


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






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase = True       )		->Optional[int]:


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

  snake_case   : Optional[Any]	  =      LoRAAttnProcessor(hidden_size=lowercase__ ,     cross_attention_dim=lowercase__       )
  snake_case   : Any	  =      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	(  __UpperCAmelCase,			__UpperCAmelCase,			unittest.TestCase     ):
       _UpperCamelCase    		=      UNetaDConditionModel
       _UpperCamelCase    		=      """sample"""

       @property
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Tuple	  =      4
        snake_case   : Dict	  =      4
        snake_case   : List[Any]	  =      4
        snake_case   : Optional[Any]	  =      (32, 32)

        snake_case   : Dict	  =      floats_tensor((batch_size, num_channels, num_frames) + sizes					).to(lowerCamelCase__					)
        snake_case   : List[str]	  =      torch.tensor([10]					).to(lowerCamelCase__					)
        snake_case   : int	  =      floats_tensor((batch_size, 4, 32)					).to(lowerCamelCase__					)

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

       @property
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return (4, 4, 32, 32)

       @property
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return (4, 4, 32, 32)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Optional[int]	  =      {
            '''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   : Dict	  =      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					):






        '''simple docstring'''






        snake_case  ,					snake_case   : Any	  =      self.prepare_init_args_and_inputs_for_common()
        snake_case   : Dict	  =      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 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : str	  =      self.prepare_init_args_and_inputs_for_common()

        snake_case   : Dict	  =      32

        snake_case   : int	  =      self.model_class(**lowerCamelCase__					)
        model.to(lowerCamelCase__					)
        model.eval()

        with torch.no_grad():
         snake_case   : Optional[Any]	  =      model(**lowerCamelCase__					)

         if isinstance(lowerCamelCase__	,						lowerCamelCase__					):
          snake_case   : int	  =      output.sample

        self.assertIsNotNone(lowerCamelCase__					)
        snake_case   : Union[str, Any]	  =      inputs_dict['''sample'''].shape
        self.assertEqual(output.shape	,						lowerCamelCase__	,						'''Input and output shapes do not match'''					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : Any	  =      self.prepare_init_args_and_inputs_for_common()
        snake_case   : str	  =      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   : Optional[Any]	  =      model(**lowerCamelCase__					)
         if isinstance(lowerCamelCase__	,						lowerCamelCase__					):
          snake_case   : Optional[Any]	  =      first.sample

         snake_case   : List[Any]	  =      model(**lowerCamelCase__					)
         if isinstance(lowerCamelCase__	,						lowerCamelCase__					):
          snake_case   : Optional[int]	  =      second.sample

        snake_case   : Tuple	  =      first.cpu().numpy()
        snake_case   : Optional[Any]	  =      second.cpu().numpy()
        snake_case   : Optional[int]	  =      out_a[~np.isnan(lowerCamelCase__					)]
        snake_case   : Any	  =      out_a[~np.isnan(lowerCamelCase__					)]
        snake_case   : Any	  =      np.amax(np.abs(out_a - out_a					)					)
        self.assertLessEqual(lowerCamelCase__	,						1e-5					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : List[Any]	  =      self.prepare_init_args_and_inputs_for_common()

        snake_case   : int	  =      8

        snake_case   : int	  =      self.model_class(**lowerCamelCase__					)
        model.to(lowerCamelCase__					)
        model.eval()

        model.set_attention_slice('''auto'''					)
        with torch.no_grad():
         snake_case   : Tuple	  =      model(**lowerCamelCase__					)
        assert output is not None

        model.set_attention_slice('''max'''					)
        with torch.no_grad():
         snake_case   : Any	  =      model(**lowerCamelCase__					)
        assert output is not None

        model.set_attention_slice(2					)
        with torch.no_grad():
         snake_case   : Dict	  =      model(**lowerCamelCase__					)
        assert output is not None

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : Optional[int]	  =      self.prepare_init_args_and_inputs_for_common()

        snake_case   : Union[str, Any]	  =      8

        snake_case   : Optional[Any]	  =      self.model_class(**lowerCamelCase__					)
        model.to(lowerCamelCase__					)

        with torch.no_grad():
         snake_case   : Optional[Any]	  =      model(**lowerCamelCase__					).sample

        snake_case   : List[Any]	  =      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   : Dict	  =      model(**lowerCamelCase__	,						cross_attention_kwargs={'''scale''': 0.0}					).sample
         snake_case   : Union[str, Any]	  =      model(**lowerCamelCase__	,						cross_attention_kwargs={'''scale''': 0.5}					).sample
         snake_case   : Optional[int]	  =      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 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : Optional[int]	  =      self.prepare_init_args_and_inputs_for_common()

        snake_case   : Optional[int]	  =      8

        torch.manual_seed(0					)
        snake_case   : int	  =      self.model_class(**lowerCamelCase__					)
        model.to(lowerCamelCase__					)

        with torch.no_grad():
         snake_case   : Any	  =      model(**lowerCamelCase__					).sample

        snake_case   : Union[str, Any]	  =      create_lora_layers(lowerCamelCase__					)
        model.set_attn_processor(lowerCamelCase__					)

        with torch.no_grad():
         snake_case   : Union[str, Any]	  =      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   : Optional[int]	  =      self.model_class(**lowerCamelCase__					)
         new_model.to(lowerCamelCase__					)
         new_model.load_attn_procs(lowerCamelCase__					)

        with torch.no_grad():
         snake_case   : Dict	  =      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 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : Tuple	  =      self.prepare_init_args_and_inputs_for_common()

        snake_case   : Optional[int]	  =      8

        torch.manual_seed(0					)
        snake_case   : List[Any]	  =      self.model_class(**lowerCamelCase__					)
        model.to(lowerCamelCase__					)

        with torch.no_grad():
         snake_case   : int	  =      model(**lowerCamelCase__					).sample

        snake_case   : str	  =      create_lora_layers(lowerCamelCase__					)
        model.set_attn_processor(lowerCamelCase__					)

        with torch.no_grad():
         snake_case   : Union[str, Any]	  =      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   : str	  =      self.model_class(**lowerCamelCase__					)
         new_model.to(lowerCamelCase__					)
         new_model.load_attn_procs(lowerCamelCase__					)

        with torch.no_grad():
         snake_case   : int	  =      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 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : Tuple	  =      self.prepare_init_args_and_inputs_for_common()

        snake_case   : Optional[int]	  =      8

        torch.manual_seed(0					)
        snake_case   : int	  =      self.model_class(**lowerCamelCase__					)
        model.to(lowerCamelCase__					)

        snake_case   : Union[str, Any]	  =      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   : List[Any]	  =      self.model_class(**lowerCamelCase__					)
         new_model.to(lowerCamelCase__					)
         new_model.load_attn_procs(lowerCamelCase__	,						weight_name='''pytorch_lora_weights.bin'''					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : Union[str, Any]	  =      self.prepare_init_args_and_inputs_for_common()

        snake_case   : List[str]	  =      8

        torch.manual_seed(0					)
        snake_case   : str	  =      self.model_class(**lowerCamelCase__					)
        model.to(lowerCamelCase__					)

        snake_case   : Dict	  =      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   : List[Any]	  =      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 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : List[str]	  =      self.prepare_init_args_and_inputs_for_common()

        snake_case   : Union[str, Any]	  =      8

        torch.manual_seed(0					)
        snake_case   : int	  =      self.model_class(**lowerCamelCase__					)
        model.to(lowerCamelCase__					)

        with torch.no_grad():
         snake_case   : Dict	  =      model(**lowerCamelCase__					).sample

        snake_case   : str	  =      create_lora_layers(lowerCamelCase__					)
        model.set_attn_processor(lowerCamelCase__					)

        with torch.no_grad():
         snake_case   : Union[str, Any]	  =      model(**lowerCamelCase__	,						cross_attention_kwargs={'''scale''': 0.0}					).sample

        model.set_attn_processor(AttnProcessor()					)

        with torch.no_grad():
         snake_case   : int	  =      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 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : Dict	  =      self.prepare_init_args_and_inputs_for_common()

        snake_case   : str	  =      4

        torch.manual_seed(0					)
        snake_case   : Any	  =      self.model_class(**lowerCamelCase__					)
        model.to(lowerCamelCase__					)
        snake_case   : Optional[Any]	  =      create_lora_layers(lowerCamelCase__					)
        model.set_attn_processor(lowerCamelCase__					)

        # default
        with torch.no_grad():
         snake_case   : Optional[int]	  =      model(**lowerCamelCase__					).sample

         model.enable_xformers_memory_efficient_attention()
         snake_case   : Tuple	  =      model(**lowerCamelCase__					).sample

         model.disable_xformers_memory_efficient_attention()
         snake_case   : int	  =      model(**lowerCamelCase__					).sample

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



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : Optional[int]	  =      self.prepare_init_args_and_inputs_for_common()
        snake_case   : List[str]	  =      32

        snake_case   : Dict	  =      self.model_class(**lowerCamelCase__					)
        model.to(lowerCamelCase__					)
        model.eval()

        with torch.no_grad():
         snake_case   : Dict	  =      model(**lowerCamelCase__					)[0]

        model.enable_forward_chunking()
        with torch.no_grad():
         snake_case   : Any	  =      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.





from itertools import permutations






def    _UpperCAmelCase	(   )		->str:


 '''simple docstring'''
 snake_case   : Any	  =      list(map(''''''.join ,     permutations('''0123456789'''       )       )       )
 return result[999999]


if __name__ == "__main__":
  print(solution())






