



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     (    lowerCAmelCase_   :					List[Any]			,lowerCAmelCase_   :					bool = True				)      -> Tuple:






						'''simple docstring'''



						UpperCAmelCase_=   {}
						for name in model.attn_processors.keys():
												UpperCAmelCase_=   name.endswith("""attn2.processor"""				) and not (
												    name.startswith("""transformer_in"""				) or """temp_attentions""" in name.split("""."""				)
												)
												UpperCAmelCase_=   model.config.cross_attention_dim if has_cross_attention else None
												if name.startswith("""mid_block"""				):
																		UpperCAmelCase_=   model.config.block_out_channels[-1]
												elif name.startswith("""up_blocks"""				):
																		UpperCAmelCase_=   int(name[len("""up_blocks."""				)]				)
																		UpperCAmelCase_=   list(reversed(model.config.block_out_channels				)				)[block_id]
												elif name.startswith("""down_blocks"""				):
																		UpperCAmelCase_=   int(name[len("""down_blocks."""				)]				)
																		UpperCAmelCase_=   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
																		UpperCAmelCase_=   8 * model.config.attention_head_dim

												UpperCAmelCase_=   LoRAAttnProcessor(hidden_size=lowerCAmelCase_			,cross_attention_dim=lowerCAmelCase_				)
												UpperCAmelCase_=   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 		lowercase						( snake_case__ ,    snake_case__ ,    unittest.TestCase):






	"""simple docstring"""


	a__      :					Union[str, Any]		   =							UNetaDConditionModel
	a__      :					List[Any]		   =							"sample"




	@property
	def   _SCREAMING_SNAKE_CASE		(	self	:						str     )			-> Union[str, Any]:
							UpperCAmelCase_=   4
							UpperCAmelCase_=   4
							UpperCAmelCase_=   4
							UpperCAmelCase_=   (32, 32)

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

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




	@property
	def   _SCREAMING_SNAKE_CASE		(	self	:						str     )			-> Optional[int]:
							return (4, 4, 32, 32)




	@property
	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> str:
							return (4, 4, 32, 32)




	def   _SCREAMING_SNAKE_CASE		(	self	:						Dict     )			-> Optional[int]:
							UpperCAmelCase_=   {
							    """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,
							}
							UpperCAmelCase_=   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   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> Optional[Any]:
							UpperCAmelCase_,   UpperCAmelCase_=   self.prepare_init_args_and_inputs_for_common()
							UpperCAmelCase_=   self.model_class(**__UpperCAmelCase     )

							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   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]     )			-> Tuple:
							UpperCAmelCase_,   UpperCAmelCase_=   self.prepare_init_args_and_inputs_for_common()

							UpperCAmelCase_=   32

							UpperCAmelCase_=   self.model_class(**__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()

							with torch.no_grad():
													UpperCAmelCase_=   model(**__UpperCAmelCase     )

													if isinstance(__UpperCAmelCase						,  __UpperCAmelCase     ):
																			UpperCAmelCase_=   output.sample

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




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> Dict:
							UpperCAmelCase_,   UpperCAmelCase_=   self.prepare_init_args_and_inputs_for_common()
							UpperCAmelCase_=   self.model_class(**__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()

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

													UpperCAmelCase_=   model(**__UpperCAmelCase     )
													if isinstance(__UpperCAmelCase						,  __UpperCAmelCase     ):
																			UpperCAmelCase_=   first.sample

													UpperCAmelCase_=   model(**__UpperCAmelCase     )
													if isinstance(__UpperCAmelCase						,  __UpperCAmelCase     ):
																			UpperCAmelCase_=   second.sample

							UpperCAmelCase_=   first.cpu().numpy()
							UpperCAmelCase_=   second.cpu().numpy()
							UpperCAmelCase_=   out_a[~np.isnan(__UpperCAmelCase     )]
							UpperCAmelCase_=   out_a[~np.isnan(__UpperCAmelCase     )]
							UpperCAmelCase_=   np.amax(np.abs(out_a - out_a     )     )
							self.assertLessEqual(__UpperCAmelCase						,  1E-5     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> Optional[Any]:
							UpperCAmelCase_,   UpperCAmelCase_=   self.prepare_init_args_and_inputs_for_common()

							UpperCAmelCase_=   8

							UpperCAmelCase_=   self.model_class(**__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()

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

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

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




	def   _SCREAMING_SNAKE_CASE		(	self	:						Any     )			-> str:
							UpperCAmelCase_,   UpperCAmelCase_=   self.prepare_init_args_and_inputs_for_common()

							UpperCAmelCase_=   8

							UpperCAmelCase_=   self.model_class(**__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )

							with torch.no_grad():
													UpperCAmelCase_=   model(**__UpperCAmelCase     ).sample

							UpperCAmelCase_=   create_lora_layers(__UpperCAmelCase     )

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

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

							with torch.no_grad():
													UpperCAmelCase_=   model(**__UpperCAmelCase						,  cross_attention_kwargs={"""scale""": 0.0}     ).sample
													UpperCAmelCase_=   model(**__UpperCAmelCase						,  cross_attention_kwargs={"""scale""": 0.5}     ).sample
													UpperCAmelCase_=   model(**__UpperCAmelCase						,  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   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> Any:
							UpperCAmelCase_,   UpperCAmelCase_=   self.prepare_init_args_and_inputs_for_common()

							UpperCAmelCase_=   8

							torch.manual_seed(0     )
							UpperCAmelCase_=   self.model_class(**__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )

							with torch.no_grad():
													UpperCAmelCase_=   model(**__UpperCAmelCase     ).sample

							UpperCAmelCase_=   create_lora_layers(__UpperCAmelCase     )
							model.set_attn_processor(__UpperCAmelCase     )

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

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

							with torch.no_grad():
													UpperCAmelCase_=   new_model(**__UpperCAmelCase						,  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   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> str:
							UpperCAmelCase_,   UpperCAmelCase_=   self.prepare_init_args_and_inputs_for_common()

							UpperCAmelCase_=   8

							torch.manual_seed(0     )
							UpperCAmelCase_=   self.model_class(**__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )

							with torch.no_grad():
													UpperCAmelCase_=   model(**__UpperCAmelCase     ).sample

							UpperCAmelCase_=   create_lora_layers(__UpperCAmelCase     )
							model.set_attn_processor(__UpperCAmelCase     )

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

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

							with torch.no_grad():
													UpperCAmelCase_=   new_model(**__UpperCAmelCase						,  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   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> Tuple:
							# enable deterministic behavior for gradient checkpointing
							UpperCAmelCase_,   UpperCAmelCase_=   self.prepare_init_args_and_inputs_for_common()

							UpperCAmelCase_=   8

							torch.manual_seed(0     )
							UpperCAmelCase_=   self.model_class(**__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )

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




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> Any:
							UpperCAmelCase_,   UpperCAmelCase_=   self.prepare_init_args_and_inputs_for_common()

							UpperCAmelCase_=   8

							torch.manual_seed(0     )
							UpperCAmelCase_=   self.model_class(**__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )

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




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> List[Any]:
							UpperCAmelCase_,   UpperCAmelCase_=   self.prepare_init_args_and_inputs_for_common()

							UpperCAmelCase_=   8

							torch.manual_seed(0     )
							UpperCAmelCase_=   self.model_class(**__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )

							with torch.no_grad():
													UpperCAmelCase_=   model(**__UpperCAmelCase     ).sample

							UpperCAmelCase_=   create_lora_layers(__UpperCAmelCase     )
							model.set_attn_processor(__UpperCAmelCase     )

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

							model.set_attn_processor(AttnProcessor()     )

							with torch.no_grad():
													UpperCAmelCase_=   model(**__UpperCAmelCase     ).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   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]     )			-> Tuple:
							# enable deterministic behavior for gradient checkpointing
							UpperCAmelCase_,   UpperCAmelCase_=   self.prepare_init_args_and_inputs_for_common()

							UpperCAmelCase_=   4

							torch.manual_seed(0     )
							UpperCAmelCase_=   self.model_class(**__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							UpperCAmelCase_=   create_lora_layers(__UpperCAmelCase     )
							model.set_attn_processor(__UpperCAmelCase     )

							# default
							with torch.no_grad():
													UpperCAmelCase_=   model(**__UpperCAmelCase     ).sample

													model.enable_xformers_memory_efficient_attention()
													UpperCAmelCase_=   model(**__UpperCAmelCase     ).sample

													model.disable_xformers_memory_efficient_attention()
													UpperCAmelCase_=   model(**__UpperCAmelCase     ).sample

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




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> Union[str, Any]:
							UpperCAmelCase_,   UpperCAmelCase_=   self.prepare_init_args_and_inputs_for_common()
							UpperCAmelCase_=   32

							UpperCAmelCase_=   self.model_class(**__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()

							with torch.no_grad():
													UpperCAmelCase_=   model(**__UpperCAmelCase     )[0]

							model.enable_forward_chunking()
							with torch.no_grad():
													UpperCAmelCase_=   model(**__UpperCAmelCase     )[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.









from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
__A				    =						{
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}



def        __a     (    lowerCAmelCase_   :					dict[str, list[str]]				)      -> set[tuple[str, str]]:






						'''simple docstring'''



						UpperCAmelCase_=   {node: {node} for node in graph}

						UpperCAmelCase_=   {node: graph[node][:] for node in graph}

						while len(lowerCAmelCase_				) > 2:
												# Choose a random edge.
												UpperCAmelCase_=   random.choice(list(graph_copy.keys()				)				)
												UpperCAmelCase_=   random.choice(graph_copy[u]				)

												# Contract edge (u, v) to new node uv
												UpperCAmelCase_=   u + v
												UpperCAmelCase_=   list(set(graph_copy[u] + graph_copy[v]				)				)
												uv_neighbors.remove(lowerCAmelCase_				)
												uv_neighbors.remove(lowerCAmelCase_				)
												UpperCAmelCase_=   uv_neighbors
												for neighbor in uv_neighbors:
																		graph_copy[neighbor].append(lowerCAmelCase_				)

												UpperCAmelCase_=   set(contracted_nodes[u].union(contracted_nodes[v]				)				)

												# Remove nodes u and v.
												del graph_copy[u]
												del graph_copy[v]
												for neighbor in uv_neighbors:
																		if u in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(lowerCAmelCase_				)
																		if v in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(lowerCAmelCase_				)

    # Find cutset.
						UpperCAmelCase_=   [contracted_nodes[node] for node in graph_copy]
						return {
						    (node, neighbor)
						    for node in groups[0]
						    for neighbor in graph[node]
						    if neighbor in groups[1]
						}


if __name__ == "__main__":
	print(partition_graph(TEST_GRAPH))




