





import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode




def        lowerCAmelCase_       (							UpperCamelCase_    ,						**UpperCamelCase_     )   ->						int:
       return RunBeamCommand(
           args.dataset    ,						args.name    ,						args.cache_dir    ,						args.beam_pipeline_options    ,						args.data_dir    ,						args.all_configs    ,						args.save_info or args.save_infos    ,						args.ignore_verifications    ,						args.force_redownload    ,						**UpperCamelCase_    ,						)







class 							_UpperCamelCase    (		lowerCAmelCase_   ):

       @staticmethod
       def lowercase       (						_SCREAMING_SNAKE_CASE:       ArgumentParser						)				->       Any:




              """simple docstring"""


              UpperCamelCase_      				=     parser.add_parser("run_beam"    ,      help="Run a Beam dataset processing pipeline"						)
              run_beam_parser.add_argument("dataset"    ,      type=_SCREAMING_SNAKE_CASE    ,      help="Name of the dataset to download"						)
              run_beam_parser.add_argument("--name"    ,      type=_SCREAMING_SNAKE_CASE    ,      default=_SCREAMING_SNAKE_CASE    ,      help="Dataset config name"						)
              run_beam_parser.add_argument(
                  "--cache_dir"    ,      type=_SCREAMING_SNAKE_CASE    ,      default=_SCREAMING_SNAKE_CASE    ,      help="Cache directory where the datasets are stored"    ,      )
              run_beam_parser.add_argument(
                  "--beam_pipeline_options"    ,      type=_SCREAMING_SNAKE_CASE    ,      default=""    ,      help="Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"    ,      )
              run_beam_parser.add_argument(
                  "--data_dir"    ,      type=_SCREAMING_SNAKE_CASE    ,      default=_SCREAMING_SNAKE_CASE    ,      help="Can be used to specify a manual directory to get the files from"    ,      )
              run_beam_parser.add_argument("--all_configs"    ,      action="store_true"    ,      help="Test all dataset configurations"						)
              run_beam_parser.add_argument("--save_info"    ,      action="store_true"    ,      help="Save the dataset infos file"						)
              run_beam_parser.add_argument(
                  "--ignore_verifications"    ,      action="store_true"    ,      help="Run the test without checksums and splits checks"						)
              run_beam_parser.add_argument("--force_redownload"    ,      action="store_true"    ,      help="Force dataset redownload"						)
              # aliases
              run_beam_parser.add_argument("--save_infos"    ,      action="store_true"    ,      help="alias for save_info"						)
              run_beam_parser.set_defaults(func=_SCREAMING_SNAKE_CASE						)

       def __init__(						self:       List[Any]    ,      _SCREAMING_SNAKE_CASE:       str    ,      _SCREAMING_SNAKE_CASE:       str    ,      _SCREAMING_SNAKE_CASE:       str    ,      _SCREAMING_SNAKE_CASE:       str    ,      _SCREAMING_SNAKE_CASE:       str    ,      _SCREAMING_SNAKE_CASE:       bool    ,      _SCREAMING_SNAKE_CASE:       bool    ,      _SCREAMING_SNAKE_CASE:       bool    ,      _SCREAMING_SNAKE_CASE:       bool    ,      **_SCREAMING_SNAKE_CASE:       str    ,      )				->       int:




              """simple docstring"""


              UpperCamelCase_      				=     dataset
              UpperCamelCase_      				=     name
              UpperCamelCase_      				=     cache_dir
              UpperCamelCase_      				=     beam_pipeline_options
              UpperCamelCase_      				=     data_dir
              UpperCamelCase_      				=     all_configs
              UpperCamelCase_      				=     save_infos
              UpperCamelCase_      				=     ignore_verifications
              UpperCamelCase_      				=     force_redownload
              UpperCamelCase_      				=     config_kwargs



       def lowercase       (						self:       Dict						)				->       int:




              """simple docstring"""


              import apache_beam as beam

              if self._name is not None and self._all_configs:
                     print("Both parameters `name` and `all_configs` can't be used at once."						)
                     exit(1						)
              UpperCamelCase_				,       UpperCamelCase_      				=     self._dataset, self._name
              UpperCamelCase_      				=     dataset_module_factory(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     import_main_class(dataset_module.module_path						)
              UpperCamelCase_      				=     []
              if self._beam_pipeline_options:
                     UpperCamelCase_      				=     beam.options.pipeline_options.PipelineOptions(
                         flags=[f'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(","						) if opt]						)
              else:
                     UpperCamelCase_      				=     None
              if self._all_configs and len(builder_cls.BUILDER_CONFIGS						) > 0:
                     for builder_config in builder_cls.BUILDER_CONFIGS:
                            builders.append(
                                builder_cls(
                                    config_name=builder_config.name    ,      data_dir=self._data_dir    ,      hash=dataset_module.hash    ,      beam_options=_SCREAMING_SNAKE_CASE    ,      cache_dir=self._cache_dir    ,      base_path=dataset_module.builder_kwargs.get("base_path"						)    ,      )						)
              else:
                     builders.append(
                         builder_cls(
                             config_name=_SCREAMING_SNAKE_CASE    ,      data_dir=self._data_dir    ,      beam_options=_SCREAMING_SNAKE_CASE    ,      cache_dir=self._cache_dir    ,      base_path=dataset_module.builder_kwargs.get("base_path"						)    ,      **self._config_kwargs    ,      )						)

              for builder in builders:
                     builder.download_and_prepare(
                         download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                         if not self._force_redownload
                         else DownloadMode.FORCE_REDOWNLOAD    ,      download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH						)    ,      verification_mode=VerificationMode.NO_CHECKS
                         if self._ignore_verifications
                         else VerificationMode.ALL_CHECKS    ,      try_from_hf_gcs=_SCREAMING_SNAKE_CASE    ,      )
                     if self._save_infos:
                            builder._save_infos()

              print("Apache beam run successful."						)

              # If save_infos=True, the dataset infos file is created next to the loaded module file.
              # 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()    ,      config.DATASETDICT_INFOS_FILENAME						)

                     UpperCamelCase_      				=     Path(_SCREAMING_SNAKE_CASE						).name + ".py"

                     UpperCamelCase_      				=     os.path.join(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)
                     if os.path.isfile(_SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     os.path.dirname(_SCREAMING_SNAKE_CASE						)
                     elif os.path.isfile(_SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     path
                     else:  # in case of a remote dataset
                            print(f'''Dataset Infos file saved at {dataset_infos_path}'''						)
                            exit(1						)

                     # Move datasetinfo back to the user
                     UpperCamelCase_      				=     os.path.join(_SCREAMING_SNAKE_CASE    ,      config.DATASETDICT_INFOS_FILENAME						)
                     copyfile(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)
                     print(f'''Dataset Infos file saved at {user_dataset_infos_path}'''						)








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				       = logging.get_logger(__name__)




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_ = True     )   ->						Any:
       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=UpperCamelCase_    ,						cross_attention_dim=UpperCamelCase_     )
              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 							_UpperCamelCase    (		lowerCAmelCase_     ,  lowerCAmelCase_     ,  unittest.TestCase   ):
       _UpperCamelCase :    Optional[Any]   			=      UNetaDConditionModel
       _UpperCamelCase :    List[Any]   			=      '''sample'''

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




              """simple docstring"""


              UpperCamelCase_      				=     4
              UpperCamelCase_      				=     4
              UpperCamelCase_      				=     4
              UpperCamelCase_      				=     (32, 32)

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

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

       @property
       def lowercase       (						self:       Tuple						)				->       str:




              """simple docstring"""


              return (4, 4, 32, 32)

       @property
       def lowercase       (						self:       List[str]						)				->       List[str]:




              """simple docstring"""


              return (4, 4, 32, 32)

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




              """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 lowercase       (						self:       str						)				->       Optional[Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)

              model.enable_xformers_memory_efficient_attention()

              assert (
                  model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
                  == "XFormersAttnProcessor"
              ), "xformers is not enabled"

       def lowercase       (						self:       List[str]						)				->       List[str]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     32

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)

                     if isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     output.sample

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

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




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

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

                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
                     if isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     first.sample

                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
                     if isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            UpperCamelCase_      				=     second.sample

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

       def lowercase       (						self:       int						)				->       List[str]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              model.set_attention_slice("auto"						)
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
              assert output is not None

              model.set_attention_slice("max"						)
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)
              assert output is not None

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

       def lowercase       (						self:       Optional[Any]						)				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)

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

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

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.0}						).sample
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

              assert (samplea - samplea).abs().max() < 3e-3
              assert (samplea - samplea).abs().max() < 3e-3

              # sample 2 and sample 3 should be different
              assert (samplea - samplea).abs().max() > 3e-3

       def lowercase       (						self:       Any						)				->       int:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

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

              with torch.no_grad():
                     UpperCamelCase_      				=     new_model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

              assert (sample - new_sample).abs().max() < 1e-3

              # LoRA and no LoRA should NOT be the same
              assert (sample - old_sample).abs().max() > 1e-4

       def lowercase       (						self:       List[str]						)				->       Optional[int]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

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

              with torch.no_grad():
                     UpperCamelCase_      				=     new_model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.5}						).sample

              assert (sample - new_sample).abs().max() < 3e-3

              # LoRA and no LoRA should NOT be the same
              assert (sample - old_sample).abs().max() > 1e-4

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




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

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

       def lowercase       (						self:       Any						)				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

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

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




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     8

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE    ,      cross_attention_kwargs={"scale": 0.0}						).sample

              model.set_attn_processor(AttnProcessor()						)

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

              assert (sample - new_sample).abs().max() < 1e-4
              assert (sample - old_sample).abs().max() < 3e-3

       @unittest.skipIf(
           torch_device != "cuda" or not is_xformers_available()    ,      reason="XFormers attention is only available with CUDA and `xformers` installed"    ,      )
       def lowercase       (						self:       Union[str, Any]						)				->       Dict:




              """simple docstring"""


              UpperCamelCase_				,       UpperCamelCase_      				=     self.prepare_init_args_and_inputs_for_common()

              UpperCamelCase_      				=     4

              torch.manual_seed(0						)
              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     create_lora_layers(_SCREAMING_SNAKE_CASE						)
              model.set_attn_processor(_SCREAMING_SNAKE_CASE						)

              # default
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

                     model.enable_xformers_memory_efficient_attention()
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

                     model.disable_xformers_memory_efficient_attention()
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						).sample

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



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




              """simple docstring"""


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

              UpperCamelCase_      				=     self.model_class(**_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()

              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)[0]

              model.enable_forward_chunking()
              with torch.no_grad():
                     UpperCamelCase_      				=     model(**_SCREAMING_SNAKE_CASE						)[0]

              self.assertEqual(output.shape    ,      output_a.shape    ,      "Shape doesn't match"						)
              assert np.abs(output.cpu() - output_a.cpu()						).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.

