
"""simple docstring"""






from __future__ import annotations

import unittest
from collections.abc import Iterator

class 				__a    :



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       None:




										'''simple docstring'''
										__lowercase						      =					label
										__lowercase						      =					parent
										__lowercase						      =					None
										__lowercase						      =					None





class 				__a    :



							'''simple docstring'''







							def __init__(    self					) ->       None:




										'''simple docstring'''
										__lowercase						      =					None
							def      SCREAMING_SNAKE_CASE	(    self					) ->       None:




										'''simple docstring'''
										__lowercase						      =					None
							def      SCREAMING_SNAKE_CASE	(    self					) ->       bool:




										'''simple docstring'''
										return self.root is None
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       None:




										'''simple docstring'''
										__lowercase						      =					self._put(self.root     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase = None					) ->       Node:




										'''simple docstring'''
										if node is None:
													__lowercase						      =					Node(_lowerCamelCase     ,						_lowerCamelCase					)
										else:
													if label < node.label:
																__lowercase						      =					self._put(node.left     ,						_lowerCamelCase     ,						_lowerCamelCase					)
													elif label > node.label:
																__lowercase						      =					self._put(node.right     ,						_lowerCamelCase     ,						_lowerCamelCase					)
													else:
																__lowercase						      =					f'''Node with label {label} already exists'''
																raise Exception(_lowerCamelCase					)

										return node
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Node:




										'''simple docstring'''
										return self._search(self.root     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Node:




										'''simple docstring'''
										if node is None:
													__lowercase						      =					f'''Node with label {label} does not exist'''
													raise Exception(_lowerCamelCase					)
										else:
													if label < node.label:
																__lowercase						      =					self._search(node.left     ,						_lowerCamelCase					)
													elif label > node.label:
																__lowercase						      =					self._search(node.right     ,						_lowerCamelCase					)

										return node
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       None:




										'''simple docstring'''
										__lowercase						      =					self.search(_lowerCamelCase					)
										if node.right and node.left:
													__lowercase						      =					self._get_lowest_node(node.right					)
													__lowercase						      =					node.left
													__lowercase						      =					node.right
													__lowercase						      =					lowest_node
													if node.right:
																__lowercase						      =					lowest_node
													self._reassign_nodes(_lowerCamelCase     ,						_lowerCamelCase					)
										elif not node.right and node.left:
													self._reassign_nodes(_lowerCamelCase     ,						node.left					)
										elif node.right and not node.left:
													self._reassign_nodes(_lowerCamelCase     ,						node.right					)
										else:
													self._reassign_nodes(_lowerCamelCase     ,						_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       None:




										'''simple docstring'''
										if new_children:
													__lowercase						      =					node.parent

										if node.parent:
													if node.parent.right == node:
																__lowercase						      =					new_children
													else:
																__lowercase						      =					new_children
										else:
													__lowercase						      =					new_children
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Node:




										'''simple docstring'''
										if node.left:
													__lowercase						      =					self._get_lowest_node(node.left					)
										else:
													__lowercase						      =					node
													self._reassign_nodes(_lowerCamelCase     ,						node.right					)

										return lowest_node
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       bool:




										'''simple docstring'''
										try:
													self.search(_lowerCamelCase					)
													return True
										except Exception:
													return False
							def      SCREAMING_SNAKE_CASE	(    self					) ->       int:




										'''simple docstring'''
										if self.root is None:
													raise Exception("Binary search tree is empty"					)

										__lowercase						      =					self.root
										while node.right is not None:
													__lowercase						      =					node.right

										return node.label
							def      SCREAMING_SNAKE_CASE	(    self					) ->       int:




										'''simple docstring'''
										if self.root is None:
													raise Exception("Binary search tree is empty"					)

										__lowercase						      =					self.root
										while node.left is not None:
													__lowercase						      =					node.left

										return node.label
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Iterator[Node]:




										'''simple docstring'''
										return self._inorder_traversal(self.root					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Iterator[Node]:




										'''simple docstring'''
										if node is not None:
													yield from self._inorder_traversal(node.left					)
													yield node
													yield from self._inorder_traversal(node.right					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Iterator[Node]:




										'''simple docstring'''
										return self._preorder_traversal(self.root					)





							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Iterator[Node]:




										'''simple docstring'''
										if node is not None:
													yield node
													yield from self._preorder_traversal(node.left					)
													yield from self._preorder_traversal(node.right					)





class 				__a    (   unittest.TestCase					):



							'''simple docstring'''







							@staticmethod
							def      SCREAMING_SNAKE_CASE	(    ) ->       BinarySearchTree:




										'''simple docstring'''
										__lowercase						      =					BinarySearchTree()
										t.put(8					)
										t.put(3					)
										t.put(6					)
										t.put(1					)
										t.put(10					)
										t.put(14					)
										t.put(13					)
										t.put(4					)
										t.put(7					)
										t.put(5					)

										return t
							def      SCREAMING_SNAKE_CASE	(    self					) ->       None:




										'''simple docstring'''
										__lowercase						      =					BinarySearchTree()
										assert t.is_empty()

										t.put(8					)
										assert t.root is not None
										assert t.root.parent is None
										assert t.root.label == 8

										t.put(10					)
										assert t.root.right is not None
										assert t.root.right.parent == t.root
										assert t.root.right.label == 10

										t.put(3					)
										assert t.root.left is not None
										assert t.root.left.parent == t.root
										assert t.root.left.label == 3

										t.put(6					)
										assert t.root.left.right is not None
										assert t.root.left.right.parent == t.root.left
										assert t.root.left.right.label == 6

										t.put(1					)
										assert t.root.left.left is not None
										assert t.root.left.left.parent == t.root.left
										assert t.root.left.left.label == 1

										with self.assertRaises(_lowerCamelCase					):  # noqa: B017
													t.put(1					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       None:




										'''simple docstring'''
										__lowercase						      =					self._get_binary_search_tree()

										__lowercase						      =					t.search(6					)
										assert node.label == 6

										__lowercase						      =					t.search(13					)
										assert node.label == 13

										with self.assertRaises(_lowerCamelCase					):  # noqa: B017
													t.search(2					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       None:




										'''simple docstring'''
										__lowercase						      =					self._get_binary_search_tree()

										t.remove(13					)
										assert t.root is not None
										assert t.root.right is not None
										assert t.root.right.right is not None
										assert t.root.right.right.right is None
										assert t.root.right.right.left is None

										t.remove(7					)
										assert t.root.left is not None
										assert t.root.left.right is not None
										assert t.root.left.right.left is not None
										assert t.root.left.right.right is None
										assert t.root.left.right.left.label == 4

										t.remove(6					)
										assert t.root.left.left is not None
										assert t.root.left.right.right is not None
										assert t.root.left.left.label == 1
										assert t.root.left.right.label == 4
										assert t.root.left.right.right.label == 5
										assert t.root.left.right.left is None
										assert t.root.left.left.parent == t.root.left
										assert t.root.left.right.parent == t.root.left

										t.remove(3					)
										assert t.root is not None
										assert t.root.left.label == 4
										assert t.root.left.right.label == 5
										assert t.root.left.left.label == 1
										assert t.root.left.parent == t.root
										assert t.root.left.left.parent == t.root.left
										assert t.root.left.right.parent == t.root.left

										t.remove(4					)
										assert t.root.left is not None
										assert t.root.left.left is not None
										assert t.root.left.label == 5
										assert t.root.left.right is None
										assert t.root.left.left.label == 1
										assert t.root.left.parent == t.root
										assert t.root.left.left.parent == t.root.left
							def      SCREAMING_SNAKE_CASE	(    self					) ->       None:




										'''simple docstring'''
										__lowercase						      =					self._get_binary_search_tree()

										t.remove(3					)
										assert t.root is not None
										assert t.root.left is not None
										assert t.root.left.left is not None
										assert t.root.left.right is not None
										assert t.root.left.right.left is not None
										assert t.root.left.right.right is not None
										assert t.root.left.label == 4
										assert t.root.left.right.label == 6
										assert t.root.left.left.label == 1
										assert t.root.left.right.right.label == 7
										assert t.root.left.right.left.label == 5
										assert t.root.left.parent == t.root
										assert t.root.left.right.parent == t.root.left
										assert t.root.left.left.parent == t.root.left
										assert t.root.left.right.left.parent == t.root.left.right
							def      SCREAMING_SNAKE_CASE	(    self					) ->       None:




										'''simple docstring'''
										__lowercase						      =					self._get_binary_search_tree()
										t.empty()
										assert t.root is None
							def      SCREAMING_SNAKE_CASE	(    self					) ->       None:




										'''simple docstring'''
										__lowercase						      =					self._get_binary_search_tree()
										assert not t.is_empty()

										t.empty()
										assert t.is_empty()
							def      SCREAMING_SNAKE_CASE	(    self					) ->       None:




										'''simple docstring'''
										__lowercase						      =					self._get_binary_search_tree()

										assert t.exists(6					)
										assert not t.exists(-1					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       None:




										'''simple docstring'''
										__lowercase						      =					self._get_binary_search_tree()

										assert t.get_max_label() == 14

										t.empty()
										with self.assertRaises(_lowerCamelCase					):  # noqa: B017
													t.get_max_label()
							def      SCREAMING_SNAKE_CASE	(    self					) ->       None:




										'''simple docstring'''
										__lowercase						      =					self._get_binary_search_tree()

										assert t.get_min_label() == 1

										t.empty()
										with self.assertRaises(_lowerCamelCase					):  # noqa: B017
													t.get_min_label()
							def      SCREAMING_SNAKE_CASE	(    self					) ->       None:




										'''simple docstring'''
										__lowercase						      =					self._get_binary_search_tree()

										__lowercase						      =					[i.label for i in t.inorder_traversal()]
										assert inorder_traversal_nodes == [1, 3, 4, 5, 6, 7, 8, 10, 13, 14]





							def      SCREAMING_SNAKE_CASE	(    self					) ->       None:




										'''simple docstring'''
										__lowercase						      =					self._get_binary_search_tree()

										__lowercase						      =					[i.label for i in t.preorder_traversal()]
										assert preorder_traversal_nodes == [8, 3, 1, 6, 4, 5, 7, 10, 14, 13]





def     lowerCAmelCase__ (		)	->None:
			__lowercase						      =					BinarySearchTree()
			t.put(8			)
			t.put(3			)
			t.put(6			)
			t.put(1			)
			t.put(1_0			)
			t.put(1_4			)
			t.put(1_3			)
			t.put(4			)
			t.put(7			)
			t.put(5			)

			print(
			    "\n            8\n           / \\\n          3   10\n         / \\    \\\n        1   6    14\n           / \\   /\n          4   7 13\n           \\\n            5\n        "			)

			print("Label 6 exists:" ,      t.exists(6			)			)
			print("Label 13 exists:" ,      t.exists(1_3			)			)
			print("Label -1 exists:" ,      t.exists(-1			)			)
			print("Label 12 exists:" ,      t.exists(1_2			)			)

			# Prints all the elements of the list in inorder traversal
			__lowercase						      =					[i.label for i in t.inorder_traversal()]
			print("Inorder traversal:" ,      __magic_name__			)

			# Prints all the elements of the list in preorder traversal
			__lowercase						      =					[i.label for i in t.preorder_traversal()]
			print("Preorder traversal:" ,      __magic_name__			)

			print("Max. label:" ,      t.get_max_label()			)
			print("Min. label:" ,      t.get_min_label()			)

			# Delete elements
			print("\nDeleting elements 13, 10, 8, 3, 6, 14"			)
			print(
			    "\n          4\n         / \\\n        1   7\n             \\\n              5\n        "			)
			t.remove(1_3			)
			t.remove(1_0			)
			t.remove(8			)
			t.remove(3			)
			t.remove(6			)
			t.remove(1_4			)

			# Prints all the elements of the list in inorder traversal after delete
			__lowercase						      =					[i.label for i in t.inorder_traversal()]
			print("Inorder traversal after delete:" ,      __magic_name__			)

			# Prints all the elements of the list in preorder traversal after delete
			__lowercase						      =					[i.label for i in t.preorder_traversal()]
			print("Preorder traversal after delete:" ,      __magic_name__			)

			print("Max. label:" ,      t.get_max_label()			)
			print("Min. label:" ,      t.get_min_label()			)


if __name__ == "__main__":
					binary_search_tree_example()







"""simple docstring"""






import functools
import math

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



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ = 4_0_9_6			)	->Dict:
			__lowercase      ,				__lowercase      ,				__lowercase						      =					key.shape[-3:]
			__lowercase						      =					value.shape[-1]
			__lowercase						      =					min(__magic_name__ ,      __magic_name__			)
			__lowercase						      =					query / jnp.sqrt(__magic_name__			)

			@functools.partial(jax.checkpoint ,      prevent_cse=__magic_name__			)
			def summarize_chunk(__magic_name__ ,      __magic_name__ ,      __magic_name__			):
						__lowercase						      =					jnp.einsum("...qhd,...khd->...qhk" ,      __magic_name__ ,      __magic_name__ ,      precision=__magic_name__			)

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

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

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

			def chunk_scanner(__magic_name__			):
						# julienne key array
						__lowercase						      =					jax.lax.dynamic_slice(
						    operand=__magic_name__ ,      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
						__lowercase						      =					jax.lax.dynamic_slice(
						    operand=__magic_name__ ,      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(__magic_name__ ,      __magic_name__ ,      __magic_name__			)

			__lowercase      ,				__lowercase      ,				__lowercase						      =					jax.lax.map(f=__magic_name__ ,      xs=jnp.arange(0 ,      __magic_name__ ,      __magic_name__			)			)

			__lowercase						      =					jnp.max(__magic_name__ ,      axis=0 ,      keepdims=__magic_name__			)
			__lowercase						      =					jnp.exp(chunk_max - global_max			)

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

			__lowercase						      =					chunk_values.sum(axis=0			)
			__lowercase						      =					jnp.expand_dims(__magic_name__ ,      -1			).sum(axis=0			)

			return all_values / all_weights



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__=jax.lax.Precision.HIGHEST ,      __magic_name__ = 1_0_2_4 ,      __magic_name__ = 4_0_9_6			)	->List[str]:
			__lowercase      ,				__lowercase      ,				__lowercase						      =					query.shape[-3:]

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

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

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

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

class 				__a    (   nn.Module					):



							'''simple docstring'''







							_lowerCamelCase				: int
							_lowerCamelCase				: int					      =      8
							_lowerCamelCase				: int					      =      64
							_lowerCamelCase				: float					      =      0.0
							_lowerCamelCase				: bool					      =      False
							_lowerCamelCase				: jnp.dtype					      =      jnp.floataa
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Any:




										'''simple docstring'''
										__lowercase						      =					self.dim_head * self.heads
										__lowercase						      =					self.dim_head**-0.5

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

										__lowercase						      =					nn.Dense(self.query_dim     ,						dtype=self.dtype     ,						name="to_out_0"					)
										__lowercase						      =					nn.Dropout(rate=self.dropout					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Optional[int]:




										'''simple docstring'''
										__lowercase      ,				__lowercase      ,				__lowercase						      =					tensor.shape
										__lowercase						      =					self.heads
										__lowercase						      =					tensor.reshape(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						dim // head_size					)
										__lowercase						      =					jnp.transpose(_lowerCamelCase     ,						(0, 2, 1, 3)					)
										__lowercase						      =					tensor.reshape(batch_size * head_size     ,						_lowerCamelCase     ,						dim // head_size					)
										return tensor
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       str:




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





							def __call__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase=None     ,						_lowerCamelCase=True					) ->       List[str]:




										'''simple docstring'''
										__lowercase						      =					hidden_states if context is None else context

										__lowercase						      =					self.query(_lowerCamelCase					)
										__lowercase						      =					self.key(_lowerCamelCase					)
										__lowercase						      =					self.value(_lowerCamelCase					)

										__lowercase						      =					self.reshape_heads_to_batch_dim(_lowerCamelCase					)
										__lowercase						      =					self.reshape_heads_to_batch_dim(_lowerCamelCase					)
										__lowercase						      =					self.reshape_heads_to_batch_dim(_lowerCamelCase					)

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

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

													__lowercase						      =					jax_memory_efficient_attention(
													    _lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						query_chunk_size=_lowerCamelCase     ,						key_chunk_size=4_096 * 4					)

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

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

										__lowercase						      =					self.reshape_batch_dim_to_heads(_lowerCamelCase					)
										__lowercase						      =					self.proj_attn(_lowerCamelCase					)
										return self.dropout_layer(_lowerCamelCase     ,						deterministic=_lowerCamelCase					)





class 				__a    (   nn.Module					):



							'''simple docstring'''







							_lowerCamelCase				: int
							_lowerCamelCase				: int
							_lowerCamelCase				: int
							_lowerCamelCase				: float					      =      0.0
							_lowerCamelCase				: bool					      =      False
							_lowerCamelCase				: jnp.dtype					      =      jnp.floataa
							_lowerCamelCase				: bool					      =      False
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




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





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




										'''simple docstring'''
										# self attention
										__lowercase						      =					hidden_states
										if self.only_cross_attention:
													__lowercase						      =					self.attna(self.norma(_lowerCamelCase					)     ,						_lowerCamelCase     ,						deterministic=_lowerCamelCase					)
										else:
													__lowercase						      =					self.attna(self.norma(_lowerCamelCase					)     ,						deterministic=_lowerCamelCase					)
										__lowercase						      =					hidden_states + residual

										# cross attention
										__lowercase						      =					hidden_states
										__lowercase						      =					self.attna(self.norma(_lowerCamelCase					)     ,						_lowerCamelCase     ,						deterministic=_lowerCamelCase					)
										__lowercase						      =					hidden_states + residual

										# feed forward
										__lowercase						      =					hidden_states
										__lowercase						      =					self.ff(self.norma(_lowerCamelCase					)     ,						deterministic=_lowerCamelCase					)
										__lowercase						      =					hidden_states + residual

										return self.dropout_layer(_lowerCamelCase     ,						deterministic=_lowerCamelCase					)





class 				__a    (   nn.Module					):



							'''simple docstring'''







							_lowerCamelCase				: int
							_lowerCamelCase				: int
							_lowerCamelCase				: int
							_lowerCamelCase				: int					      =      1
							_lowerCamelCase				: float					      =      0.0
							_lowerCamelCase				: bool					      =      False
							_lowerCamelCase				: bool					      =      False
							_lowerCamelCase				: jnp.dtype					      =      jnp.floataa
							_lowerCamelCase				: bool					      =      False
							def      SCREAMING_SNAKE_CASE	(    self					) ->       int:




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

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

										__lowercase						      =					[
										    FlaxBasicTransformerBlock(
										        _lowerCamelCase     ,						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:
													__lowercase						      =					nn.Dense(_lowerCamelCase     ,						dtype=self.dtype					)
										else:
													__lowercase						      =					nn.Conv(
													    _lowerCamelCase     ,						kernel_size=(1, 1)     ,						strides=(1, 1)     ,						padding="VALID"     ,						dtype=self.dtype     ,						)

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





							def __call__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=True					) ->       int:




										'''simple docstring'''
										__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase						      =					hidden_states.shape
										__lowercase						      =					hidden_states
										__lowercase						      =					self.norm(_lowerCamelCase					)
										if self.use_linear_projection:
													__lowercase						      =					hidden_states.reshape(_lowerCamelCase     ,						height * width     ,						_lowerCamelCase					)
													__lowercase						      =					self.proj_in(_lowerCamelCase					)
										else:
													__lowercase						      =					self.proj_in(_lowerCamelCase					)
													__lowercase						      =					hidden_states.reshape(_lowerCamelCase     ,						height * width     ,						_lowerCamelCase					)

										for transformer_block in self.transformer_blocks:
													__lowercase						      =					transformer_block(_lowerCamelCase     ,						_lowerCamelCase     ,						deterministic=_lowerCamelCase					)

										if self.use_linear_projection:
													__lowercase						      =					self.proj_out(_lowerCamelCase					)
													__lowercase						      =					hidden_states.reshape(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										else:
													__lowercase						      =					hidden_states.reshape(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
													__lowercase						      =					self.proj_out(_lowerCamelCase					)

										__lowercase						      =					hidden_states + residual
										return self.dropout_layer(_lowerCamelCase     ,						deterministic=_lowerCamelCase					)





class 				__a    (   nn.Module					):



							'''simple docstring'''







							_lowerCamelCase				: int
							_lowerCamelCase				: float					      =      0.0
							_lowerCamelCase				: jnp.dtype					      =      jnp.floataa
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




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





							def __call__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase=True					) ->       int:




										'''simple docstring'''
										__lowercase						      =					self.net_a(_lowerCamelCase     ,						deterministic=_lowerCamelCase					)
										__lowercase						      =					self.net_a(_lowerCamelCase					)
										return hidden_states





class 				__a    (   nn.Module					):



							'''simple docstring'''







							_lowerCamelCase				: int
							_lowerCamelCase				: float					      =      0.0
							_lowerCamelCase				: jnp.dtype					      =      jnp.floataa
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




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





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




										'''simple docstring'''
										__lowercase						      =					self.proj(_lowerCamelCase					)
										__lowercase      ,				__lowercase						      =					jnp.split(_lowerCamelCase     ,						2     ,						axis=2					)
										return self.dropout_layer(hidden_linear * nn.gelu(_lowerCamelCase					)     ,						deterministic=_lowerCamelCase					)





