






'''simple docstring'''





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 __UpperCAmelCase (			A			:	str	,   **A			:	str						)  -> 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	,   **A	,   )







class 				snake_case__     (			UpperCamelCase):




					@staticmethod
					def 						A     (       _A       :						ArgumentParser     )  ->    int:
								UpperCAmelCase_     :   Tuple       =   parser.add_parser('''run_beam'''							,				help='''Run a Beam dataset processing pipeline'''     )
								run_beam_parser.add_argument('''dataset'''							,				type=_A							,				help='''Name of the dataset to download'''     )
								run_beam_parser.add_argument('''--name'''							,				type=_A							,				default=_A							,				help='''Dataset config name'''     )
								run_beam_parser.add_argument(
								    '''--cache_dir'''							,				type=_A							,				default=_A							,				help='''Cache directory where the datasets are stored'''							,				)
								run_beam_parser.add_argument(
								    '''--beam_pipeline_options'''							,				type=_A							,				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=_A							,				default=_A							,				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=_A     )




					def __init__(       self       :						Union[str, Any]							,				_A       :						str							,				_A       :						str							,				_A       :						str							,				_A       :						str							,				_A       :						str							,				_A       :						bool							,				_A       :						bool							,				_A       :						bool							,				_A       :						bool							,				**_A       :						int							,				)  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   dataset
								UpperCAmelCase_     :   Dict       =   name
								UpperCAmelCase_     :   List[str]       =   cache_dir
								UpperCAmelCase_     :   List[Any]       =   beam_pipeline_options
								UpperCAmelCase_     :   Any       =   data_dir
								UpperCAmelCase_     :   Union[str, Any]       =   all_configs
								UpperCAmelCase_     :   str       =   save_infos
								UpperCAmelCase_     :   Optional[Any]       =   ignore_verifications
								UpperCAmelCase_     :   int       =   force_redownload
								UpperCAmelCase_     :   int       =   config_kwargs





					def 						A     (       self       :						int     )  ->    int:
								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_     :   Tuple       =   self._dataset, self._name
								UpperCAmelCase_     :   Union[str, Any]       =   dataset_module_factory(_A     )
								UpperCAmelCase_     :   Union[str, Any]       =   import_main_class(dataset_module.module_path     )
								UpperCAmelCase_     :   List[DatasetBuilder]       =   []
								if self._beam_pipeline_options:
											UpperCAmelCase_     :   Union[str, Any]       =   beam.options.pipeline_options.PipelineOptions(
											    flags=[F"--{opt.strip()}" for opt in self._beam_pipeline_options.split(''','''     ) if opt]     )
								else:
											UpperCAmelCase_     :   Any       =   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=_A							,				cache_dir=self._cache_dir							,				base_path=dataset_module.builder_kwargs.get('''base_path'''     )							,				)     )
								else:
											builders.append(
											    builder_cls(
											        config_name=_A							,				data_dir=self._data_dir							,				beam_options=_A							,				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=_A							,				)
											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_     :   str       =   os.path.join(builder_cls.get_imported_module_dir()							,				config.DATASETDICT_INFOS_FILENAME     )

											UpperCAmelCase_     :   Optional[Any]       =   Path(_A     ).name + '''.py'''

											UpperCAmelCase_     :   Any       =   os.path.join(_A							,				_A     )
											if os.path.isfile(_A     ):
														UpperCAmelCase_     :   Optional[int]       =   os.path.dirname(_A     )
											elif os.path.isfile(_A     ):
														UpperCAmelCase_     :   Union[str, Any]       =   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_     :   Dict       =   os.path.join(_A							,				config.DATASETDICT_INFOS_FILENAME     )
											copyfile(_A							,				_A     )
											print(F"Dataset Infos file saved at {user_dataset_infos_path}"     )










'''simple docstring'''





import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

_UpperCamelCase		:		Dict								=			logging.get_logger(__name__)
def __UpperCAmelCase (			A			:	List[Any]	,   A			:	bool = True						)  -> List[Any]:
			UpperCAmelCase_     :   Any       =   {}
			for name in model.attn_processors.keys():
						UpperCAmelCase_     :   List[Any]       =   name.endswith('''attn2.processor'''						) and not (
						    name.startswith('''transformer_in'''						) or '''temp_attentions''' in name.split('''.'''						)
						)
						UpperCAmelCase_     :   Optional[Any]       =   model.config.cross_attention_dim if has_cross_attention else None
						if name.startswith('''mid_block'''						):
									UpperCAmelCase_     :   Dict       =   model.config.block_out_channels[-1]
						elif name.startswith('''up_blocks'''						):
									UpperCAmelCase_     :   Any       =   int(name[len('''up_blocks.'''						)]						)
									UpperCAmelCase_     :   int       =   list(reversed(model.config.block_out_channels						)						)[block_id]
						elif name.startswith('''down_blocks'''						):
									UpperCAmelCase_     :   Dict       =   int(name[len('''down_blocks.'''						)]						)
									UpperCAmelCase_     :   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
									UpperCAmelCase_     :   int       =   8 * model.config.attention_head_dim

						UpperCAmelCase_     :   Optional[int]       =   LoRAAttnProcessor(hidden_size=A	,   cross_attention_dim=A						)
						UpperCAmelCase_     :   Tuple       =   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 				snake_case__     (			UpperCamelCase      ,							UpperCamelCase      ,							unittest.TestCase):
					a_       =			UNetaDConditionModel
					a_       =			"sample"




					@property
					def 						A     (       self       :						Tuple     )  ->    int:
								UpperCAmelCase_     :   Optional[Any]       =   4
								UpperCAmelCase_     :   int       =   4
								UpperCAmelCase_     :   List[Any]       =   4
								UpperCAmelCase_     :   int       =   (32, 32)

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

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




					@property
					def 						A     (       self       :						Dict     )  ->    Dict:
								return (4, 4, 32, 32)




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




					def 						A     (       self       :						Dict     )  ->    List[Any]:
								UpperCAmelCase_     :   List[str]       =   {
								    '''block_out_channels''': (32, 64),
								    '''down_block_types''': (
								        '''CrossAttnDownBlock3D''',
								        '''DownBlock3D''',
								    ),
								    '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
								    '''cross_attention_dim''': 32,
								    '''attention_head_dim''': 8,
								    '''out_channels''': 4,
								    '''in_channels''': 4,
								    '''layers_per_block''': 1,
								    '''sample_size''': 32,
								}
								UpperCAmelCase_     :   Union[str, Any]       =   self.dummy_input
								return init_dict, inputs_dict




					@unittest.skipIf(
					    torch_device != '''cuda''' or not is_xformers_available()							,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''							,				)
					def 						A     (       self       :						Union[str, Any]     )  ->    Optional[int]:
								UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[int]       =   self.prepare_init_args_and_inputs_for_common()
								UpperCAmelCase_     :   List[str]       =   self.model_class(**_A     )

								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       :						Optional[Any]     )  ->    List[str]:
								UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   self.prepare_init_args_and_inputs_for_common()

								UpperCAmelCase_     :   str       =   32

								UpperCAmelCase_     :   Union[str, Any]       =   self.model_class(**_A     )
								model.to(_A     )
								model.eval()

								with torch.no_grad():
											UpperCAmelCase_     :   Any       =   model(**_A     )

											if isinstance(_A							,				_A     ):
														UpperCAmelCase_     :   Optional[Any]       =   output.sample

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




					def 						A     (       self       :						Union[str, Any]     )  ->    Optional[int]:
								UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[Any]       =   self.prepare_init_args_and_inputs_for_common()
								UpperCAmelCase_     :   Optional[Any]       =   self.model_class(**_A     )
								model.to(_A     )
								model.eval()

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

											UpperCAmelCase_     :   List[Any]       =   model(**_A     )
											if isinstance(_A							,				_A     ):
														UpperCAmelCase_     :   List[Any]       =   first.sample

											UpperCAmelCase_     :   Tuple       =   model(**_A     )
											if isinstance(_A							,				_A     ):
														UpperCAmelCase_     :   Union[str, Any]       =   second.sample

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




					def 						A     (       self       :						str     )  ->    str:
								UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[Any]       =   self.prepare_init_args_and_inputs_for_common()

								UpperCAmelCase_     :   Union[str, Any]       =   8

								UpperCAmelCase_     :   Tuple       =   self.model_class(**_A     )
								model.to(_A     )
								model.eval()

								model.set_attention_slice('''auto'''     )
								with torch.no_grad():
											UpperCAmelCase_     :   Dict       =   model(**_A     )
								assert output is not None

								model.set_attention_slice('''max'''     )
								with torch.no_grad():
											UpperCAmelCase_     :   Union[str, Any]       =   model(**_A     )
								assert output is not None

								model.set_attention_slice(2     )
								with torch.no_grad():
											UpperCAmelCase_     :   int       =   model(**_A     )
								assert output is not None




					def 						A     (       self       :						List[Any]     )  ->    Any:
								UpperCAmelCase_  ,     UpperCAmelCase_     :   List[Any]       =   self.prepare_init_args_and_inputs_for_common()

								UpperCAmelCase_     :   Dict       =   8

								UpperCAmelCase_     :   List[Any]       =   self.model_class(**_A     )
								model.to(_A     )

								with torch.no_grad():
											UpperCAmelCase_     :   int       =   model(**_A     ).sample

								UpperCAmelCase_     :   Any       =   create_lora_layers(_A     )

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

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

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

								UpperCAmelCase_     :   str       =   8

								torch.manual_seed(0     )
								UpperCAmelCase_     :   Optional[int]       =   self.model_class(**_A     )
								model.to(_A     )

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

								UpperCAmelCase_     :   int       =   create_lora_layers(_A     )
								model.set_attn_processor(_A     )

								with torch.no_grad():
											UpperCAmelCase_     :   Optional[int]       =   model(**_A							,				cross_attention_kwargs={'''scale''': 0.5}     ).sample

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

								with torch.no_grad():
											UpperCAmelCase_     :   Dict       =   new_model(**_A							,				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       :						Tuple     )  ->    Union[str, Any]:
								UpperCAmelCase_  ,     UpperCAmelCase_     :   Dict       =   self.prepare_init_args_and_inputs_for_common()

								UpperCAmelCase_     :   List[str]       =   8

								torch.manual_seed(0     )
								UpperCAmelCase_     :   str       =   self.model_class(**_A     )
								model.to(_A     )

								with torch.no_grad():
											UpperCAmelCase_     :   Dict       =   model(**_A     ).sample

								UpperCAmelCase_     :   Tuple       =   create_lora_layers(_A     )
								model.set_attn_processor(_A     )

								with torch.no_grad():
											UpperCAmelCase_     :   Dict       =   model(**_A							,				cross_attention_kwargs={'''scale''': 0.5}     ).sample

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

								with torch.no_grad():
											UpperCAmelCase_     :   Union[str, Any]       =   new_model(**_A							,				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       :						Union[str, Any]     )  ->    Dict:
								# enable deterministic behavior for gradient checkpointing
								UpperCAmelCase_  ,     UpperCAmelCase_     :   List[Any]       =   self.prepare_init_args_and_inputs_for_common()

								UpperCAmelCase_     :   Any       =   8

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

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




					def 						A     (       self       :						Optional[Any]     )  ->    Optional[int]:
								UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[int]       =   self.prepare_init_args_and_inputs_for_common()

								UpperCAmelCase_     :   List[Any]       =   8

								torch.manual_seed(0     )
								UpperCAmelCase_     :   Dict       =   self.model_class(**_A     )
								model.to(_A     )

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




					def 						A     (       self       :						int     )  ->    Tuple:
								UpperCAmelCase_  ,     UpperCAmelCase_     :   Any       =   self.prepare_init_args_and_inputs_for_common()

								UpperCAmelCase_     :   Dict       =   8

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

								with torch.no_grad():
											UpperCAmelCase_     :   str       =   model(**_A     ).sample

								UpperCAmelCase_     :   str       =   create_lora_layers(_A     )
								model.set_attn_processor(_A     )

								with torch.no_grad():
											UpperCAmelCase_     :   Optional[int]       =   model(**_A							,				cross_attention_kwargs={'''scale''': 0.0}     ).sample

								model.set_attn_processor(AttnProcessor()     )

								with torch.no_grad():
											UpperCAmelCase_     :   int       =   model(**_A     ).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       :						str     )  ->    Optional[Any]:
								# enable deterministic behavior for gradient checkpointing
								UpperCAmelCase_  ,     UpperCAmelCase_     :   Any       =   self.prepare_init_args_and_inputs_for_common()

								UpperCAmelCase_     :   Dict       =   4

								torch.manual_seed(0     )
								UpperCAmelCase_     :   List[Any]       =   self.model_class(**_A     )
								model.to(_A     )
								UpperCAmelCase_     :   List[str]       =   create_lora_layers(_A     )
								model.set_attn_processor(_A     )

								# default
								with torch.no_grad():
											UpperCAmelCase_     :   List[str]       =   model(**_A     ).sample

											model.enable_xformers_memory_efficient_attention()
											UpperCAmelCase_     :   List[str]       =   model(**_A     ).sample

											model.disable_xformers_memory_efficient_attention()
											UpperCAmelCase_     :   Any       =   model(**_A     ).sample

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





					def 						A     (       self       :						Tuple     )  ->    Union[str, Any]:
								UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[int]       =   self.prepare_init_args_and_inputs_for_common()
								UpperCAmelCase_     :   int       =   32

								UpperCAmelCase_     :   Any       =   self.model_class(**_A     )
								model.to(_A     )
								model.eval()

								with torch.no_grad():
											UpperCAmelCase_     :   Tuple       =   model(**_A     )[0]

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


