




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()

__lowerCAmelCase		:    Union[str, Any]	      =		logging.get_logger(__name__)




def       __magic_name__    (			A		: Dict,  A		: Any = True       ):






			'''simple docstring'''







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

						a   =      LoRAAttnProcessor(hidden_size=A,  cross_attention_dim=A       )
						a   =      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    snake_case__							(lowerCAmelCase__   ,	lowerCAmelCase__   ,	unittest.TestCase       ):
	"""simple docstring"""



	SCREAMING_SNAKE_CASE_	:							Union[str, Any]										= UNetaDConditionModel
	SCREAMING_SNAKE_CASE_	:							str										= """sample"""





	@property
	def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Union[str, Any]:
				a   =      4
				a   =      4
				a   =      4
				a   =      (32, 32)

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

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





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





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





	def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Union[str, Any]:
				a   =      {
				    '''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,
				}
				a   =      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 							__UpperCAmelCase							(				self    :					Dict							) ->					Any:
				a   =      self.prepare_init_args_and_inputs_for_common()
				a   =      self.model_class(**_A							)

				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 							__UpperCAmelCase							(				self    :					List[str]							) ->					Optional[int]:
				a   =      self.prepare_init_args_and_inputs_for_common()

				a   =      32

				a   =      self.model_class(**_A							)
				model.to(_A							)
				model.eval()

				with torch.no_grad():
							a   =      model(**_A							)

							if isinstance(_A ,				_A							):
										a   =      output.sample

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





	def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Tuple:
				a   =      self.prepare_init_args_and_inputs_for_common()
				a   =      self.model_class(**_A							)
				model.to(_A							)
				model.eval()

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

							a   =      model(**_A							)
							if isinstance(_A ,				_A							):
										a   =      first.sample

							a   =      model(**_A							)
							if isinstance(_A ,				_A							):
										a   =      second.sample

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





	def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					Optional[int]:
				a   =      self.prepare_init_args_and_inputs_for_common()

				a   =      8

				a   =      self.model_class(**_A							)
				model.to(_A							)
				model.eval()

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

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

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





	def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					int:
				a   =      self.prepare_init_args_and_inputs_for_common()

				a   =      8

				a   =      self.model_class(**_A							)
				model.to(_A							)

				with torch.no_grad():
							a   =      model(**_A							).sample

				a   =      create_lora_layers(_A							)

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

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

				with torch.no_grad():
							a   =      model(**_A ,				cross_attention_kwargs={"scale": 0.0}							).sample
							a   =      model(**_A ,				cross_attention_kwargs={"scale": 0.5}							).sample
							a   =      model(**_A ,				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 							__UpperCAmelCase							(				self    :					str							) ->					Optional[int]:
				a   =      self.prepare_init_args_and_inputs_for_common()

				a   =      8

				torch.manual_seed(0							)
				a   =      self.model_class(**_A							)
				model.to(_A							)

				with torch.no_grad():
							a   =      model(**_A							).sample

				a   =      create_lora_layers(_A							)
				model.set_attn_processor(_A							)

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

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

				with torch.no_grad():
							a   =      new_model(**_A ,				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 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					List[str]:
				a   =      self.prepare_init_args_and_inputs_for_common()

				a   =      8

				torch.manual_seed(0							)
				a   =      self.model_class(**_A							)
				model.to(_A							)

				with torch.no_grad():
							a   =      model(**_A							).sample

				a   =      create_lora_layers(_A							)
				model.set_attn_processor(_A							)

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

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

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

				a   =      8

				torch.manual_seed(0							)
				a   =      self.model_class(**_A							)
				model.to(_A							)

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





	def 							__UpperCAmelCase							(				self    :					List[str]							) ->					List[str]:
				a   =      self.prepare_init_args_and_inputs_for_common()

				a   =      8

				torch.manual_seed(0							)
				a   =      self.model_class(**_A							)
				model.to(_A							)

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





	def 							__UpperCAmelCase							(				self    :					str							) ->					Tuple:
				a   =      self.prepare_init_args_and_inputs_for_common()

				a   =      8

				torch.manual_seed(0							)
				a   =      self.model_class(**_A							)
				model.to(_A							)

				with torch.no_grad():
							a   =      model(**_A							).sample

				a   =      create_lora_layers(_A							)
				model.set_attn_processor(_A							)

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

				model.set_attn_processor(AttnProcessor()							)

				with torch.no_grad():
							a   =      model(**_A							).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 							__UpperCAmelCase							(				self    :					List[str]							) ->					str:
				a   =      self.prepare_init_args_and_inputs_for_common()

				a   =      4

				torch.manual_seed(0							)
				a   =      self.model_class(**_A							)
				model.to(_A							)
				a   =      create_lora_layers(_A							)
				model.set_attn_processor(_A							)

				# default
				with torch.no_grad():
							a   =      model(**_A							).sample

							model.enable_xformers_memory_efficient_attention()
							a   =      model(**_A							).sample

							model.disable_xformers_memory_efficient_attention()
							a   =      model(**_A							).sample

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





	def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					Tuple:
				a   =      self.prepare_init_args_and_inputs_for_common()
				a   =      32

				a   =      self.model_class(**_A							)
				model.to(_A							)
				model.eval()

				with torch.no_grad():
							a   =      model(**_A							)[0]

				model.enable_forward_chunking()
				with torch.no_grad():
							a   =      model(**_A							)[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 ..utils import DummyObject, requires_backends




class    snake_case__							(metaclass=_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[str]										= ["""torch""", """transformers""", """k_diffusion"""]





				def __init__(				self    :					Optional[int] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					List[Any]							) ->					Optional[Any]:
							requires_backends(self ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					str ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Tuple							) ->					List[Any]:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					Optional[Any] ,				*__lowerCamelCase    :					List[str] ,				**__lowerCamelCase    :					int							) ->					Dict:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)






