





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_												=   logging.get_logger(__name__)





def   lowerCamelCase__   (   UpperCamelCase__	:				List[Any]				,			UpperCamelCase__	:				bool = True  )	->       List[str]:



					'''simple docstring'''




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

										_snake_case													= LoRAAttnProcessor(hidden_size=lowercase_				,			cross_attention_dim=lowercase_  )
										_snake_case													= 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 	UpperCamelCase_						(     __UpperCAmelCase							,     __UpperCAmelCase							,     unittest.TestCase    ):
					lowerCAmelCase_							=		UNetaDConditionModel
					lowerCAmelCase_							=		'''sample'''




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

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

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




					@property
					def 		lowerCAmelCase	(						self     )						->							str:
										return (4, 4, 32, 32)




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




					def 		lowerCAmelCase	(						self     )						->							Tuple:
										_snake_case													= {
										    '''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,
										}
										_snake_case													= 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 		lowerCAmelCase	(						self     )						->							Tuple:
										_snake_case													= self.prepare_init_args_and_inputs_for_common()
										_snake_case													= 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 		lowerCAmelCase	(						self     )						->							Union[str, Any]:
										_snake_case													= self.prepare_init_args_and_inputs_for_common()

										_snake_case													= 32

										_snake_case													= self.model_class(**_lowerCamelCase     )
										model.to(_lowerCamelCase     )
										model.eval()

										with torch.no_grad():
															_snake_case													= model(**_lowerCamelCase     )

															if isinstance(_lowerCamelCase ,  _lowerCamelCase     ):
																				_snake_case													= output.sample

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




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

															_snake_case													= model(**_lowerCamelCase     )
															if isinstance(_lowerCamelCase ,  _lowerCamelCase     ):
																				_snake_case													= first.sample

															_snake_case													= model(**_lowerCamelCase     )
															if isinstance(_lowerCamelCase ,  _lowerCamelCase     ):
																				_snake_case													= second.sample

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




					def 		lowerCAmelCase	(						self     )						->							List[str]:
										_snake_case													= self.prepare_init_args_and_inputs_for_common()

										_snake_case													= 8

										_snake_case													= self.model_class(**_lowerCamelCase     )
										model.to(_lowerCamelCase     )
										model.eval()

										model.set_attention_slice('auto'     )
										with torch.no_grad():
															_snake_case													= model(**_lowerCamelCase     )
										assert output is not None

										model.set_attention_slice('max'     )
										with torch.no_grad():
															_snake_case													= model(**_lowerCamelCase     )
										assert output is not None

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




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

										_snake_case													= 8

										_snake_case													= self.model_class(**_lowerCamelCase     )
										model.to(_lowerCamelCase     )

										with torch.no_grad():
															_snake_case													= model(**_lowerCamelCase     ).sample

										_snake_case													= 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():
															_snake_case													= model(**_lowerCamelCase ,  cross_attention_kwargs={'scale': 0.0}     ).sample
															_snake_case													= model(**_lowerCamelCase ,  cross_attention_kwargs={'scale': 0.5}     ).sample
															_snake_case													= 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 		lowerCAmelCase	(						self     )						->							Dict:
										_snake_case													= self.prepare_init_args_and_inputs_for_common()

										_snake_case													= 8

										torch.manual_seed(0     )
										_snake_case													= self.model_class(**_lowerCamelCase     )
										model.to(_lowerCamelCase     )

										with torch.no_grad():
															_snake_case													= model(**_lowerCamelCase     ).sample

										_snake_case													= create_lora_layers(_lowerCamelCase     )
										model.set_attn_processor(_lowerCamelCase     )

										with torch.no_grad():
															_snake_case													= 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     )
															_snake_case													= self.model_class(**_lowerCamelCase     )
															new_model.to(_lowerCamelCase     )
															new_model.load_attn_procs(_lowerCamelCase     )

										with torch.no_grad():
															_snake_case													= 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 		lowerCAmelCase	(						self     )						->							Tuple:
										_snake_case													= self.prepare_init_args_and_inputs_for_common()

										_snake_case													= 8

										torch.manual_seed(0     )
										_snake_case													= self.model_class(**_lowerCamelCase     )
										model.to(_lowerCamelCase     )

										with torch.no_grad():
															_snake_case													= model(**_lowerCamelCase     ).sample

										_snake_case													= create_lora_layers(_lowerCamelCase     )
										model.set_attn_processor(_lowerCamelCase     )

										with torch.no_grad():
															_snake_case													= 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     )
															_snake_case													= self.model_class(**_lowerCamelCase     )
															new_model.to(_lowerCamelCase     )
															new_model.load_attn_procs(_lowerCamelCase     )

										with torch.no_grad():
															_snake_case													= 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 		lowerCAmelCase	(						self     )						->							Tuple:
										# enable deterministic behavior for gradient checkpointing
										_snake_case													= self.prepare_init_args_and_inputs_for_common()

										_snake_case													= 8

										torch.manual_seed(0     )
										_snake_case													= self.model_class(**_lowerCamelCase     )
										model.to(_lowerCamelCase     )

										_snake_case													= 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     )
															_snake_case													= self.model_class(**_lowerCamelCase     )
															new_model.to(_lowerCamelCase     )
															new_model.load_attn_procs(_lowerCamelCase ,  weight_name='pytorch_lora_weights.bin'     )




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

										_snake_case													= 8

										torch.manual_seed(0     )
										_snake_case													= self.model_class(**_lowerCamelCase     )
										model.to(_lowerCamelCase     )

										_snake_case													= 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     )
															_snake_case													= 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 		lowerCAmelCase	(						self     )						->							Any:
										_snake_case													= self.prepare_init_args_and_inputs_for_common()

										_snake_case													= 8

										torch.manual_seed(0     )
										_snake_case													= self.model_class(**_lowerCamelCase     )
										model.to(_lowerCamelCase     )

										with torch.no_grad():
															_snake_case													= model(**_lowerCamelCase     ).sample

										_snake_case													= create_lora_layers(_lowerCamelCase     )
										model.set_attn_processor(_lowerCamelCase     )

										with torch.no_grad():
															_snake_case													= model(**_lowerCamelCase ,  cross_attention_kwargs={'scale': 0.0}     ).sample

										model.set_attn_processor(AttnProcessor()     )

										with torch.no_grad():
															_snake_case													= 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 		lowerCAmelCase	(						self     )						->							Optional[int]:
										# enable deterministic behavior for gradient checkpointing
										_snake_case													= self.prepare_init_args_and_inputs_for_common()

										_snake_case													= 4

										torch.manual_seed(0     )
										_snake_case													= self.model_class(**_lowerCamelCase     )
										model.to(_lowerCamelCase     )
										_snake_case													= create_lora_layers(_lowerCamelCase     )
										model.set_attn_processor(_lowerCamelCase     )

										# default
										with torch.no_grad():
															_snake_case													= model(**_lowerCamelCase     ).sample

															model.enable_xformers_memory_efficient_attention()
															_snake_case													= model(**_lowerCamelCase     ).sample

															model.disable_xformers_memory_efficient_attention()
															_snake_case													= model(**_lowerCamelCase     ).sample

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






					def 		lowerCAmelCase	(						self     )						->							str:
										_snake_case													= self.prepare_init_args_and_inputs_for_common()
										_snake_case													= 32

										_snake_case													= self.model_class(**_lowerCamelCase     )
										model.to(_lowerCamelCase     )
										model.eval()

										with torch.no_grad():
															_snake_case													= model(**_lowerCamelCase     )[0]

										model.enable_forward_chunking()
										with torch.no_grad():
															_snake_case													= 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.










"""simple docstring"""





import numpy as np




def 		lowerCAmelCase_	(  lowercase_   :							str		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Dict												=       np.exp(lowercase_		)

 # Add up the all the exponentials
 __SCREAMING_SNAKE_CASE :			Tuple												=       np.sum(lowercase_		)

 # Divide every exponent by the sum of all exponents
 __SCREAMING_SNAKE_CASE :			List[Any]												=       exponent_vector / sum_of_exponents

 return softmax_vector


if __name__ == "__main__":
    print(softmax((0,)))

