





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class 			lowercase				(							nn.Module     ):



      """simple docstring"""

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





          '''simple docstring'''







          super().__init__()
          _snake_case :			Dict  						=   nn.ModuleList(
              [
                  TransformeraDModel(
                      num_attention_heads=lowerCamelCase_ ,				attention_head_dim=lowerCamelCase_ ,				in_channels=lowerCamelCase_ ,				num_layers=lowerCamelCase_ ,				dropout=lowerCamelCase_ ,				norm_num_groups=lowerCamelCase_ ,				cross_attention_dim=lowerCamelCase_ ,				attention_bias=lowerCamelCase_ ,				sample_size=lowerCamelCase_ ,				num_vector_embeds=lowerCamelCase_ ,				activation_fn=lowerCamelCase_ ,				num_embeds_ada_norm=lowerCamelCase_ ,				)
                  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 :			List[Any]  						=   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[Any]  						=   [77, 2_57]

          # 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 :			Optional[Any]  						=   [1, 0]







      def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Optional[Any] ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		int=None ,				lowerCamelCase_			:		Dict=None ,				lowerCamelCase_			:		Any=None ,				lowerCamelCase_			:		bool = True ,				):





          '''simple docstring'''







          _snake_case :			List[str]  						=   hidden_states

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

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

          if not return_dict:
              return (output_states,)

          return TransformeraDModelOutput(sample=lowerCamelCase_					)













from math import factorial

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				if successes > trials:
								raise ValueError('successes must be lower or equal to trials' )
				if trials < 0 or successes < 0:
								raise ValueError('the function is defined for non-negative integers' )
				if not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ) or not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ):
								raise ValueError('the function is defined for non-negative integers' )
				if not 0 < prob < 1:
								raise ValueError('prob has to be in range of 1 - 0' )
				_snake_case :			Optional[int]  						=   (prob**successes) * ((1 - prob) ** (trials - successes))
				# Calculate the binomial coefficient: n! / k!(n-k)!
				_snake_case :			List[Any]  						=   float(factorial(__lowerCAmelCase ) )
				coefficient /= factorial(__lowerCAmelCase ) * factorial(trials - successes )
				return probability * coefficient


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		print('''Probability of 2 successes out of 4 trails''')
		print('''with probability of 0.75 is:''', end=''' ''')
		print(binomial_distribution(2, 4, 0.75))






