'''simple docstring'''
from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a=  1_0







def  _UpperCamelCase (     _a	:	int				,     _a	:	int				,     _a	:	list[int]				,     _a	:	int			):




    """simple docstring"""



    for i in range(_a				,     _a			):
        if array[i] == target:
            return i
    return -1







def  _UpperCamelCase (     _a	:	list[int]				,     _a	:	int			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  0
    __UpperCamelCase	:	Tuple		    =  len(_a			)
    while left <= right:
        if right - left < precision:
            return lin_search(_a				,     _a				,     _a				,     _a			)

        __UpperCamelCase	:	Union[str, Any]		    =  (left + right) // 3 + 1
        __UpperCamelCase	:	Optional[int]		    =  2 * (left + right) // 3 + 1

        if array[one_third] == target:
            return one_third
        elif array[two_third] == target:
            return two_third

        elif target < array[one_third]:
            __UpperCamelCase	:	Union[str, Any]		    =  one_third - 1
        elif array[two_third] < target:
            __UpperCamelCase	:	Tuple		    =  two_third + 1

        else:
            __UpperCamelCase	:	str		    =  one_third + 1
            __UpperCamelCase	:	Optional[Any]		    =  two_third - 1
    else:
        return -1







def  _UpperCamelCase (     _a	:	int				,     _a	:	int				,     _a	:	list[int]				,     _a	:	int			):




    """simple docstring"""



    if left < right:
        if right - left < precision:
            return lin_search(_a				,     _a				,     _a				,     _a			)
        __UpperCamelCase	:	List[str]		    =  (left + right) // 3 + 1
        __UpperCamelCase	:	Optional[Any]		    =  2 * (left + right) // 3 + 1

        if array[one_third] == target:
            return one_third
        elif array[two_third] == target:
            return two_third

        elif target < array[one_third]:
            return rec_ternary_search(_a				,     one_third - 1				,     _a				,     _a			)
        elif array[two_third] < target:
            return rec_ternary_search(two_third + 1				,     _a				,     _a				,     _a			)
        else:
            return rec_ternary_search(one_third + 1				,     two_third - 1				,     _a				,     _a			)
    else:
        return -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       a=  input('''Enter numbers separated by comma:\n''').strip()
       a=  [int(item.strip()) for item in user_input.split(''',''')]
       assert collection == sorted(collection), F"List must be ordered.\n{collection}."
       a=  int(input('''Enter the number to be found in the list:\n''').strip())
       a=  ite_ternary_search(collection, target)
       a=  rec_ternary_search(0, len(collection) - 1, collection, target)
       if resulta != -1:
              print(F"""Iterative search: {target} found at positions: {resulta}""")
              print(F"""Recursive search: {target} found at positions: {resulta}""")
       else:
              print('''Not found''')




'''simple docstring'''
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase						):







     """simple docstring"""






     @register_to_config
     def __init__(		self     ,			*,
         _lowerCamelCase = 4     ,			_lowerCamelCase = 7_6_8     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
         super().__init__()

         __UpperCamelCase	:	Optional[int]		    =  nn.Parameter(torch.zeros(_lowerCamelCase    )    )

         # parameters for additional clip time embeddings
         __UpperCamelCase	:	Dict		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )

         # parameters for encoder hidden states
         __UpperCamelCase	:	Optional[Any]		    =  clip_extra_context_tokens
         __UpperCamelCase	:	Dict		    =  nn.Linear(
             _lowerCamelCase     ,			self.clip_extra_context_tokens * cross_attention_dim    )
         __UpperCamelCase	:	str		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  nn.LayerNorm(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*, _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         if do_classifier_free_guidance:
             # Add the classifier free guidance embeddings to the image embeddings
             __UpperCamelCase	:	int		    =  image_embeddings.shape[0]
             __UpperCamelCase	:	Optional[Any]		    =  self.learned_classifier_free_guidance_embeddings.unsqueeze(0    )
             __UpperCamelCase	:	List[Any]		    =  classifier_free_guidance_embeddings.expand(
                 _lowerCamelCase     ,			-1    )
             __UpperCamelCase	:	Optional[Any]		    =  torch.cat([classifier_free_guidance_embeddings, image_embeddings]     ,			dim=0    )

         # The image embeddings batch size and the text embeddings batch size are equal
         assert image_embeddings.shape[0] == prompt_embeds.shape[0]

         __UpperCamelCase	:	Optional[Any]		    =  prompt_embeds.shape[0]

         # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
         # adding CLIP embeddings to the existing timestep embedding, ...
         __UpperCamelCase	:	Optional[int]		    =  self.embedding_proj(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  self.clip_image_embeddings_project_to_time_embeddings(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  time_projected_image_embeddings + time_projected_prompt_embeds

         # ... and by projecting CLIP embeddings into four
         # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
         __UpperCamelCase	:	Optional[int]		    =  self.clip_extra_context_tokens_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  clip_extra_context_tokens.reshape(_lowerCamelCase     ,			-1     ,			self.clip_extra_context_tokens    )
         __UpperCamelCase	:	Dict		    =  clip_extra_context_tokens.permute(0     ,			2     ,			1    )

         __UpperCamelCase	:	Union[str, Any]		    =  self.encoder_hidden_states_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[Any]		    =  self.text_encoder_hidden_states_norm(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]     ,			dim=1    )

         return text_encoder_hidden_states, additive_clip_time_embeddings



