

'''simple docstring'''
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class 					_snake_case		(nn.Module):






			def __init__(  self			,_snake_case = 16			,_snake_case = 88			,_snake_case = None			,_snake_case = 1			,_snake_case = 0.0			,_snake_case = 32			,_snake_case = None			,_snake_case = False			,_snake_case = None			,_snake_case = None			,_snake_case = "geglu"			,_snake_case = None			,):
				super().__init__()
				UpperCAmelCase_     :      Optional[Any]						=  nn.ModuleList(
				    [
				        TransformeraDModel(
				            num_attention_heads=_snake_case			,attention_head_dim=_snake_case			,in_channels=_snake_case			,num_layers=_snake_case			,dropout=_snake_case			,norm_num_groups=_snake_case			,cross_attention_dim=_snake_case			,attention_bias=_snake_case			,sample_size=_snake_case			,num_vector_embeds=_snake_case			,activation_fn=_snake_case			,num_embeds_ada_norm=_snake_case			,)
				        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_     :      List[str]						=  0.5

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






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case=None			,_snake_case=None			,_snake_case=None			,_snake_case = True			,):
				UpperCAmelCase_     :      List[str]						=  hidden_states

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

				UpperCAmelCase_     :      Dict						=  encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
				UpperCAmelCase_     :      List[Any]						=  output_states + input_states

				if not return_dict:
					return (output_states,)

				return TransformeraDModelOutput(sample=_snake_case     )












import numpy as np





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case = 1E-12       ,			__snake_case = 100       ,			):
     assert np.shape(__snake_case							)[0] == np.shape(__snake_case							)[1]
     # Ensure proper dimensionality.
     assert np.shape(__snake_case							)[0] == np.shape(__snake_case							)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(__snake_case							) == np.iscomplexobj(__snake_case							)
     __lowerCAmelCase             =       np.iscomplexobj(__snake_case							)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(__snake_case       ,			input_matrix.conj().T							)

     # Set convergence to False. Will define convergence when we exceed max_iterations
     # or when we have small changes from one iteration to next.

     __lowerCAmelCase             =       False
     __lowerCAmelCase             =       0
     __lowerCAmelCase             =       0
     __lowerCAmelCase             =       1E12

     while not convergence:
          # Multiple matrix by the vector.
          __lowerCAmelCase             =       np.dot(__snake_case       ,			__snake_case							)
          # Normalize the resulting output vector.
          __lowerCAmelCase             =       w / np.linalg.norm(__snake_case							)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          __lowerCAmelCase             =       vector.conj().T if is_complex else vector.T
          __lowerCAmelCase             =       np.dot(__snake_case       ,			np.dot(__snake_case       ,			__snake_case							)							)

          # Check convergence.
          __lowerCAmelCase             =       np.abs(lambda_ - lambda_previous							) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               __lowerCAmelCase             =       True

          __lowerCAmelCase             =       lambda_

     if is_complex:
          __lowerCAmelCase             =       np.real(lambda_							)

     return lambda_, vector





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]							)
     __lowerCAmelCase             =       np.array([41, 4, 20]							)
     __lowerCAmelCase             =       real_input_matrix.astype(np.complexaaa							)
     __lowerCAmelCase             =       np.triu(1J * complex_input_matrix       ,			1							)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     __lowerCAmelCase             =       np.array([41, 4, 20]							).astype(np.complexaaa							)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               __lowerCAmelCase             =       real_input_matrix
               __lowerCAmelCase             =       real_vector
          elif problem_type == "complex":
               __lowerCAmelCase             =       complex_input_matrix
               __lowerCAmelCase             =       complex_vector

          # Our implementation.
          __lowerCAmelCase      ,			__lowerCAmelCase             =       power_iteration(__snake_case       ,			__snake_case							)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          __lowerCAmelCase      ,			__lowerCAmelCase             =       np.linalg.eigh(__snake_case							)
          # Last eigenvalue is the maximum one.
          __lowerCAmelCase             =       eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          __lowerCAmelCase             =       eigen_vectors[:, -1]

          # Check our implementation and numpy gives close answers.
          assert np.abs(eigen_value - eigen_value_max							) <= 1E-6
          # Take absolute values element wise of each eigenvector.
          # as they are only unique to a minus sign.
          assert np.linalg.norm(np.abs(__snake_case							) - np.abs(__snake_case							)							) <= 1E-6


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 test_power_iteration()






