

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput





class 	snake_case_   (		nn.Module					):







			'''simple docstring'''

			def __init__(     self :				Dict							,   _UpperCamelCase :				int = 1_6							,   _UpperCamelCase :				int = 8_8							,   _UpperCamelCase :				Optional[int] = None							,   _UpperCamelCase :				int = 1							,   _UpperCamelCase :				float = 0.0							,   _UpperCamelCase :				int = 3_2							,   _UpperCamelCase :				Optional[int] = None							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				Optional[int] = None							,   _UpperCamelCase :				Optional[int] = None							,   _UpperCamelCase :				str = "geglu"							,   _UpperCamelCase :				Optional[int] = None							,   )      ->Any:
									super().__init__()
									snake_case_   =	nn.ModuleList(
									    [
									        TransformeraDModel(
									            num_attention_heads=_UpperCamelCase							,   attention_head_dim=_UpperCamelCase							,   in_channels=_UpperCamelCase							,   num_layers=_UpperCamelCase							,   dropout=_UpperCamelCase							,   norm_num_groups=_UpperCamelCase							,   cross_attention_dim=_UpperCamelCase							,   attention_bias=_UpperCamelCase							,   sample_size=_UpperCamelCase							,   num_vector_embeds=_UpperCamelCase							,   activation_fn=_UpperCamelCase							,   num_embeds_ada_norm=_UpperCamelCase							,   )
									        for _ in range(2							)
									    ]							)

									# Variables that can be set by a pipeline:

									# The ratio of transformer1 to transformer2's output states to be combined during inference
									snake_case_   =	0.5

									# The shape of `encoder_hidden_states` is expected to be
									# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
									snake_case_   =	[7_7, 2_5_7]

									# Which transformer to use to encode which condition.
									# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
									snake_case_   =	[1, 0]







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				str							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Optional[Any]=None							,   _UpperCamelCase :				List[Any]=None							,   _UpperCamelCase :				List[str]=None							,   _UpperCamelCase :				bool = True							,   )      ->Optional[Any]:
									snake_case_   =	hidden_states

									snake_case_   =	[]
									snake_case_   =	0
									# attention_mask is not used yet
									for i in range(2							):
															# for each of the two transformers, pass the corresponding condition tokens
															snake_case_   =	encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
															snake_case_   =	self.transformer_index_for_condition[i]
															snake_case_   =	self.transformers[transformer_index](
															    _UpperCamelCase							,   encoder_hidden_states=_UpperCamelCase							,   timestep=_UpperCamelCase							,   cross_attention_kwargs=_UpperCamelCase							,   return_dict=_UpperCamelCase							,   )[0]
															encoded_states.append(encoded_state - input_states							)
															tokens_start += self.condition_lengths[i]

									snake_case_   =	encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
									snake_case_   =	output_states + input_states

									if not return_dict:
															return (output_states,)

									return TransformeraDModelOutput(sample=_UpperCamelCase							)


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


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):

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

						snake_case_   =	precision
						snake_case_   =	ceil(precision / 14       )
						snake_case_   =	426880 * Decimal(10005       ).sqrt()
						snake_case_   =	1
						snake_case_   =	13591409
						snake_case_   =	Decimal(SCREAMING_SNAKE_CASE__       )
						for k in range(1  ,		SCREAMING_SNAKE_CASE__       ):
												snake_case_   =	factorial(6 * k       ) // (factorial(3 * k       ) * factorial(SCREAMING_SNAKE_CASE__       ) ** 3)
												linear_term += 545140134
												exponential_term *= -262537412640768000
												partial_sum += Decimal(multinomial_term * linear_term       ) / exponential_term
						return str(constant_term / partial_sum       )[:-1]


if __name__ == "__main__":
					lowerCAmelCase_       	=	50
					print(f"""The first {n} digits of pi is: {pi(n)}""")