

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	:   List[str]	=					logging.get_logger(__name__)




def        __UpperCamelCase     (      _A					:							Tuple      ,  _A					:							bool = True		)				->Any:



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

														lowerCamelCase_								=LoRAAttnProcessor(hidden_size=_A      ,  cross_attention_dim=_A		)
														lowerCamelCase_								=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 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__ ,    lowerCAmelCase__ ,    unittest.TestCase):
						_UpperCamelCase:Any	=		UNetaDConditionModel
						_UpperCamelCase:List[Any]	=		"sample"







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

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

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







						@property
						def        _snake_case				(			self							)->							List[Any]:
													return (4, 4, 32, 32)







						@property
						def        _snake_case				(			self							)->							Any:
													return (4, 4, 32, 32)







						def        _snake_case				(			self							)->							str:
													lowerCamelCase_								={
													    """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,
													}
													lowerCamelCase_								=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        _snake_case				(			self							)->							Dict:
													lowerCamelCase_	,   lowerCamelCase_								=self.prepare_init_args_and_inputs_for_common()
													lowerCamelCase_								=self.model_class(**_SCREAMING_SNAKE_CASE							)

													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        _snake_case				(			self							)->							Optional[int]:
													lowerCamelCase_	,   lowerCamelCase_								=self.prepare_init_args_and_inputs_for_common()

													lowerCamelCase_								=32

													lowerCamelCase_								=self.model_class(**_SCREAMING_SNAKE_CASE							)
													model.to(_SCREAMING_SNAKE_CASE							)
													model.eval()

													with torch.no_grad():
																				lowerCamelCase_								=model(**_SCREAMING_SNAKE_CASE							)

																				if isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							):
																											lowerCamelCase_								=output.sample

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







						def        _snake_case				(			self							)->							Optional[int]:
													lowerCamelCase_	,   lowerCamelCase_								=self.prepare_init_args_and_inputs_for_common()
													lowerCamelCase_								=self.model_class(**_SCREAMING_SNAKE_CASE							)
													model.to(_SCREAMING_SNAKE_CASE							)
													model.eval()

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

																				lowerCamelCase_								=model(**_SCREAMING_SNAKE_CASE							)
																				if isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							):
																											lowerCamelCase_								=first.sample

																				lowerCamelCase_								=model(**_SCREAMING_SNAKE_CASE							)
																				if isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							):
																											lowerCamelCase_								=second.sample

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







						def        _snake_case				(			self							)->							Optional[Any]:
													lowerCamelCase_	,   lowerCamelCase_								=self.prepare_init_args_and_inputs_for_common()

													lowerCamelCase_								=8

													lowerCamelCase_								=self.model_class(**_SCREAMING_SNAKE_CASE							)
													model.to(_SCREAMING_SNAKE_CASE							)
													model.eval()

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

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

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







						def        _snake_case				(			self							)->							str:
													lowerCamelCase_	,   lowerCamelCase_								=self.prepare_init_args_and_inputs_for_common()

													lowerCamelCase_								=8

													lowerCamelCase_								=self.model_class(**_SCREAMING_SNAKE_CASE							)
													model.to(_SCREAMING_SNAKE_CASE							)

													with torch.no_grad():
																				lowerCamelCase_								=model(**_SCREAMING_SNAKE_CASE							).sample

													lowerCamelCase_								=create_lora_layers(_SCREAMING_SNAKE_CASE							)

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

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

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

													lowerCamelCase_								=8

													torch.manual_seed(0							)
													lowerCamelCase_								=self.model_class(**_SCREAMING_SNAKE_CASE							)
													model.to(_SCREAMING_SNAKE_CASE							)

													with torch.no_grad():
																				lowerCamelCase_								=model(**_SCREAMING_SNAKE_CASE							).sample

													lowerCamelCase_								=create_lora_layers(_SCREAMING_SNAKE_CASE							)
													model.set_attn_processor(_SCREAMING_SNAKE_CASE							)

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

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

													with torch.no_grad():
																				lowerCamelCase_								=new_model(**_SCREAMING_SNAKE_CASE     ,						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        _snake_case				(			self							)->							Dict:
													lowerCamelCase_	,   lowerCamelCase_								=self.prepare_init_args_and_inputs_for_common()

													lowerCamelCase_								=8

													torch.manual_seed(0							)
													lowerCamelCase_								=self.model_class(**_SCREAMING_SNAKE_CASE							)
													model.to(_SCREAMING_SNAKE_CASE							)

													with torch.no_grad():
																				lowerCamelCase_								=model(**_SCREAMING_SNAKE_CASE							).sample

													lowerCamelCase_								=create_lora_layers(_SCREAMING_SNAKE_CASE							)
													model.set_attn_processor(_SCREAMING_SNAKE_CASE							)

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

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

													with torch.no_grad():
																				lowerCamelCase_								=new_model(**_SCREAMING_SNAKE_CASE     ,						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        _snake_case				(			self							)->							Dict:
													# enable deterministic behavior for gradient checkpointing
													lowerCamelCase_	,   lowerCamelCase_								=self.prepare_init_args_and_inputs_for_common()

													lowerCamelCase_								=8

													torch.manual_seed(0							)
													lowerCamelCase_								=self.model_class(**_SCREAMING_SNAKE_CASE							)
													model.to(_SCREAMING_SNAKE_CASE							)

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







						def        _snake_case				(			self							)->							Any:
													lowerCamelCase_	,   lowerCamelCase_								=self.prepare_init_args_and_inputs_for_common()

													lowerCamelCase_								=8

													torch.manual_seed(0							)
													lowerCamelCase_								=self.model_class(**_SCREAMING_SNAKE_CASE							)
													model.to(_SCREAMING_SNAKE_CASE							)

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







						def        _snake_case				(			self							)->							str:
													lowerCamelCase_	,   lowerCamelCase_								=self.prepare_init_args_and_inputs_for_common()

													lowerCamelCase_								=8

													torch.manual_seed(0							)
													lowerCamelCase_								=self.model_class(**_SCREAMING_SNAKE_CASE							)
													model.to(_SCREAMING_SNAKE_CASE							)

													with torch.no_grad():
																				lowerCamelCase_								=model(**_SCREAMING_SNAKE_CASE							).sample

													lowerCamelCase_								=create_lora_layers(_SCREAMING_SNAKE_CASE							)
													model.set_attn_processor(_SCREAMING_SNAKE_CASE							)

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

													model.set_attn_processor(AttnProcessor()							)

													with torch.no_grad():
																				lowerCamelCase_								=model(**_SCREAMING_SNAKE_CASE							).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        _snake_case				(			self							)->							Tuple:
													# enable deterministic behavior for gradient checkpointing
													lowerCamelCase_	,   lowerCamelCase_								=self.prepare_init_args_and_inputs_for_common()

													lowerCamelCase_								=4

													torch.manual_seed(0							)
													lowerCamelCase_								=self.model_class(**_SCREAMING_SNAKE_CASE							)
													model.to(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=create_lora_layers(_SCREAMING_SNAKE_CASE							)
													model.set_attn_processor(_SCREAMING_SNAKE_CASE							)

													# default
													with torch.no_grad():
																				lowerCamelCase_								=model(**_SCREAMING_SNAKE_CASE							).sample

																				model.enable_xformers_memory_efficient_attention()
																				lowerCamelCase_								=model(**_SCREAMING_SNAKE_CASE							).sample

																				model.disable_xformers_memory_efficient_attention()
																				lowerCamelCase_								=model(**_SCREAMING_SNAKE_CASE							).sample

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







						def        _snake_case				(			self							)->							List[Any]:
													lowerCamelCase_	,   lowerCamelCase_								=self.prepare_init_args_and_inputs_for_common()
													lowerCamelCase_								=32

													lowerCamelCase_								=self.model_class(**_SCREAMING_SNAKE_CASE							)
													model.to(_SCREAMING_SNAKE_CASE							)
													model.eval()

													with torch.no_grad():
																				lowerCamelCase_								=model(**_SCREAMING_SNAKE_CASE							)[0]

													model.enable_forward_chunking()
													with torch.no_grad():
																				lowerCamelCase_								=model(**_SCREAMING_SNAKE_CASE							)[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




def        __UpperCamelCase     (      _A					:							int      ,  _A					:							list[int]      ,  _A					:							list[int]      ,  _A					:							int		)				->int:



							"""simple docstring"""

							# Base Case
							if counter == 0 or capacity == 0:
														return 0

							# If weight of the nth item is more than Knapsack of capacity,
							#   then this item cannot be included in the optimal solution,
							# else return the maximum of two cases:
							#   (1) nth item included
							#   (2) not included
							if weights[counter - 1] > capacity:
														return knapsack(_A      ,  _A      ,  _A      ,  counter - 1		)
							else:
														lowerCamelCase_								=capacity - weights[counter - 1]
														lowerCamelCase_								=values[counter - 1] + knapsack(
														    _A      ,  _A      ,  _A      ,  counter - 1		)
														lowerCamelCase_								=knapsack(_A      ,  _A      ,  _A      ,  counter - 1		)
														return max(_A      ,  _A		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
