






"""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 :    Dict      =			logging.get_logger(__name__)




def 					a			(  UpperCamelCase_				:					int  ,						UpperCamelCase_				:					bool = True		)	->		Union[str, Any]:
 __A						:							Optional[Any]      =						{}
 for name in model.attn_processors.keys():
  __A						:							str      =						name.endswith('attn2.processor'		) and not (
      name.startswith('transformer_in'		) or 'temp_attentions' in name.split('.'		)
  )
  __A						:							Tuple      =						model.config.cross_attention_dim if has_cross_attention else None
  if name.startswith('mid_block'		):
   __A						:							str      =						model.config.block_out_channels[-1]
  elif name.startswith('up_blocks'		):
   __A						:							Union[str, Any]      =						int(name[len('up_blocks.'		)]		)
   __A						:							Any      =						list(reversed(model.config.block_out_channels		)		)[block_id]
  elif name.startswith('down_blocks'		):
   __A						:							List[Any]      =						int(name[len('down_blocks.'		)]		)
   __A						:							str      =						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						:							int      =						8 * model.config.attention_head_dim

  __A						:							Any      =						LoRAAttnProcessor(hidden_size=UpperCamelCase_  ,						cross_attention_dim=UpperCamelCase_		)
  __A						:							Dict      =						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 ( snake_case_			,snake_case_			,unittest.TestCase					):
  __lowercase     :     List[Any]       						=       UNetaDConditionModel
  __lowercase     :     List[Any]       						=       'sample'



  @property
  def     A_					(self	:		Union[str, Any] )		->				Union[str, Any]:
   __A						:							Optional[int]      =						4
   __A						:							Optional[int]      =						4
   __A						:							Optional[Any]      =						4
   __A						:							Optional[Any]      =						(32, 32)

   __A						:							Optional[Any]      =						floats_tensor((batch_size, num_channels, num_frames) + sizes ).to(lowercase_ )
   __A						:							Optional[int]      =						torch.tensor([10] ).to(lowercase_ )
   __A						:							List[Any]      =						floats_tensor((batch_size, 4, 32) ).to(lowercase_ )

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



  @property
  def     A_					(self	:		str )		->				str:
   return (4, 4, 32, 32)



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



  def     A_					(self	:		Tuple )		->				int:
   __A						:							str      =						{
       '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						:							Any      =						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 )		->				Tuple:
   __A     ,       __A						:							Union[str, Any]      =						self.prepare_init_args_and_inputs_for_common()
   __A						:							int      =						self.model_class(**lowercase_ )

   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	:		str )		->				str:
   __A     ,       __A						:							Dict      =						self.prepare_init_args_and_inputs_for_common()

   __A						:							Dict      =						32

   __A						:							Tuple      =						self.model_class(**lowercase_ )
   model.to(lowercase_ )
   model.eval()

   with torch.no_grad():
    __A						:							List[Any]      =						model(**lowercase_ )

    if isinstance(lowercase_					,      lowercase_ ):
     __A						:							Dict      =						output.sample

   self.assertIsNotNone(lowercase_ )
   __A						:							Optional[int]      =						inputs_dict['sample'].shape
   self.assertEqual(output.shape					,      lowercase_					,      'Input and output shapes do not match' )



  def     A_					(self	:		str )		->				Tuple:
   __A     ,       __A						:							int      =						self.prepare_init_args_and_inputs_for_common()
   __A						:							Tuple      =						self.model_class(**lowercase_ )
   model.to(lowercase_ )
   model.eval()

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

    __A						:							int      =						model(**lowercase_ )
    if isinstance(lowercase_					,      lowercase_ ):
     __A						:							Dict      =						first.sample

    __A						:							Optional[Any]      =						model(**lowercase_ )
    if isinstance(lowercase_					,      lowercase_ ):
     __A						:							Tuple      =						second.sample

   __A						:							Any      =						first.cpu().numpy()
   __A						:							Tuple      =						second.cpu().numpy()
   __A						:							List[str]      =						out_a[~np.isnan(lowercase_ )]
   __A						:							Optional[int]      =						out_a[~np.isnan(lowercase_ )]
   __A						:							List[Any]      =						np.amax(np.abs(out_a - out_a ) )
   self.assertLessEqual(lowercase_					,      1E-5 )



  def     A_					(self	:		int )		->				Union[str, Any]:
   __A     ,       __A						:							Tuple      =						self.prepare_init_args_and_inputs_for_common()

   __A						:							List[Any]      =						8

   __A						:							Optional[int]      =						self.model_class(**lowercase_ )
   model.to(lowercase_ )
   model.eval()

   model.set_attention_slice('auto' )
   with torch.no_grad():
    __A						:							Union[str, Any]      =						model(**lowercase_ )
   assert output is not None

   model.set_attention_slice('max' )
   with torch.no_grad():
    __A						:							Optional[int]      =						model(**lowercase_ )
   assert output is not None

   model.set_attention_slice(2 )
   with torch.no_grad():
    __A						:							Tuple      =						model(**lowercase_ )
   assert output is not None



  def     A_					(self	:		Union[str, Any] )		->				Dict:
   __A     ,       __A						:							Dict      =						self.prepare_init_args_and_inputs_for_common()

   __A						:							Optional[Any]      =						8

   __A						:							int      =						self.model_class(**lowercase_ )
   model.to(lowercase_ )

   with torch.no_grad():
    __A						:							int      =						model(**lowercase_ ).sample

   __A						:							Union[str, Any]      =						create_lora_layers(lowercase_ )

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

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

   with torch.no_grad():
    __A						:							Tuple      =						model(**lowercase_					,      cross_attention_kwargs={'scale': 0.0} ).sample
    __A						:							Any      =						model(**lowercase_					,      cross_attention_kwargs={'scale': 0.5} ).sample
    __A						:							List[Any]      =						model(**lowercase_					,      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[int] )		->				int:
   __A     ,       __A						:							Dict      =						self.prepare_init_args_and_inputs_for_common()

   __A						:							int      =						8

   torch.manual_seed(0 )
   __A						:							List[str]      =						self.model_class(**lowercase_ )
   model.to(lowercase_ )

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

   __A						:							Optional[Any]      =						create_lora_layers(lowercase_ )
   model.set_attn_processor(lowercase_ )

   with torch.no_grad():
    __A						:							List[str]      =						model(**lowercase_					,      cross_attention_kwargs={'scale': 0.5} ).sample

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

   with torch.no_grad():
    __A						:							Any      =						new_model(**lowercase_					,      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 )		->				Tuple:
   __A     ,       __A						:							List[str]      =						self.prepare_init_args_and_inputs_for_common()

   __A						:							Dict      =						8

   torch.manual_seed(0 )
   __A						:							str      =						self.model_class(**lowercase_ )
   model.to(lowercase_ )

   with torch.no_grad():
    __A						:							Optional[Any]      =						model(**lowercase_ ).sample

   __A						:							int      =						create_lora_layers(lowercase_ )
   model.set_attn_processor(lowercase_ )

   with torch.no_grad():
    __A						:							int      =						model(**lowercase_					,      cross_attention_kwargs={'scale': 0.5} ).sample

   with tempfile.TemporaryDirectory() as tmpdirname:
    model.save_attn_procs(lowercase_					,      safe_serialization=lowercase_ )
    self.assertTrue(os.path.isfile(os.path.join(lowercase_					,      'pytorch_lora_weights.safetensors' ) ) )
    torch.manual_seed(0 )
    __A						:							Optional[Any]      =						self.model_class(**lowercase_ )
    new_model.to(lowercase_ )
    new_model.load_attn_procs(lowercase_ )

   with torch.no_grad():
    __A						:							Tuple      =						new_model(**lowercase_					,      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	:		Tuple )		->				Optional[Any]:
   # enable deterministic behavior for gradient checkpointing
   __A     ,       __A						:							Optional[int]      =						self.prepare_init_args_and_inputs_for_common()

   __A						:							Union[str, Any]      =						8

   torch.manual_seed(0 )
   __A						:							List[str]      =						self.model_class(**lowercase_ )
   model.to(lowercase_ )

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



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

   __A						:							Optional[Any]      =						8

   torch.manual_seed(0 )
   __A						:							Dict      =						self.model_class(**lowercase_ )
   model.to(lowercase_ )

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



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

   __A						:							Any      =						8

   torch.manual_seed(0 )
   __A						:							Tuple      =						self.model_class(**lowercase_ )
   model.to(lowercase_ )

   with torch.no_grad():
    __A						:							List[str]      =						model(**lowercase_ ).sample

   __A						:							Any      =						create_lora_layers(lowercase_ )
   model.set_attn_processor(lowercase_ )

   with torch.no_grad():
    __A						:							List[str]      =						model(**lowercase_					,      cross_attention_kwargs={'scale': 0.0} ).sample

   model.set_attn_processor(AttnProcessor() )

   with torch.no_grad():
    __A						:							Any      =						model(**lowercase_ ).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	:		Union[str, Any] )		->				List[Any]:
   # enable deterministic behavior for gradient checkpointing
   __A     ,       __A						:							Tuple      =						self.prepare_init_args_and_inputs_for_common()

   __A						:							List[str]      =						4

   torch.manual_seed(0 )
   __A						:							List[Any]      =						self.model_class(**lowercase_ )
   model.to(lowercase_ )
   __A						:							Any      =						create_lora_layers(lowercase_ )
   model.set_attn_processor(lowercase_ )

   # default
   with torch.no_grad():
    __A						:							str      =						model(**lowercase_ ).sample

    model.enable_xformers_memory_efficient_attention()
    __A						:							int      =						model(**lowercase_ ).sample

    model.disable_xformers_memory_efficient_attention()
    __A						:							str      =						model(**lowercase_ ).sample

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



  def     A_					(self	:		Union[str, Any] )		->				Tuple:
   __A     ,       __A						:							Dict      =						self.prepare_init_args_and_inputs_for_common()
   __A						:							Any      =						32

   __A						:							str      =						self.model_class(**lowercase_ )
   model.to(lowercase_ )
   model.eval()

   with torch.no_grad():
    __A						:							Optional[int]      =						model(**lowercase_ )[0]

   model.enable_forward_chunking()
   with torch.no_grad():
    __A						:							Any      =						model(**lowercase_ )[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"""


import torch
from torch import autograd






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		Optional[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float					,      lowercase_	:		bool )		->				List[str]:
   __A						:							str      =						inputs.numel()
   __A						:							str      =						int(0.005 * nb_elems ) + 1
   if sigmoid:
    __A						:							Dict      =						(torch.sigmoid(lowercase_ ) > threshold).type(inputs.type() )
   else:
    __A						:							Tuple      =						(inputs > threshold).type(inputs.type() )
   if mask.sum() < nb_min:
    # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
    __A						:							Any      =						inputs.flatten().kthvalue(max(nb_elems - nb_min					,      1 ) ).values
    __A						:							Union[str, Any]      =						(inputs > k_threshold).type(inputs.type() )
   return mask



  @staticmethod
  def     A_					(lowercase_	:		str					,      lowercase_	:		Any )		->				int:
   return gradOutput, None, None






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		List[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				Dict:
   __A						:							List[Any]      =						inputs.clone()
   __A     ,       __A						:							Optional[int]      =						inputs.flatten().sort(descending=lowercase_ )
   __A						:							Tuple      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							Optional[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Any      =						1
   return mask



  @staticmethod
  def     A_					(lowercase_	:		Tuple					,      lowercase_	:		Optional[Any] )		->				str:
   return gradOutput, None






class __a ( snake_case_					):



  @staticmethod
  def     A_					(lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				List[str]:
   __A						:							List[str]      =						inputs.clone()
   __A     ,       __A						:							List[Any]      =						inputs.abs().flatten().sort(descending=lowercase_ )
   __A						:							Dict      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							List[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Tuple      =						1
   return mask





