



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()

lowercase_  =   logging.get_logger(__name__)



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

						lowercase__         = LoRAAttnProcessor(hidden_size=SCREAMING_SNAKE_CASE_  ,     cross_attention_dim=SCREAMING_SNAKE_CASE_     )
						lowercase__         = 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	(    a_			,		a_			,		unittest.TestCase):
				UpperCamelCase__ :    int   						=UNetaDConditionModel
				UpperCamelCase__ :    Dict   						="""sample"""




				@property
				def 							A__					(  self    :  Tuple  ):
							lowercase__         = 4
							lowercase__         = 4
							lowercase__         = 4
							lowercase__         = (32, 32)

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

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




				@property
				def 							A__					(  self    :  Tuple  ):
							return (4, 4, 32, 32)




				@property
				def 							A__					(  self    :  str  ):
							return (4, 4, 32, 32)




				def 							A__					(  self    :  List[Any]  ):
							lowercase__         = {
							    '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,
							}
							lowercase__         = 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  ):
							lowercase__         = self.prepare_init_args_and_inputs_for_common()
							lowercase__         = 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    :  str  ):
							lowercase__         = self.prepare_init_args_and_inputs_for_common()

							lowercase__         = 32

							lowercase__         = self.model_class(**__lowercase  )
							model.to(__lowercase  )
							model.eval()

							with torch.no_grad():
										lowercase__         = model(**__lowercase  )

										if isinstance(__lowercase,    __lowercase  ):
													lowercase__         = output.sample

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




				def 							A__					(  self    :  Dict  ):
							lowercase__         = self.prepare_init_args_and_inputs_for_common()
							lowercase__         = 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  )

										lowercase__         = model(**__lowercase  )
										if isinstance(__lowercase,    __lowercase  ):
													lowercase__         = first.sample

										lowercase__         = model(**__lowercase  )
										if isinstance(__lowercase,    __lowercase  ):
													lowercase__         = second.sample

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




				def 							A__					(  self    :  Optional[int]  ):
							lowercase__         = self.prepare_init_args_and_inputs_for_common()

							lowercase__         = 8

							lowercase__         = self.model_class(**__lowercase  )
							model.to(__lowercase  )
							model.eval()

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

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

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




				def 							A__					(  self    :  List[Any]  ):
							lowercase__         = self.prepare_init_args_and_inputs_for_common()

							lowercase__         = 8

							lowercase__         = self.model_class(**__lowercase  )
							model.to(__lowercase  )

							with torch.no_grad():
										lowercase__         = model(**__lowercase  ).sample

							lowercase__         = 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():
										lowercase__         = model(**__lowercase,    cross_attention_kwargs={"scale": 0.0}  ).sample
										lowercase__         = model(**__lowercase,    cross_attention_kwargs={"scale": 0.5}  ).sample
										lowercase__         = 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    :  str  ):
							lowercase__         = self.prepare_init_args_and_inputs_for_common()

							lowercase__         = 8

							torch.manual_seed(0  )
							lowercase__         = self.model_class(**__lowercase  )
							model.to(__lowercase  )

							with torch.no_grad():
										lowercase__         = model(**__lowercase  ).sample

							lowercase__         = create_lora_layers(__lowercase  )
							model.set_attn_processor(__lowercase  )

							with torch.no_grad():
										lowercase__         = 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  )
										lowercase__         = self.model_class(**__lowercase  )
										new_model.to(__lowercase  )
										new_model.load_attn_procs(__lowercase  )

							with torch.no_grad():
										lowercase__         = 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    :  Union[str, Any]  ):
							lowercase__         = self.prepare_init_args_and_inputs_for_common()

							lowercase__         = 8

							torch.manual_seed(0  )
							lowercase__         = self.model_class(**__lowercase  )
							model.to(__lowercase  )

							with torch.no_grad():
										lowercase__         = model(**__lowercase  ).sample

							lowercase__         = create_lora_layers(__lowercase  )
							model.set_attn_processor(__lowercase  )

							with torch.no_grad():
										lowercase__         = 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  )
										lowercase__         = self.model_class(**__lowercase  )
										new_model.to(__lowercase  )
										new_model.load_attn_procs(__lowercase  )

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

							lowercase__         = 8

							torch.manual_seed(0  )
							lowercase__         = self.model_class(**__lowercase  )
							model.to(__lowercase  )

							lowercase__         = 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  )
										lowercase__         = self.model_class(**__lowercase  )
										new_model.to(__lowercase  )
										new_model.load_attn_procs(__lowercase,    weight_name="pytorch_lora_weights.bin"  )




				def 							A__					(  self    :  List[Any]  ):
							lowercase__         = self.prepare_init_args_and_inputs_for_common()

							lowercase__         = 8

							torch.manual_seed(0  )
							lowercase__         = self.model_class(**__lowercase  )
							model.to(__lowercase  )

							lowercase__         = 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  )
										lowercase__         = 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    :  Optional[int]  ):
							lowercase__         = self.prepare_init_args_and_inputs_for_common()

							lowercase__         = 8

							torch.manual_seed(0  )
							lowercase__         = self.model_class(**__lowercase  )
							model.to(__lowercase  )

							with torch.no_grad():
										lowercase__         = model(**__lowercase  ).sample

							lowercase__         = create_lora_layers(__lowercase  )
							model.set_attn_processor(__lowercase  )

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

							model.set_attn_processor(AttnProcessor()  )

							with torch.no_grad():
										lowercase__         = 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    :  List[str]  ):
							# enable deterministic behavior for gradient checkpointing
							lowercase__         = self.prepare_init_args_and_inputs_for_common()

							lowercase__         = 4

							torch.manual_seed(0  )
							lowercase__         = self.model_class(**__lowercase  )
							model.to(__lowercase  )
							lowercase__         = create_lora_layers(__lowercase  )
							model.set_attn_processor(__lowercase  )

							# default
							with torch.no_grad():
										lowercase__         = model(**__lowercase  ).sample

										model.enable_xformers_memory_efficient_attention()
										lowercase__         = model(**__lowercase  ).sample

										model.disable_xformers_memory_efficient_attention()
										lowercase__         = model(**__lowercase  ).sample

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




				def 							A__					(  self    :  Optional[Any]  ):
							lowercase__         = self.prepare_init_args_and_inputs_for_common()
							lowercase__         = 32

							lowercase__         = self.model_class(**__lowercase  )
							model.to(__lowercase  )
							model.eval()

							with torch.no_grad():
										lowercase__         = model(**__lowercase  )[0]

							model.enable_forward_chunking()
							with torch.no_grad():
										lowercase__         = 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'''







import math






def       lowerCAmelCase__       (				lowerCamelCase     :			int   ):
       _A     :	int      =					math.log(2  ,10   ) * number
       _A     :	List[Any]      =					round((log_number - int(lowerCamelCase   ))  ,15   )

       return difference






def       lowerCAmelCase__       (				lowerCamelCase     :			int = 678910   ):
       _A     :	List[str]      =					90
       _A     :	List[Any]      =					0

       _A     :	List[str]      =					math.log(1.23  ,10   )
       _A     :	Any      =					math.log(1.24  ,10   )
       _A     :	Union[str, Any]      =					0

       while position < number:
              _A     :	Dict      =					log_difference(lowerCamelCase   )

              if difference >= upper_limit:
                     power_iterator += 93

              elif difference < lower_limit:
                     power_iterator += 196

              else:
                     _A     :	str      =					power_iterator
                     power_iterator += 196
                     position += 1

       return previous_power


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"""{solution() = }""")






