




'''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[int]					      =						logging.get_logger(__name__)







def      _UpperCamelCase	(_lowerCamelCase							:       Dict				,		_lowerCamelCase							:       bool = True		)->				Tuple:




     '''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=_lowerCamelCase				,		cross_attention_dim=_lowerCamelCase		)
          __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       lowerCAmelCase				(	__lowerCAmelCase			,				__lowerCAmelCase			,				unittest.TestCase):
       __lowercase					: Union[str, Any]	    =     UNetaDConditionModel
       __lowercase					: Tuple	    =     '''sample'''


       @property
       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            __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(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					torch.tensor([10]					).to(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					floats_tensor((batch_size, 4, 32)					).to(__SCREAMING_SNAKE_CASE					)

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


       @property
       def  lowerCAmelCase (				self					) ->					List[str]:



            '''simple docstring'''




            return (4, 4, 32, 32)


       @property
       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            return (4, 4, 32, 32)


       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            __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:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()
            __snake_case         =					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  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					32

            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)

                 if isinstance(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					):
                      __snake_case         =					output.sample

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


       def  lowerCAmelCase (				self					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()
            __snake_case         =					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					)

                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)
                 if isinstance(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					):
                      __snake_case         =					first.sample

                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)
                 if isinstance(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					):
                      __snake_case         =					second.sample

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


       def  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					8

            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()

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

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

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


       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					8

            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					).sample

            __snake_case         =					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():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE      ,					cross_attention_kwargs={'''scale''': 0.0}					).sample
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE      ,					cross_attention_kwargs={'''scale''': 0.5}					).sample
                 __snake_case         =					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  lowerCAmelCase (				self					) ->					Tuple:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					8

            torch.manual_seed(0					)
            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					).sample

            __snake_case         =					create_lora_layers(__SCREAMING_SNAKE_CASE					)
            model.set_attn_processor(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					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					)
                 __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
                 new_model.to(__SCREAMING_SNAKE_CASE					)
                 new_model.load_attn_procs(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					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  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					8

            torch.manual_seed(0					)
            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					).sample

            __snake_case         =					create_lora_layers(__SCREAMING_SNAKE_CASE					)
            model.set_attn_processor(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					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					)
                 __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
                 new_model.to(__SCREAMING_SNAKE_CASE					)
                 new_model.load_attn_procs(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					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  lowerCAmelCase (				self					) ->					Union[str, Any]:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					8

            torch.manual_seed(0					)
            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)

            __snake_case         =					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					)
                 __snake_case         =					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  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					8

            torch.manual_seed(0					)
            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)

            __snake_case         =					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					)
                 __snake_case         =					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  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					8

            torch.manual_seed(0					)
            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					).sample

            __snake_case         =					create_lora_layers(__SCREAMING_SNAKE_CASE					)
            model.set_attn_processor(__SCREAMING_SNAKE_CASE					)

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

            model.set_attn_processor(AttnProcessor()					)

            with torch.no_grad():
                 __snake_case         =					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  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					4

            torch.manual_seed(0					)
            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					create_lora_layers(__SCREAMING_SNAKE_CASE					)
            model.set_attn_processor(__SCREAMING_SNAKE_CASE					)

            # default
            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					).sample

                 model.enable_xformers_memory_efficient_attention()
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					).sample

                 model.disable_xformers_memory_efficient_attention()
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					).sample

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




       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()
            __snake_case         =					32

            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()

            with torch.no_grad():
                 __snake_case         =					model(**__SCREAMING_SNAKE_CASE					)[0]

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









'''simple docstring'''



from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class       lowerCAmelCase				:


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE					) ->					None:



            '''simple docstring'''




            __snake_case         =					data
            __snake_case         =					None




       def __str__(				self					) ->					str:



            '''simple docstring'''




            return F'''{self.data}'''





class       lowerCAmelCase				:


       def __init__(				self					) ->					None:



            '''simple docstring'''




            __snake_case         =					None
            __snake_case         =					None


       def __iter__(				self					) ->					Iterator[Any]:



            '''simple docstring'''




            __snake_case         =					self.front
            while node:
                 yield node.data
                 __snake_case         =					node.next


       def __len__(				self					) ->					int:



            '''simple docstring'''




            return len(tuple(iter(self					)					)					)


       def __str__(				self					) ->					str:



            '''simple docstring'''




            return " <- ".join(str(__SCREAMING_SNAKE_CASE					) for item in self					)


       def  lowerCAmelCase (				self					) ->					bool:



            '''simple docstring'''




            return len(self					) == 0


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					None:



            '''simple docstring'''




            __snake_case         =					Node(__SCREAMING_SNAKE_CASE					)
            if self.is_empty():
                 __snake_case         =					__snake_case         =					node
            else:
                 assert isinstance(self.rear      ,					__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					node
                 __snake_case         =					node


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            if self.is_empty():
                 raise IndexError('''dequeue from empty queue'''					)
            assert isinstance(self.front      ,					__SCREAMING_SNAKE_CASE					)
            __snake_case         =					self.front
            __snake_case         =					self.front.next
            if self.front is None:
                 __snake_case         =					None
            return node.data




       def  lowerCAmelCase (				self					) ->					None:



            '''simple docstring'''




            __snake_case         =					__snake_case         =					None


if __name__ == "__main__":
     from doctest import testmod

     testmod()






