

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						:int            = logging.get_logger(__name__)

def 			__snake_case	(   __UpperCamelCase  :   Any							,__UpperCamelCase  :   Optional[int] = True						):



      """simple docstring"""

      A_	     =							{}
      for name in model.attn_processors.keys():
            A_	     =							name.endswith("attn2.processor"						) and not (
                name.startswith("transformer_in"						) or "temp_attentions" in name.split("."						)
            )
            A_	     =							model.config.cross_attention_dim if has_cross_attention else None
            if name.startswith("mid_block"						):
                  A_	     =							model.config.block_out_channels[-1]
            elif name.startswith("up_blocks"						):
                  A_	     =							int(name[len("up_blocks."						)]						)
                  A_	     =							list(reversed(model.config.block_out_channels						)						)[block_id]
            elif name.startswith("down_blocks"						):
                  A_	     =							int(name[len("down_blocks."						)]						)
                  A_	     =							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
                  A_	     =							8 * model.config.attention_head_dim

            A_	     =							LoRAAttnProcessor(hidden_size=lowercase__							,cross_attention_dim=lowercase__						)
            A_	     =							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				( __UpperCAmelCase    ,							__UpperCAmelCase    ,							unittest.TestCase							):




      """simple docstring"""

      _lowerCamelCase					: List[Any]					=					UNetaDConditionModel
      _lowerCamelCase					: int					=					"""sample"""





      @property
      def 		__A  (      self       :							List[Any]  ):
            A_	     =							4
            A_	     =							4
            A_	     =							4
            A_	     =							(32, 32)

            A_	     =							floats_tensor((batch_size, num_channels, num_frames) + sizes  ).to(lowerCamelCase__  )
            A_	     =							torch.tensor([10]  ).to(lowerCamelCase__  )
            A_	     =							floats_tensor((batch_size, 4, 32)  ).to(lowerCamelCase__  )

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





      @property
      def 		__A  (      self       :							int  ):
            return (4, 4, 32, 32)





      @property
      def 		__A  (      self       :							List[str]  ):
            return (4, 4, 32, 32)





      def 		__A  (      self       :							Optional[int]  ):
            A_	     =							{
                "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,
            }
            A_	     =							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 		__A  (      self       :							str  ):
            A_     ,    A_	     =							self.prepare_init_args_and_inputs_for_common()
            A_	     =							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 		__A  (      self       :							Union[str, Any]  ):
            A_     ,    A_	     =							self.prepare_init_args_and_inputs_for_common()

            A_	     =							32

            A_	     =							self.model_class(**lowerCamelCase__  )
            model.to(lowerCamelCase__  )
            model.eval()

            with torch.no_grad():
                  A_	     =							model(**lowerCamelCase__  )

                  if isinstance(lowerCamelCase__	, lowerCamelCase__  ):
                        A_	     =							output.sample

            self.assertIsNotNone(lowerCamelCase__  )
            A_	     =							inputs_dict["sample"].shape
            self.assertEqual(output.shape	, lowerCamelCase__	, "Input and output shapes do not match"  )





      def 		__A  (      self       :							Dict  ):
            A_     ,    A_	     =							self.prepare_init_args_and_inputs_for_common()
            A_	     =							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  )

                  A_	     =							model(**lowerCamelCase__  )
                  if isinstance(lowerCamelCase__	, lowerCamelCase__  ):
                        A_	     =							first.sample

                  A_	     =							model(**lowerCamelCase__  )
                  if isinstance(lowerCamelCase__	, lowerCamelCase__  ):
                        A_	     =							second.sample

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





      def 		__A  (      self       :							Any  ):
            A_     ,    A_	     =							self.prepare_init_args_and_inputs_for_common()

            A_	     =							8

            A_	     =							self.model_class(**lowerCamelCase__  )
            model.to(lowerCamelCase__  )
            model.eval()

            model.set_attention_slice("auto"  )
            with torch.no_grad():
                  A_	     =							model(**lowerCamelCase__  )
            assert output is not None

            model.set_attention_slice("max"  )
            with torch.no_grad():
                  A_	     =							model(**lowerCamelCase__  )
            assert output is not None

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





      def 		__A  (      self       :							Optional[int]  ):
            A_     ,    A_	     =							self.prepare_init_args_and_inputs_for_common()

            A_	     =							8

            A_	     =							self.model_class(**lowerCamelCase__  )
            model.to(lowerCamelCase__  )

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

            A_	     =							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():
                  A_	     =							model(**lowerCamelCase__	, cross_attention_kwargs={"scale": 0.0}  ).sample
                  A_	     =							model(**lowerCamelCase__	, cross_attention_kwargs={"scale": 0.5}  ).sample
                  A_	     =							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 		__A  (      self       :							Optional[Any]  ):
            A_     ,    A_	     =							self.prepare_init_args_and_inputs_for_common()

            A_	     =							8

            torch.manual_seed(0  )
            A_	     =							self.model_class(**lowerCamelCase__  )
            model.to(lowerCamelCase__  )

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

            A_	     =							create_lora_layers(lowerCamelCase__  )
            model.set_attn_processor(lowerCamelCase__  )

            with torch.no_grad():
                  A_	     =							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  )
                  A_	     =							self.model_class(**lowerCamelCase__  )
                  new_model.to(lowerCamelCase__  )
                  new_model.load_attn_procs(lowerCamelCase__  )

            with torch.no_grad():
                  A_	     =							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 		__A  (      self       :							str  ):
            A_     ,    A_	     =							self.prepare_init_args_and_inputs_for_common()

            A_	     =							8

            torch.manual_seed(0  )
            A_	     =							self.model_class(**lowerCamelCase__  )
            model.to(lowerCamelCase__  )

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

            A_	     =							create_lora_layers(lowerCamelCase__  )
            model.set_attn_processor(lowerCamelCase__  )

            with torch.no_grad():
                  A_	     =							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  )
                  A_	     =							self.model_class(**lowerCamelCase__  )
                  new_model.to(lowerCamelCase__  )
                  new_model.load_attn_procs(lowerCamelCase__  )

            with torch.no_grad():
                  A_	     =							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 		__A  (      self       :							List[Any]  ):
            A_     ,    A_	     =							self.prepare_init_args_and_inputs_for_common()

            A_	     =							8

            torch.manual_seed(0  )
            A_	     =							self.model_class(**lowerCamelCase__  )
            model.to(lowerCamelCase__  )

            A_	     =							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  )
                  A_	     =							self.model_class(**lowerCamelCase__  )
                  new_model.to(lowerCamelCase__  )
                  new_model.load_attn_procs(lowerCamelCase__	, weight_name="pytorch_lora_weights.bin"  )





      def 		__A  (      self       :							Optional[Any]  ):
            A_     ,    A_	     =							self.prepare_init_args_and_inputs_for_common()

            A_	     =							8

            torch.manual_seed(0  )
            A_	     =							self.model_class(**lowerCamelCase__  )
            model.to(lowerCamelCase__  )

            A_	     =							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  )
                  A_	     =							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 		__A  (      self       :							int  ):
            A_     ,    A_	     =							self.prepare_init_args_and_inputs_for_common()

            A_	     =							8

            torch.manual_seed(0  )
            A_	     =							self.model_class(**lowerCamelCase__  )
            model.to(lowerCamelCase__  )

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

            A_	     =							create_lora_layers(lowerCamelCase__  )
            model.set_attn_processor(lowerCamelCase__  )

            with torch.no_grad():
                  A_	     =							model(**lowerCamelCase__	, cross_attention_kwargs={"scale": 0.0}  ).sample

            model.set_attn_processor(AttnProcessor()  )

            with torch.no_grad():
                  A_	     =							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 		__A  (      self       :							List[str]  ):
            A_     ,    A_	     =							self.prepare_init_args_and_inputs_for_common()

            A_	     =							4

            torch.manual_seed(0  )
            A_	     =							self.model_class(**lowerCamelCase__  )
            model.to(lowerCamelCase__  )
            A_	     =							create_lora_layers(lowerCamelCase__  )
            model.set_attn_processor(lowerCamelCase__  )

            # default
            with torch.no_grad():
                  A_	     =							model(**lowerCamelCase__  ).sample

                  model.enable_xformers_memory_efficient_attention()
                  A_	     =							model(**lowerCamelCase__  ).sample

                  model.disable_xformers_memory_efficient_attention()
                  A_	     =							model(**lowerCamelCase__  ).sample

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







      def 		__A  (      self       :							Optional[Any]  ):
            A_     ,    A_	     =							self.prepare_init_args_and_inputs_for_common()
            A_	     =							32

            A_	     =							self.model_class(**lowerCamelCase__  )
            model.to(lowerCamelCase__  )
            model.eval()

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

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


import pytest

from datasets.utils.version import Version

@pytest.mark.parametrize(
    "other, expected_equality"							,[
        (Version("1.0.0"						), True),
        ("1.0.0", True),
        (Version("2.0.0"						), False),
        ("2.0.0", False),
        ("1", False),
        ("a", False),
        (1, False),
        (None, False),
    ]							,)
def 			__snake_case	(   __UpperCamelCase  :   Any							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							Version("1.0.0"						)
      assert (version == other) is expected_equality
      assert (version != other) is not expected_equality
      assert (hash(__UpperCamelCase						) == hash(__UpperCamelCase						)) is expected_equality