'''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()

_A										= logging.get_logger(__name__)

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

          lowercase_       :							List[str]  					=       LoRAAttnProcessor(hidden_size=SCREAMING_SNAKE_CASE_	,							cross_attention_dim=SCREAMING_SNAKE_CASE_				)
          lowercase_       :							Optional[int]  					=       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__	(							_snake_case       ,  _snake_case       ,  unittest.TestCase      ):






   """simple docstring"""
   A				:							Optional[Any]			     =     UNetaDConditionModel
   A				:							List[str]			     =     '''sample'''
   @property
   def 		_lowerCamelCase			(self					)     ->  List[Any]:
        lowercase_       :							Tuple  					=       4
        lowercase_       :							List[str]  					=       4
        lowercase_       :							List[Any]  					=       4
        lowercase_       :							Optional[Any]  					=       (32, 32)

        lowercase_       :							Optional[int]  					=       floats_tensor((batch_size, num_channels, num_frames) + sizes					).to(_a					)
        lowercase_       :							str  					=       torch.tensor([10]					).to(_a					)
        lowercase_       :							List[Any]  					=       floats_tensor((batch_size, 4, 32)					).to(_a					)

        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[Any]:
        return (4, 4, 32, 32)
   def 		_lowerCamelCase			(self					)     ->  Any:
        lowercase_       :							Any  					=       {
            '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_       :							int  					=       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					)     ->  Optional[Any]:
        lowercase_					,lowercase_       :							Optional[Any]  					=       self.prepare_init_args_and_inputs_for_common()
        lowercase_       :							Union[str, Any]  					=       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 		_lowerCamelCase			(self					)     ->  List[Any]:
        lowercase_					,lowercase_       :							Union[str, Any]  					=       self.prepare_init_args_and_inputs_for_common()

        lowercase_       :							Tuple  					=       32

        lowercase_       :							Union[str, Any]  					=       self.model_class(**_a					)
        model.to(_a					)
        model.eval()

        with torch.no_grad():
             lowercase_       :							str  					=       model(**_a					)

             if isinstance(_a							,					_a					):
                  lowercase_       :							List[Any]  					=       output.sample

        self.assertIsNotNone(_a					)
        lowercase_       :							List[Any]  					=       inputs_dict['sample'].shape
        self.assertEqual(output.shape							,					_a							,					'Input and output shapes do not match'					)
   def 		_lowerCamelCase			(self					)     ->  int:
        lowercase_					,lowercase_       :							Optional[Any]  					=       self.prepare_init_args_and_inputs_for_common()
        lowercase_       :							List[Any]  					=       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					)

             lowercase_       :							List[Any]  					=       model(**_a					)
             if isinstance(_a							,					_a					):
                  lowercase_       :							Optional[int]  					=       first.sample

             lowercase_       :							Union[str, Any]  					=       model(**_a					)
             if isinstance(_a							,					_a					):
                  lowercase_       :							Union[str, Any]  					=       second.sample

        lowercase_       :							Dict  					=       first.cpu().numpy()
        lowercase_       :							List[Any]  					=       second.cpu().numpy()
        lowercase_       :							List[str]  					=       out_a[~np.isnan(_a					)]
        lowercase_       :							Tuple  					=       out_a[~np.isnan(_a					)]
        lowercase_       :							int  					=       np.amax(np.abs(out_a - out_a					)					)
        self.assertLessEqual(_a							,					1e-5					)
   def 		_lowerCamelCase			(self					)     ->  Tuple:
        lowercase_					,lowercase_       :							Optional[Any]  					=       self.prepare_init_args_and_inputs_for_common()

        lowercase_       :							Optional[int]  					=       8

        lowercase_       :							Dict  					=       self.model_class(**_a					)
        model.to(_a					)
        model.eval()

        model.set_attention_slice('auto'					)
        with torch.no_grad():
             lowercase_       :							Optional[int]  					=       model(**_a					)
        assert output is not None

        model.set_attention_slice('max'					)
        with torch.no_grad():
             lowercase_       :							Optional[Any]  					=       model(**_a					)
        assert output is not None

        model.set_attention_slice(2					)
        with torch.no_grad():
             lowercase_       :							Any  					=       model(**_a					)
        assert output is not None
   def 		_lowerCamelCase			(self					)     ->  Optional[Any]:
        lowercase_					,lowercase_       :							Dict  					=       self.prepare_init_args_and_inputs_for_common()

        lowercase_       :							Any  					=       8

        lowercase_       :							List[str]  					=       self.model_class(**_a					)
        model.to(_a					)

        with torch.no_grad():
             lowercase_       :							Dict  					=       model(**_a					).sample

        lowercase_       :							Dict  					=       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():
             lowercase_       :							Optional[int]  					=       model(**_a							,					cross_attention_kwargs={'scale': 0.0}					).sample
             lowercase_       :							List[Any]  					=       model(**_a							,					cross_attention_kwargs={'scale': 0.5}					).sample
             lowercase_       :							Optional[Any]  					=       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 		_lowerCamelCase			(self					)     ->  Union[str, Any]:
        lowercase_					,lowercase_       :							Dict  					=       self.prepare_init_args_and_inputs_for_common()

        lowercase_       :							Tuple  					=       8

        torch.manual_seed(0					)
        lowercase_       :							Any  					=       self.model_class(**_a					)
        model.to(_a					)

        with torch.no_grad():
             lowercase_       :							Any  					=       model(**_a					).sample

        lowercase_       :							Optional[int]  					=       create_lora_layers(_a					)
        model.set_attn_processor(_a					)

        with torch.no_grad():
             lowercase_       :							int  					=       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					)
             lowercase_       :							Dict  					=       self.model_class(**_a					)
             new_model.to(_a					)
             new_model.load_attn_procs(_a					)

        with torch.no_grad():
             lowercase_       :							Tuple  					=       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 		_lowerCamelCase			(self					)     ->  Union[str, Any]:
        lowercase_					,lowercase_       :							List[Any]  					=       self.prepare_init_args_and_inputs_for_common()

        lowercase_       :							Optional[int]  					=       8

        torch.manual_seed(0					)
        lowercase_       :							int  					=       self.model_class(**_a					)
        model.to(_a					)

        with torch.no_grad():
             lowercase_       :							Optional[int]  					=       model(**_a					).sample

        lowercase_       :							Optional[Any]  					=       create_lora_layers(_a					)
        model.set_attn_processor(_a					)

        with torch.no_grad():
             lowercase_       :							Optional[Any]  					=       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					)
             lowercase_       :							Tuple  					=       self.model_class(**_a					)
             new_model.to(_a					)
             new_model.load_attn_procs(_a					)

        with torch.no_grad():
             lowercase_       :							Any  					=       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 		_lowerCamelCase			(self					)     ->  Union[str, Any]:
        # enable deterministic behavior for gradient checkpointing
        lowercase_					,lowercase_       :							Union[str, Any]  					=       self.prepare_init_args_and_inputs_for_common()

        lowercase_       :							List[str]  					=       8

        torch.manual_seed(0					)
        lowercase_       :							Dict  					=       self.model_class(**_a					)
        model.to(_a					)

        lowercase_       :							int  					=       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					)
             lowercase_       :							Any  					=       self.model_class(**_a					)
             new_model.to(_a					)
             new_model.load_attn_procs(_a							,					weight_name='pytorch_lora_weights.bin'					)
   def 		_lowerCamelCase			(self					)     ->  Tuple:
        lowercase_					,lowercase_       :							Dict  					=       self.prepare_init_args_and_inputs_for_common()

        lowercase_       :							Optional[Any]  					=       8

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

        lowercase_       :							str  					=       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					)
             lowercase_       :							Any  					=       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 		_lowerCamelCase			(self					)     ->  Dict:
        lowercase_					,lowercase_       :							int  					=       self.prepare_init_args_and_inputs_for_common()

        lowercase_       :							Any  					=       8

        torch.manual_seed(0					)
        lowercase_       :							Any  					=       self.model_class(**_a					)
        model.to(_a					)

        with torch.no_grad():
             lowercase_       :							List[Any]  					=       model(**_a					).sample

        lowercase_       :							Dict  					=       create_lora_layers(_a					)
        model.set_attn_processor(_a					)

        with torch.no_grad():
             lowercase_       :							Dict  					=       model(**_a							,					cross_attention_kwargs={'scale': 0.0}					).sample

        model.set_attn_processor(AttnProcessor()					)

        with torch.no_grad():
             lowercase_       :							int  					=       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 		_lowerCamelCase			(self					)     ->  int:
        # enable deterministic behavior for gradient checkpointing
        lowercase_					,lowercase_       :							Any  					=       self.prepare_init_args_and_inputs_for_common()

        lowercase_       :							int  					=       4

        torch.manual_seed(0					)
        lowercase_       :							Optional[int]  					=       self.model_class(**_a					)
        model.to(_a					)
        lowercase_       :							Any  					=       create_lora_layers(_a					)
        model.set_attn_processor(_a					)

        # default
        with torch.no_grad():
             lowercase_       :							Tuple  					=       model(**_a					).sample

             model.enable_xformers_memory_efficient_attention()
             lowercase_       :							List[str]  					=       model(**_a					).sample

             model.disable_xformers_memory_efficient_attention()
             lowercase_       :							int  					=       model(**_a					).sample

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





   def 		_lowerCamelCase			(self					)     ->  List[Any]:
        lowercase_					,lowercase_       :							int  					=       self.prepare_init_args_and_inputs_for_common()
        lowercase_       :							Optional[Any]  					=       32

        lowercase_       :							Optional[int]  					=       self.model_class(**_a					)
        model.to(_a					)
        model.eval()

        with torch.no_grad():
             lowercase_       :							str  					=       model(**_a					)[0]

        model.enable_forward_chunking()
        with torch.no_grad():
             lowercase_       :							str  					=       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.





'''simple docstring'''




def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     return " ".join([word[::-1] for word in input_str.split()]				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




