

"""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()

A									=						logging.get_logger(__name__)







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					bool = True   )						->     List[Any]:



       """simple docstring"""







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

              __magic_name__			:		Optional[Any]        =     LoRAAttnProcessor(hidden_size=lowerCamelCase     , cross_attention_dim=lowerCamelCase   )
              __magic_name__			:		List[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        _UpperCamelCase	(						lowerCamelCase__   ,				lowerCamelCase__   ,				unittest.TestCase	):


  """simple docstring"""





  snake_case_        =    UNetaDConditionModel
  snake_case_        =    'sample'



  @property
  def 							_UpperCAmelCase      ( self					: Optional[Any]      )	->			str:


         '''simple docstring'''





         __magic_name__			:		str        =     4
         __magic_name__			:		List[str]        =     4
         __magic_name__			:		List[str]        =     4
         __magic_name__			:		str        =     (32, 32)

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

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



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


         '''simple docstring'''





         return (4, 4, 32, 32)



  @property
  def 							_UpperCAmelCase      ( self					: Tuple      )	->			Dict:


         '''simple docstring'''





         return (4, 4, 32, 32)



  def 							_UpperCAmelCase      ( self					: Optional[Any]      )	->			Optional[Any]:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     {
             '''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,
         }
         __magic_name__			:		Optional[int]        =     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					: Tuple      )	->			Any:


         '''simple docstring'''





         __magic_name__       ,							__magic_name__			:		Optional[Any]        =     self.prepare_init_args_and_inputs_for_common()
         __magic_name__			:		Tuple        =     self.model_class(**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      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__       ,							__magic_name__			:		List[str]        =     self.prepare_init_args_and_inputs_for_common()

         __magic_name__			:		Any        =     32

         __magic_name__			:		List[Any]        =     self.model_class(**snake_case      )
         model.to(snake_case      )
         model.eval()

         with torch.no_grad():
                __magic_name__			:		int        =     model(**snake_case      )

                if isinstance(snake_case       ,       snake_case      ):
                       __magic_name__			:		str        =     output.sample

         self.assertIsNotNone(snake_case      )
         __magic_name__			:		Dict        =     inputs_dict['''sample'''].shape
         self.assertEqual(output.shape       ,       snake_case       ,       '''Input and output shapes do not match'''      )



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


         '''simple docstring'''





         __magic_name__       ,							__magic_name__			:		Any        =     self.prepare_init_args_and_inputs_for_common()
         __magic_name__			:		Tuple        =     self.model_class(**snake_case      )
         model.to(snake_case      )
         model.eval()

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

                __magic_name__			:		Dict        =     model(**snake_case      )
                if isinstance(snake_case       ,       snake_case      ):
                       __magic_name__			:		Any        =     first.sample

                __magic_name__			:		str        =     model(**snake_case      )
                if isinstance(snake_case       ,       snake_case      ):
                       __magic_name__			:		Dict        =     second.sample

         __magic_name__			:		Tuple        =     first.cpu().numpy()
         __magic_name__			:		int        =     second.cpu().numpy()
         __magic_name__			:		int        =     out_a[~np.isnan(snake_case      )]
         __magic_name__			:		Union[str, Any]        =     out_a[~np.isnan(snake_case      )]
         __magic_name__			:		Union[str, Any]        =     np.amax(np.abs(out_a - out_a      )      )
         self.assertLessEqual(snake_case       ,       1e-5      )



  def 							_UpperCAmelCase      ( self					: Optional[int]      )	->			Optional[int]:


         '''simple docstring'''





         __magic_name__       ,							__magic_name__			:		int        =     self.prepare_init_args_and_inputs_for_common()

         __magic_name__			:		Union[str, Any]        =     8

         __magic_name__			:		int        =     self.model_class(**snake_case      )
         model.to(snake_case      )
         model.eval()

         model.set_attention_slice('''auto'''      )
         with torch.no_grad():
                __magic_name__			:		List[str]        =     model(**snake_case      )
         assert output is not None

         model.set_attention_slice('''max'''      )
         with torch.no_grad():
                __magic_name__			:		Any        =     model(**snake_case      )
         assert output is not None

         model.set_attention_slice(2      )
         with torch.no_grad():
                __magic_name__			:		Dict        =     model(**snake_case      )
         assert output is not None



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


         '''simple docstring'''





         __magic_name__       ,							__magic_name__			:		List[Any]        =     self.prepare_init_args_and_inputs_for_common()

         __magic_name__			:		Union[str, Any]        =     8

         __magic_name__			:		List[Any]        =     self.model_class(**snake_case      )
         model.to(snake_case      )

         with torch.no_grad():
                __magic_name__			:		Any        =     model(**snake_case      ).sample

         __magic_name__			:		Any        =     create_lora_layers(snake_case      )

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

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

         with torch.no_grad():
                __magic_name__			:		Tuple        =     model(**snake_case       ,       cross_attention_kwargs={'''scale''': 0.0}      ).sample
                __magic_name__			:		str        =     model(**snake_case       ,       cross_attention_kwargs={'''scale''': 0.5}      ).sample
                __magic_name__			:		Union[str, Any]        =     model(**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					: int      )	->			Tuple:


         '''simple docstring'''





         __magic_name__       ,							__magic_name__			:		str        =     self.prepare_init_args_and_inputs_for_common()

         __magic_name__			:		str        =     8

         torch.manual_seed(0      )
         __magic_name__			:		str        =     self.model_class(**snake_case      )
         model.to(snake_case      )

         with torch.no_grad():
                __magic_name__			:		Tuple        =     model(**snake_case      ).sample

         __magic_name__			:		Optional[int]        =     create_lora_layers(snake_case      )
         model.set_attn_processor(snake_case      )

         with torch.no_grad():
                __magic_name__			:		Optional[Any]        =     model(**snake_case       ,       cross_attention_kwargs={'''scale''': 0.5}      ).sample

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

         with torch.no_grad():
                __magic_name__			:		Tuple        =     new_model(**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					: int      )	->			Dict:


         '''simple docstring'''





         __magic_name__       ,							__magic_name__			:		Dict        =     self.prepare_init_args_and_inputs_for_common()

         __magic_name__			:		Any        =     8

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

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

         __magic_name__			:		Dict        =     create_lora_layers(snake_case      )
         model.set_attn_processor(snake_case      )

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

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

         with torch.no_grad():
                __magic_name__			:		Dict        =     new_model(**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					: List[str]      )	->			str:


         '''simple docstring'''





         __magic_name__       ,							__magic_name__			:		Dict        =     self.prepare_init_args_and_inputs_for_common()

         __magic_name__			:		Tuple        =     8

         torch.manual_seed(0      )
         __magic_name__			:		Any        =     self.model_class(**snake_case      )
         model.to(snake_case      )

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



  def 							_UpperCAmelCase      ( self					: List[Any]      )	->			str:


         '''simple docstring'''





         __magic_name__       ,							__magic_name__			:		Tuple        =     self.prepare_init_args_and_inputs_for_common()

         __magic_name__			:		Tuple        =     8

         torch.manual_seed(0      )
         __magic_name__			:		Optional[Any]        =     self.model_class(**snake_case      )
         model.to(snake_case      )

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



  def 							_UpperCAmelCase      ( self					: Optional[Any]      )	->			str:


         '''simple docstring'''





         __magic_name__       ,							__magic_name__			:		List[str]        =     self.prepare_init_args_and_inputs_for_common()

         __magic_name__			:		List[Any]        =     8

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

         with torch.no_grad():
                __magic_name__			:		Tuple        =     model(**snake_case      ).sample

         __magic_name__			:		Union[str, Any]        =     create_lora_layers(snake_case      )
         model.set_attn_processor(snake_case      )

         with torch.no_grad():
                __magic_name__			:		Optional[Any]        =     model(**snake_case       ,       cross_attention_kwargs={'''scale''': 0.0}      ).sample

         model.set_attn_processor(AttnProcessor()      )

         with torch.no_grad():
                __magic_name__			:		int        =     model(**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					: Dict      )	->			Any:


         '''simple docstring'''





         __magic_name__       ,							__magic_name__			:		Optional[int]        =     self.prepare_init_args_and_inputs_for_common()

         __magic_name__			:		int        =     4

         torch.manual_seed(0      )
         __magic_name__			:		List[str]        =     self.model_class(**snake_case      )
         model.to(snake_case      )
         __magic_name__			:		Optional[int]        =     create_lora_layers(snake_case      )
         model.set_attn_processor(snake_case      )

         # default
         with torch.no_grad():
                __magic_name__			:		List[Any]        =     model(**snake_case      ).sample

                model.enable_xformers_memory_efficient_attention()
                __magic_name__			:		Dict        =     model(**snake_case      ).sample

                model.disable_xformers_memory_efficient_attention()
                __magic_name__			:		Optional[int]        =     model(**snake_case      ).sample

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




  def 							_UpperCAmelCase      ( self					: Any      )	->			Optional[Any]:


         '''simple docstring'''





         __magic_name__       ,							__magic_name__			:		Union[str, Any]        =     self.prepare_init_args_and_inputs_for_common()
         __magic_name__			:		Optional[Any]        =     32

         __magic_name__			:		Any        =     self.model_class(**snake_case      )
         model.to(snake_case      )
         model.eval()

         with torch.no_grad():
                __magic_name__			:		Tuple        =     model(**snake_case      )[0]

         model.enable_forward_chunking()
         with torch.no_grad():
                __magic_name__			:		Optional[Any]        =     model(**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.



"""simple docstring"""
from collections import defaultdict







def       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     dict:



       """simple docstring"""







       __magic_name__			:		defaultdict[str, int]        =     defaultdict(lowerCamelCase   )
       # Creating a dictionary containing count of each word
       for word in sentence.split():
              occurrence[word] += 1
       return occurrence


if __name__ == "__main__":
 for word, count in word_occurrence("""INPUT STRING""").items():
  print(F"""{word}: {count}""")
