


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.
UpperCAmelCase__   	=							10
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)     ->			int:







 """simple docstring"""


 for i in range(__snake_case     ,						__snake_case						):
  if array[i] == target:
   return i
 return -1
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			int:







 """simple docstring"""


 _lowercase							      =0
 _lowercase							      =len(__snake_case						)
 while left <= right:
  if right - left < precision:
   return lin_search(__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)

  _lowercase							      =(left + right) // 3 + 1
  _lowercase							      =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]:
   _lowercase							      =one_third - 1
  elif array[two_third] < target:
   _lowercase							      =two_third + 1

  else:
   _lowercase							      =one_third + 1
   _lowercase							      =two_third - 1
 else:
  return -1
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)     ->			int:







 """simple docstring"""


 if left < right:
  if right - left < precision:
   return lin_search(__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)
  _lowercase							      =(left + right) // 3 + 1
  _lowercase							      =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(__snake_case     ,						one_third - 1     ,						__snake_case     ,						__snake_case						)
  elif array[two_third] < target:
   return rec_ternary_search(two_third + 1     ,						__snake_case     ,						__snake_case     ,						__snake_case						)
  else:
   return rec_ternary_search(one_third + 1     ,						two_third - 1     ,						__snake_case     ,						__snake_case						)
 else:
  return -1


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCAmelCase__   	=							input('''Enter numbers separated by comma:\n''').strip()
  UpperCAmelCase__   	=							[int(item.strip()) for item in user_input.split(''',''')]
  assert collection == sorted(collection), f"List must be ordered.\n{collection}."
  UpperCAmelCase__   	=							int(input('''Enter the number to be found in the list:\n''').strip())
  UpperCAmelCase__   	=							ite_ternary_search(collection, target)
  UpperCAmelCase__   	=							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''')





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class       lowerCamelCase__   (       nn.Module):





     def __init__(self		,     UpperCAmelCase = 1_6		,     UpperCAmelCase = 8_8		,     UpperCAmelCase = None		,     UpperCAmelCase = 1		,     UpperCAmelCase = 0.0		,     UpperCAmelCase = 3_2		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = "geglu"		,     UpperCAmelCase = None		,     )     ->   Any:
      super().__init__()
      _lowercase							      =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
      _lowercase							      =0.5

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase = True		,     )     ->   str:
      _lowercase							      =hidden_states

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

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

      if not return_dict:
       return (output_states,)

      return TransformeraDModelOutput(sample=UpperCAmelCase       )

