






"""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					=     logging.get_logger(__name__)






def 							A(    snake_case_		,  snake_case_ = True						):
       """simple docstring"""





       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[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__:							int         =     int(name[len("up_blocks."						)]						)
                     lowercase__:							Optional[int]         =     list(reversed(model.config.block_out_channels						)						)[block_id]
              elif name.startswith("down_blocks"						):
                     lowercase__:							str         =     int(name[len("down_blocks."						)]						)
                     lowercase__:							Tuple         =     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__:							Optional[Any]         =     8 * model.config.attention_head_dim

              lowercase__:							Dict         =     LoRAAttnProcessor(hidden_size=__lowerCamelCase		,  cross_attention_dim=__lowerCamelCase						)
              lowercase__:							Tuple         =     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 						_a  (		lowercase__							,	lowercase__							,	unittest.TestCase       ):





   '''simple docstring'''






   UpperCamelCase__     	=							UNetaDConditionModel
   UpperCamelCase__     	=							"""sample"""





   @property
   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




          lowercase__:							Tuple         =     4
          lowercase__:							Dict         =     4
          lowercase__:							int         =     4
          lowercase__:							Union[str, Any]         =     (32, 32)

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

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





   @property
   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




          return (4, 4, 32, 32)





   @property
   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




          return (4, 4, 32, 32)





   def __lowercase							(       self)			->   Optional[Any]:




          '''simple docstring'''




          lowercase__:							Union[str, 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 __lowercase							(       self)			->   Dict:




          '''simple docstring'''




          lowercase__:							Optional[int]         =     self.prepare_init_args_and_inputs_for_common()
          lowercase__:							List[str]         =     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 __lowercase							(       self)			->   str:




          '''simple docstring'''




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

          lowercase__:							Tuple         =     32

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

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

                 if isinstance(lowercase_					, lowercase_):
                        lowercase__:							List[str]         =     output.sample

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





   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




          lowercase__:							List[Any]         =     self.prepare_init_args_and_inputs_for_common()
          lowercase__:							List[Any]         =     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__:							Union[str, Any]         =     model(**lowercase_)
                 if isinstance(lowercase_					, lowercase_):
                        lowercase__:							Tuple         =     first.sample

                 lowercase__:							List[str]         =     model(**lowercase_)
                 if isinstance(lowercase_					, lowercase_):
                        lowercase__:							Dict         =     second.sample

          lowercase__:							List[str]         =     first.cpu().numpy()
          lowercase__:							Optional[int]         =     second.cpu().numpy()
          lowercase__:							int         =     out_a[~np.isnan(lowercase_)]
          lowercase__:							List[str]         =     out_a[~np.isnan(lowercase_)]
          lowercase__:							Optional[int]         =     np.amax(np.abs(out_a - out_a))
          self.assertLessEqual(lowercase_					, 1E-5)





   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




          lowercase__:							Tuple         =     self.prepare_init_args_and_inputs_for_common()

          lowercase__:							Tuple         =     8

          lowercase__:							Optional[Any]         =     self.model_class(**lowercase_)
          model.to(lowercase_)
          model.eval()

          model.set_attention_slice("auto")
          with torch.no_grad():
                 lowercase__:							List[Any]         =     model(**lowercase_)
          assert output is not None

          model.set_attention_slice("max")
          with torch.no_grad():
                 lowercase__:							List[Any]         =     model(**lowercase_)
          assert output is not None

          model.set_attention_slice(2)
          with torch.no_grad():
                 lowercase__:							Union[str, Any]         =     model(**lowercase_)
          assert output is not None





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




          '''simple docstring'''




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

          lowercase__:							Optional[int]         =     8

          lowercase__:							str         =     self.model_class(**lowercase_)
          model.to(lowercase_)

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

          lowercase__:							int         =     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__:							List[Any]         =     model(**lowercase_					, cross_attention_kwargs={"scale": 0.0}).sample
                 lowercase__:							str         =     model(**lowercase_					, cross_attention_kwargs={"scale": 0.5}).sample
                 lowercase__:							List[Any]         =     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 __lowercase							(       self)			->   Dict:




          '''simple docstring'''




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

          lowercase__:							str         =     8

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

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

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

          with torch.no_grad():
                 lowercase__:							Tuple         =     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__:							List[Any]         =     self.model_class(**lowercase_)
                 new_model.to(lowercase_)
                 new_model.load_attn_procs(lowercase_)

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




          '''simple docstring'''




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

          lowercase__:							List[str]         =     8

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

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

          lowercase__:							Tuple         =     create_lora_layers(lowercase_)
          model.set_attn_processor(lowercase_)

          with torch.no_grad():
                 lowercase__:							Tuple         =     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__:							Dict         =     self.model_class(**lowercase_)
                 new_model.to(lowercase_)
                 new_model.load_attn_procs(lowercase_)

          with torch.no_grad():
                 lowercase__:							Optional[Any]         =     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 __lowercase							(       self)			->   List[str]:




          '''simple docstring'''




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

          lowercase__:							Optional[int]         =     8

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

          lowercase__:							str         =     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__:							Dict         =     self.model_class(**lowercase_)
                 new_model.to(lowercase_)
                 new_model.load_attn_procs(lowercase_					, weight_name="pytorch_lora_weights.bin")





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




          '''simple docstring'''




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

          lowercase__:							str         =     8

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

          lowercase__:							Tuple         =     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__:							int         =     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 __lowercase							(       self)			->   Union[str, Any]:




          '''simple docstring'''




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

          lowercase__:							List[Any]         =     8

          torch.manual_seed(0)
          lowercase__:							str         =     self.model_class(**lowercase_)
          model.to(lowercase_)

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

          lowercase__:							Any         =     create_lora_layers(lowercase_)
          model.set_attn_processor(lowercase_)

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

          model.set_attn_processor(AttnProcessor())

          with torch.no_grad():
                 lowercase__:							Optional[Any]         =     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 __lowercase							(       self)			->   int:




          '''simple docstring'''




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

          lowercase__:							List[str]         =     4

          torch.manual_seed(0)
          lowercase__:							Any         =     self.model_class(**lowercase_)
          model.to(lowercase_)
          lowercase__:							int         =     create_lora_layers(lowercase_)
          model.set_attn_processor(lowercase_)

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

                 model.enable_xformers_memory_efficient_attention()
                 lowercase__:							Tuple         =     model(**lowercase_).sample

                 model.disable_xformers_memory_efficient_attention()
                 lowercase__:							Tuple         =     model(**lowercase_).sample

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





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




          '''simple docstring'''




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

          lowercase__:							str         =     self.model_class(**lowercase_)
          model.to(lowercase_)
          model.eval()

          with torch.no_grad():
                 lowercase__:							Optional[Any]         =     model(**lowercase_)[0]

          model.enable_forward_chunking()
          with torch.no_grad():
                 lowercase__:							Optional[Any]         =     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 cva
import numpy as np







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):

   try:
      return int(image[x_coordinate][y_coordinate] >= center    )
   except (IndexError, TypeError):
      return 0







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[int]     	=       image[x_coordinate][y_coordinate]
   UpperCAmelCase_   :   int     	=       [1, 2, 4, 8, 16, 32, 64, 128]

   # skip get_neighbors_pixel if center is null
   if center is None:
      return 0

   # Starting from the top right, assigning value to pixels clockwise
   UpperCAmelCase_   :   List[Any]     	=       [
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						__lowerCamelCase,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						__lowerCamelCase,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						__lowerCamelCase,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						y_coordinate - 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						__lowerCamelCase,						y_coordinate - 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						y_coordinate - 1,						__lowerCamelCase    ),
   ]

   # Converting the binary value to decimal.
   return sum(
       binary_value * power for binary_value, power in zip(__lowerCamelCase,						__lowerCamelCase    )    )


if __name__ == "__main__":
    # Reading the image and converting it to grayscale.
    _a									=				cva.imread(
        'digital_image_processing/image_data/lena.jpg', cva.IMREAD_GRAYSCALE
    )

    # Create a numpy array as the same height and width of read image
    _a									=				np.zeros((image.shape[0], image.shape[1]))

    # Iterating through the image and calculating the
    # local binary pattern value for each pixel.
    for i in range(0, image.shape[0]):
        for j in range(0, image.shape[1]):
            _a									=				local_binary_value(image, i, j)

    cva.imshow('local binary pattern', lbp_image)
    cva.waitKey(0)
    cva.destroyAllWindows()


