"""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()

UpperCamelCase_							:							Optional[Any]				 =		logging.get_logger(__name__)


def 	a__   (		UpperCAmelCase__							:      Any		,      UpperCAmelCase__							:      bool = True      )->						Tuple:
						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=UpperCAmelCase__		,      cross_attention_dim=UpperCAmelCase__      )
												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		( __lowerCamelCase   ,   __lowerCamelCase   ,   unittest.TestCase	):
				lowerCamelCase__							=							UNetaDConditionModel
				lowerCamelCase__							=							'sample'

				@property
				def 							A			(  self							:     Dict  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		4
										UpperCAmelCase__       						=		4
										UpperCAmelCase__       						=		4
										UpperCAmelCase__       						=		(3_2, 3_2)

										UpperCAmelCase__       						=		floats_tensor((batch_size, num_channels, num_frames) + sizes  ).to(_lowercase  )
										UpperCAmelCase__       						=		torch.tensor([1_0]  ).to(_lowercase  )
										UpperCAmelCase__       						=		floats_tensor((batch_size, 4, 3_2)  ).to(_lowercase  )

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

				@property
				def 							A			(  self							:     str  ):


										'''simple docstring'''
										return (4, 4, 3_2, 3_2)

				@property
				def 							A			(  self							:     int  ):


										'''simple docstring'''
										return (4, 4, 3_2, 3_2)

				def 							A			(  self							:     Dict  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		{
										    """block_out_channels""": (3_2, 6_4),
										    """down_block_types""": (
										        """CrossAttnDownBlock3D""",
										        """DownBlock3D""",
										    ),
										    """up_block_types""": ("""UpBlock3D""", """CrossAttnUpBlock3D"""),
										    """cross_attention_dim""": 3_2,
										    """attention_head_dim""": 8,
										    """out_channels""": 4,
										    """in_channels""": 4,
										    """layers_per_block""": 1,
										    """sample_size""": 3_2,
										}
										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 							A			(  self							:     Tuple  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self.prepare_init_args_and_inputs_for_common()
										UpperCAmelCase__       						=		self.model_class(**_lowercase  )

										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							:     Union[str, Any]  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self.prepare_init_args_and_inputs_for_common()

										UpperCAmelCase__       						=		3_2

										UpperCAmelCase__       						=		self.model_class(**_lowercase  )
										model.to(_lowercase  )
										model.eval()

										with torch.no_grad():
																UpperCAmelCase__       						=		model(**_lowercase  )

																if isinstance(_lowercase							,     _lowercase  ):
																						UpperCAmelCase__       						=		output.sample

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

				def 							A			(  self							:     Union[str, Any]  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self.prepare_init_args_and_inputs_for_common()
										UpperCAmelCase__       						=		self.model_class(**_lowercase  )
										model.to(_lowercase  )
										model.eval()

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

																UpperCAmelCase__       						=		model(**_lowercase  )
																if isinstance(_lowercase							,     _lowercase  ):
																						UpperCAmelCase__       						=		first.sample

																UpperCAmelCase__       						=		model(**_lowercase  )
																if isinstance(_lowercase							,     _lowercase  ):
																						UpperCAmelCase__       						=		second.sample

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

				def 							A			(  self							:     List[Any]  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self.prepare_init_args_and_inputs_for_common()

										UpperCAmelCase__       						=		8

										UpperCAmelCase__       						=		self.model_class(**_lowercase  )
										model.to(_lowercase  )
										model.eval()

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

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

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

				def 							A			(  self							:     List[Any]  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self.prepare_init_args_and_inputs_for_common()

										UpperCAmelCase__       						=		8

										UpperCAmelCase__       						=		self.model_class(**_lowercase  )
										model.to(_lowercase  )

										with torch.no_grad():
																UpperCAmelCase__       						=		model(**_lowercase  ).sample

										UpperCAmelCase__       						=		create_lora_layers(_lowercase  )

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

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

										with torch.no_grad():
																UpperCAmelCase__       						=		model(**_lowercase							,     cross_attention_kwargs={"""scale""": 0.0}  ).sample
																UpperCAmelCase__       						=		model(**_lowercase							,     cross_attention_kwargs={"""scale""": 0.5}  ).sample
																UpperCAmelCase__       						=		model(**_lowercase							,     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							:     Union[str, Any]  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self.prepare_init_args_and_inputs_for_common()

										UpperCAmelCase__       						=		8

										torch.manual_seed(0  )
										UpperCAmelCase__       						=		self.model_class(**_lowercase  )
										model.to(_lowercase  )

										with torch.no_grad():
																UpperCAmelCase__       						=		model(**_lowercase  ).sample

										UpperCAmelCase__       						=		create_lora_layers(_lowercase  )
										model.set_attn_processor(_lowercase  )

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

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

										with torch.no_grad():
																UpperCAmelCase__       						=		new_model(**_lowercase							,     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							:     Tuple  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self.prepare_init_args_and_inputs_for_common()

										UpperCAmelCase__       						=		8

										torch.manual_seed(0  )
										UpperCAmelCase__       						=		self.model_class(**_lowercase  )
										model.to(_lowercase  )

										with torch.no_grad():
																UpperCAmelCase__       						=		model(**_lowercase  ).sample

										UpperCAmelCase__       						=		create_lora_layers(_lowercase  )
										model.set_attn_processor(_lowercase  )

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

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

										with torch.no_grad():
																UpperCAmelCase__       						=		new_model(**_lowercase							,     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							:     str  ):


										'''simple docstring'''
										# 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(**_lowercase  )
										model.to(_lowercase  )

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

				def 							A			(  self							:     Optional[int]  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self.prepare_init_args_and_inputs_for_common()

										UpperCAmelCase__       						=		8

										torch.manual_seed(0  )
										UpperCAmelCase__       						=		self.model_class(**_lowercase  )
										model.to(_lowercase  )

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

				def 							A			(  self							:     List[Any]  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self.prepare_init_args_and_inputs_for_common()

										UpperCAmelCase__       						=		8

										torch.manual_seed(0  )
										UpperCAmelCase__       						=		self.model_class(**_lowercase  )
										model.to(_lowercase  )

										with torch.no_grad():
																UpperCAmelCase__       						=		model(**_lowercase  ).sample

										UpperCAmelCase__       						=		create_lora_layers(_lowercase  )
										model.set_attn_processor(_lowercase  )

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

										model.set_attn_processor(AttnProcessor()  )

										with torch.no_grad():
																UpperCAmelCase__       						=		model(**_lowercase  ).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							:     Dict  ):


										'''simple docstring'''
										# 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(**_lowercase  )
										model.to(_lowercase  )
										UpperCAmelCase__       						=		create_lora_layers(_lowercase  )
										model.set_attn_processor(_lowercase  )

										# default
										with torch.no_grad():
																UpperCAmelCase__       						=		model(**_lowercase  ).sample

																model.enable_xformers_memory_efficient_attention()
																UpperCAmelCase__       						=		model(**_lowercase  ).sample

																model.disable_xformers_memory_efficient_attention()
																UpperCAmelCase__       						=		model(**_lowercase  ).sample

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

				def 							A			(  self							:     List[Any]  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self.prepare_init_args_and_inputs_for_common()
										UpperCAmelCase__       						=		3_2

										UpperCAmelCase__       						=		self.model_class(**_lowercase  )
										model.to(_lowercase  )
										model.eval()

										with torch.no_grad():
																UpperCAmelCase__       						=		model(**_lowercase  )[0]

										model.enable_forward_chunking()
										with torch.no_grad():
																UpperCAmelCase__       						=		model(**_lowercase  )[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.


"""simple docstring"""







def 	a__   (		UpperCAmelCase__							:      list[int]      )->						int:
						UpperCAmelCase__       						=		len(UpperCAmelCase__      )
						if n in (0, 1):
												return 0
						return sum(query * (n - i - 1) for i, query in enumerate(sorted(UpperCAmelCase__      )      )      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

