



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


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=						{
    """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_  (   __snake_case   ):
						__magic_name__:     Dict	 =    'gptj'
						__magic_name__:     int	 =    {
						    'max_position_embeddings': 'n_positions',
						    'hidden_size': 'n_embd',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}






						def __init__( self : List[Any]						,				_A : str=50400						,				_A : Optional[Any]=2048						,				_A : Optional[int]=4096						,				_A : Tuple=28						,				_A : Tuple=16						,				_A : Union[str, Any]=64						,				_A : int=None						,				_A : Dict="gelu_new"						,				_A : Any=0.0						,				_A : Tuple=0.0						,				_A : Union[str, Any]=0.0						,				_A : int=1E-5						,				_A : int=0.0_2						,				_A : int=True						,				_A : int=50256						,				_A : Optional[int]=50256						,				_A : int=False						,				**_A : Dict						,				)							->      int:







							"""simple docstring"""


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

							snake_case_						:     Optional[int]     =		bos_token_id
							snake_case_						:     Optional[Any]     =		eos_token_id

							super().__init__(
							    bos_token_id=_A						,				eos_token_id=_A						,				tie_word_embeddings=_A						,				**_A		)
class SCREAMING_SNAKE_CASE_  (   __snake_case   ):






						def __init__( self : Dict						,				_A : int						,				_A : Optional[int] = "default"						,				_A : Tuple = None						,				_A : Optional[int] = False						,				)							->      Union[str, Any]:







							"""simple docstring"""


							super().__init__(_A						,				task=_A						,				patching_specs=_A						,				use_past=_A		)
							if not getattr(self._config						,				'pad_token_id'						,				_A		):
								# TODO: how to do that better?
								snake_case_						:     Any     =		0






						@property
						def UpperCAmelCase_			( self : Any		)							->      Mapping[str, Mapping[int, str]]:







							"""simple docstring"""


							snake_case_						:     Dict     =		OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}		)
							if self.use_past:
								self.fill_with_past_key_values_(_A						,				direction='inputs'		)
								snake_case_						:     Optional[Any]     =		{0: 'batch', 1: 'past_sequence + sequence'}
							else:
								snake_case_						:     str     =		{0: 'batch', 1: 'sequence'}

							return common_inputs






						@property
						def UpperCAmelCase_			( self : Any		)							->      int:







							"""simple docstring"""


							return self._config.n_layer






						@property
						def UpperCAmelCase_			( self : str		)							->      int:







							"""simple docstring"""


							return self._config.n_head






						def UpperCAmelCase_			( self : Tuple						,				_A : Optional[int]						,				_A : Dict = -1						,				_A : List[str] = -1						,				_A : Optional[Any] = False						,				_A : Dict = None						,				)							->      Mapping[str, Any]:







							"""simple docstring"""


							snake_case_						:     Dict     =		super(_A						,				self		).generate_dummy_inputs(
							    _A						,				batch_size=_A						,				seq_length=_A						,				is_pair=_A						,				framework=_A		)

							# We need to order the input in the way they appears in the forward()
							snake_case_						:     str     =		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_  ,snake_case_						:     Union[str, Any]     =		common_inputs['input_ids'].shape
									# Not using the same length for past_key_values
									snake_case_						:     int     =		seqlen + 2
									snake_case_						:     Any     =		(
									    batch,
									    self.num_attention_heads,
									    past_key_values_length,
									    self._config.hidden_size // self.num_attention_heads,
									)
									snake_case_						:     str     =		[
									    (torch.zeros(_A		), torch.zeros(_A		)) for _ in range(self.num_layers		)
									]

							snake_case_						:     Union[str, Any]     =		common_inputs['attention_mask']
							if self.use_past:
								snake_case_						:     Tuple     =		ordered_inputs['attention_mask'].dtype
								snake_case_						:     Any     =		torch.cat(
								    [ordered_inputs['attention_mask'], torch.ones(_A						,				_A						,				dtype=_A		)]						,				dim=1		)

							return ordered_inputs






						@property
						def UpperCAmelCase_			( self : List[Any]		)							->      int:







							"""simple docstring"""


							return 13








from decimal import Decimal, getcontext
from math import ceil, factorial


def 							SCREAMING_SNAKE_CASE__  (      __a				):

	if not isinstance(__a				,	__a				):
		raise TypeError('Undefined for non-integers'				)
	elif precision < 1:
		raise ValueError('Undefined for non-natural numbers'				)

	snake_case_						:     Dict     =		precision
	snake_case_						:     str     =		ceil(precision / 14				)
	snake_case_						:     str     =		42_68_80 * Decimal(1_00_05				).sqrt()
	snake_case_						:     Tuple     =		1
	snake_case_						:     int     =		13_59_14_09
	snake_case_						:     Tuple     =		Decimal(__a				)
	for k in range(1				,	__a				):
		snake_case_						:     List[Any]     =		factorial(6 * k				) // (factorial(3 * k				) * factorial(__a				) ** 3)
		linear_term += 5_45_14_01_34
		exponential_term *= -26_25_37_41_26_40_76_80_00
		partial_sum += Decimal(multinomial_term * linear_term				) / exponential_term
	return str(constant_term / partial_sum				)[:-1]


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						50
						print(F'''The first {n} digits of pi is: {pi(n)}''')

