

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     						=      logging.get_logger(__name__)



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase = True	)				->      Optional[int]:



  """simple docstring"""






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

    _snake_case		=				LoRAAttnProcessor(hidden_size=A_					,			cross_attention_dim=A_	)
    _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     lowercase_       (    snake_case__  ,				snake_case__  ,				unittest.TestCase	):
      UpperCamelCase_     :						Union[str, Any]		 =							UNetaDConditionModel
      UpperCamelCase_     :						List[str]		 =							"sample"



      @property
      def 		UpperCamelCase_						(     self :							Any						)       ->	Dict:
        _snake_case		=				4
        _snake_case		=				4
        _snake_case		=				4
        _snake_case		=				(32, 32)

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

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



      @property
      def 		UpperCamelCase_						(     self :							int						)       ->	Union[str, Any]:
        return (4, 4, 32, 32)



      @property
      def 		UpperCamelCase_						(     self :							Dict						)       ->	Union[str, Any]:
        return (4, 4, 32, 32)



      def 		UpperCamelCase_						(     self :							List[Any]						)       ->	List[str]:
        _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,
        }
        _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 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	Dict:
        _snake_case		=				self.prepare_init_args_and_inputs_for_common()
        _snake_case		=				self.model_class(**__SCREAMING_SNAKE_CASE						)

        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 :							Dict						)       ->	Any:
        _snake_case		=				self.prepare_init_args_and_inputs_for_common()

        _snake_case		=				32

        _snake_case		=				self.model_class(**__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()

        with torch.no_grad():
          _snake_case		=				model(**__SCREAMING_SNAKE_CASE						)

          if isinstance(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE						):
            _snake_case		=				output.sample

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



      def 		UpperCamelCase_						(     self :							str						)       ->	List[Any]:
        _snake_case		=				self.prepare_init_args_and_inputs_for_common()
        _snake_case		=				self.model_class(**__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()

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

          _snake_case		=				model(**__SCREAMING_SNAKE_CASE						)
          if isinstance(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE						):
            _snake_case		=				first.sample

          _snake_case		=				model(**__SCREAMING_SNAKE_CASE						)
          if isinstance(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE						):
            _snake_case		=				second.sample

        _snake_case		=				first.cpu().numpy()
        _snake_case		=				second.cpu().numpy()
        _snake_case		=				out_a[~np.isnan(__SCREAMING_SNAKE_CASE						)]
        _snake_case		=				out_a[~np.isnan(__SCREAMING_SNAKE_CASE						)]
        _snake_case		=				np.amax(np.abs(out_a - out_a						)						)
        self.assertLessEqual(__SCREAMING_SNAKE_CASE     ,   1e-5						)



      def 		UpperCamelCase_						(     self :							int						)       ->	Optional[int]:
        _snake_case		=				self.prepare_init_args_and_inputs_for_common()

        _snake_case		=				8

        _snake_case		=				self.model_class(**__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()

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

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

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



      def 		UpperCamelCase_						(     self :							Optional[Any]						)       ->	Tuple:
        _snake_case		=				self.prepare_init_args_and_inputs_for_common()

        _snake_case		=				8

        _snake_case		=				self.model_class(**__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)

        with torch.no_grad():
          _snake_case		=				model(**__SCREAMING_SNAKE_CASE						).sample

        _snake_case		=				create_lora_layers(__SCREAMING_SNAKE_CASE						)

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

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

        with torch.no_grad():
          _snake_case		=				model(**__SCREAMING_SNAKE_CASE     ,   cross_attention_kwargs={'''scale''': 0.0}						).sample
          _snake_case		=				model(**__SCREAMING_SNAKE_CASE     ,   cross_attention_kwargs={'''scale''': 0.5}						).sample
          _snake_case		=				model(**__SCREAMING_SNAKE_CASE     ,   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 :							List[Any]						)       ->	List[Any]:
        _snake_case		=				self.prepare_init_args_and_inputs_for_common()

        _snake_case		=				8

        torch.manual_seed(0						)
        _snake_case		=				self.model_class(**__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)

        with torch.no_grad():
          _snake_case		=				model(**__SCREAMING_SNAKE_CASE						).sample

        _snake_case		=				create_lora_layers(__SCREAMING_SNAKE_CASE						)
        model.set_attn_processor(__SCREAMING_SNAKE_CASE						)

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

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

        with torch.no_grad():
          _snake_case		=				new_model(**__SCREAMING_SNAKE_CASE     ,   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 :							str						)       ->	Optional[Any]:
        _snake_case		=				self.prepare_init_args_and_inputs_for_common()

        _snake_case		=				8

        torch.manual_seed(0						)
        _snake_case		=				self.model_class(**__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)

        with torch.no_grad():
          _snake_case		=				model(**__SCREAMING_SNAKE_CASE						).sample

        _snake_case		=				create_lora_layers(__SCREAMING_SNAKE_CASE						)
        model.set_attn_processor(__SCREAMING_SNAKE_CASE						)

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

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

        with torch.no_grad():
          _snake_case		=				new_model(**__SCREAMING_SNAKE_CASE     ,   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 :							str						)       ->	Optional[Any]:
        _snake_case		=				self.prepare_init_args_and_inputs_for_common()

        _snake_case		=				8

        torch.manual_seed(0						)
        _snake_case		=				self.model_class(**__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)

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



      def 		UpperCamelCase_						(     self :							Any						)       ->	Optional[Any]:
        _snake_case		=				self.prepare_init_args_and_inputs_for_common()

        _snake_case		=				8

        torch.manual_seed(0						)
        _snake_case		=				self.model_class(**__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)

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



      def 		UpperCamelCase_						(     self :							Optional[int]						)       ->	int:
        _snake_case		=				self.prepare_init_args_and_inputs_for_common()

        _snake_case		=				8

        torch.manual_seed(0						)
        _snake_case		=				self.model_class(**__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)

        with torch.no_grad():
          _snake_case		=				model(**__SCREAMING_SNAKE_CASE						).sample

        _snake_case		=				create_lora_layers(__SCREAMING_SNAKE_CASE						)
        model.set_attn_processor(__SCREAMING_SNAKE_CASE						)

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

        model.set_attn_processor(AttnProcessor()						)

        with torch.no_grad():
          _snake_case		=				model(**__SCREAMING_SNAKE_CASE						).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]						)       ->	int:
        _snake_case		=				self.prepare_init_args_and_inputs_for_common()

        _snake_case		=				4

        torch.manual_seed(0						)
        _snake_case		=				self.model_class(**__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        _snake_case		=				create_lora_layers(__SCREAMING_SNAKE_CASE						)
        model.set_attn_processor(__SCREAMING_SNAKE_CASE						)

        # default
        with torch.no_grad():
          _snake_case		=				model(**__SCREAMING_SNAKE_CASE						).sample

          model.enable_xformers_memory_efficient_attention()
          _snake_case		=				model(**__SCREAMING_SNAKE_CASE						).sample

          model.disable_xformers_memory_efficient_attention()
          _snake_case		=				model(**__SCREAMING_SNAKE_CASE						).sample

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







      def 		UpperCamelCase_						(     self :							Optional[Any]						)       ->	Optional[Any]:
        _snake_case		=				self.prepare_init_args_and_inputs_for_common()
        _snake_case		=				32

        _snake_case		=				self.model_class(**__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()

        with torch.no_grad():
          _snake_case		=				model(**__SCREAMING_SNAKE_CASE						)[0]

        model.enable_forward_chunking()
        with torch.no_grad():
          _snake_case		=				model(**__SCREAMING_SNAKE_CASE						)[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 	_lowerCamelCase  (		A_  :	float							,  A_  :	float							,  A_  :	int      )			->			float:

   '''simple docstring'''






   UpperCamelCase__ :			Union[str, Any] 						=[1 / first_term]
   UpperCamelCase__ :			Tuple 						=1 / first_term
   for _ in range(number_of_terms - 1      ):
      first_term += common_difference
      arithmetic_progression.append(A_      )
   UpperCamelCase__ :			List[str] 						=[1 / step for step in arithmetic_progression]
   return sum(A_      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(sum_of_harmonic_progression(1 / 2, 2, 2))

