
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


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "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       a		(   __UpperCAmelCase					):
							lowercase_		:						Any   							=  'gptj'
							lowercase_		:						str   							=  {
							    'max_position_embeddings': 'n_positions',
							    'hidden_size': 'n_embd',
							    'num_attention_heads': 'n_head',
							    'num_hidden_layers': 'n_layer',
							}







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







								__lowerCAmelCase         =							vocab_size
								__lowerCAmelCase         =							n_positions
								__lowerCAmelCase         =							n_embd
								__lowerCAmelCase         =							n_layer
								__lowerCAmelCase         =							n_head
								__lowerCAmelCase         =							n_inner
								__lowerCAmelCase         =							rotary_dim
								__lowerCAmelCase         =							activation_function
								__lowerCAmelCase         =							resid_pdrop
								__lowerCAmelCase         =							embd_pdrop
								__lowerCAmelCase         =							attn_pdrop
								__lowerCAmelCase         =							layer_norm_epsilon
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							use_cache

								__lowerCAmelCase         =							bos_token_id
								__lowerCAmelCase         =							eos_token_id

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

class       a		(   __UpperCAmelCase					):







							def __init__(						self  :  str      ,				snake_case__  :  PretrainedConfig      ,				snake_case__  :  str = "default"      ,				snake_case__  :  List[PatchingSpec] = None      ,				snake_case__  :  bool = False      ,				):
								"""simple docstring"""







								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?
									__lowerCAmelCase         =							0







							@property
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
								if self.use_past:
									self.fill_with_past_key_values_(snake_case__      ,				direction="inputs" )
									__lowerCAmelCase         =							{0: "batch", 1: "past_sequence + sequence"}
								else:
									__lowerCAmelCase         =							{0: "batch", 1: "sequence"}

								return common_inputs







							@property
							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								return self._config.n_layer







							@property
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								return self._config.n_head







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  PreTrainedTokenizer      ,				snake_case__  :  int = -1      ,				snake_case__  :  int = -1      ,				snake_case__  :  bool = False      ,				snake_case__  :  Optional[TensorType] = None      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							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()
								__lowerCAmelCase         =							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

										__lowerCAmelCase   ,						__lowerCAmelCase         =							common_inputs["input_ids"].shape
										# Not using the same length for past_key_values
										__lowerCAmelCase         =							seqlen + 2
										__lowerCAmelCase         =							(
										    batch,
										    self.num_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // self.num_attention_heads,
										)
										__lowerCAmelCase         =							[
										    (torch.zeros(snake_case__ ), torch.zeros(snake_case__ )) for _ in range(self.num_layers )
										]

								__lowerCAmelCase         =							common_inputs["attention_mask"]
								if self.use_past:
									__lowerCAmelCase         =							ordered_inputs["attention_mask"].dtype
									__lowerCAmelCase         =							torch.cat(
									    [ordered_inputs["attention_mask"], torch.ones(snake_case__      ,				snake_case__      ,				dtype=snake_case__ )]      ,				dim=1 )

								return ordered_inputs







							@property
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								return 13








from __future__ import annotations

UpperCamelCase_   	=	[True] * 1_0_0_0_0_0_1
UpperCamelCase_   	=	2
while i * i <= 1_0_0_0_0_0_0:
					if seive[i]:
										for j in range(i * i, 1_0_0_0_0_0_1, i):
															UpperCamelCase_   	=	False
					i += 1






def 		_UpperCAmelCase   (	UpperCamelCase:     int  ):






	"""simple docstring"""


	return seive[n]






def 		_UpperCAmelCase   (	UpperCamelCase:     int  ):






	"""simple docstring"""


	return any(digit in "02468" for digit in str(UpperCamelCase  )  )






def 		_UpperCAmelCase   (	UpperCamelCase:     int = 1_0_0_0_0_0_0  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[2]  # result already includes the number 2.
	for num in range(3			,							limit + 1			,							2  ):
		if is_prime(UpperCamelCase  ) and not contains_an_even_digit(UpperCamelCase  ):
			__lowerCAmelCase         =							str(UpperCamelCase  )
			__lowerCAmelCase         =							[int(str_num[j:] + str_num[:j]  ) for j in range(len(UpperCamelCase  )  )]
			if all(is_prime(UpperCamelCase  ) for i in list_nums  ):
				result.append(UpperCamelCase  )
	return result






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


	return len(find_circular_primes()  )


if __name__ == "__main__":
					print(f'''{len(find_circular_primes()) = }''')






