
"""simple docstring"""




from __future__ import annotations

import unittest
from collections.abc import Iterator




class 					UpperCamelCase     :


				def __init__(    self		,     lowercase__		,     lowercase__      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     label
									SCREAMING_SNAKE_CASE           =     parent
									SCREAMING_SNAKE_CASE           =     None
									SCREAMING_SNAKE_CASE           =     None






class 					UpperCamelCase     :


				def __init__(    self      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     None


				def 							A (    self      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     None


				def 							A (    self      )   ->   bool:
									"""simple docstring"""







									return self.root is None


				def 							A (    self		,     lowercase__      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._put(self.root		,     lowercase__      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__ = None      )   ->   Node:
									"""simple docstring"""







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

									return node


				def 							A (    self		,     lowercase__      )   ->   Node:
									"""simple docstring"""







									return self._search(self.root		,     lowercase__      )


				def 							A (    self		,     lowercase__		,     lowercase__      )   ->   Node:
									"""simple docstring"""







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

									return node


				def 							A (    self		,     lowercase__      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.search(lowercase__      )
									if node.right and node.left:
														SCREAMING_SNAKE_CASE           =     self._get_lowest_node(node.right      )
														SCREAMING_SNAKE_CASE           =     node.left
														SCREAMING_SNAKE_CASE           =     node.right
														SCREAMING_SNAKE_CASE           =     lowest_node
														if node.right:
																			SCREAMING_SNAKE_CASE           =     lowest_node
														self._reassign_nodes(lowercase__		,     lowercase__      )
									elif not node.right and node.left:
														self._reassign_nodes(lowercase__		,     node.left      )
									elif node.right and not node.left:
														self._reassign_nodes(lowercase__		,     node.right      )
									else:
														self._reassign_nodes(lowercase__		,     lowercase__      )


				def 							A (    self		,     lowercase__		,     lowercase__      )   ->   None:
									"""simple docstring"""







									if new_children:
														SCREAMING_SNAKE_CASE           =     node.parent

									if node.parent:
														if node.parent.right == node:
																			SCREAMING_SNAKE_CASE           =     new_children
														else:
																			SCREAMING_SNAKE_CASE           =     new_children
									else:
														SCREAMING_SNAKE_CASE           =     new_children


				def 							A (    self		,     lowercase__      )   ->   Node:
									"""simple docstring"""







									if node.left:
														SCREAMING_SNAKE_CASE           =     self._get_lowest_node(node.left      )
									else:
														SCREAMING_SNAKE_CASE           =     node
														self._reassign_nodes(lowercase__		,     node.right      )

									return lowest_node


				def 							A (    self		,     lowercase__      )   ->   bool:
									"""simple docstring"""







									try:
														self.search(lowercase__      )
														return True
									except Exception:
														return False


				def 							A (    self      )   ->   int:
									"""simple docstring"""







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

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

									return node.label


				def 							A (    self      )   ->   int:
									"""simple docstring"""







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

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

									return node.label


				def 							A (    self      )   ->   Iterator[Node]:
									"""simple docstring"""







									return self._inorder_traversal(self.root      )


				def 							A (    self		,     lowercase__      )   ->   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 							A (    self      )   ->   Iterator[Node]:
									"""simple docstring"""







									return self._preorder_traversal(self.root      )




				def 							A (    self		,     lowercase__      )   ->   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 					UpperCamelCase     (	unittest.TestCase				):


				@staticmethod
				def 							A (    )   ->   BinarySearchTree:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     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 							A (    self      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     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(lowercase__      ):  # noqa: B017
														t.put(1      )


				def 							A (    self      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._get_binary_search_tree()

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

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

									with self.assertRaises(lowercase__      ):  # noqa: B017
														t.search(2      )


				def 							A (    self      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     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 							A (    self      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     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 							A (    self      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._get_binary_search_tree()
									t.empty()
									assert t.root is None


				def 							A (    self      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._get_binary_search_tree()
									assert not t.is_empty()

									t.empty()
									assert t.is_empty()


				def 							A (    self      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._get_binary_search_tree()

									assert t.exists(6      )
									assert not t.exists(-1      )


				def 							A (    self      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._get_binary_search_tree()

									assert t.get_max_label() == 14

									t.empty()
									with self.assertRaises(lowercase__      ):  # noqa: B017
														t.get_max_label()


				def 							A (    self      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._get_binary_search_tree()

									assert t.get_min_label() == 1

									t.empty()
									with self.assertRaises(lowercase__      ):  # noqa: B017
														t.get_min_label()


				def 							A (    self      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._get_binary_search_tree()

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




				def 							A (    self      )   ->   None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._get_binary_search_tree()

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






def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE           =     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
					SCREAMING_SNAKE_CASE           =     [i.label for i in t.inorder_traversal()]
					print('Inorder traversal:',					SCREAMING_SNAKE_CASE_      )

					# Prints all the elements of the list in preorder traversal
					SCREAMING_SNAKE_CASE           =     [i.label for i in t.preorder_traversal()]
					print('Preorder traversal:',					SCREAMING_SNAKE_CASE_      )

					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
					SCREAMING_SNAKE_CASE           =     [i.label for i in t.inorder_traversal()]
					print('Inorder traversal after delete:',					SCREAMING_SNAKE_CASE_      )

					# Prints all the elements of the list in preorder traversal after delete
					SCREAMING_SNAKE_CASE           =     [i.label for i in t.preorder_traversal()]
					print('Preorder traversal after delete:',					SCREAMING_SNAKE_CASE_      )

					print('Max. label:',					t.get_max_label()      )
					print('Min. label:',					t.get_min_label()      )


if __name__ == "__main__":
	binary_search_tree_example()


"""simple docstring"""




from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin




@dataclass
class 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				torch.FloatTensor






class 					UpperCamelCase     (	__magic_name__  ,				__magic_name__				):

				"""simple docstring"""



				@register_to_config
				def __init__(    self		,     lowercase__ = 16		,     lowercase__ = 88		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = 1		,     lowercase__ = 0.0		,     lowercase__ = 32		,     lowercase__ = None		,     lowercase__ = False		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = "geglu"		,     lowercase__ = None		,     lowercase__ = False		,     lowercase__ = False		,     lowercase__ = False		,     lowercase__ = "layer_norm"		,     lowercase__ = True		,     )   ->   List[Any]:
									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE           =     use_linear_projection
									SCREAMING_SNAKE_CASE           =     num_attention_heads
									SCREAMING_SNAKE_CASE           =     attention_head_dim
									SCREAMING_SNAKE_CASE           =     num_attention_heads * attention_head_dim

									# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
									# Define whether input is continuous or discrete depending on configuration
									SCREAMING_SNAKE_CASE           =     (in_channels is not None) and (patch_size is None)
									SCREAMING_SNAKE_CASE           =     num_vector_embeds is not None
									SCREAMING_SNAKE_CASE           =     in_channels is not None and patch_size is not None

									if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
														SCREAMING_SNAKE_CASE           =     (
														    f'''The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'''
														    ' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'
														    ' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'
														    ' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'
														    ' would be very nice if you could open a Pull request for the `transformer/config.json` file'
														)
														deprecate('norm_type!=num_embeds_ada_norm'		,     '1.0.0'		,     lowercase__		,     standard_warn=lowercase__      )
														SCREAMING_SNAKE_CASE           =     'ada_norm'

									if self.is_input_continuous and self.is_input_vectorized:
														raise ValueError(
														    f'''Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'''
														    ' sure that either `in_channels` or `num_vector_embeds` is None.'      )
									elif self.is_input_vectorized and self.is_input_patches:
														raise ValueError(
														    f'''Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'''
														    ' sure that either `num_vector_embeds` or `num_patches` is None.'      )
									elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
														raise ValueError(
														    f'''Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'''
														    f''' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.'''      )

									# 2. Define input layers
									if self.is_input_continuous:
														SCREAMING_SNAKE_CASE           =     in_channels

														SCREAMING_SNAKE_CASE           =     torch.nn.GroupNorm(num_groups=lowercase__		,     num_channels=lowercase__		,     eps=1E-6		,     affine=lowercase__      )
														if use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     nn.Convad(lowercase__		,     lowercase__		,     kernel_size=1		,     stride=1		,     padding=0      )
									elif self.is_input_vectorized:
														assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
														assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

														SCREAMING_SNAKE_CASE           =     sample_size
														SCREAMING_SNAKE_CASE           =     sample_size
														SCREAMING_SNAKE_CASE           =     num_vector_embeds
														SCREAMING_SNAKE_CASE           =     self.height * self.width

														SCREAMING_SNAKE_CASE           =     ImagePositionalEmbeddings(
														    num_embed=lowercase__		,     embed_dim=lowercase__		,     height=self.height		,     width=self.width      )
									elif self.is_input_patches:
														assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

														SCREAMING_SNAKE_CASE           =     sample_size
														SCREAMING_SNAKE_CASE           =     sample_size

														SCREAMING_SNAKE_CASE           =     patch_size
														SCREAMING_SNAKE_CASE           =     PatchEmbed(
														    height=lowercase__		,     width=lowercase__		,     patch_size=lowercase__		,     in_channels=lowercase__		,     embed_dim=lowercase__		,     )

									# 3. Define transformers blocks
									SCREAMING_SNAKE_CASE           =     nn.ModuleList(
									    [
									        BasicTransformerBlock(
									            lowercase__		,     lowercase__		,     lowercase__		,     dropout=lowercase__		,     cross_attention_dim=lowercase__		,     activation_fn=lowercase__		,     num_embeds_ada_norm=lowercase__		,     attention_bias=lowercase__		,     only_cross_attention=lowercase__		,     upcast_attention=lowercase__		,     norm_type=lowercase__		,     norm_elementwise_affine=lowercase__		,     )
									        for d in range(lowercase__      )
									    ]      )

									# 4. Define output layers
									SCREAMING_SNAKE_CASE           =     in_channels if out_channels is None else out_channels
									if self.is_input_continuous:
														# TODO: should use out_channels for continuous projections
														if use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     nn.Convad(lowercase__		,     lowercase__		,     kernel_size=1		,     stride=1		,     padding=0      )
									elif self.is_input_vectorized:
														SCREAMING_SNAKE_CASE           =     nn.LayerNorm(lowercase__      )
														SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     self.num_vector_embeds - 1      )
									elif self.is_input_patches:
														SCREAMING_SNAKE_CASE           =     nn.LayerNorm(lowercase__		,     elementwise_affine=lowercase__		,     eps=1E-6      )
														SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     2 * inner_dim      )
														SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     patch_size * patch_size * self.out_channels      )




				def 							A (    self		,     lowercase__		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = True		,     )   ->   List[Any]:
									"""simple docstring"""







									if attention_mask is not None and attention_mask.ndim == 2:
														# assume that mask is expressed as:
														#   (1 = keep,      0 = discard)
														# convert mask into a bias that can be added to attention scores:
														#       (keep = +0,     discard = -10000.0)
														SCREAMING_SNAKE_CASE           =     (1 - attention_mask.to(hidden_states.dtype      )) * -10_000.0
														SCREAMING_SNAKE_CASE           =     attention_mask.unsqueeze(1      )

									# convert encoder_attention_mask to a bias the same way we do for attention_mask
									if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
														SCREAMING_SNAKE_CASE           =     (1 - encoder_attention_mask.to(hidden_states.dtype      )) * -10_000.0
														SCREAMING_SNAKE_CASE           =     encoder_attention_mask.unsqueeze(1      )

									# 1. Input
									if self.is_input_continuous:
														SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     hidden_states.shape
														SCREAMING_SNAKE_CASE           =     hidden_states

														SCREAMING_SNAKE_CASE           =     self.norm(lowercase__      )
														if not self.use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     self.proj_in(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     hidden_states.shape[1]
																			SCREAMING_SNAKE_CASE           =     hidden_states.permute(0		,     2		,     3		,     1      ).reshape(lowercase__		,     height * width		,     lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     hidden_states.shape[1]
																			SCREAMING_SNAKE_CASE           =     hidden_states.permute(0		,     2		,     3		,     1      ).reshape(lowercase__		,     height * width		,     lowercase__      )
																			SCREAMING_SNAKE_CASE           =     self.proj_in(lowercase__      )
									elif self.is_input_vectorized:
														SCREAMING_SNAKE_CASE           =     self.latent_image_embedding(lowercase__      )
									elif self.is_input_patches:
														SCREAMING_SNAKE_CASE           =     self.pos_embed(lowercase__      )

									# 2. Blocks
									for block in self.transformer_blocks:
														SCREAMING_SNAKE_CASE           =     block(
														    lowercase__		,     attention_mask=lowercase__		,     encoder_hidden_states=lowercase__		,     encoder_attention_mask=lowercase__		,     timestep=lowercase__		,     cross_attention_kwargs=lowercase__		,     class_labels=lowercase__		,     )

									# 3. Output
									if self.is_input_continuous:
														if not self.use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     hidden_states.reshape(lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      ).permute(0		,     3		,     1		,     2      ).contiguous()
																			SCREAMING_SNAKE_CASE           =     self.proj_out(lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     self.proj_out(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     hidden_states.reshape(lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      ).permute(0		,     3		,     1		,     2      ).contiguous()

														SCREAMING_SNAKE_CASE           =     hidden_states + residual
									elif self.is_input_vectorized:
														SCREAMING_SNAKE_CASE           =     self.norm_out(lowercase__      )
														SCREAMING_SNAKE_CASE           =     self.out(lowercase__      )
														# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
														SCREAMING_SNAKE_CASE           =     logits.permute(0		,     2		,     1      )

														# log(p(x_0))
														SCREAMING_SNAKE_CASE           =     F.log_softmax(logits.double()		,     dim=1      ).float()
									elif self.is_input_patches:
														# TODO: cleanup!
														SCREAMING_SNAKE_CASE           =     self.transformer_blocks[0].norma.emb(
														    lowercase__		,     lowercase__		,     hidden_dtype=hidden_states.dtype      )
														SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     self.proj_out_a(F.silu(lowercase__      )      ).chunk(2		,     dim=1      )
														SCREAMING_SNAKE_CASE           =     self.norm_out(lowercase__      ) * (1 + scale[:, None]) + shift[:, None]
														SCREAMING_SNAKE_CASE           =     self.proj_out_a(lowercase__      )

														# unpatchify
														SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     int(hidden_states.shape[1] ** 0.5      )
														SCREAMING_SNAKE_CASE           =     hidden_states.reshape(
														    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)      )
														SCREAMING_SNAKE_CASE           =     torch.einsum('nhwpqc->nchpwq'		,     lowercase__      )
														SCREAMING_SNAKE_CASE           =     hidden_states.reshape(
														    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)      )

									if not return_dict:
														return (output,)

									return TransformeraDModelOutput(sample=lowercase__      )
