



'''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__    :     int			  =   logging.get_logger(__name__)



def     UpperCAmelCase     (    a_      ,		a_ = True		)   ->       Union[str, Any]:



   """simple docstring"""






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

      A_  :		Any						=					LoRAAttnProcessor(hidden_size=a_      ,		cross_attention_dim=a_		)
      A_  :		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 							_lowerCAmelCase	(  __A,							__A,							unittest.TestCase							):


 """simple docstring"""

 lowerCamelCase									=     UNetaDConditionModel
 lowerCamelCase									=     '''sample'''







 @property
 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    A_  :		List[Any]						=					4
    A_  :		Optional[Any]						=					4
    A_  :		Any						=					4
    A_  :		Any						=					(32, 32)

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

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







 @property
 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    return (4, 4, 32, 32)







 @property
 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    return (4, 4, 32, 32)







 def       UpperCAmelCase_       (      self  )    ->    str:
    A_  :		int						=					{
        """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_  :		Dict						=					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       UpperCAmelCase_       (      self  )    ->    int:
    A_						,  A_  :		Any						=					self.prepare_init_args_and_inputs_for_common()
    A_  :		Optional[Any]						=					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       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    A_						,  A_  :		Tuple						=					self.prepare_init_args_and_inputs_for_common()

    A_  :		int						=					32

    A_  :		Any						=					self.model_class(**_lowerCamelCase  )
    model.to(_lowerCamelCase  )
    model.eval()

    with torch.no_grad():
       A_  :		List[str]						=					model(**_lowerCamelCase  )

       if isinstance(_lowerCamelCase					,    _lowerCamelCase  ):
          A_  :		List[str]						=					output.sample

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







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    A_						,  A_  :		Optional[Any]						=					self.prepare_init_args_and_inputs_for_common()
    A_  :		Dict						=					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_  :		Optional[Any]						=					model(**_lowerCamelCase  )
       if isinstance(_lowerCamelCase					,    _lowerCamelCase  ):
          A_  :		str						=					first.sample

       A_  :		List[str]						=					model(**_lowerCamelCase  )
       if isinstance(_lowerCamelCase					,    _lowerCamelCase  ):
          A_  :		str						=					second.sample

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







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_						,  A_  :		int						=					self.prepare_init_args_and_inputs_for_common()

    A_  :		Tuple						=					8

    A_  :		Tuple						=					self.model_class(**_lowerCamelCase  )
    model.to(_lowerCamelCase  )
    model.eval()

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

    model.set_attention_slice("""max"""  )
    with torch.no_grad():
       A_  :		Optional[Any]						=					model(**_lowerCamelCase  )
    assert output is not None

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







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    A_						,  A_  :		List[str]						=					self.prepare_init_args_and_inputs_for_common()

    A_  :		Union[str, Any]						=					8

    A_  :		List[Any]						=					self.model_class(**_lowerCamelCase  )
    model.to(_lowerCamelCase  )

    with torch.no_grad():
       A_  :		Union[str, Any]						=					model(**_lowerCamelCase  ).sample

    A_  :		List[str]						=					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_  :		List[str]						=					model(**_lowerCamelCase					,    cross_attention_kwargs={"""scale""": 0.0}  ).sample
       A_  :		List[str]						=					model(**_lowerCamelCase					,    cross_attention_kwargs={"""scale""": 0.5}  ).sample
       A_  :		List[Any]						=					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       UpperCAmelCase_       (      self  )    ->    Any:
    A_						,  A_  :		Union[str, Any]						=					self.prepare_init_args_and_inputs_for_common()

    A_  :		List[Any]						=					8

    torch.manual_seed(0  )
    A_  :		int						=					self.model_class(**_lowerCamelCase  )
    model.to(_lowerCamelCase  )

    with torch.no_grad():
       A_  :		List[Any]						=					model(**_lowerCamelCase  ).sample

    A_  :		int						=					create_lora_layers(_lowerCamelCase  )
    model.set_attn_processor(_lowerCamelCase  )

    with torch.no_grad():
       A_  :		Union[str, Any]						=					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_  :		int						=					self.model_class(**_lowerCamelCase  )
       new_model.to(_lowerCamelCase  )
       new_model.load_attn_procs(_lowerCamelCase  )

    with torch.no_grad():
       A_  :		Union[str, Any]						=					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       UpperCAmelCase_       (      self  )    ->    Any:
    A_						,  A_  :		Optional[int]						=					self.prepare_init_args_and_inputs_for_common()

    A_  :		Tuple						=					8

    torch.manual_seed(0  )
    A_  :		Dict						=					self.model_class(**_lowerCamelCase  )
    model.to(_lowerCamelCase  )

    with torch.no_grad():
       A_  :		Union[str, Any]						=					model(**_lowerCamelCase  ).sample

    A_  :		Optional[Any]						=					create_lora_layers(_lowerCamelCase  )
    model.set_attn_processor(_lowerCamelCase  )

    with torch.no_grad():
       A_  :		Tuple						=					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_  :		int						=					self.model_class(**_lowerCamelCase  )
       new_model.to(_lowerCamelCase  )
       new_model.load_attn_procs(_lowerCamelCase  )

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

    A_  :		int						=					8

    torch.manual_seed(0  )
    A_  :		Optional[int]						=					self.model_class(**_lowerCamelCase  )
    model.to(_lowerCamelCase  )

    A_  :		List[Any]						=					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_  :		Union[str, Any]						=					self.model_class(**_lowerCamelCase  )
       new_model.to(_lowerCamelCase  )
       new_model.load_attn_procs(_lowerCamelCase					,    weight_name="""pytorch_lora_weights.bin"""  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_						,  A_  :		str						=					self.prepare_init_args_and_inputs_for_common()

    A_  :		Any						=					8

    torch.manual_seed(0  )
    A_  :		Any						=					self.model_class(**_lowerCamelCase  )
    model.to(_lowerCamelCase  )

    A_  :		str						=					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_  :		List[str]						=					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       UpperCAmelCase_       (      self  )    ->    int:
    A_						,  A_  :		str						=					self.prepare_init_args_and_inputs_for_common()

    A_  :		Union[str, Any]						=					8

    torch.manual_seed(0  )
    A_  :		str						=					self.model_class(**_lowerCamelCase  )
    model.to(_lowerCamelCase  )

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

    A_  :		Optional[int]						=					create_lora_layers(_lowerCamelCase  )
    model.set_attn_processor(_lowerCamelCase  )

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

    model.set_attn_processor(AttnProcessor()  )

    with torch.no_grad():
       A_  :		Tuple						=					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       UpperCAmelCase_       (      self  )    ->    List[str]:
    # enable deterministic behavior for gradient checkpointing
    A_						,  A_  :		Dict						=					self.prepare_init_args_and_inputs_for_common()

    A_  :		Optional[int]						=					4

    torch.manual_seed(0  )
    A_  :		List[Any]						=					self.model_class(**_lowerCamelCase  )
    model.to(_lowerCamelCase  )
    A_  :		Union[str, Any]						=					create_lora_layers(_lowerCamelCase  )
    model.set_attn_processor(_lowerCamelCase  )

    # default
    with torch.no_grad():
       A_  :		Optional[Any]						=					model(**_lowerCamelCase  ).sample

       model.enable_xformers_memory_efficient_attention()
       A_  :		Tuple						=					model(**_lowerCamelCase  ).sample

       model.disable_xformers_memory_efficient_attention()
       A_  :		Optional[Any]						=					model(**_lowerCamelCase  ).sample

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







 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    A_						,  A_  :		Optional[Any]						=					self.prepare_init_args_and_inputs_for_common()
    A_  :		Any						=					32

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

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

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





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__    :     List[Any]			  =   {
    'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
    'tokenization_layoutlm': ['LayoutLMTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     Any			  =   ['LayoutLMTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     Optional[Any]			  =   [
      'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'LayoutLMForMaskedLM',
      'LayoutLMForSequenceClassification',
      'LayoutLMForTokenClassification',
      'LayoutLMForQuestionAnswering',
      'LayoutLMModel',
      'LayoutLMPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     str			  =   [
      'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLayoutLMForMaskedLM',
      'TFLayoutLMForSequenceClassification',
      'TFLayoutLMForTokenClassification',
      'TFLayoutLMForQuestionAnswering',
      'TFLayoutLMMainLayer',
      'TFLayoutLMModel',
      'TFLayoutLMPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
  from .tokenization_layoutlm import LayoutLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )

else:
  import sys

  UpperCamelCase__    :     List[str]			  =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
