





from __future__ import annotations

from decimal import Decimal

from numpy import array






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						list[list[float]]:






				"""simple docstring"""


				snake_case_						:   Union[str, Any]           =  Decimal

				# Check if the provided matrix has 2 rows and 2 columns
				# since this implementation only works for 2x2 matrices
				if len(_UpperCamelCase			) == 2 and len(matrix[0]			) == 2 and len(matrix[1]			) == 2:
								# Calculate the determinant of the matrix
								snake_case_						:   Optional[Any]           =  float(
								    d(matrix[0][0]			) * d(matrix[1][1]			) - d(matrix[1][0]			) * d(matrix[0][1]			)			)
								if determinant == 0:
												raise ValueError('''This matrix has no inverse.'''			)

								# Creates a copy of the matrix with swapped positions of the elements
								snake_case_						:   List[Any]           =  [[0.0, 0.0], [0.0, 0.0]]
								snake_case_      ,							snake_case_						:   List[Any]           =  matrix[1][1], matrix[0][0]
								snake_case_      ,							snake_case_						:   Tuple           =  -matrix[1][0], -matrix[0][1]

								# Calculate the inverse of the matrix
								return [
								    [(float(d(_UpperCamelCase			)			) / determinant) or 0.0 for n in row] for row in swapped_matrix
								]
				elif (
				    len(_UpperCamelCase			) == 3
				    and len(matrix[0]			) == 3
				    and len(matrix[1]			) == 3
				    and len(matrix[2]			) == 3
				):
								# Calculate the determinant of the matrix using Sarrus rule
								snake_case_						:   List[str]           =  float(
								    (
								        (d(matrix[0][0]			) * d(matrix[1][1]			) * d(matrix[2][2]			))
								        + (d(matrix[0][1]			) * d(matrix[1][2]			) * d(matrix[2][0]			))
								        + (d(matrix[0][2]			) * d(matrix[1][0]			) * d(matrix[2][1]			))
								    )
								    - (
								        (d(matrix[0][2]			) * d(matrix[1][1]			) * d(matrix[2][0]			))
								        + (d(matrix[0][1]			) * d(matrix[1][0]			) * d(matrix[2][2]			))
								        + (d(matrix[0][0]			) * d(matrix[1][2]			) * d(matrix[2][1]			))
								    )			)
								if determinant == 0:
												raise ValueError('''This matrix has no inverse.'''			)

								# Creating cofactor matrix
								snake_case_						:   str           =  [
								    [d(0.0			), d(0.0			), d(0.0			)],
								    [d(0.0			), d(0.0			), d(0.0			)],
								    [d(0.0			), d(0.0			), d(0.0			)],
								]
								snake_case_						:   int           =  (d(matrix[1][1]			) * d(matrix[2][2]			)) - (
								    d(matrix[1][2]			) * d(matrix[2][1]			)
								)
								snake_case_						:   Any           =  -(
								    (d(matrix[1][0]			) * d(matrix[2][2]			)) - (d(matrix[1][2]			) * d(matrix[2][0]			))
								)
								snake_case_						:   Optional[Any]           =  (d(matrix[1][0]			) * d(matrix[2][1]			)) - (
								    d(matrix[1][1]			) * d(matrix[2][0]			)
								)
								snake_case_						:   Tuple           =  -(
								    (d(matrix[0][1]			) * d(matrix[2][2]			)) - (d(matrix[0][2]			) * d(matrix[2][1]			))
								)
								snake_case_						:   Optional[int]           =  (d(matrix[0][0]			) * d(matrix[2][2]			)) - (
								    d(matrix[0][2]			) * d(matrix[2][0]			)
								)
								snake_case_						:   Dict           =  -(
								    (d(matrix[0][0]			) * d(matrix[2][1]			)) - (d(matrix[0][1]			) * d(matrix[2][0]			))
								)
								snake_case_						:   Any           =  (d(matrix[0][1]			) * d(matrix[1][2]			)) - (
								    d(matrix[0][2]			) * d(matrix[1][1]			)
								)
								snake_case_						:   Dict           =  -(
								    (d(matrix[0][0]			) * d(matrix[1][2]			)) - (d(matrix[0][2]			) * d(matrix[1][0]			))
								)
								snake_case_						:   List[str]           =  (d(matrix[0][0]			) * d(matrix[1][1]			)) - (
								    d(matrix[0][1]			) * d(matrix[1][0]			)
								)

								# Transpose the cofactor matrix (Adjoint matrix)
								snake_case_						:   List[Any]           =  array(_UpperCamelCase			)
								for i in range(3			):
												for j in range(3			):
																snake_case_						:   int           =  cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
								snake_case_						:   Optional[Any]           =  array(_UpperCamelCase			)
								for i in range(3			):
												for j in range(3			):
																inverse_matrix[i][j] /= d(_UpperCamelCase			)

        # Calculate the inverse of the matrix
								return [[float(d(_UpperCamelCase			)			) or 0.0 for n in row] for row in inverse_matrix]
				raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''			)







import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD



class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   Tuple           =  []
							snake_case_						:   List[str]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   Tuple           =  self.in_channels if i == 0 else self.out_channels

											snake_case_						:   Dict           =  FlaxResnetBlockaD(
											    in_channels=__magic_name__ ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

											snake_case_						:   str           =  FlaxTransformeraDModel(
											    in_channels=self.out_channels ,							n_heads=self.num_attention_heads ,							d_head=self.out_channels // self.num_attention_heads ,							depth=1 ,							use_linear_projection=self.use_linear_projection ,							only_cross_attention=self.only_cross_attention ,							use_memory_efficient_attention=self.use_memory_efficient_attention ,							dtype=self.dtype ,							)
											attentions.append(__magic_name__   )

							snake_case_						:   Union[str, Any]           =  resnets
							snake_case_						:   Union[str, Any]           =  attentions

							if self.add_downsample:
											snake_case_						:   List[str]           =  FlaxDownsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   str           =  ()

							for resnet, attn in zip(self.resnets ,							self.attentions   ):
											snake_case_						:   Optional[Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											snake_case_						:   List[str]           =  attn(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											output_states += (hidden_states,)

							if self.add_downsample:
											snake_case_						:   Union[str, Any]           =  self.downsamplers_a(__magic_name__   )
											output_states += (hidden_states,)

							return hidden_states, output_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   List[Any]           =  self.in_channels if i == 0 else self.out_channels

											snake_case_						:   Tuple           =  FlaxResnetBlockaD(
											    in_channels=__magic_name__ ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )
							snake_case_						:   Dict           =  resnets

							if self.add_downsample:
											snake_case_						:   Optional[int]           =  FlaxDownsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  ()

							for resnet in self.resnets:
											snake_case_						:   List[Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											output_states += (hidden_states,)

							if self.add_downsample:
											snake_case_						:   str           =  self.downsamplers_a(__magic_name__   )
											output_states += (hidden_states,)

							return hidden_states, output_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  []
							snake_case_						:   Optional[Any]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   List[Any]           =  self.in_channels if (i == self.num_layers - 1) else self.out_channels
											snake_case_						:   Optional[int]           =  self.prev_output_channel if i == 0 else self.out_channels

											snake_case_						:   Dict           =  FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

											snake_case_						:   List[str]           =  FlaxTransformeraDModel(
											    in_channels=self.out_channels ,							n_heads=self.num_attention_heads ,							d_head=self.out_channels // self.num_attention_heads ,							depth=1 ,							use_linear_projection=self.use_linear_projection ,							only_cross_attention=self.only_cross_attention ,							use_memory_efficient_attention=self.use_memory_efficient_attention ,							dtype=self.dtype ,							)
											attentions.append(__magic_name__   )

							snake_case_						:   List[Any]           =  resnets
							snake_case_						:   Tuple           =  attentions

							if self.add_upsample:
											snake_case_						:   List[Any]           =  FlaxUpsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       Union[str, Any]:


							'''simple docstring'''



							for resnet, attn in zip(self.resnets ,							self.attentions   ):
											# pop res hidden states
											snake_case_						:   Dict           =  res_hidden_states_tuple[-1]
											snake_case_						:   List[Any]           =  res_hidden_states_tuple[:-1]
											snake_case_						:   Optional[int]           =  jnp.concatenate((hidden_states, res_hidden_states) ,							axis=-1   )

											snake_case_						:   Tuple           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											snake_case_						:   Tuple           =  attn(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )

							if self.add_upsample:
											snake_case_						:   Optional[Any]           =  self.upsamplers_a(__magic_name__   )

							return hidden_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   Tuple           =  self.in_channels if (i == self.num_layers - 1) else self.out_channels
											snake_case_						:   Optional[Any]           =  self.prev_output_channel if i == 0 else self.out_channels

											snake_case_						:   int           =  FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

							snake_case_						:   Tuple           =  resnets

							if self.add_upsample:
											snake_case_						:   List[str]           =  FlaxUpsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       List[Any]:


							'''simple docstring'''



							for resnet in self.resnets:
											# pop res hidden states
											snake_case_						:   Tuple           =  res_hidden_states_tuple[-1]
											snake_case_						:   List[Any]           =  res_hidden_states_tuple[:-1]
											snake_case_						:   Dict           =  jnp.concatenate((hidden_states, res_hidden_states) ,							axis=-1   )

											snake_case_						:   Optional[Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )

							if self.add_upsample:
											snake_case_						:   Optional[int]           =  self.upsamplers_a(__magic_name__   )

							return hidden_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Dict           =  [
							    FlaxResnetBlockaD(
							        in_channels=self.in_channels ,							out_channels=self.in_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
							]

							snake_case_						:   int           =  []

							for _ in range(self.num_layers   ):
											snake_case_						:   str           =  FlaxTransformeraDModel(
											    in_channels=self.in_channels ,							n_heads=self.num_attention_heads ,							d_head=self.in_channels // self.num_attention_heads ,							depth=1 ,							use_linear_projection=self.use_linear_projection ,							use_memory_efficient_attention=self.use_memory_efficient_attention ,							dtype=self.dtype ,							)
											attentions.append(__magic_name__   )

											snake_case_						:   Dict           =  FlaxResnetBlockaD(
											    in_channels=self.in_channels ,							out_channels=self.in_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

							snake_case_						:   Optional[Any]           =  resnets
							snake_case_						:   Optional[int]           =  attentions






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  self.resnets[0](__magic_name__ ,							__magic_name__   )
							for attn, resnet in zip(self.attentions ,							self.resnets[1:]   ):
											snake_case_						:   Tuple           =  attn(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											snake_case_						:   Union[str, Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )

							return hidden_states
