'''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


A_        =   logging.get_logger(__name__)

A_        =   {
    "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
    "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
    "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
    "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
    "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
}




class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			__magic_name__   :							Tuple             =					"""gpt2"""
			__magic_name__   :							List[Any]             =					["""past_key_values"""]
			__magic_name__   :							Dict             =					{
			    """hidden_size""": """n_embd""",
			    """max_position_embeddings""": """n_positions""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}



			def __init__(       self							, snake_case_=50_257							, snake_case_=1_024							, snake_case_=768							, snake_case_=12							, snake_case_=12							, snake_case_=None							, snake_case_="gelu_new"							, snake_case_=0.1							, snake_case_=0.1							, snake_case_=0.1							, snake_case_=1e-5							, snake_case_=0.02							, snake_case_="cls_index"							, snake_case_=True							, snake_case_=None							, snake_case_=True							, snake_case_=0.1							, snake_case_=True							, snake_case_=True							, snake_case_=50_256							, snake_case_=50_256							, snake_case_=False							, snake_case_=False							, **snake_case_							, ):
									snake_case   :					int							=						vocab_size
									snake_case   :					List[str]							=						n_positions
									snake_case   :					Union[str, Any]							=						n_embd
									snake_case   :					Union[str, Any]							=						n_layer
									snake_case   :					Optional[int]							=						n_head
									snake_case   :					Any							=						n_inner
									snake_case   :					Tuple							=						activation_function
									snake_case   :					Optional[int]							=						resid_pdrop
									snake_case   :					Optional[int]							=						embd_pdrop
									snake_case   :					Optional[int]							=						attn_pdrop
									snake_case   :					str							=						layer_norm_epsilon
									snake_case   :					List[Any]							=						initializer_range
									snake_case   :					Dict							=						summary_type
									snake_case   :					str							=						summary_use_proj
									snake_case   :					List[str]							=						summary_activation
									snake_case   :					str							=						summary_first_dropout
									snake_case   :					Dict							=						summary_proj_to_labels
									snake_case   :					Optional[Any]							=						scale_attn_weights
									snake_case   :					Any							=						use_cache
									snake_case   :					Dict							=						scale_attn_by_inverse_layer_idx
									snake_case   :					Dict							=						reorder_and_upcast_attn

									snake_case   :					Optional[int]							=						bos_token_id
									snake_case   :					Union[str, Any]							=						eos_token_id

									super().__init__(bos_token_id=snake_case_							, eos_token_id=snake_case_							, **snake_case_				)







class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			def __init__(       self							, snake_case_							, snake_case_ = "default"							, snake_case_ = None							, snake_case_ = False							, ):
									super().__init__(snake_case_							, task=snake_case_							, patching_specs=snake_case_							, use_past=snake_case_				)
									if not getattr(self._config							, "pad_token_id"							, snake_case_				):
															# TODO: how to do that better?
															snake_case   :					int							=						0



			@property
			def      lowerCamelCase   (       self				):
									snake_case   :					List[Any]							=						OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}				)
									if self.use_past:
															self.fill_with_past_key_values_(snake_case_							, direction="inputs"				)
															snake_case   :					List[str]							=						{0: "batch", 1: "past_sequence + sequence"}
									else:
															snake_case   :					List[str]							=						{0: "batch", 1: "sequence"}

									return common_inputs



			@property
			def      lowerCamelCase   (       self				):
									return self._config.n_layer



			@property
			def      lowerCamelCase   (       self				):
									return self._config.n_head



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_ = -1							, snake_case_ = -1							, snake_case_ = False							, snake_case_ = None							, ):
									snake_case   :					Union[str, Any]							=						super(snake_case_							, self				).generate_dummy_inputs(
									    snake_case_							, batch_size=snake_case_							, seq_length=snake_case_							, is_pair=snake_case_							, framework=snake_case_				)

									# We need to order the input in the way they appears in the forward()
									snake_case   :					Dict							=						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

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

									snake_case   :					Optional[int]							=						common_inputs["attention_mask"]
									if self.use_past:
															snake_case   :					Any							=						ordered_inputs["attention_mask"].dtype
															snake_case   :					Optional[Any]							=						torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(snake_case_							, snake_case_							, dtype=snake_case_				)]							, dim=1				)

									return ordered_inputs






			@property
			def      lowerCamelCase   (       self				):
									return 13






'''simple docstring'''

import math


def 					UpperCAmelCase__      (							_lowerCAmelCase						: 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(_lowerCAmelCase) + 1),			6):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int = 200_0000)						->		int:



						"""simple docstring"""

						return sum(num for num in range(3,			_lowerCAmelCase,			2) if is_prime(_lowerCAmelCase)) + 2 if n > 2 else 0


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





