UpperCamelCase			:			List[str]   			=						n_positions
										_UpperCamelCase			:			Union[str, Any]   			=						n_embd
										_UpperCamelCase			:			Union[str, Any]   			=						n_layer
										_UpperCamelCase			:			Optional[Any]   			=						n_head
										_UpperCamelCase			:			Dict   			=						n_inner
										_UpperCamelCase			:			Optional[Any]   			=						rotary_dim
										_UpperCamelCase			:			Tuple   			=						activation_function
										_UpperCamelCase			:			List[Any]   			=						resid_pdrop
										_UpperCamelCase			:			Any   			=						embd_pdrop
										_UpperCamelCase			:			Optional[Any]   			=						attn_pdrop
										_UpperCamelCase			:			Optional[Any]   			=						layer_norm_epsilon
										_UpperCamelCase			:			Union[str, Any]   			=						initializer_range
										_UpperCamelCase			:			Optional[int]   			=						use_cache

										_UpperCamelCase			:			str   			=						bos_token_id
										_UpperCamelCase			:			Any   			=						eos_token_id

										super().__init__(
										    bos_token_id=lowercase__       ,		eos_token_id=lowercase__       ,		tie_word_embeddings=lowercase__       ,		**lowercase__					)





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						Optional[int]       ,		lowercase__					:						PretrainedConfig       ,		lowercase__					:						str = "default"       ,		lowercase__					:						List[PatchingSpec] = None       ,		lowercase__					:						bool = False       ,		)							->Union[str, Any]:


										'''simple docstring'''




										super().__init__(lowercase__       ,		task=lowercase__       ,		patching_specs=lowercase__       ,		use_past=lowercase__					)
										if not getattr(self._config       ,		"pad_token_id"       ,		lowercase__					):
																# TODO: how to do that better?
																_UpperCamelCase			:			Optional[int]   			=						0





				@property
				def  snake_case__				(						self					:						List[str]					)							->Mapping[str, Mapping[int, str]]:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}					)
										if self.use_past:
																self.fill_with_past_key_values_(lowercase__       ,		direction="inputs"					)
																_UpperCamelCase			:			str   			=						{0: "batch", 1: "past_sequence + sequence"}
										else:
																_UpperCamelCase			:			Optional[Any]   			=						{0: "batch", 1: "sequence"}

										return common_inputs





				@property
				def  snake_case__				(						self					:						int					)							->int:


										'''simple docstring'''




										return self._config.n_layer





				@property
				def  snake_case__				(						self					:						Dict					)							->int:


										'''simple docstring'''




										return self._config.n_head





				def  snake_case__				(						self					:						int       ,		lowercase__					:						PreTrainedTokenizer       ,		lowercase__					:						int = -1       ,		lowercase__					:						int = -1       ,		lowercase__					:						bool = False       ,		lowercase__					:						Optional[TensorType] = None       ,		)							->Mapping[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						super(lowercase__       ,		self					).generate_dummy_inputs(
										    lowercase__       ,		batch_size=lowercase__       ,		seq_length=lowercase__       ,		is_pair=lowercase__       ,		framework=lowercase__					)

										# We need to order the input in the way they appears in the forward()
										_UpperCamelCase			:			Tuple   			=						OrderedDict({"input_ids": common_inputs["input_ids"]}					)

										# Need to add the past_keys
										if self.use_past:
																if not is_torch_available():
																						raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
																else:
																						import torch

																						_UpperCamelCase			,       _UpperCamelCase			:			Optional[Any]   			=						common_inputs["input_ids"].shape
																						# Not using the same length for past_key_values
																						_UpperCamelCase			:			Optional[int]   			=						seqlen + 2
																						_UpperCamelCase			:			str   			=						(
																						    batch,
																						    self.num_attention_heads,
																						    past_key_values_length,
																						    self._config.hidden_size // self.num_attention_heads,
																						)
																						_UpperCamelCase			:			Dict   			=						[
																						    (torch.zeros(lowercase__					), torch.zeros(lowercase__					)) for _ in range(self.num_layers					)
																						]

										_UpperCamelCase			:			str   			=						common_inputs["attention_mask"]
										if self.use_past:
																_UpperCamelCase			:			int   			=						ordered_inputs["attention_mask"].dtype
																_UpperCamelCase			:			Optional[int]   			=						torch.cat(
																    [ordered_inputs["attention_mask"], torch.ones(lowercase__       ,		lowercase__       ,		dtype=lowercase__					)]       ,		dim=1					)

										return ordered_inputs







				@property
				def  snake_case__				(						self					:						Tuple					)							->int:


										'''simple docstring'''




										return 13


