code: 
  '''simple docstring'''
  
  
  
  import argparse
  import logging
  import sys
  from unittest.mock import patch
  
  import run_glue_with_pabee
  
  from transformers.testing_utils import TestCasePlus
  
  
  logging.basicConfig(level=logging.DEBUG)
  
  __A          =    logging.getLogger()
  
  
  
  
  
  def  _A		(							):
  						lowercase__							 =       argparse.ArgumentParser()
  						parser.add_argument("""-f"""	)
  						lowercase__							 =       parser.parse_args()
  						return args.f
  
  
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       logging.StreamHandler(sys.stdout       )
  									logger.addHandler(lowerCamelCase__       )
  
  									lowercase__							 =       self.get_auto_remove_tmp_dir()
  									lowercase__							 =       F'''
              run_glue_with_pabee.py
              --model_type albert
              --model_name_or_path albert-base-v2
              --data_dir ./tests/fixtures/tests_samples/MRPC/
              --output_dir {tmp_dir}
              --overwrite_output_dir
              --task_name mrpc
              --do_train
              --do_eval
              --per_gpu_train_batch_size=2
              --per_gpu_eval_batch_size=1
              --learning_rate=2e-5
              --max_steps=50
              --warmup_steps=2
              --seed=42
              --max_seq_length=128
              '''.split()
  
  									with patch.object(lowerCamelCase__       ,     """argv"""       ,     lowerCamelCase__       ):
  															lowercase__							 =       run_glue_with_pabee.main()
  															for value in result.values():
  																					self.assertGreaterEqual(lowerCamelCase__       ,     0.75       )
  
  
  
  
  
  
  
style_context: 
  '''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  _A		(							lowercase__		,		lowercase__ = True	):
  						lowercase__							 =       {}
  						for name in model.attn_processors.keys():
  												lowercase__							 =       name.endswith("""attn2.processor"""	) and not (
  												    name.startswith("""transformer_in"""	) or """temp_attentions""" in name.split("""."""	)
  												)
  												lowercase__							 =       model.config.cross_attention_dim if has_cross_attention else None
  												if name.startswith("""mid_block"""	):
  																		lowercase__							 =       model.config.block_out_channels[-1]
  												elif name.startswith("""up_blocks"""	):
  																		lowercase__							 =       int(name[len("""up_blocks."""	)]	)
  																		lowercase__							 =       list(reversed(model.config.block_out_channels	)	)[block_id]
  												elif name.startswith("""down_blocks"""	):
  																		lowercase__							 =       int(name[len("""down_blocks."""	)]	)
  																		lowercase__							 =       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
  																		lowercase__							 =       8 * model.config.attention_head_dim
  
  												lowercase__							 =       LoRAAttnProcessor(hidden_size=lowercase__		,		cross_attention_dim=lowercase__	)
  												lowercase__							 =       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							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							List[Any]       		=  UNetaDConditionModel
  			lowerCamelCase							:							int       		=  """sample"""
  
  
  
  			@property
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       4
  									lowercase__							 =       4
  									lowercase__							 =       4
  									lowercase__							 =       (32, 32)
  
  									lowercase__							 =       floats_tensor((batch_size, num_channels, num_frames) + sizes       ).to(lowerCamelCase__       )
  									lowercase__							 =       torch.tensor([10]       ).to(lowerCamelCase__       )
  									lowercase__							 =       floats_tensor((batch_size, 4, 32)       ).to(lowerCamelCase__       )
  
  									return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
  
  
  
  			@property
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return (4, 4, 32, 32)
  
  
  
  			@property
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return (4, 4, 32, 32)
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       {
  									    """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,
  									}
  									lowercase__							 =       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:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  									lowercase__							 =       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 A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       32
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       output.sample
  
  									self.assertIsNotNone(lowerCamelCase__       )
  									lowercase__							 =       inputs_dict["""sample"""].shape
  									self.assertEqual(output.shape       ,     lowerCamelCase__       ,     """Input and output shapes do not match"""       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  									lowercase__							 =       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       )
  
  															lowercase__							 =       model(**lowerCamelCase__       )
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       first.sample
  
  															lowercase__							 =       model(**lowerCamelCase__       )
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       second.sample
  
  									lowercase__							 =       first.cpu().numpy()
  									lowercase__							 =       second.cpu().numpy()
  									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
  									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
  									lowercase__							 =       np.amax(np.abs(out_a - out_a       )       )
  									self.assertLessEqual(lowerCamelCase__       ,     1e-5       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									model.set_attention_slice("""auto"""       )
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  									assert output is not None
  
  									model.set_attention_slice("""max"""       )
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  									assert output is not None
  
  									model.set_attention_slice(2       )
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  									assert output is not None
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       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():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  															lowercase__							 =       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 A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       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       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															new_model.load_attn_procs(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       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 A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       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       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															new_model.load_attn_procs(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       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 A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									lowercase__							 =       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       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															new_model.load_attn_procs(lowerCamelCase__       ,     weight_name="""pytorch_lora_weights.bin"""       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									lowercase__							 =       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       )
  															lowercase__							 =       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 A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample
  
  									model.set_attn_processor(AttnProcessor()       )
  
  									with torch.no_grad():
  															lowercase__							 =       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 A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       4
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									# default
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  															model.enable_xformers_memory_efficient_attention()
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  															model.disable_xformers_memory_efficient_attention()
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									assert (sample - on_sample).abs().max() < 1e-4
  									assert (sample - off_sample).abs().max() < 1e-4
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  									lowercase__							 =       32
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )[0]
  
  									model.enable_forward_chunking()
  									with torch.no_grad():
  															lowercase__							 =       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.
  
  
  
  
  
  
  