


'''simple docstring'''






from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class 						__UpperCamelCase	(						nn.Module							):





							def __init__(  self		:Optional[int]  ,_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  ,):
									super().__init__()
									snake_case_		:							List[Any]			       =						nn.ModuleList(
									    [
									        TransformeraDModel(
									            num_attention_heads=_snake_case  ,attention_head_dim=_snake_case  ,in_channels=_snake_case  ,num_layers=_snake_case  ,dropout=_snake_case  ,norm_num_groups=_snake_case  ,cross_attention_dim=_snake_case  ,attention_bias=_snake_case  ,sample_size=_snake_case  ,num_vector_embeds=_snake_case  ,activation_fn=_snake_case  ,num_embeds_ada_norm=_snake_case  ,)
									        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_		:							Tuple			       =						0.5

									# The shape of `encoder_hidden_states` is expected to be
									# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
									snake_case_		:							Optional[int]			       =						[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_		:							Union[str, Any]			       =						[1, 0]





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:str=None  ,_UpperCamelCase		:Optional[int]=None  ,_UpperCamelCase		:Union[str, Any]=None  ,_UpperCamelCase		:bool = True  ,):
									snake_case_		:							Union[str, Any]			       =						hidden_states

									snake_case_		:							Any			       =						[]
									snake_case_		:							str			       =						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_		:							str			       =						encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
											snake_case_		:							int			       =						self.transformer_index_for_condition[i]
											snake_case_		:							Tuple			       =						self.transformers[transformer_index](
											    _snake_case  ,encoder_hidden_states=_snake_case  ,timestep=_snake_case  ,cross_attention_kwargs=_snake_case  ,return_dict=_snake_case  ,)[0]
											encoded_states.append(encoded_state - input_states			)
											tokens_start += self.condition_lengths[i]

									snake_case_		:							Union[str, Any]			       =						encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
									snake_case_		:							List[Any]			       =						output_states + input_states

									if not return_dict:
											return (output_states,)

									return TransformeraDModelOutput(sample=_snake_case			)



'''simple docstring'''






import math





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						len(lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
		snake_case_		:							str			       =						0
		while arr[min(lowerCamelCase_				,      lowerCamelCase_ ) - 1] < x:
				snake_case_		:							Any			       =						step
				step += int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
				if prev >= n:
						return -1

		while arr[prev] < x:
				snake_case_		:							Optional[Any]			       =						prev + 1
				if prev == min(lowerCamelCase_				,      lowerCamelCase_ ):
						return -1
		if arr[prev] == x:
				return prev
		return -1


if __name__ == "__main__":
							__A	:	Any     =     input('Enter numbers separated by a comma:\n').strip()
							__A	:	Union[str, Any]     =     [int(item) for item in user_input.split(',')]
							__A	:	Optional[int]     =     int(input('Enter the number to be searched:\n'))
							__A	:	int     =     jump_search(arr, x)
							if res == -1:
														print('Number not found!')
							else:
														print(F'Number {x} is at index {res}')