




'''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()

lowercase	:      Optional[Any]				=       logging.get_logger(__name__)




def 		a_( lowerCamelCase   ,				lowerCamelCase = True      )				->Optional[Any]:






       '''simple docstring'''




       lowercase  :		List[Any]						=      {}
       for name in model.attn_processors.keys():
              lowercase  :		Optional[int]						=      name.endswith('''attn2.processor'''      ) and not (
                  name.startswith('''transformer_in'''      ) or '''temp_attentions''' in name.split('''.'''      )
              )
              lowercase  :		Optional[Any]						=      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  :		List[Any]						=      int(name[len('''up_blocks.'''      )]      )
                     lowercase  :		Optional[Any]						=      list(reversed(model.config.block_out_channels      )      )[block_id]
              elif name.startswith('''down_blocks'''      ):
                     lowercase  :		Dict						=      int(name[len('''down_blocks.'''      )]      )
                     lowercase  :		Any						=      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  :		str						=      8 * model.config.attention_head_dim

              lowercase  :		str						=      LoRAAttnProcessor(hidden_size=lowerCamelCase   ,				cross_attention_dim=lowerCamelCase      )
              lowercase  :		Union[str, 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   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_		,     lowerCamelCase_		,     unittest.TestCase				):
 __snake_case	:     Optional[Any]			   =      UNetaDConditionModel
 __snake_case	:     Dict			   =      "sample"



 @property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		str						=      4
        lowercase  :		List[Any]						=      4
        lowercase  :		Optional[int]						=      4
        lowercase  :		List[str]						=      (32, 32)

        lowercase  :		Union[str, Any]						=      floats_tensor((batch_size, num_channels, num_frames) + sizes ).to(A )
        lowercase  :		List[str]						=      torch.tensor([10] ).to(A )
        lowercase  :		Union[str, Any]						=      floats_tensor((batch_size, 4, 32) ).to(A )

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



 @property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return (4, 4, 32, 32)



 @property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return (4, 4, 32, 32)



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Tuple						=      {
            '''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  :		List[Any]						=      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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      self.prepare_init_args_and_inputs_for_common()
        lowercase  :		List[str]						=      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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Tuple						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		int						=      32

        lowercase  :		Tuple						=      self.model_class(**A )
        model.to(A )
        model.eval()

        with torch.no_grad():
               lowercase  :		List[str]						=      model(**A )

               if isinstance(A   ,		A ):
                      lowercase  :		str						=      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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Tuple						=      self.prepare_init_args_and_inputs_for_common()
        lowercase  :		Dict						=      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  :		str						=      model(**A )
               if isinstance(A   ,		A ):
                      lowercase  :		Optional[Any]						=      first.sample

               lowercase  :		int						=      model(**A )
               if isinstance(A   ,		A ):
                      lowercase  :		int						=      second.sample

        lowercase  :		Dict						=      first.cpu().numpy()
        lowercase  :		Optional[Any]						=      second.cpu().numpy()
        lowercase  :		Dict						=      out_a[~np.isnan(A )]
        lowercase  :		Any						=      out_a[~np.isnan(A )]
        lowercase  :		Optional[Any]						=      np.amax(np.abs(out_a - out_a ) )
        self.assertLessEqual(A   ,		1E-5 )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		Any						=      8

        lowercase  :		Optional[int]						=      self.model_class(**A )
        model.to(A )
        model.eval()

        model.set_attention_slice('''auto''' )
        with torch.no_grad():
               lowercase  :		Dict						=      model(**A )
        assert output is not None

        model.set_attention_slice('''max''' )
        with torch.no_grad():
               lowercase  :		List[str]						=      model(**A )
        assert output is not None

        model.set_attention_slice(2 )
        with torch.no_grad():
               lowercase  :		Optional[Any]						=      model(**A )
        assert output is not None



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Tuple						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		str						=      8

        lowercase  :		Any						=      self.model_class(**A )
        model.to(A )

        with torch.no_grad():
               lowercase  :		Optional[int]						=      model(**A ).sample

        lowercase  :		Optional[int]						=      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  :		Tuple						=      model(**A   ,		cross_attention_kwargs={'''scale''': 0.0} ).sample
               lowercase  :		Optional[Any]						=      model(**A   ,		cross_attention_kwargs={'''scale''': 0.5} ).sample
               lowercase  :		Tuple						=      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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

        lowercase  :		int						=      8

        torch.manual_seed(0 )
        lowercase  :		int						=      self.model_class(**A )
        model.to(A )

        with torch.no_grad():
               lowercase  :		List[Any]						=      model(**A ).sample

        lowercase  :		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  :		Optional[Any]						=      self.model_class(**A )
               new_model.to(A )
               new_model.load_attn_procs(A )

        with torch.no_grad():
               lowercase  :		List[str]						=      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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Tuple						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		Tuple						=      8

        torch.manual_seed(0 )
        lowercase  :		List[str]						=      self.model_class(**A )
        model.to(A )

        with torch.no_grad():
               lowercase  :		Any						=      model(**A ).sample

        lowercase  :		Tuple						=      create_lora_layers(A )
        model.set_attn_processor(A )

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

        with torch.no_grad():
               lowercase  :		List[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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		Any						=      8

        torch.manual_seed(0 )
        lowercase  :		Optional[Any]						=      self.model_class(**A )
        model.to(A )

        lowercase  :		Optional[Any]						=      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  :		Optional[int]						=      self.model_class(**A )
               new_model.to(A )
               new_model.load_attn_procs(A   ,		weight_name='''pytorch_lora_weights.bin''' )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Dict						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		List[Any]						=      8

        torch.manual_seed(0 )
        lowercase  :		Union[str, Any]						=      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  :		Tuple						=      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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Any						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		List[Any]						=      8

        torch.manual_seed(0 )
        lowercase  :		Union[str, Any]						=      self.model_class(**A )
        model.to(A )

        with torch.no_grad():
               lowercase  :		Tuple						=      model(**A ).sample

        lowercase  :		int						=      create_lora_layers(A )
        model.set_attn_processor(A )

        with torch.no_grad():
               lowercase  :		Any						=      model(**A   ,		cross_attention_kwargs={'''scale''': 0.0} ).sample

        model.set_attn_processor(AttnProcessor() )

        with torch.no_grad():
               lowercase  :		str						=      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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Any						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		Union[str, Any]						=      4

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

        # default
        with torch.no_grad():
               lowercase  :		int						=      model(**A ).sample

               model.enable_xformers_memory_efficient_attention()
               lowercase  :		Union[str, Any]						=      model(**A ).sample

               model.disable_xformers_memory_efficient_attention()
               lowercase  :		Optional[Any]						=      model(**A ).sample

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





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		str						=      self.prepare_init_args_and_inputs_for_common()
        lowercase  :		List[str]						=      32

        lowercase  :		Tuple						=      self.model_class(**A )
        model.to(A )
        model.eval()

        with torch.no_grad():
               lowercase  :		List[Any]						=      model(**A )[0]

        model.enable_forward_chunking()
        with torch.no_grad():
               lowercase  :		List[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'''






import numpy as np
from PIL import Image




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       return (gray > 127) & (gray <= 255)




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      np.zeros_like(lowerCamelCase      )
       lowercase  :		Any						=      np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

       # Copy image to padded image
       lowercase  :		List[str]						=      image

       # Iterate over image & apply kernel
       for x in range(image.shape[1]      ):
              for y in range(image.shape[0]      ):
                     lowercase  :		Union[str, Any]						=      (
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     lowercase  :		Any						=      int(summation == 5      )
       return output


# kernel to be applied
lowercase	:      List[Any]				=       np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      lowercase	:      Dict				=       np.array(Image.open(r'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      lowercase	:      List[str]				=       erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      lowercase	:      Optional[Any]				=       Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')




