


'''simple docstring'''

import functools
import math

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





def __snake_case (				UpperCAmelCase_						:						Union[str, Any] ,    UpperCAmelCase_						:						Dict ,    UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						Optional[int] ,    UpperCAmelCase_						:						int = 4096		):
 lowerCamelCase_			,lowerCamelCase_			,lowerCamelCase_								=  key.shape[-3:]
 lowerCamelCase_								=  value.shape[-1]
 lowerCamelCase_								=  min(UpperCAmelCase_ ,    UpperCAmelCase_		)
 lowerCamelCase_								=  query / jnp.sqrt(UpperCAmelCase_		)

 @functools.partial(jax.checkpoint ,    prevent_cse=UpperCAmelCase_		)
 def summarize_chunk(UpperCAmelCase_						:						Optional[int] ,    UpperCAmelCase_						:						int ,    UpperCAmelCase_						:						Dict		):
  lowerCamelCase_								=  jnp.einsum("...qhd,...khd->...qhk" ,    UpperCAmelCase_ ,    UpperCAmelCase_ ,    precision=UpperCAmelCase_		)

  lowerCamelCase_								=  jnp.max(UpperCAmelCase_ ,    axis=-1 ,    keepdims=UpperCAmelCase_		)
  lowerCamelCase_								=  jax.lax.stop_gradient(UpperCAmelCase_		)
  lowerCamelCase_								=  jnp.exp(attn_weights - max_score		)

  lowerCamelCase_								=  jnp.einsum("...vhf,...qhv->...qhf" ,    UpperCAmelCase_ ,    UpperCAmelCase_ ,    precision=UpperCAmelCase_		)
  lowerCamelCase_								=  jnp.einsum("...qhk->...qh" ,    UpperCAmelCase_		)

  return (exp_values, exp_weights.sum(axis=-1		), max_score)

 def chunk_scanner(UpperCAmelCase_						:						int		):
  # julienne key array
  lowerCamelCase_								=  jax.lax.dynamic_slice(
      operand=UpperCAmelCase_ ,    start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0] ,    slice_sizes=list(key.shape[:-3]		) + [key_chunk_size, num_heads, k_features] ,    )

  # julienne value array
  lowerCamelCase_								=  jax.lax.dynamic_slice(
      operand=UpperCAmelCase_ ,    start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0] ,    slice_sizes=list(value.shape[:-3]		) + [key_chunk_size, num_heads, v_features] ,    )

  return summarize_chunk(UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_		)

 lowerCamelCase_			,lowerCamelCase_			,lowerCamelCase_								=  jax.lax.map(f=UpperCAmelCase_ ,    xs=jnp.arange(0 ,    UpperCAmelCase_ ,    UpperCAmelCase_		)		)

 lowerCamelCase_								=  jnp.max(UpperCAmelCase_ ,    axis=0 ,    keepdims=UpperCAmelCase_		)
 lowerCamelCase_								=  jnp.exp(chunk_max - global_max		)

 chunk_values *= jnp.expand_dims(UpperCAmelCase_ ,    axis=-1		)
 chunk_weights *= max_diffs

 lowerCamelCase_								=  chunk_values.sum(axis=0		)
 lowerCamelCase_								=  jnp.expand_dims(UpperCAmelCase_ ,    -1		).sum(axis=0		)

 return all_values / all_weights





def __snake_case (				UpperCAmelCase_						:						Optional[int] ,    UpperCAmelCase_						:						List[Any] ,    UpperCAmelCase_						:						Optional[Any] ,    UpperCAmelCase_						:						int=jax.lax.Precision.HIGHEST ,    UpperCAmelCase_						:						int = 1024 ,    UpperCAmelCase_						:						int = 4096		):
 lowerCamelCase_			,lowerCamelCase_			,lowerCamelCase_								=  query.shape[-3:]

 def chunk_scanner(UpperCAmelCase_						:						List[str] ,    UpperCAmelCase_						:						List[str]		):
  # julienne query array
  lowerCamelCase_								=  jax.lax.dynamic_slice(
      operand=UpperCAmelCase_ ,    start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0] ,    slice_sizes=list(query.shape[:-3]		) + [min(UpperCAmelCase_ ,    UpperCAmelCase_		), num_heads, q_features] ,    )

  return (
      chunk_idx + query_chunk_size,  # unused ignore it
      _query_chunk_attention(
          query=UpperCAmelCase_ ,    key=UpperCAmelCase_ ,    value=UpperCAmelCase_ ,    precision=UpperCAmelCase_ ,    key_chunk_size=UpperCAmelCase_		),
  )

 lowerCamelCase_			,lowerCamelCase_								=  jax.lax.scan(
     f=UpperCAmelCase_ ,    init=0 ,    xs=UpperCAmelCase_ ,    length=math.ceil(num_q / query_chunk_size		)		)

 return jnp.concatenate(UpperCAmelCase_ ,    axis=-3		)  # fuse the chunked result back





class 					snake_case   (   nn.Module      ):





  """simple docstring"""




  _lowerCamelCase		   =  42
  _lowerCamelCase		   =  8
  _lowerCamelCase		   =  64
  _lowerCamelCase		   =  0.0
  _lowerCamelCase		   =  False
  _lowerCamelCase		   =  jnp.floataa






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.dim_head * self.heads
   lowerCamelCase_								=  self.dim_head**-0.5

   # Weights were exported with old names {to_q, to_k, to_v, to_out}
   lowerCamelCase_								=  nn.Dense(UpperCamelCase      ,  use_bias=UpperCamelCase      ,  dtype=self.dtype      ,  name="to_q"					)
   lowerCamelCase_								=  nn.Dense(UpperCamelCase      ,  use_bias=UpperCamelCase      ,  dtype=self.dtype      ,  name="to_k"					)
   lowerCamelCase_								=  nn.Dense(UpperCamelCase      ,  use_bias=UpperCamelCase      ,  dtype=self.dtype      ,  name="to_v"					)

   lowerCamelCase_								=  nn.Dense(self.query_dim      ,  dtype=self.dtype      ,  name="to_out_0"					)
   lowerCamelCase_								=  nn.Dropout(rate=self.dropout					)






  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_			,lowerCamelCase_			,lowerCamelCase_								=  tensor.shape
   lowerCamelCase_								=  self.heads
   lowerCamelCase_								=  tensor.reshape(UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  dim // head_size					)
   lowerCamelCase_								=  jnp.transpose(UpperCamelCase      ,  (0, 2, 1, 3)					)
   lowerCamelCase_								=  tensor.reshape(batch_size * head_size      ,  UpperCamelCase      ,  dim // head_size					)
   return tensor






  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_			,lowerCamelCase_			,lowerCamelCase_								=  tensor.shape
   lowerCamelCase_								=  self.heads
   lowerCamelCase_								=  tensor.reshape(batch_size // head_size      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase					)
   lowerCamelCase_								=  jnp.transpose(UpperCamelCase      ,  (0, 2, 1, 3)					)
   lowerCamelCase_								=  tensor.reshape(batch_size // head_size      ,  UpperCamelCase      ,  dim * head_size					)
   return tensor







  def __call__(   self      ,  UpperCamelCase      ,  UpperCamelCase=None      ,  UpperCamelCase=True					):







   """simple docstring"""
   lowerCamelCase_								=  hidden_states if context is None else context

   lowerCamelCase_								=  self.query(UpperCamelCase					)
   lowerCamelCase_								=  self.key(UpperCamelCase					)
   lowerCamelCase_								=  self.value(UpperCamelCase					)

   lowerCamelCase_								=  self.reshape_heads_to_batch_dim(UpperCamelCase					)
   lowerCamelCase_								=  self.reshape_heads_to_batch_dim(UpperCamelCase					)
   lowerCamelCase_								=  self.reshape_heads_to_batch_dim(UpperCamelCase					)

   if self.use_memory_efficient_attention:
    lowerCamelCase_								=  query_states.transpose(1      ,  0      ,  2					)
    lowerCamelCase_								=  key_states.transpose(1      ,  0      ,  2					)
    lowerCamelCase_								=  value_states.transpose(1      ,  0      ,  2					)

    # this if statement create a chunk size for each layer of the unet
    # the chunk size is equal to the query_length dimension of the deepest layer of the unet

    lowerCamelCase_								=  query_states.shape[-3]
    if flatten_latent_dim % 64 == 0:
     lowerCamelCase_								=  int(flatten_latent_dim / 64					)
    elif flatten_latent_dim % 16 == 0:
     lowerCamelCase_								=  int(flatten_latent_dim / 16					)
    elif flatten_latent_dim % 4 == 0:
     lowerCamelCase_								=  int(flatten_latent_dim / 4					)
    else:
     lowerCamelCase_								=  int(UpperCamelCase					)

    lowerCamelCase_								=  jax_memory_efficient_attention(
        UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  query_chunk_size=UpperCamelCase      ,  key_chunk_size=4096 * 4					)

    lowerCamelCase_								=  hidden_states.transpose(1      ,  0      ,  2					)
   else:
    # compute attentions
    lowerCamelCase_								=  jnp.einsum("b i d, b j d->b i j"      ,  UpperCamelCase      ,  UpperCamelCase					)
    lowerCamelCase_								=  attention_scores * self.scale
    lowerCamelCase_								=  nn.softmax(UpperCamelCase      ,  axis=2					)

    # attend to values
    lowerCamelCase_								=  jnp.einsum("b i j, b j d -> b i d"      ,  UpperCamelCase      ,  UpperCamelCase					)

   lowerCamelCase_								=  self.reshape_batch_dim_to_heads(UpperCamelCase					)
   lowerCamelCase_								=  self.proj_attn(UpperCamelCase					)
   return self.dropout_layer(UpperCamelCase      ,  deterministic=UpperCamelCase					)





class 					snake_case   (   nn.Module      ):





  """simple docstring"""




  _lowerCamelCase		   =  42
  _lowerCamelCase		   =  42
  _lowerCamelCase		   =  42
  _lowerCamelCase		   =  0.0
  _lowerCamelCase		   =  False
  _lowerCamelCase		   =  jnp.floataa
  _lowerCamelCase		   =  False






  def   snake_case		(   self					):







   """simple docstring"""
   # self attention (or cross_attention if only_cross_attention is True)
   lowerCamelCase_								=  FlaxAttention(
       self.dim      ,  self.n_heads      ,  self.d_head      ,  self.dropout      ,  self.use_memory_efficient_attention      ,  dtype=self.dtype					)
   # cross attention
   lowerCamelCase_								=  FlaxAttention(
       self.dim      ,  self.n_heads      ,  self.d_head      ,  self.dropout      ,  self.use_memory_efficient_attention      ,  dtype=self.dtype					)
   lowerCamelCase_								=  FlaxFeedForward(dim=self.dim      ,  dropout=self.dropout      ,  dtype=self.dtype					)
   lowerCamelCase_								=  nn.LayerNorm(epsilon=1e-5      ,  dtype=self.dtype					)
   lowerCamelCase_								=  nn.LayerNorm(epsilon=1e-5      ,  dtype=self.dtype					)
   lowerCamelCase_								=  nn.LayerNorm(epsilon=1e-5      ,  dtype=self.dtype					)
   lowerCamelCase_								=  nn.Dropout(rate=self.dropout					)







  def __call__(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase=True					):







   """simple docstring"""
   # self attention
   lowerCamelCase_								=  hidden_states
   if self.only_cross_attention:
    lowerCamelCase_								=  self.attna(self.norma(UpperCamelCase					)      ,  UpperCamelCase      ,  deterministic=UpperCamelCase					)
   else:
    lowerCamelCase_								=  self.attna(self.norma(UpperCamelCase					)      ,  deterministic=UpperCamelCase					)
   lowerCamelCase_								=  hidden_states + residual

   # cross attention
   lowerCamelCase_								=  hidden_states
   lowerCamelCase_								=  self.attna(self.norma(UpperCamelCase					)      ,  UpperCamelCase      ,  deterministic=UpperCamelCase					)
   lowerCamelCase_								=  hidden_states + residual

   # feed forward
   lowerCamelCase_								=  hidden_states
   lowerCamelCase_								=  self.ff(self.norma(UpperCamelCase					)      ,  deterministic=UpperCamelCase					)
   lowerCamelCase_								=  hidden_states + residual

   return self.dropout_layer(UpperCamelCase      ,  deterministic=UpperCamelCase					)





class 					snake_case   (   nn.Module      ):





  """simple docstring"""




  _lowerCamelCase		   =  42
  _lowerCamelCase		   =  42
  _lowerCamelCase		   =  42
  _lowerCamelCase		   =  1
  _lowerCamelCase		   =  0.0
  _lowerCamelCase		   =  False
  _lowerCamelCase		   =  False
  _lowerCamelCase		   =  jnp.floataa
  _lowerCamelCase		   =  False






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  nn.GroupNorm(num_groups=32      ,  epsilon=1e-5					)

   lowerCamelCase_								=  self.n_heads * self.d_head
   if self.use_linear_projection:
    lowerCamelCase_								=  nn.Dense(UpperCamelCase      ,  dtype=self.dtype					)
   else:
    lowerCamelCase_								=  nn.Conv(
        UpperCamelCase      ,  kernel_size=(1, 1)      ,  strides=(1, 1)      ,  padding="VALID"      ,  dtype=self.dtype      ,  )

   lowerCamelCase_								=  [
       FlaxBasicTransformerBlock(
           UpperCamelCase      ,  self.n_heads      ,  self.d_head      ,  dropout=self.dropout      ,  only_cross_attention=self.only_cross_attention      ,  dtype=self.dtype      ,  use_memory_efficient_attention=self.use_memory_efficient_attention      ,  )
       for _ in range(self.depth					)
   ]

   if self.use_linear_projection:
    lowerCamelCase_								=  nn.Dense(UpperCamelCase      ,  dtype=self.dtype					)
   else:
    lowerCamelCase_								=  nn.Conv(
        UpperCamelCase      ,  kernel_size=(1, 1)      ,  strides=(1, 1)      ,  padding="VALID"      ,  dtype=self.dtype      ,  )

   lowerCamelCase_								=  nn.Dropout(rate=self.dropout					)







  def __call__(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase=True					):







   """simple docstring"""
   lowerCamelCase_			,lowerCamelCase_			,lowerCamelCase_			,lowerCamelCase_								=  hidden_states.shape
   lowerCamelCase_								=  hidden_states
   lowerCamelCase_								=  self.norm(UpperCamelCase					)
   if self.use_linear_projection:
    lowerCamelCase_								=  hidden_states.reshape(UpperCamelCase      ,  height * width      ,  UpperCamelCase					)
    lowerCamelCase_								=  self.proj_in(UpperCamelCase					)
   else:
    lowerCamelCase_								=  self.proj_in(UpperCamelCase					)
    lowerCamelCase_								=  hidden_states.reshape(UpperCamelCase      ,  height * width      ,  UpperCamelCase					)

   for transformer_block in self.transformer_blocks:
    lowerCamelCase_								=  transformer_block(UpperCamelCase      ,  UpperCamelCase      ,  deterministic=UpperCamelCase					)

   if self.use_linear_projection:
    lowerCamelCase_								=  self.proj_out(UpperCamelCase					)
    lowerCamelCase_								=  hidden_states.reshape(UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase					)
   else:
    lowerCamelCase_								=  hidden_states.reshape(UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase					)
    lowerCamelCase_								=  self.proj_out(UpperCamelCase					)

   lowerCamelCase_								=  hidden_states + residual
   return self.dropout_layer(UpperCamelCase      ,  deterministic=UpperCamelCase					)





class 					snake_case   (   nn.Module      ):





  """simple docstring"""




  _lowerCamelCase		   =  42
  _lowerCamelCase		   =  0.0
  _lowerCamelCase		   =  jnp.floataa






  def   snake_case		(   self					):







   """simple docstring"""
   # The second linear layer needs to be called
   # net_2 for now to match the index of the Sequential layer
   lowerCamelCase_								=  FlaxGEGLU(self.dim      ,  self.dropout      ,  self.dtype					)
   lowerCamelCase_								=  nn.Dense(self.dim      ,  dtype=self.dtype					)







  def __call__(   self      ,  UpperCamelCase      ,  UpperCamelCase=True					):







   """simple docstring"""
   lowerCamelCase_								=  self.net_a(UpperCamelCase      ,  deterministic=UpperCamelCase					)
   lowerCamelCase_								=  self.net_a(UpperCamelCase					)
   return hidden_states





class 					snake_case   (   nn.Module      ):





  """simple docstring"""




  _lowerCamelCase		   =  42
  _lowerCamelCase		   =  0.0
  _lowerCamelCase		   =  jnp.floataa






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.dim * 4
   lowerCamelCase_								=  nn.Dense(inner_dim * 2      ,  dtype=self.dtype					)
   lowerCamelCase_								=  nn.Dropout(rate=self.dropout					)







  def __call__(   self      ,  UpperCamelCase      ,  UpperCamelCase=True					):







   """simple docstring"""
   lowerCamelCase_								=  self.proj(UpperCamelCase					)
   lowerCamelCase_			,lowerCamelCase_								=  jnp.split(UpperCamelCase      ,  2      ,  axis=2					)
   return self.dropout_layer(hidden_linear * nn.gelu(UpperCamelCase					)      ,  deterministic=UpperCamelCase					)







'''simple docstring'''

import numpy as np





class 					snake_case   :





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase=5      ,  UpperCamelCase=5					):







   """simple docstring"""
   lowerCamelCase_								=  depth
   lowerCamelCase_								=  0
   lowerCamelCase_								=  None
   lowerCamelCase_								=  None
   lowerCamelCase_								=  min_leaf_size
   lowerCamelCase_								=  None






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   if labels.ndim != 1:
    print("Error: Input labels must be one dimensional"					)

   return np.mean((labels - prediction) ** 2					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   if x.ndim != 1:
    print("Error: Input data set must be one dimensional"					)
    return
   if len(UpperCamelCase					) != len(UpperCamelCase					):
    print("Error: X and y have different lengths"					)
    return
   if y.ndim != 1:
    print("Error: Data set labels must be one dimensional"					)
    return

   if len(UpperCamelCase					) < 2 * self.min_leaf_size:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)
    return

   if self.depth == 1:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)
    return

   lowerCamelCase_								=  0
   lowerCamelCase_								=  self.mean_squared_error(UpperCamelCase      ,  np.mean(UpperCamelCase					)					) * 2
   for i in range(len(UpperCamelCase					)					):
    if len(x[:i]					) < self.min_leaf_size:
     continue
    elif len(x[i:]					) < self.min_leaf_size:
     continue
    else:
     lowerCamelCase_								=  self.mean_squared_error(x[:i]      ,  np.mean(y[:i]					)					)
     lowerCamelCase_								=  self.mean_squared_error(x[i:]      ,  np.mean(y[i:]					)					)
     lowerCamelCase_								=  error_left + error_right
     if error < min_error:
      lowerCamelCase_								=  i
      lowerCamelCase_								=  error

   if best_split != 0:
    lowerCamelCase_								=  x[:best_split]
    lowerCamelCase_								=  y[:best_split]
    lowerCamelCase_								=  x[best_split:]
    lowerCamelCase_								=  y[best_split:]

    lowerCamelCase_								=  x[best_split]
    lowerCamelCase_								=  DecisionTree(
        depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size					)
    lowerCamelCase_								=  DecisionTree(
        depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size					)
    self.left.train(UpperCamelCase      ,  UpperCamelCase					)
    self.right.train(UpperCamelCase      ,  UpperCamelCase					)
   else:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)

   return







  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   if self.prediction is not None:
    return self.prediction
   elif self.left or self.right is not None:
    if x >= self.decision_boundary:
     return self.right.predict(UpperCamelCase					)
    else:
     return self.left.predict(UpperCamelCase					)
   else:
    print("Error: Decision tree not yet trained"					)
    return None





class 					snake_case   :





  """simple docstring"""




  @staticmethod
  def   snake_case		(   UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  float(0					)
   for label in labels:
    squared_error_sum += (label - prediction) ** 2

   return float(squared_error_sum / labels.size					)





def __snake_case (				):
 lowerCamelCase_								=  np.arange(-1.0 ,    1.0 ,    0.005		)
 lowerCamelCase_								=  np.sin(UpperCAmelCase_		)

 lowerCamelCase_								=  DecisionTree(depth=10 ,    min_leaf_size=10		)
 tree.train(UpperCAmelCase_ ,    UpperCAmelCase_		)

 lowerCamelCase_								=  (np.random.rand(10		) * 2) - 1
 lowerCamelCase_								=  np.array([tree.predict(UpperCAmelCase_		) for x in test_cases]		)
 lowerCamelCase_								=  np.mean((predictions - test_cases) ** 2		)

 print("Test values: " + str(UpperCAmelCase_		)		)
 print("Predictions: " + str(UpperCAmelCase_		)		)
 print("Average error: " + str(UpperCAmelCase_		)		)


if __name__ == "__main__":
     main()
     import doctest

     doctest.testmod(name="""mean_squarred_error""", verbose=True)



