code: 
  """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
  
  
  SCREAMING_SNAKE_CASE_     				=   get_logger(__name__)
  def 			__UpperCAmelCase		(		a_			:		Optional[int]				)     ->       Optional[int]:
     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     __a			(					lowerCAmelCase_			):
         _lowerCamelCase							:							Optional[Any]       =			False  # to tell pytest it's not a test class
  
  
  
  
  
  
  
         @staticmethod
         def    _snake_case			(					lowerCamelCase_		):
  
  
  
            '''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							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,):
  
  
  
            '''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		):
  
  
  
            '''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."		)
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  import collections
  import inspect
  import unittest
  
  from transformers import NatConfig
  from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  
  from ...test_backbone_common import BackboneTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
    import torch
    from torch import nn
  
    from transformers import NatBackbone, NatForImageClassification, NatModel
    from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST
  
  if is_vision_available():
    from PIL import Image
  
    from transformers import AutoImageProcessor
  
  
  
  
  
  
  class     __a			:
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_							,lowerCamelCase_=13							,lowerCamelCase_=64							,lowerCamelCase_=4							,lowerCamelCase_=3							,lowerCamelCase_=16							,lowerCamelCase_=[1, 2, 1]							,lowerCamelCase_=[2, 4, 8]							,lowerCamelCase_=3							,lowerCamelCase_=2.0							,lowerCamelCase_=True							,lowerCamelCase_=0.0							,lowerCamelCase_=0.0							,lowerCamelCase_=0.1							,lowerCamelCase_="gelu"							,lowerCamelCase_=True							,lowerCamelCase_=0.0_2							,lowerCamelCase_=1e-5							,lowerCamelCase_=True							,lowerCamelCase_=None							,lowerCamelCase_=True							,lowerCamelCase_=10							,lowerCamelCase_=["stage1", "stage2"]							,lowerCamelCase_=[1, 2]							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					parent
            _UpperCAmelCase   	=					batch_size
            _UpperCAmelCase   	=					image_size
            _UpperCAmelCase   	=					patch_size
            _UpperCAmelCase   	=					num_channels
            _UpperCAmelCase   	=					embed_dim
            _UpperCAmelCase   	=					depths
            _UpperCAmelCase   	=					num_heads
            _UpperCAmelCase   	=					kernel_size
            _UpperCAmelCase   	=					mlp_ratio
            _UpperCAmelCase   	=					qkv_bias
            _UpperCAmelCase   	=					hidden_dropout_prob
            _UpperCAmelCase   	=					attention_probs_dropout_prob
            _UpperCAmelCase   	=					drop_path_rate
            _UpperCAmelCase   	=					hidden_act
            _UpperCAmelCase   	=					patch_norm
            _UpperCAmelCase   	=					layer_norm_eps
            _UpperCAmelCase   	=					initializer_range
            _UpperCAmelCase   	=					is_training
            _UpperCAmelCase   	=					scope
            _UpperCAmelCase   	=					use_labels
            _UpperCAmelCase   	=					num_labels
            _UpperCAmelCase   	=					out_features
            _UpperCAmelCase   	=					out_indices
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
  
            _UpperCAmelCase   	=					None
            if self.use_labels:
               _UpperCAmelCase   	=					ids_tensor([self.batch_size]							,self.num_labels		)
  
            _UpperCAmelCase   	=					self.get_config()
  
            return config, pixel_values, labels
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            return NatConfig(
                num_labels=self.num_labels							,image_size=self.image_size							,patch_size=self.patch_size							,num_channels=self.num_channels							,embed_dim=self.embed_dim							,depths=self.depths							,num_heads=self.num_heads							,kernel_size=self.kernel_size							,mlp_ratio=self.mlp_ratio							,qkv_bias=self.qkv_bias							,hidden_dropout_prob=self.hidden_dropout_prob							,attention_probs_dropout_prob=self.attention_probs_dropout_prob							,drop_path_rate=self.drop_path_rate							,hidden_act=self.hidden_act							,patch_norm=self.patch_norm							,layer_norm_eps=self.layer_norm_eps							,initializer_range=self.initializer_range							,out_features=self.out_features							,out_indices=self.out_indices							,)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					NatModel(config=lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(lowerCamelCase_		)
  
            _UpperCAmelCase   	=					_UpperCAmelCase   	=					(config.image_size // config.patch_size) // (2 ** (len(config.depths		) - 1))
            _UpperCAmelCase   	=					int(config.embed_dim * 2 ** (len(config.depths		) - 1)		)
  
            self.parent.assertEqual(
                result.last_hidden_state.shape							,(self.batch_size, expected_height, expected_width, expected_dim)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					NatForImageClassification(lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(lowerCamelCase_							,labels=lowerCamelCase_		)
            self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)		)
  
            # test greyscale images
            _UpperCAmelCase   	=					1
            _UpperCAmelCase   	=					NatForImageClassification(lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
  
            _UpperCAmelCase   	=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
            _UpperCAmelCase   	=					model(lowerCamelCase_		)
            self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					NatBackbone(config=lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(lowerCamelCase_		)
  
            # verify hidden states
            self.parent.assertEqual(len(result.feature_maps		)							,len(config.out_features		)		)
            self.parent.assertListEqual(list(result.feature_maps[0].shape		)							,[self.batch_size, model.channels[0], 16, 16]		)
  
            # verify channels
            self.parent.assertEqual(len(model.channels		)							,len(config.out_features		)		)
  
            # verify backbone works with out_features=None
            _UpperCAmelCase   	=					None
            _UpperCAmelCase   	=					NatBackbone(config=lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(lowerCamelCase_		)
  
            # verify feature maps
            self.parent.assertEqual(len(result.feature_maps		)							,1		)
            self.parent.assertListEqual(list(result.feature_maps[0].shape		)							,[self.batch_size, model.channels[-1], 4, 4]		)
  
            # verify channels
            self.parent.assertEqual(len(model.channels		)							,1		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.prepare_config_and_inputs()
            _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					config_and_inputs
            _UpperCAmelCase   	=					{"pixel_values": pixel_values}
            return config, inputs_dict
  
  
  
  
  
  
  @require_natten
  @require_torch
  class     __a			(					lowerCAmelCase_				,	lowerCAmelCase_				,	unittest.TestCase			):
         _lowerCamelCase							:							str       =			(
             (
                 NatModel,
                 NatForImageClassification,
                 NatBackbone,
             )
             if is_torch_available()
             else ()
         )
         _lowerCamelCase							:							int       =			(
             {'feature-extraction': NatModel, 'image-classification': NatForImageClassification}
             if is_torch_available()
             else {}
         )
         _lowerCamelCase							:							Optional[Any]       =			False
  
         _lowerCamelCase							:							Optional[Any]       =			False
         _lowerCamelCase							:							Optional[int]       =			False
         _lowerCamelCase							:							List[str]       =			False
         _lowerCamelCase							:							str       =			False
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					NatModelTester(self		)
            _UpperCAmelCase   	=					ConfigTester(self							,config_class=lowerCamelCase_							,embed_dim=37		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            self.create_and_test_config_common_properties()
            self.config_tester.create_and_test_config_to_json_string()
            self.config_tester.create_and_test_config_to_json_file()
            self.config_tester.create_and_test_config_from_and_save_pretrained()
            self.config_tester.create_and_test_config_with_num_labels()
            self.config_tester.check_config_can_be_init_without_params()
            self.config_tester.check_config_arguments_init()
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            return
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_backbone(*lowerCamelCase_		)
  
  
  
  
  
  
  
         @unittest.skip(reason="Nat does not use inputs_embeds"		)
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            pass
  
  
  
  
  
  
  
         @unittest.skip(reason="Nat does not use feedforward chunking"		)
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            pass
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()
  
            for model_class in self.all_model_classes:
               _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
               self.assertIsInstance(model.get_input_embeddings()							,(nn.Module)		)
               _UpperCAmelCase   	=					model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCamelCase_							,nn.Linear		)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()
  
            for model_class in self.all_model_classes:
               _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
               _UpperCAmelCase   	=					inspect.signature(model.forward		)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _UpperCAmelCase   	=					[*signature.parameters.keys()]
  
               _UpperCAmelCase   	=					["pixel_values"]
               self.assertListEqual(arg_names[:1]							,lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            self.skipTest("Nat's attention operation is handled entirely by NATTEN."		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
  
            with torch.no_grad():
               _UpperCAmelCase   	=					model(**self._prepare_for_class(lowerCamelCase_							,lowerCamelCase_		)		)
  
            _UpperCAmelCase   	=					outputs.hidden_states
  
            _UpperCAmelCase   	=					getattr(
                self.model_tester							,"expected_num_hidden_layers"							,len(self.model_tester.depths		) + 1		)
            self.assertEqual(len(lowerCamelCase_		)							,lowerCamelCase_		)
  
            # Nat has a different seq_length
            _UpperCAmelCase   	=					(
                config.patch_size
                if isinstance(config.patch_size							,collections.abc.Iterable		)
                else (config.patch_size, config.patch_size)
            )
  
            _UpperCAmelCase   	=					image_size[0] // patch_size[0]
            _UpperCAmelCase   	=					image_size[1] // patch_size[1]
  
            self.assertListEqual(
                list(hidden_states[0].shape[-3:]		)							,[height, width, self.model_tester.embed_dim]							,)
  
            if model_class.__name__ != "NatBackbone":
               _UpperCAmelCase   	=					outputs.reshaped_hidden_states
               self.assertEqual(len(lowerCamelCase_		)							,lowerCamelCase_		)
  
               _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					reshaped_hidden_states[0].shape
               _UpperCAmelCase   	=					(
                   reshaped_hidden_states[0].view(lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		).permute(0							,2							,3							,1		)
               )
               self.assertListEqual(
                   list(reshaped_hidden_states.shape[-3:]		)							,[height, width, self.model_tester.embed_dim]							,)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()
  
            _UpperCAmelCase   	=					(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size							,collections.abc.Iterable		)
                else (self.model_tester.image_size, self.model_tester.image_size)
            )
  
            for model_class in self.all_model_classes:
               _UpperCAmelCase   	=					True
               self.check_hidden_states_output(lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		)
  
               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               _UpperCAmelCase   	=					True
  
               self.check_hidden_states_output(lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		)
  
  
  
  
  
  
  
         @slow
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase   	=					NatModel.from_pretrained(lowerCamelCase_		)
               self.assertIsNotNone(lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()
  
            _UpperCAmelCase   	=					_config_zero_init(lowerCamelCase_		)
            for model_class in self.all_model_classes:
               _UpperCAmelCase   	=					model_class(config=lowerCamelCase_		)
               for name, param in model.named_parameters():
                  if "embeddings" not in name and param.requires_grad:
                     self.assertIn(
                         ((param.data.mean() * 1e9).round() / 1e9).item()							,[0.0, 1.0]							,msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''							,)
  
  
  
  
  
  
  @require_natten
  @require_vision
  @require_torch
  class     __a			(					unittest.TestCase			):
  
  
  
  
  
  
  
         @cached_property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            return AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224"		) if is_vision_available() else None
  
  
  
  
  
  
  
         @slow
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					NatForImageClassification.from_pretrained("shi-labs/nat-mini-in1k-224"		).to(lowerCamelCase_		)
            _UpperCAmelCase   	=					self.default_image_processor
  
            _UpperCAmelCase   	=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
            _UpperCAmelCase   	=					image_processor(images=lowerCamelCase_							,return_tensors="pt"		).to(lowerCamelCase_		)
  
            # forward pass
            with torch.no_grad():
               _UpperCAmelCase   	=					model(**lowerCamelCase_		)
  
            # verify the logits
            _UpperCAmelCase   	=					torch.Size((1, 1000)		)
            self.assertEqual(outputs.logits.shape							,lowerCamelCase_		)
            _UpperCAmelCase   	=					torch.tensor([0.3_8_0_5, -0.8_6_7_6, -0.3_9_1_2]		).to(lowerCamelCase_		)
            self.assertTrue(torch.allclose(outputs.logits[0, :3]							,lowerCamelCase_							,atol=1e-4		)		)
  
  
  
  
  
  
  @require_torch
  @require_natten
  class     __a			(					unittest.TestCase				,	lowerCAmelCase_			):
         _lowerCamelCase							:							Any       =			(NatBackbone,) if is_torch_available() else ()
         _lowerCamelCase							:							Dict       =			NatConfig
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					NatModelTester(self		)
  
  