





'''simple docstring'''

import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


lowercase__		:    List[str] =       get_logger(__name__)







def       a__       (       lowercase      :  Optional[Any]   )	->	Optional[int]:





     """simple docstring"""

     return TestCommand(
         args.dataset,      args.name,      args.cache_dir,      args.data_dir,      args.all_configs,      args.save_info or args.save_infos,      args.ignore_verifications,      args.force_redownload,      args.clear_cache,      )




class        __lowerCAmelCase  (					__magic_name__				):






       """simple docstring"""
       _snake_case			:  List[Any]		       =					False  # to tell pytest it's not a test class





       @staticmethod
       def snake_case__		(							lowerCAmelCase__     :	ArgumentParser  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase       					= parser.add_parser('''test''' ,				help='''Test dataset implementation.'''  )
            test_parser.add_argument('''--name''' ,				type=lowerCAmelCase__ ,				default=lowerCAmelCase__ ,				help='''Dataset processing name'''  )
            test_parser.add_argument(
                '''--cache_dir''' ,				type=lowerCAmelCase__ ,				default=lowerCAmelCase__ ,				help='''Cache directory where the datasets are stored.''' ,				)
            test_parser.add_argument(
                '''--data_dir''' ,				type=lowerCAmelCase__ ,				default=lowerCAmelCase__ ,				help='''Can be used to specify a manual directory to get the files from.''' ,				)
            test_parser.add_argument('''--all_configs''' ,				action='''store_true''' ,				help='''Test all dataset configurations'''  )
            test_parser.add_argument(
                '''--save_info''' ,				action='''store_true''' ,				help='''Save the dataset infos in the dataset card (README.md)'''  )
            test_parser.add_argument(
                '''--ignore_verifications''' ,				action='''store_true''' ,				help='''Run the test without checksums and splits checks.''' ,				)
            test_parser.add_argument('''--force_redownload''' ,				action='''store_true''' ,				help='''Force dataset redownload'''  )
            test_parser.add_argument(
                '''--clear_cache''' ,				action='''store_true''' ,				help='''Remove downloaded files and cached datasets after each config test''' ,				)
            # aliases
            test_parser.add_argument('''--save_infos''' ,				action='''store_true''' ,				help='''alias to save_info'''  )
            test_parser.add_argument('''dataset''' ,				type=lowerCAmelCase__ ,				help='''Name of the dataset to download'''  )
            test_parser.set_defaults(func=_test_command_factory  )





       def __init__(							self     :	Dict ,				lowerCAmelCase__     :	str ,				lowerCAmelCase__     :	str ,				lowerCAmelCase__     :	str ,				lowerCAmelCase__     :	str ,				lowerCAmelCase__     :	bool ,				lowerCAmelCase__     :	bool ,				lowerCAmelCase__     :	bool ,				lowerCAmelCase__     :	bool ,				lowerCAmelCase__     :	bool ,				)					->  Dict:

            '''simple docstring'''




            _UpperCamelCase       					= dataset
            _UpperCamelCase       					= name
            _UpperCamelCase       					= cache_dir
            _UpperCamelCase       					= data_dir
            _UpperCamelCase       					= all_configs
            _UpperCamelCase       					= save_infos
            _UpperCamelCase       					= ignore_verifications
            _UpperCamelCase       					= force_redownload
            _UpperCamelCase       					= clear_cache
            if clear_cache and not cache_dir:
                 print(
                     '''When --clear_cache is used, specifying a cache directory is mandatory.\n'''
                     '''The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'''
                     '''Please provide a --cache_dir that will be used to test the dataset script.'''  )
                 exit(1  )
            if save_infos:
                 _UpperCamelCase       					= True







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

            '''simple docstring'''




            fl_logger().setLevel(lowerCAmelCase__  )
            if self._name is not None and self._all_configs:
                 print('''Both parameters `config` and `all_configs` can\'t be used at once.'''  )
                 exit(1  )
            _UpperCamelCase					,       _UpperCamelCase       					= self._dataset, self._name
            _UpperCamelCase       					= dataset_module_factory(lowerCAmelCase__  )
            _UpperCamelCase       					= import_main_class(module.module_path  )
            _UpperCamelCase       					= len(builder_cls.BUILDER_CONFIGS  ) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

            def get_builders() -> Generator[DatasetBuilder, None, None]:
                 if self._all_configs and builder_cls.BUILDER_CONFIGS:
                      for i, config in enumerate(builder_cls.BUILDER_CONFIGS  ):
                           if "config_name" in module.builder_kwargs:
                                yield builder_cls(
                                    cache_dir=self._cache_dir ,				data_dir=self._data_dir ,				**module.builder_kwargs ,				)
                           else:
                                yield builder_cls(
                                    config_name=config.name ,				cache_dir=self._cache_dir ,				data_dir=self._data_dir ,				**module.builder_kwargs ,				)
                 else:
                      if "config_name" in module.builder_kwargs:
                           yield builder_cls(cache_dir=self._cache_dir ,				data_dir=self._data_dir ,				**module.builder_kwargs  )
                      else:
                           yield builder_cls(
                               config_name=lowerCAmelCase__ ,				cache_dir=self._cache_dir ,				data_dir=self._data_dir ,				**module.builder_kwargs ,				)

            for j, builder in enumerate(get_builders()  ):
                 print(f"""Testing builder '{builder.config.name}' ({j + 1}/{n_builders})"""  )
                 _UpperCamelCase       					= os.path.exists(
                     os.path.join(builder.get_imported_module_dir() ,				datasets.config.DATASETDICT_INFOS_FILENAME  )  )  # record checksums only if we need to update a (deprecated) dataset_infos.json
                 builder.download_and_prepare(
                     download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                     if not self._force_redownload
                     else DownloadMode.FORCE_REDOWNLOAD ,				verification_mode=VerificationMode.NO_CHECKS
                     if self._ignore_verifications
                     else VerificationMode.ALL_CHECKS ,				try_from_hf_gcs=lowerCAmelCase__ ,				)
                 builder.as_dataset()
                 if self._save_infos:
                      builder._save_infos()

                 # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
                 # The dataset_infos are saved in the YAML part of the README.md

                 # Let's move it to the original directory of the dataset script, to allow the user to
                 # upload them on S3 at the same time afterwards.
                 if self._save_infos:
                      _UpperCamelCase       					= os.path.join(builder_cls.get_imported_module_dir() ,				'''README.md'''  )
                      _UpperCamelCase       					= Path(lowerCAmelCase__  ).name + '''.py'''
                      _UpperCamelCase       					= os.path.join(lowerCAmelCase__ ,				lowerCAmelCase__  )
                      if os.path.isfile(lowerCAmelCase__  ):
                           _UpperCamelCase       					= os.path.dirname(lowerCAmelCase__  )
                      elif os.path.isfile(lowerCAmelCase__  ):
                           _UpperCamelCase       					= path
                      elif os.path.isdir(lowerCAmelCase__  ):  # for local directories containing only data files
                           _UpperCamelCase       					= path
                      else:  # in case of a remote dataset
                           _UpperCamelCase       					= None
                           print(f"""Dataset card saved at {dataset_readme_path}"""  )

                      # Move dataset_info back to the user
                      if dataset_dir is not None:
                           _UpperCamelCase       					= os.path.join(lowerCAmelCase__ ,				'''README.md'''  )
                           copyfile(lowerCAmelCase__ ,				lowerCAmelCase__  )
                           print(f"""Dataset card saved at {user_dataset_readme_path}"""  )

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
                 if self._clear_cache:
                      if os.path.isdir(builder._cache_dir  ):
                           logger.warning(f"""Clearing cache at {builder._cache_dir}"""  )
                           rmtree(builder._cache_dir  )
                      _UpperCamelCase       					= os.path.join(self._cache_dir ,				datasets.config.DOWNLOADED_DATASETS_DIR  )
                      if os.path.isdir(lowerCAmelCase__  ):
                           logger.warning(f"""Clearing cache at {download_dir}"""  )
                           rmtree(lowerCAmelCase__  )

            print('''Test successful.'''  )










'''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()

lowercase__		:    Union[str, Any] =       logging.get_logger(__name__)







def       a__       (       lowercase      :  Dict,      lowercase      :  bool = True   )	->	int:





     """simple docstring"""

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

          _UpperCamelCase       					= LoRAAttnProcessor(hidden_size=lowercase,      cross_attention_dim=lowercase   )
          _UpperCamelCase       					= 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        __lowerCAmelCase  (					__magic_name__		,		__magic_name__		,		unittest.TestCase				):






       """simple docstring"""
       _snake_case			:  int		       =					UNetaDConditionModel
       _snake_case			:  List[str]		       =					'sample'





       @property
       def snake_case__		(							self     :	Optional[int]  )					->  Any:

            '''simple docstring'''




            _UpperCamelCase       					= 4
            _UpperCamelCase       					= 4
            _UpperCamelCase       					= 4
            _UpperCamelCase       					= (32, 32)

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

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





       @property
       def snake_case__		(							self     :	Tuple  )					->  Optional[int]:

            '''simple docstring'''




            return (4, 4, 32, 32)





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

            '''simple docstring'''




            return (4, 4, 32, 32)





       def snake_case__		(							self     :	Optional[Any]  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase       					= {
                '''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,
            }
            _UpperCamelCase       					= 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 snake_case__		(							self     :	Union[str, Any]  )					->  int:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()
            _UpperCamelCase       					= 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 snake_case__		(							self     :	Tuple  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 32

            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  )

                 if isinstance(lowerCAmelCase__ ,				lowerCAmelCase__  ):
                      _UpperCamelCase       					= output.sample

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





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

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()
            _UpperCamelCase       					= 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  )

                 _UpperCamelCase       					= model(**lowerCAmelCase__  )
                 if isinstance(lowerCAmelCase__ ,				lowerCAmelCase__  ):
                      _UpperCamelCase       					= first.sample

                 _UpperCamelCase       					= model(**lowerCAmelCase__  )
                 if isinstance(lowerCAmelCase__ ,				lowerCAmelCase__  ):
                      _UpperCamelCase       					= second.sample

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





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

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()

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

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

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





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

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

            _UpperCamelCase       					= 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():
                 _UpperCamelCase       					= model(**lowerCAmelCase__ ,				cross_attention_kwargs={'''scale''': 0.0}  ).sample
                 _UpperCamelCase       					= model(**lowerCAmelCase__ ,				cross_attention_kwargs={'''scale''': 0.5}  ).sample
                 _UpperCamelCase       					= 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 snake_case__		(							self     :	str  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= 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  )
                 _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
                 new_model.to(lowerCAmelCase__  )
                 new_model.load_attn_procs(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= 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 snake_case__		(							self     :	str  )					->  List[Any]:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= 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  )
                 _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
                 new_model.to(lowerCAmelCase__  )
                 new_model.load_attn_procs(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= 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 snake_case__		(							self     :	Tuple  )					->  List[Any]:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            _UpperCamelCase       					= 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  )
                 _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
                 new_model.to(lowerCAmelCase__  )
                 new_model.load_attn_procs(lowerCAmelCase__ ,				weight_name='''pytorch_lora_weights.bin'''  )





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

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            _UpperCamelCase       					= 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  )
                 _UpperCamelCase       					= 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 snake_case__		(							self     :	Tuple  )					->  List[Any]:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )

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

            model.set_attn_processor(AttnProcessor()  )

            with torch.no_grad():
                 _UpperCamelCase       					= 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 snake_case__		(							self     :	List[str]  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 4

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )

            # default
            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

                 model.enable_xformers_memory_efficient_attention()
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

                 model.disable_xformers_memory_efficient_attention()
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

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







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

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()
            _UpperCamelCase       					= 32

            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  )[0]

            model.enable_forward_chunking()
            with torch.no_grad():
                 _UpperCamelCase       					= 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.



