
from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray


def 		UpperCAmelCase     (      a_						,  a_						,  a_						,  a_						,  )						->      list[float]:







				"""simple docstring"""



				__A     ,				__A       =					coefficient_matrix.shape
				__A     ,				__A       =					constant_matrix.shape

				if rowsa != colsa:
								__A       =					F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
								raise ValueError(a_		)

				if colsa != 1:
								__A       =					F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
								raise ValueError(a_		)

				if rowsa != rowsa:
								__A       =					(
								    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
								    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
								)
								raise ValueError(a_		)

				if len(a_		) != rowsa:
								__A       =					(
								    "Number of initial values must be equal to number of rows in coefficient "
								    F'''matrix but received {len(a_		)} and {rowsa}'''
								)
								raise ValueError(a_		)

				if iterations <= 0:
								raise ValueError("Iterations must be at least 1"		)

				__A       =					np.concatenate(
				    (coefficient_matrix, constant_matrix)						,  axis=1		)

				__A     ,				__A       =					table.shape

				strictly_diagonally_dominant(a_		)

				# Iterates the whole matrix for given number of times
				for _ in range(a_		):
								__A       =					[]
								for row in range(a_		):
												__A       =					0
												for col in range(a_		):
																if col == row:
																				__A       =					table[row][col]
																elif col == cols - 1:
																				__A       =					table[row][col]
																else:
																				temp += (-1) * table[row][col] * init_val[col]
												__A       =					(temp + val) / denom
												new_val.append(a_		)
								__A       =					new_val

				return [float(a_		) for i in new_val]


def 		UpperCAmelCase     (      a_		)						->      bool:







				"""simple docstring"""



				__A     ,				__A       =					table.shape

				__A       =					True

				for i in range(0						,  a_		):
								__A       =					0
								for j in range(0						,  cols - 1		):
												if i == j:
																continue
												else:
																total += table[i][j]

								if table[i][i] <= total:
												raise ValueError("Coefficient matrix is not strictly diagonally dominant"		)

				return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
					import doctest

					doctest.testmod()



'''simple docstring'''




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class 							lowercase_						(		nn.Module			):

  '''simple docstring'''





  def __init__(  self ,			a_ = 1_6 ,			a_ = 8_8 ,			a_ = None ,			a_ = 1 ,			a_ = 0.0 ,			a_ = 3_2 ,			a_ = None ,			a_ = False ,			a_ = None ,			a_ = None ,			a_ = "geglu" ,			a_ = None ,			)				->					List[str]:


    """simple docstring"""
    super().__init__()
    UpperCAmelCase    				=						nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=a_ ,			attention_head_dim=a_ ,			in_channels=a_ ,			num_layers=a_ ,			dropout=a_ ,			norm_num_groups=a_ ,			cross_attention_dim=a_ ,			attention_bias=a_ ,			sample_size=a_ ,			num_vector_embeds=a_ ,			activation_fn=a_ ,			num_embeds_ada_norm=a_ ,			)
            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    				=						0.5

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





  def     snake_case_      (  self ,			a_ ,			a_ ,			a_=None ,			a_=None ,			a_=None ,			a_ = True ,			)				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						hidden_states

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

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

    if not return_dict:
      return (output_states,)

    return TransformeraDModelOutput(sample=a_  )


