






from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class      lowercase__     (			nn.Module	):

	def __init__(							self		,    __UpperCAmelCase = 16		,    __UpperCAmelCase = 88		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 1		,    __UpperCAmelCase = 0.0		,    __UpperCAmelCase = 32		,    __UpperCAmelCase = None		,    __UpperCAmelCase = False		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    __UpperCAmelCase = "geglu"		,    __UpperCAmelCase = None		,    )->					int:







							'''simple docstring'''





							super().__init__()
							lowerCAmelCase__       =						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
							lowerCAmelCase__       =						0.5

							# The shape of `encoder_hidden_states` is expected to be
							# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
							lowerCAmelCase__       =						[77, 257]

							# 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])`
							lowerCAmelCase__       =						[1, 0]


	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase=None		,    __UpperCAmelCase=None		,    __UpperCAmelCase=None		,    __UpperCAmelCase = True		,    )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						hidden_states

							lowerCAmelCase__       =						[]
							lowerCAmelCase__       =						0
							# attention_mask is not used yet
							for i in range(2       ):
													# for each of the two transformers, pass the corresponding condition tokens
													lowerCAmelCase__       =						encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
													lowerCAmelCase__       =						self.transformer_index_for_condition[i]
													lowerCAmelCase__       =						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]

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

							if not return_dict:
													return (output_states,)

							return TransformeraDModelOutput(sample=__UpperCAmelCase       )














from __future__ import annotations

from collections.abc import Generator


def        _a  (     ) ->   Generator[int, None, None]:
						"""simple docstring"""







						lowerCAmelCase__       =						{}
						lowerCAmelCase__       =						2
						while True:
												lowerCAmelCase__       =						factor_map.pop(UpperCamelCase_    ,   UpperCamelCase_   )
												if factor:
																		lowerCAmelCase__       =						factor + prime
																		while x in factor_map:
																								x += factor
																		lowerCAmelCase__       =						factor
												else:
																		lowerCAmelCase__       =						prime
																		yield prime
												prime += 1


def        _a  (     UpperCamelCase_					:				float = 1e1_0   ) ->   int:
						"""simple docstring"""







						lowerCAmelCase__       =						sieve()

						lowerCAmelCase__       =						1
						while True:
												lowerCAmelCase__       =						next(UpperCamelCase_   )
												if (2 * prime * n) > limit:
																		return n
												# Ignore the next prime as the reminder will be 2.
												next(UpperCamelCase_   )
												n += 2


if __name__ == "__main__":
	print(solution())






