


from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput






class      UpperCamelCase					(		nn.Module     ):


			'''simple docstring'''







			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   ,							):
								super().__init__()
								lowercase_       :List[str]     =       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_       :Any     =       0.5

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




			def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None   ,							UpperCamelCase_ = True   ,							):
								lowercase_       :Optional[int]     =       hidden_states

								lowercase_       :Any     =       []
								lowercase_       :Dict     =       0
								# attention_mask is not used yet
								for i in range(2		):
													# for each of the two transformers, pass the corresponding condition tokens
													lowercase_       :int     =       encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
													lowercase_       :List[str]     =       self.transformer_index_for_condition[i]
													lowercase_       :List[Any]     =       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_       :Dict     =       encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
								lowercase_       :Any     =       output_states + input_states

								if not return_dict:
													return (output_states,)

								return TransformeraDModelOutput(sample=UpperCamelCase_		)





def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							int:
     '''simple docstring'''





     def count_of_possible_combinations(_a		) -> int:
          if target < 0:
               return 0
          if target == 0:
               return 1
          return sum(count_of_possible_combinations(target - item		) for item in array		)

     return count_of_possible_combinations(_a		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							int:
     '''simple docstring'''





     def count_of_possible_combinations_with_dp_array(
         _a       ,		_a		) -> int:
          if target < 0:
               return 0
          if target == 0:
               return 1
          if dp_array[target] != -1:
               return dp_array[target]
          lowercase_       :Optional[int]     =       sum(
              count_of_possible_combinations_with_dp_array(target - item       ,		_a		)
              for item in array		)
          lowercase_       :List[Any]     =       answer
          return answer

     lowercase_       :Dict     =       [-1] * (target + 1)
     return count_of_possible_combinations_with_dp_array(_a       ,		_a		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							int:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       [0] * (target + 1)
     lowercase_       :int     =       1

     for i in range(1       ,		target + 1		):
          for j in range(_a		):
               if i - array[j] >= 0:
                    dp_array[i] += dp_array[i - array[j]]

     return dp_array[target]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   SCREAMING_SNAKE_CASE       :		Optional[Any]         =				3
   SCREAMING_SNAKE_CASE       :		Tuple         =				5
   SCREAMING_SNAKE_CASE       :		List[Any]         =				[1, 2, 5]
   print(combination_sum_iv(n, array, target))

