




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()

SCREAMING_SNAKE_CASE_						:					List[Any]			   =       logging.get_logger(__name__)
def        __lowerCAmelCase (UpperCAmelCase_     :   List[Any]   ,    UpperCAmelCase_     :   Any = True		):
    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=UpperCAmelCase_   ,    cross_attention_dim=UpperCAmelCase_		)
        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 		__UpperCAmelCase(		lowercase__       ,	lowercase__       ,	unittest.TestCase ):





     """simple docstring"""



     __UpperCAmelCase	: Optional[Any]						=    UNetaDConditionModel
     __UpperCAmelCase	: List[Any]						=    """sample"""

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			int:






         """simple docstring"""
         a__      =       4
         a__      =       4
         a__      =       4
         a__      =       (32, 32)

         a__      =       floats_tensor((batch_size, num_channels, num_frames) + sizes    ).to(A    )
         a__      =       torch.tensor([10]    ).to(A    )
         a__      =       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 :       List[str]    )			->			Tuple:






         """simple docstring"""
         return (4, 4, 32, 32)

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			Any:






         """simple docstring"""
         return (4, 4, 32, 32)

     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			Union[str, Any]:






         """simple docstring"""
         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 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			Union[str, Any]:






         """simple docstring"""
         a__ ,		a__      =       self.prepare_init_args_and_inputs_for_common()
         a__      =       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 :       Any    )			->			Any:






         """simple docstring"""
         a__ ,		a__      =       self.prepare_init_args_and_inputs_for_common()

         a__      =       32

         a__      =       self.model_class(**A    )
         model.to(A    )
         model.eval()

         with torch.no_grad():
             a__      =       model(**A    )

             if isinstance(A						,				A    ):
                 a__      =       output.sample

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

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			Dict:






         """simple docstring"""
         a__ ,		a__      =       self.prepare_init_args_and_inputs_for_common()
         a__      =       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    )

             a__      =       model(**A    )
             if isinstance(A						,				A    ):
                 a__      =       first.sample

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

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

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			List[Any]:






         """simple docstring"""
         a__ ,		a__      =       self.prepare_init_args_and_inputs_for_common()

         a__      =       8

         a__      =       self.model_class(**A    )
         model.to(A    )
         model.eval()

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

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

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

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			Dict:






         """simple docstring"""
         a__ ,		a__      =       self.prepare_init_args_and_inputs_for_common()

         a__      =       8

         a__      =       self.model_class(**A    )
         model.to(A    )

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

         a__      =       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():
             a__      =       model(**A						,				cross_attention_kwargs={"""scale""": 0.0}    ).sample
             a__      =       model(**A						,				cross_attention_kwargs={"""scale""": 0.5}    ).sample
             a__      =       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 :       Any    )			->			Any:






         """simple docstring"""
         a__ ,		a__      =       self.prepare_init_args_and_inputs_for_common()

         a__      =       8

         torch.manual_seed(0    )
         a__      =       self.model_class(**A    )
         model.to(A    )

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

         a__      =       create_lora_layers(A    )
         model.set_attn_processor(A    )

         with torch.no_grad():
             a__      =       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    )
             a__      =       self.model_class(**A    )
             new_model.to(A    )
             new_model.load_attn_procs(A    )

         with torch.no_grad():
             a__      =       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 :       Any    )			->			Any:






         """simple docstring"""
         a__ ,		a__      =       self.prepare_init_args_and_inputs_for_common()

         a__      =       8

         torch.manual_seed(0    )
         a__      =       self.model_class(**A    )
         model.to(A    )

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

         a__      =       create_lora_layers(A    )
         model.set_attn_processor(A    )

         with torch.no_grad():
             a__      =       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    )
             a__      =       self.model_class(**A    )
             new_model.to(A    )
             new_model.load_attn_procs(A    )

         with torch.no_grad():
             a__      =       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 :       int    )			->			Union[str, Any]:






         """simple docstring"""
         a__ ,		a__      =       self.prepare_init_args_and_inputs_for_common()

         a__      =       8

         torch.manual_seed(0    )
         a__      =       self.model_class(**A    )
         model.to(A    )

         a__      =       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    )
             a__      =       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 :       str    )			->			Any:






         """simple docstring"""
         a__ ,		a__      =       self.prepare_init_args_and_inputs_for_common()

         a__      =       8

         torch.manual_seed(0    )
         a__      =       self.model_class(**A    )
         model.to(A    )

         a__      =       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    )
             a__      =       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 :       Tuple    )			->			List[str]:






         """simple docstring"""
         a__ ,		a__      =       self.prepare_init_args_and_inputs_for_common()

         a__      =       8

         torch.manual_seed(0    )
         a__      =       self.model_class(**A    )
         model.to(A    )

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

         a__      =       create_lora_layers(A    )
         model.set_attn_processor(A    )

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

         model.set_attn_processor(AttnProcessor()    )

         with torch.no_grad():
             a__      =       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 :       str    )			->			int:






         """simple docstring"""
         a__ ,		a__      =       self.prepare_init_args_and_inputs_for_common()

         a__      =       4

         torch.manual_seed(0    )
         a__      =       self.model_class(**A    )
         model.to(A    )
         a__      =       create_lora_layers(A    )
         model.set_attn_processor(A    )

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

             model.enable_xformers_memory_efficient_attention()
             a__      =       model(**A    ).sample

             model.disable_xformers_memory_efficient_attention()
             a__      =       model(**A    ).sample

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






     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			Any:






         """simple docstring"""
         a__ ,		a__      =       self.prepare_init_args_and_inputs_for_common()
         a__      =       32

         a__      =       self.model_class(**A    )
         model.to(A    )
         model.eval()

         with torch.no_grad():
             a__      =       model(**A    )[0]

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_						:					List[str]			   =       {'''configuration_vit_hybrid''': ['''VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTHybridConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_						:					Tuple			   =       [
     '''VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ViTHybridForImageClassification''',
     '''ViTHybridModel''',
     '''ViTHybridPreTrainedModel''',
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_						:					int			   =       ['''ViTHybridImageProcessor''']


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

 SCREAMING_SNAKE_CASE_						:					Any			   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






