
'''simple docstring'''



from __future__ import annotations

import unittest
from collections.abc import Iterator





class 			A							:



			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		int:




									'''simple docstring'''



									lowercase__							 =       label
									lowercase__							 =       parent
									lowercase__							 =       None
									lowercase__							 =       None





class 			A							:



			def __init__(					self       )			->		List[str]:




									'''simple docstring'''



									lowercase__							 =       None



			def A__ (					self       )			->		Tuple:




									'''simple docstring'''



									lowercase__							 =       None



			def A__ (					self       )			->		Tuple:




									'''simple docstring'''



									return self.root is None



			def A__ (					self       ,     lowerCamelCase__       )			->		Tuple:




									'''simple docstring'''



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



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       )			->		List[str]:




									'''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 A__ (					self       ,     lowerCamelCase__       )			->		Optional[Any]:




									'''simple docstring'''



									return self._search(self.root       ,     _lowerCamelCase       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:




									'''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 A__ (					self       ,     lowerCamelCase__       )			->		Any:




									'''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 A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[int]:




									'''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 A__ (					self       ,     lowerCamelCase__       )			->		Optional[Any]:




									'''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 A__ (					self       ,     lowerCamelCase__       )			->		int:




									'''simple docstring'''



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



			def A__ (					self       )			->		Dict:




									'''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 A__ (					self       )			->		List[Any]:




									'''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 A__ (					self       )			->		List[str]:




									'''simple docstring'''



									return self._inorder_traversal(self.root       )



			def A__ (					self       ,     lowerCamelCase__       )			->		int:




									'''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       )			->		List[str]:




									'''simple docstring'''



									return self._preorder_traversal(self.root       )



			def A__ (					self       ,     lowerCamelCase__       )			->		Union[str, Any]:




									'''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				):



			@staticmethod
			def A__ (					)			->		Dict:




									'''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 A__ (					self       )			->		Union[str, Any]:




									'''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 A__ (					self       )			->		Any:




									'''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 A__ (					self       )			->		Union[str, Any]:




									'''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 A__ (					self       )			->		str:




									'''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 A__ (					self       )			->		List[str]:




									'''simple docstring'''



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



			def A__ (					self       )			->		int:




									'''simple docstring'''



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

									t.empty()
									assert t.is_empty()



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__							 =       self._get_binary_search_tree()

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



			def A__ (					self       )			->		Optional[Any]:




									'''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 A__ (					self       )			->		Any:




									'''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 A__ (					self       )			->		List[str]:




									'''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 A__ (					self       )			->		Union[str, Any]:




									'''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  _A		(							):
						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	)

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

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

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

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

						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(
						    """
          4
         / \\
        1   7
             \\
              5
        """	)
						t.remove(13	)
						t.remove(10	)
						t.remove(8	)
						t.remove(3	)
						t.remove(6	)
						t.remove(14	)

						# 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:"""		,		lowercase__	)

						# 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:"""		,		lowercase__	)

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


if __name__ == "__main__":
				binary_search_tree_example()








'''simple docstring'''



import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

__A          =    logging.get_logger(__name__)





def  _A		(							lowercase__		,		lowercase__ = True	):
						lowercase__							 =       {}
						for name in model.attn_processors.keys():
												lowercase__							 =       name.endswith("""attn2.processor"""	) and not (
												    name.startswith("""transformer_in"""	) or """temp_attentions""" in name.split("""."""	)
												)
												lowercase__							 =       model.config.cross_attention_dim if has_cross_attention else None
												if name.startswith("""mid_block"""	):
																		lowercase__							 =       model.config.block_out_channels[-1]
												elif name.startswith("""up_blocks"""	):
																		lowercase__							 =       int(name[len("""up_blocks."""	)]	)
																		lowercase__							 =       list(reversed(model.config.block_out_channels	)	)[block_id]
												elif name.startswith("""down_blocks"""	):
																		lowercase__							 =       int(name[len("""down_blocks."""	)]	)
																		lowercase__							 =       model.config.block_out_channels[block_id]
												elif name.startswith("""transformer_in"""	):
																		# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
																		lowercase__							 =       8 * model.config.attention_head_dim

												lowercase__							 =       LoRAAttnProcessor(hidden_size=lowercase__		,		cross_attention_dim=lowercase__	)
												lowercase__							 =       lora_attn_procs[name].to(model.device	)

												if mock_weights:
																		# add 1 to weights to mock trained weights
																		with torch.no_grad():
																								lora_attn_procs[name].to_q_lora.up.weight += 1
																								lora_attn_procs[name].to_k_lora.up.weight += 1
																								lora_attn_procs[name].to_v_lora.up.weight += 1
																								lora_attn_procs[name].to_out_lora.up.weight += 1

						return lora_attn_procs







@skip_mps
class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
			lowerCamelCase							:							List[Any]       		=  UNetaDConditionModel
			lowerCamelCase							:							int       		=  """sample"""



			@property
			def A__ (					self       )			->		Optional[Any]:




									'''simple docstring'''



									lowercase__							 =       4
									lowercase__							 =       4
									lowercase__							 =       4
									lowercase__							 =       (32, 32)

									lowercase__							 =       floats_tensor((batch_size, num_channels, num_frames) + sizes       ).to(lowerCamelCase__       )
									lowercase__							 =       torch.tensor([10]       ).to(lowerCamelCase__       )
									lowercase__							 =       floats_tensor((batch_size, 4, 32)       ).to(lowerCamelCase__       )

									return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}



			@property
			def A__ (					self       )			->		Any:




									'''simple docstring'''



									return (4, 4, 32, 32)



			@property
			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



									return (4, 4, 32, 32)



			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__							 =       {
									    """block_out_channels""": (32, 64),
									    """down_block_types""": (
									        """CrossAttnDownBlock3D""",
									        """DownBlock3D""",
									    ),
									    """up_block_types""": ("""UpBlock3D""", """CrossAttnUpBlock3D"""),
									    """cross_attention_dim""": 32,
									    """attention_head_dim""": 8,
									    """out_channels""": 4,
									    """in_channels""": 4,
									    """layers_per_block""": 1,
									    """sample_size""": 32,
									}
									lowercase__							 =       self.dummy_input
									return init_dict, inputs_dict



			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()       ,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,     )
			def A__ (					self       )			->		str:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
									lowercase__							 =       self.model_class(**lowerCamelCase__       )

									model.enable_xformers_memory_efficient_attention()

									assert (
									    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
									    == "XFormersAttnProcessor"
									), "xformers is not enabled"



			def A__ (					self       )			->		Dict:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       32

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )

															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					lowercase__							 =       output.sample

									self.assertIsNotNone(lowerCamelCase__       )
									lowercase__							 =       inputs_dict["""sample"""].shape
									self.assertEqual(output.shape       ,     lowerCamelCase__       ,     """Input and output shapes do not match"""       )



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									with torch.no_grad():
															# Warmup pass when using mps (see #372)
															if torch_device == "mps" and isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					model(**self.dummy_input       )

															lowercase__							 =       model(**lowerCamelCase__       )
															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					lowercase__							 =       first.sample

															lowercase__							 =       model(**lowerCamelCase__       )
															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					lowercase__							 =       second.sample

									lowercase__							 =       first.cpu().numpy()
									lowercase__							 =       second.cpu().numpy()
									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
									lowercase__							 =       np.amax(np.abs(out_a - out_a       )       )
									self.assertLessEqual(lowerCamelCase__       ,     1e-5       )



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									model.set_attention_slice("""auto"""       )
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )
									assert output is not None

									model.set_attention_slice("""max"""       )
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )
									assert output is not None

									model.set_attention_slice(2       )
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )
									assert output is not None



			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )

									# make sure we can set a list of attention processors
									model.set_attn_processor(lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									# test that attn processors can be set to itself
									model.set_attn_processor(model.attn_processors       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									assert (samplea - samplea).abs().max() < 3e-3
									assert (samplea - samplea).abs().max() < 3e-3

									# sample 2 and sample 3 should be different
									assert (samplea - samplea).abs().max() > 3e-3



			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															new_model.load_attn_procs(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       new_model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									assert (sample - new_sample).abs().max() < 1e-3

									# LoRA and no LoRA should NOT be the same
									assert (sample - old_sample).abs().max() > 1e-4



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       ,     safe_serialization=lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.safetensors"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															new_model.load_attn_procs(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       new_model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									assert (sample - new_sample).abs().max() < 3e-3

									# LoRA and no LoRA should NOT be the same
									assert (sample - old_sample).abs().max() > 1e-4



			def A__ (					self       )			->		Dict:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )
									# Saving as torch, properly reloads with directly filename
									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															new_model.load_attn_procs(lowerCamelCase__       ,     weight_name="""pytorch_lora_weights.bin"""       )



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )
									# Saving as torch, properly reloads with directly filename
									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															with self.assertRaises(lowerCamelCase__       ) as e:
																					new_model.load_attn_procs(lowerCamelCase__       ,     use_safetensors=lowerCamelCase__       )
															self.assertIn("""Error no file named pytorch_lora_weights.safetensors"""       ,     str(e.exception       )       )



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample

									model.set_attn_processor(AttnProcessor()       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									assert (sample - new_sample).abs().max() < 1e-4
									assert (sample - old_sample).abs().max() < 3e-3



			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()       ,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,     )
			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       4

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									# default
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

															model.enable_xformers_memory_efficient_attention()
															lowercase__							 =       model(**lowerCamelCase__       ).sample

															model.disable_xformers_memory_efficient_attention()
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									assert (sample - on_sample).abs().max() < 1e-4
									assert (sample - off_sample).abs().max() < 1e-4



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
									lowercase__							 =       32

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )[0]

									model.enable_forward_chunking()
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )[0]

									self.assertEqual(output.shape       ,     output_a.shape       ,     """Shape doesn't match"""       )
									assert np.abs(output.cpu() - output_a.cpu()       ).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.






