






'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp






class      __SCREAMING_SNAKE_CASE      (			nn.Module				):
 snake_case_  							=							42
 snake_case_  							=							jnp.floataa




 def      __magic_name__	(      self	:							List[str]      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=nn.Conv(
           self.out_channels					,      kernel_size=(3, 3)					,      strides=(1, 1)					,      padding=((1, 1), (1, 1))					,      dtype=self.dtype					,      )




 def __call__(      self	:							Dict					,      __lowercase	:							Tuple      )	->						Dict:
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[Any]									=hidden_states.shape
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=jax.image.resize(
           __lowercase					,      shape=(batch, height * 2, width * 2, channels)					,      method='''nearest'''					,      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.conv(__lowercase      )
       return hidden_states






class      __SCREAMING_SNAKE_CASE      (			nn.Module				):
 snake_case_  							=							42
 snake_case_  							=							jnp.floataa




 def      __magic_name__	(      self	:							List[str]      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=nn.Conv(
           self.out_channels					,      kernel_size=(3, 3)					,      strides=(2, 2)					,      padding=((1, 1), (1, 1))					,      dtype=self.dtype					,      )




 def __call__(      self	:							Optional[Any]					,      __lowercase	:							Optional[int]      )	->						Optional[Any]:
       # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
       # hidden_states = jnp.pad(hidden_states, pad_width=pad)
       SCREAMING_SNAKE_CASE__     :				int									=self.conv(__lowercase      )
       return hidden_states






class      __SCREAMING_SNAKE_CASE      (			nn.Module				):
 snake_case_  							=							42
 snake_case_  							=							None
 snake_case_  							=							0.0
 snake_case_  							=							None
 snake_case_  							=							jnp.floataa




 def      __magic_name__	(      self	:							int      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.in_channels if self.out_channels is None else self.out_channels

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=nn.GroupNorm(num_groups=32					,      epsilon=1e-5      )
       SCREAMING_SNAKE_CASE__     :				str									=nn.Conv(
           __lowercase					,      kernel_size=(3, 3)					,      strides=(1, 1)					,      padding=((1, 1), (1, 1))					,      dtype=self.dtype					,      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=nn.Dense(__lowercase					,      dtype=self.dtype      )

       SCREAMING_SNAKE_CASE__     :				str									=nn.GroupNorm(num_groups=32					,      epsilon=1e-5      )
       SCREAMING_SNAKE_CASE__     :				str									=nn.Dropout(self.dropout_prob      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=nn.Conv(
           __lowercase					,      kernel_size=(3, 3)					,      strides=(1, 1)					,      padding=((1, 1), (1, 1))					,      dtype=self.dtype					,      )

       SCREAMING_SNAKE_CASE__     :				str									=self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

       SCREAMING_SNAKE_CASE__     :				Any									=None
       if use_nin_shortcut:
             SCREAMING_SNAKE_CASE__     :				List[Any]									=nn.Conv(
                 __lowercase					,      kernel_size=(1, 1)					,      strides=(1, 1)					,      padding='''VALID'''					,      dtype=self.dtype					,      )




 def __call__(      self	:							int					,      __lowercase	:							List[Any]					,      __lowercase	:							Dict					,      __lowercase	:							List[Any]=True      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				List[str]									=hidden_states
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.norma(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=nn.swish(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Dict									=self.conva(__lowercase      )

       SCREAMING_SNAKE_CASE__     :				List[str]									=self.time_emb_proj(nn.swish(__lowercase      )      )
       SCREAMING_SNAKE_CASE__     :				int									=jnp.expand_dims(jnp.expand_dims(__lowercase					,      1      )					,      1      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=hidden_states + temb

       SCREAMING_SNAKE_CASE__     :				Dict									=self.norma(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=nn.swish(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.dropout(__lowercase					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.conva(__lowercase      )

       if self.conv_shortcut is not None:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.conv_shortcut(__lowercase      )

       return hidden_states + residual







'''simple docstring'''
from __future__ import annotations

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





def  _a(  UpperCamelCase__				:						NDArray[floataa],	UpperCamelCase__				:						NDArray[floataa],	UpperCamelCase__				:						list[int],	UpperCamelCase__				:						int,	):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Tuple									=coefficient_matrix.shape
      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Tuple									=constant_matrix.shape

      if rowsa != colsa:
            SCREAMING_SNAKE_CASE__     :				Any									=f"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
            raise ValueError(UpperCamelCase__							)

      if colsa != 1:
            SCREAMING_SNAKE_CASE__     :				str									=f"Constant matrix must be nx1 but received {rowsa}x{colsa}"
            raise ValueError(UpperCamelCase__							)

      if rowsa != rowsa:
            SCREAMING_SNAKE_CASE__     :				str									=(
                '''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
                f"received {rowsa}x{colsa} and {rowsa}x{colsa}"
            )
            raise ValueError(UpperCamelCase__							)

      if len(UpperCamelCase__							) != rowsa:
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(
                '''Number of initial values must be equal to number of rows in coefficient '''
                f"matrix but received {len(UpperCamelCase__							)} and {rowsa}"
            )
            raise ValueError(UpperCamelCase__							)

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

      SCREAMING_SNAKE_CASE__     :				NDArray[floataa]									=np.concatenate(
          (coefficient_matrix, constant_matrix),	axis=1							)

      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Any									=table.shape

      strictly_diagonally_dominant(UpperCamelCase__							)

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

      return [float(UpperCamelCase__							) for i in new_val]





def  _a(  UpperCamelCase__				:						NDArray[floataa]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Optional[int]									=table.shape

      SCREAMING_SNAKE_CASE__     :				Any									=True

      for i in range(0,	UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				int									=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()