





"""simple docstring"""





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 				snake_case				(      nn.Module			):

      '''simple docstring'''



      def __init__(				self							:   Optional[Any]    ,					__lowercase							:   int = 16    ,					__lowercase							:   int = 88    ,					__lowercase							:   Optional[int] = None    ,					__lowercase							:   int = 1    ,					__lowercase							:   float = 0.0    ,					__lowercase							:   int = 32    ,					__lowercase							:   Optional[int] = None    ,					__lowercase							:   bool = False    ,					__lowercase							:   Optional[int] = None    ,					__lowercase							:   Optional[int] = None    ,					__lowercase							:   str = "geglu"    ,					__lowercase							:   Optional[int] = None    ,					):


        '''simple docstring'''




        super().__init__()
        __UpperCAmelCase     :    str							=    nn.ModuleList(
            [
                TransformeraDModel(
                    num_attention_heads=lowercase_    ,					attention_head_dim=lowercase_    ,					in_channels=lowercase_    ,					num_layers=lowercase_    ,					dropout=lowercase_    ,					norm_num_groups=lowercase_    ,					cross_attention_dim=lowercase_    ,					attention_bias=lowercase_    ,					sample_size=lowercase_    ,					num_vector_embeds=lowercase_    ,					activation_fn=lowercase_    ,					num_embeds_ada_norm=lowercase_    ,					)
                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
        __UpperCAmelCase     :    Tuple							=    0.5

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







      def    A_       (				self							:   str    ,					__lowercase							:   int    ,					__lowercase							:   Dict    ,					__lowercase							:   Tuple=None    ,					__lowercase							:   Optional[Any]=None    ,					__lowercase							:   str=None    ,					__lowercase							:   bool = True    ,					):


        '''simple docstring'''




        __UpperCAmelCase     :    Optional[Any]							=    hidden_states

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

        __UpperCAmelCase     :    Any							=    encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        __UpperCAmelCase     :    Optional[Any]							=    output_states + input_states

        if not return_dict:
          return (output_states,)

        return TransformeraDModelOutput(sample=lowercase_			)
'''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.
__snake_case						  =		10

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

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  lowercase_     =       0
  lowercase_     =       len(SCREAMING_SNAKE_CASE_						)
  while left <= right:
    if right - left < precision:
      return lin_search(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_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   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  if left < right:
    if right - left < precision:
      return lin_search(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_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(SCREAMING_SNAKE_CASE_ ,	one_third - 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    elif array[two_third] < target:
      return rec_ternary_search(two_third + 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    else:
      return rec_ternary_search(one_third + 1 ,	two_third - 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  else:
    return -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()

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

