

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__					: List[Any]								=      logging.get_logger(__name__)


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Union[str, Any]  ,				_SCREAMING_SNAKE_CASE   :						Optional[int] = True    ):





    """simple docstring"""


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

        SCREAMING_SNAKE_CASE_													=  LoRAAttnProcessor(hidden_size=A__  ,				cross_attention_dim=A__    )
        SCREAMING_SNAKE_CASE_													=  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  __snake_case					(      _lowerCamelCase       ,	_lowerCamelCase       ,	unittest.TestCase    ):
  __lowerCAmelCase       :		Union[str, Any]  	=					UNetaDConditionModel
  __lowerCAmelCase       :		Dict  	=					'sample'

  @property
  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  4
      SCREAMING_SNAKE_CASE_													=  4
      SCREAMING_SNAKE_CASE_													=  4
      SCREAMING_SNAKE_CASE_													=  (32, 32)

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

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

  @property
  def   lowerCAmelCase__       (   self):
      return (4, 4, 32, 32)

  @property
  def   lowerCAmelCase__       (   self):
      return (4, 4, 32, 32)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  {
          '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,
      }
      SCREAMING_SNAKE_CASE_													=  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   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_init_args_and_inputs_for_common()
      SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)

      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   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_init_args_and_inputs_for_common()

      SCREAMING_SNAKE_CASE_													=  32

      SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)
      model.to(__UpperCamelCase)
      model.eval()

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase)

          if isinstance(__UpperCamelCase  ,						__UpperCamelCase):
              SCREAMING_SNAKE_CASE_													=  output.sample

      self.assertIsNotNone(__UpperCamelCase)
      SCREAMING_SNAKE_CASE_													=  inputs_dict['sample'].shape
      self.assertEqual(output.shape  ,						__UpperCamelCase  ,						'Input and output shapes do not match')

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_init_args_and_inputs_for_common()
      SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)
      model.to(__UpperCamelCase)
      model.eval()

      with torch.no_grad():
          # Warmup pass when using mps (see #372)
          if torch_device == "mps" and isinstance(__UpperCamelCase  ,						__UpperCamelCase):
              model(**self.dummy_input)

          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase)
          if isinstance(__UpperCamelCase  ,						__UpperCamelCase):
              SCREAMING_SNAKE_CASE_													=  first.sample

          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase)
          if isinstance(__UpperCamelCase  ,						__UpperCamelCase):
              SCREAMING_SNAKE_CASE_													=  second.sample

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

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_init_args_and_inputs_for_common()

      SCREAMING_SNAKE_CASE_													=  8

      SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)
      model.to(__UpperCamelCase)
      model.eval()

      model.set_attention_slice('auto')
      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase)
      assert output is not None

      model.set_attention_slice('max')
      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase)
      assert output is not None

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

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_init_args_and_inputs_for_common()

      SCREAMING_SNAKE_CASE_													=  8

      SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)
      model.to(__UpperCamelCase)

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase).sample

      SCREAMING_SNAKE_CASE_													=  create_lora_layers(__UpperCamelCase)

      # make sure we can set a list of attention processors
      model.set_attn_processor(__UpperCamelCase)
      model.to(__UpperCamelCase)

      # test that attn processors can be set to itself
      model.set_attn_processor(model.attn_processors)

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase  ,						cross_attention_kwargs={'scale': 0.0}).sample
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase  ,						cross_attention_kwargs={'scale': 0.5}).sample
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase  ,						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   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_init_args_and_inputs_for_common()

      SCREAMING_SNAKE_CASE_													=  8

      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)
      model.to(__UpperCamelCase)

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase).sample

      SCREAMING_SNAKE_CASE_													=  create_lora_layers(__UpperCamelCase)
      model.set_attn_processor(__UpperCamelCase)

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase  ,						cross_attention_kwargs={'scale': 0.5}).sample

      with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_attn_procs(__UpperCamelCase)
          self.assertTrue(os.path.isfile(os.path.join(__UpperCamelCase  ,						'pytorch_lora_weights.bin')))
          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)
          new_model.to(__UpperCamelCase)
          new_model.load_attn_procs(__UpperCamelCase)

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  new_model(**__UpperCamelCase  ,						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   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_init_args_and_inputs_for_common()

      SCREAMING_SNAKE_CASE_													=  8

      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)
      model.to(__UpperCamelCase)

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase).sample

      SCREAMING_SNAKE_CASE_													=  create_lora_layers(__UpperCamelCase)
      model.set_attn_processor(__UpperCamelCase)

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase  ,						cross_attention_kwargs={'scale': 0.5}).sample

      with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_attn_procs(__UpperCamelCase  ,						safe_serialization=__UpperCamelCase)
          self.assertTrue(os.path.isfile(os.path.join(__UpperCamelCase  ,						'pytorch_lora_weights.safetensors')))
          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)
          new_model.to(__UpperCamelCase)
          new_model.load_attn_procs(__UpperCamelCase)

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  new_model(**__UpperCamelCase  ,						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   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_init_args_and_inputs_for_common()

      SCREAMING_SNAKE_CASE_													=  8

      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)
      model.to(__UpperCamelCase)

      SCREAMING_SNAKE_CASE_													=  create_lora_layers(__UpperCamelCase  ,						mock_weights=__UpperCamelCase)
      model.set_attn_processor(__UpperCamelCase)
      # Saving as torch, properly reloads with directly filename
      with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_attn_procs(__UpperCamelCase)
          self.assertTrue(os.path.isfile(os.path.join(__UpperCamelCase  ,						'pytorch_lora_weights.bin')))
          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)
          new_model.to(__UpperCamelCase)
          new_model.load_attn_procs(__UpperCamelCase  ,						weight_name='pytorch_lora_weights.bin')

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_init_args_and_inputs_for_common()

      SCREAMING_SNAKE_CASE_													=  8

      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)
      model.to(__UpperCamelCase)

      SCREAMING_SNAKE_CASE_													=  create_lora_layers(__UpperCamelCase  ,						mock_weights=__UpperCamelCase)
      model.set_attn_processor(__UpperCamelCase)
      # Saving as torch, properly reloads with directly filename
      with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_attn_procs(__UpperCamelCase)
          self.assertTrue(os.path.isfile(os.path.join(__UpperCamelCase  ,						'pytorch_lora_weights.bin')))
          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)
          new_model.to(__UpperCamelCase)
          with self.assertRaises(__UpperCamelCase) as e:
              new_model.load_attn_procs(__UpperCamelCase  ,						use_safetensors=__UpperCamelCase)
          self.assertIn('Error no file named pytorch_lora_weights.safetensors'  ,						str(e.exception))

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_init_args_and_inputs_for_common()

      SCREAMING_SNAKE_CASE_													=  8

      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)
      model.to(__UpperCamelCase)

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase).sample

      SCREAMING_SNAKE_CASE_													=  create_lora_layers(__UpperCamelCase)
      model.set_attn_processor(__UpperCamelCase)

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase  ,						cross_attention_kwargs={'scale': 0.0}).sample

      model.set_attn_processor(AttnProcessor())

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase).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   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_init_args_and_inputs_for_common()

      SCREAMING_SNAKE_CASE_													=  4

      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)
      model.to(__UpperCamelCase)
      SCREAMING_SNAKE_CASE_													=  create_lora_layers(__UpperCamelCase)
      model.set_attn_processor(__UpperCamelCase)

      # default
      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase).sample

          model.enable_xformers_memory_efficient_attention()
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase).sample

          model.disable_xformers_memory_efficient_attention()
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase).sample

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


  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_init_args_and_inputs_for_common()
      SCREAMING_SNAKE_CASE_													=  32

      SCREAMING_SNAKE_CASE_													=  self.model_class(**__UpperCamelCase)
      model.to(__UpperCamelCase)
      model.eval()

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase)[0]

      model.enable_forward_chunking()
      with torch.no_grad():
          SCREAMING_SNAKE_CASE_													=  model(**__UpperCamelCase)[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.





def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    if power < 0:
        return -1
    base %= mod
    SCREAMING_SNAKE_CASE_													=  1

    while power > 0:
        if power & 1:
            SCREAMING_SNAKE_CASE_													=  (result * base) % mod
        SCREAMING_SNAKE_CASE_													=  power >> 1
        SCREAMING_SNAKE_CASE_													=  (base * base) % mod

    return result


def    _UpperCAmelCase    (					):





    """simple docstring"""


    print(modular_exponential(3	,				200	,				13    )    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       main()


