

import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator







def a_		(       UpperCamelCase_   :							List[str]  )							->   Optional[int]:







 """simple docstring"""

 lowerCamelCase      						=    fname.split(os.path.sep  )[-1]
 return re.search(R'^(.*)_\d+\.jpg$'   ,					UpperCamelCase_  ).groups()[0]


class     lowerCAmelCase				(    __UpperCamelCase				):


       '''simple docstring'''







       def __init__(	self					:  int						,	__snake_case					:  Union[str, Any]						,	__snake_case					:  Optional[int]=None						,	__snake_case					:  Union[str, Any]=None     )   -> str:







        '''simple docstring'''

        lowerCamelCase      						=    file_names
        lowerCamelCase      						=    image_transform
        lowerCamelCase      						=    label_to_id

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







        '''simple docstring'''

        return len(self.file_names     )





       def __getitem__(	self					:  List[str]						,	__snake_case					:  Union[str, Any]     )   -> Optional[Any]:







        '''simple docstring'''

        lowerCamelCase      						=    self.file_names[idx]
        lowerCamelCase      						=    PIL.Image.open(__snake_case     )
        lowerCamelCase      						=    raw_image.convert('RGB'     )
        if self.image_transform is not None:
         lowerCamelCase      						=    self.image_transform(__snake_case     )
        lowerCamelCase      						=    extract_label(__snake_case     )
        if self.label_to_id is not None:
         lowerCamelCase      						=    self.label_to_id[label]
        return {"image": image, "label": label}







def a_		(       UpperCamelCase_   :							Any   ,					UpperCamelCase_   :							str  )							->   Optional[Any]:







 """simple docstring"""

 lowerCamelCase      						=    Accelerator(cpu=args.cpu   ,					mixed_precision=args.mixed_precision  )

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowerCamelCase      						=    config['lr']
 lowerCamelCase      						=    int(config['num_epochs']  )
 lowerCamelCase      						=    int(config['seed']  )
 lowerCamelCase      						=    int(config['batch_size']  )
 lowerCamelCase      						=    config['image_size']
 if not isinstance(UpperCamelCase_   ,					(list, tuple)  ):
  lowerCamelCase      						=    (image_size, image_size)

 # Grab all the image filenames
 lowerCamelCase      						=    [os.path.join(args.data_dir   ,					UpperCamelCase_  ) for fname in os.listdir(args.data_dir  ) if fname.endswith('.jpg'  )]

 # Build the label correspondences
 lowerCamelCase      						=    [extract_label(UpperCamelCase_  ) for fname in file_names]
 lowerCamelCase      						=    list(set(UpperCamelCase_  )  )
 id_to_label.sort()
 lowerCamelCase      						=    {lbl: i for i, lbl in enumerate(UpperCamelCase_  )}

 # Set the seed before splitting the data.
 np.random.seed(UpperCamelCase_  )
 torch.manual_seed(UpperCamelCase_  )
 torch.cuda.manual_seed_all(UpperCamelCase_  )

 # Split our filenames between train and validation
 lowerCamelCase      						=    np.random.permutation(len(UpperCamelCase_  )  )
 lowerCamelCase      						=    int(0.8 * len(UpperCamelCase_  )  )
 lowerCamelCase      						=    random_perm[:cut]
 lowerCamelCase      						=    random_perm[cut:]

 # For training we use a simple RandomResizedCrop
 lowerCamelCase      						=    Compose([RandomResizedCrop(UpperCamelCase_   ,					scale=(0.5, 1.0)  ), ToTensor()]  )
 lowerCamelCase      						=    PetsDataset(
     [file_names[i] for i in train_split]   ,					image_transform=UpperCamelCase_   ,					label_to_id=UpperCamelCase_  )

 # For evaluation, we use a deterministic Resize
 lowerCamelCase      						=    Compose([Resize(UpperCamelCase_  ), ToTensor()]  )
 lowerCamelCase      						=    PetsDataset([file_names[i] for i in eval_split]   ,					image_transform=UpperCamelCase_   ,					label_to_id=UpperCamelCase_  )

 # Instantiate dataloaders.
 lowerCamelCase      						=    DataLoader(UpperCamelCase_   ,					shuffle=UpperCamelCase_   ,					batch_size=UpperCamelCase_   ,					num_workers=4  )
 lowerCamelCase      						=    DataLoader(UpperCamelCase_   ,					shuffle=UpperCamelCase_   ,					batch_size=UpperCamelCase_   ,					num_workers=4  )

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 lowerCamelCase      						=    create_model('resnet50d'   ,					pretrained=UpperCamelCase_   ,					num_classes=len(UpperCamelCase_  )  )

 # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
 # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
 # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
 lowerCamelCase      						=    model.to(accelerator.device  )

 # Freezing the base model
 for param in model.parameters():
  lowerCamelCase      						=    False
 for param in model.get_classifier().parameters():
  lowerCamelCase      						=    True

 # We normalize the batches of images to be a bit faster.
 lowerCamelCase      						=    torch.tensor(model.default_cfg['mean']  )[None, :, None, None].to(accelerator.device  )
 lowerCamelCase      						=    torch.tensor(model.default_cfg['std']  )[None, :, None, None].to(accelerator.device  )

 # Instantiate optimizer
 lowerCamelCase      						=    torch.optim.Adam(params=model.parameters()   ,					lr=lr / 2_5  )

 # Instantiate learning rate scheduler
 lowerCamelCase      						=    OneCycleLR(optimizer=UpperCamelCase_   ,					max_lr=UpperCamelCase_   ,					epochs=UpperCamelCase_   ,					steps_per_epoch=len(UpperCamelCase_  )  )

 # Prepare everything
 # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
 # prepare method.
 lowerCamelCase						,	lowerCamelCase						,	lowerCamelCase						,	lowerCamelCase						,	lowerCamelCase      						=    accelerator.prepare(
     UpperCamelCase_   ,					UpperCamelCase_   ,					UpperCamelCase_   ,					UpperCamelCase_   ,					UpperCamelCase_  )

 # Now we train the model
 for epoch in range(UpperCamelCase_  ):
  model.train()
  for step, batch in enumerate(UpperCamelCase_  ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   lowerCamelCase      						=    {k: v.to(accelerator.device  ) for k, v in batch.items()}
   lowerCamelCase      						=    (batch['image'] - mean) / std
   lowerCamelCase      						=    model(UpperCamelCase_  )
   lowerCamelCase      						=    torch.nn.functional.cross_entropy(UpperCamelCase_   ,					batch['label']  )
   accelerator.backward(UpperCamelCase_  )
   optimizer.step()
   lr_scheduler.step()
   optimizer.zero_grad()

  model.eval()
  lowerCamelCase      						=    0
  lowerCamelCase      						=    0
  for _, batch in enumerate(UpperCamelCase_  ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   lowerCamelCase      						=    {k: v.to(accelerator.device  ) for k, v in batch.items()}
   lowerCamelCase      						=    (batch['image'] - mean) / std
   with torch.no_grad():
    lowerCamelCase      						=    model(UpperCamelCase_  )
   lowerCamelCase      						=    outputs.argmax(dim=-1  )
   lowerCamelCase						,	lowerCamelCase      						=    accelerator.gather_for_metrics((predictions, batch['label'])  )
   lowerCamelCase      						=    predictions == references
   num_elems += accurate_preds.shape[0]
   accurate += accurate_preds.long().sum()

  lowerCamelCase      						=    accurate.item() / num_elems
  # Use accelerator.print to print only on the main process.
  accelerator.print(f'''epoch {epoch}: {1_0_0 * eval_metric:.2f}'''  )







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







 """simple docstring"""

 lowerCamelCase      						=    argparse.ArgumentParser(description='Simple example of training script.'  )
 parser.add_argument('--data_dir'   ,					required=UpperCamelCase_   ,					help='The data folder on disk.'  )
 parser.add_argument(
     '--mixed_precision'   ,					type=UpperCamelCase_   ,					default=UpperCamelCase_   ,					choices=['no', 'fp16', 'bf16', 'fp8']   ,					help='Whether to use mixed precision. Choose'
     'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
     'and an Nvidia Ampere GPU.'   ,					)
 parser.add_argument(
     '--checkpointing_steps'   ,					type=UpperCamelCase_   ,					default=UpperCamelCase_   ,					help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'   ,					)
 parser.add_argument('--cpu'   ,					action='store_true'   ,					help='If passed, will train on the CPU.'  )
 lowerCamelCase      						=    parser.parse_args()
 lowerCamelCase      						=    {'lr': 3E-2, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 6_4, 'image_size': 2_2_4}
 training_function(UpperCamelCase_   ,					UpperCamelCase_  )


if __name__ == "__main__":
     main()







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     lowerCAmelCase				:


       '''simple docstring'''







       def __init__(	self					:  Union[str, Any]						,	__snake_case					:  Optional[Any]						,	__snake_case					:  Tuple=13						,	__snake_case					:  Optional[Any]=64						,	__snake_case					:  str=4						,	__snake_case					:  List[Any]=3						,	__snake_case					:  Dict=16						,	__snake_case					:  str=[1, 2, 1]						,	__snake_case					:  Any=[2, 4, 8]						,	__snake_case					:  Union[str, Any]=3						,	__snake_case					:  List[str]=2.0						,	__snake_case					:  Optional[Any]=True						,	__snake_case					:  str=0.0						,	__snake_case					:  Optional[Any]=0.0						,	__snake_case					:  Any=0.1						,	__snake_case					:  List[Any]="gelu"						,	__snake_case					:  str=True						,	__snake_case					:  str=0.02						,	__snake_case					:  Optional[Any]=1e-5						,	__snake_case					:  int=True						,	__snake_case					:  int=None						,	__snake_case					:  str=True						,	__snake_case					:  Any=10						,	__snake_case					:  List[str]=["stage1", "stage2"]						,	__snake_case					:  int=[1, 2]						,	)   -> int:







        '''simple docstring'''

        lowerCamelCase      						=    parent
        lowerCamelCase      						=    batch_size
        lowerCamelCase      						=    image_size
        lowerCamelCase      						=    patch_size
        lowerCamelCase      						=    num_channels
        lowerCamelCase      						=    embed_dim
        lowerCamelCase      						=    depths
        lowerCamelCase      						=    num_heads
        lowerCamelCase      						=    kernel_size
        lowerCamelCase      						=    mlp_ratio
        lowerCamelCase      						=    qkv_bias
        lowerCamelCase      						=    hidden_dropout_prob
        lowerCamelCase      						=    attention_probs_dropout_prob
        lowerCamelCase      						=    drop_path_rate
        lowerCamelCase      						=    hidden_act
        lowerCamelCase      						=    patch_norm
        lowerCamelCase      						=    layer_norm_eps
        lowerCamelCase      						=    initializer_range
        lowerCamelCase      						=    is_training
        lowerCamelCase      						=    scope
        lowerCamelCase      						=    use_labels
        lowerCamelCase      						=    num_labels
        lowerCamelCase      						=    out_features
        lowerCamelCase      						=    out_indices

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







        '''simple docstring'''

        lowerCamelCase      						=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

        lowerCamelCase      						=    None
        if self.use_labels:
         lowerCamelCase      						=    ids_tensor([self.batch_size]						,	self.num_labels     )

        lowerCamelCase      						=    self.get_config()

        return config, pixel_values, labels

       def   lowerCamelCase__						(	self					:  Tuple     )   -> int:







        '''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   lowerCamelCase__						(	self					:  str						,	__snake_case					:  Optional[Any]						,	__snake_case					:  List[Any]						,	__snake_case					:  List[str]     )   -> int:







        '''simple docstring'''

        lowerCamelCase      						=    NatModel(config=__snake_case     )
        model.to(__snake_case     )
        model.eval()
        lowerCamelCase      						=    model(__snake_case     )

        lowerCamelCase      						=    lowerCamelCase      						=    (config.image_size // config.patch_size) // (2 ** (len(config.depths     ) - 1))
        lowerCamelCase      						=    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   lowerCamelCase__						(	self					:  Tuple						,	__snake_case					:  List[Any]						,	__snake_case					:  Optional[int]						,	__snake_case					:  Union[str, Any]     )   -> str:







        '''simple docstring'''

        lowerCamelCase      						=    NatForImageClassification(__snake_case     )
        model.to(__snake_case     )
        model.eval()
        lowerCamelCase      						=    model(__snake_case						,	labels=__snake_case     )
        self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)     )

        # test greyscale images
        lowerCamelCase      						=    1
        lowerCamelCase      						=    NatForImageClassification(__snake_case     )
        model.to(__snake_case     )
        model.eval()

        lowerCamelCase      						=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
        lowerCamelCase      						=    model(__snake_case     )
        self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)     )

       def   lowerCamelCase__						(	self					:  Union[str, Any]						,	__snake_case					:  Any						,	__snake_case					:  Any						,	__snake_case					:  Dict     )   -> List[Any]:







        '''simple docstring'''

        lowerCamelCase      						=    NatBackbone(config=__snake_case     )
        model.to(__snake_case     )
        model.eval()
        lowerCamelCase      						=    model(__snake_case     )

        # 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
        lowerCamelCase      						=    None
        lowerCamelCase      						=    NatBackbone(config=__snake_case     )
        model.to(__snake_case     )
        model.eval()
        lowerCamelCase      						=    model(__snake_case     )

        # 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   lowerCamelCase__						(	self					:  Tuple     )   -> Union[str, Any]:







        '''simple docstring'''

        lowerCamelCase      						=    self.prepare_config_and_inputs()
        lowerCamelCase						,	lowerCamelCase						,	lowerCamelCase      						=    config_and_inputs
        lowerCamelCase      						=    {'pixel_values': pixel_values}
        return config, inputs_dict


@require_natten
@require_torch
class     lowerCAmelCase				(    __UpperCamelCase   ,				__UpperCamelCase   ,				unittest.TestCase				):


       '''simple docstring'''







       snake_case    							=	(
           (
               NatModel,
               NatForImageClassification,
               NatBackbone,
           )
           if is_torch_available()
           else ()
       )
       snake_case    							=	(
           {'feature-extraction': NatModel, 'image-classification': NatForImageClassification}
           if is_torch_available()
           else {}
       )
       snake_case    							=	False

       snake_case    							=	False
       snake_case    							=	False
       snake_case    							=	False
       snake_case    							=	False

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







        '''simple docstring'''

        lowerCamelCase      						=    NatModelTester(self     )
        lowerCamelCase      						=    ConfigTester(self						,	config_class=__snake_case						,	embed_dim=37     )

       def   lowerCamelCase__						(	self					:  Dict     )   -> str:







        '''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   lowerCamelCase__						(	self					:  Optional[Any]     )   -> Union[str, Any]:







        '''simple docstring'''

        return

       def   lowerCamelCase__						(	self					:  List[Any]     )   -> List[str]:







        '''simple docstring'''

        lowerCamelCase      						=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__snake_case     )

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







        '''simple docstring'''

        lowerCamelCase      						=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__snake_case     )

       def   lowerCamelCase__						(	self					:  List[str]     )   -> Dict:







        '''simple docstring'''

        lowerCamelCase      						=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*__snake_case     )

       @unittest.skip(reason='Nat does not use inputs_embeds'     )
       def   lowerCamelCase__						(	self					:  Tuple     )   -> Optional[int]:







        '''simple docstring'''

        pass

       @unittest.skip(reason='Nat does not use feedforward chunking'     )
       def   lowerCamelCase__						(	self					:  Optional[Any]     )   -> List[str]:







        '''simple docstring'''

        pass

       def   lowerCamelCase__						(	self					:  Tuple     )   -> str:







        '''simple docstring'''

        lowerCamelCase						,	lowerCamelCase      						=    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCamelCase      						=    model_class(__snake_case     )
         self.assertIsInstance(model.get_input_embeddings()						,	(nn.Module)     )
         lowerCamelCase      						=    model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(__snake_case						,	nn.Linear     )     )

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







        '''simple docstring'''

        lowerCamelCase						,	lowerCamelCase      						=    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCamelCase      						=    model_class(__snake_case     )
         lowerCamelCase      						=    inspect.signature(model.forward     )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCamelCase      						=    [*signature.parameters.keys()]

         lowerCamelCase      						=    ['pixel_values']
         self.assertListEqual(arg_names[:1]						,	__snake_case     )

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







        '''simple docstring'''

        self.skipTest('Nat\'s attention operation is handled entirely by NATTEN.'     )

       def   lowerCamelCase__						(	self					:  str						,	__snake_case					:  List[str]						,	__snake_case					:  Optional[Any]						,	__snake_case					:  Tuple						,	__snake_case					:  Union[str, Any]     )   -> int:







        '''simple docstring'''

        lowerCamelCase      						=    model_class(__snake_case     )
        model.to(__snake_case     )
        model.eval()

        with torch.no_grad():
         lowerCamelCase      						=    model(**self._prepare_for_class(__snake_case						,	__snake_case     )     )

        lowerCamelCase      						=    outputs.hidden_states

        lowerCamelCase      						=    getattr(
            self.model_tester						,	'expected_num_hidden_layers'						,	len(self.model_tester.depths     ) + 1     )
        self.assertEqual(len(__snake_case     )						,	__snake_case     )

        # Nat has a different seq_length
        lowerCamelCase      						=    (
            config.patch_size
            if isinstance(config.patch_size						,	collections.abc.Iterable     )
            else (config.patch_size, config.patch_size)
        )

        lowerCamelCase      						=    image_size[0] // patch_size[0]
        lowerCamelCase      						=    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":
         lowerCamelCase      						=    outputs.reshaped_hidden_states
         self.assertEqual(len(__snake_case     )						,	__snake_case     )

         lowerCamelCase						,	lowerCamelCase						,	lowerCamelCase						,	lowerCamelCase      						=    reshaped_hidden_states[0].shape
         lowerCamelCase      						=    (
             reshaped_hidden_states[0].view(__snake_case						,	__snake_case						,	__snake_case						,	__snake_case     ).permute(0						,	2						,	3						,	1     )
         )
         self.assertListEqual(
             list(reshaped_hidden_states.shape[-3:]     )						,	[height, width, self.model_tester.embed_dim]						,	)

       def   lowerCamelCase__						(	self					:  Dict     )   -> Tuple:







        '''simple docstring'''

        lowerCamelCase						,	lowerCamelCase      						=    self.model_tester.prepare_config_and_inputs_for_common()

        lowerCamelCase      						=    (
            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:
         lowerCamelCase      						=    True
         self.check_hidden_states_output(__snake_case						,	__snake_case						,	__snake_case						,	__snake_case     )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCamelCase      						=    True

         self.check_hidden_states_output(__snake_case						,	__snake_case						,	__snake_case						,	__snake_case     )

       @slow
       def   lowerCamelCase__						(	self					:  Any     )   -> Optional[int]:







        '''simple docstring'''

        for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCamelCase      						=    NatModel.from_pretrained(__snake_case     )
         self.assertIsNotNone(__snake_case     )





       def   lowerCamelCase__						(	self					:  Any     )   -> Any:







        '''simple docstring'''

        lowerCamelCase						,	lowerCamelCase      						=    self.model_tester.prepare_config_and_inputs_for_common()

        lowerCamelCase      						=    _config_zero_init(__snake_case     )
        for model_class in self.all_model_classes:
         lowerCamelCase      						=    model_class(config=__snake_case     )
         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     lowerCAmelCase				(    unittest.TestCase				):


       '''simple docstring'''







       @cached_property
       def   lowerCamelCase__						(	self					:  Union[str, Any]     )   -> Dict:







        '''simple docstring'''

        return AutoImageProcessor.from_pretrained('shi-labs/nat-mini-in1k-224'     ) if is_vision_available() else None





       @slow
       def   lowerCamelCase__						(	self					:  List[str]     )   -> Tuple:







        '''simple docstring'''

        lowerCamelCase      						=    NatForImageClassification.from_pretrained('shi-labs/nat-mini-in1k-224'     ).to(__snake_case     )
        lowerCamelCase      						=    self.default_image_processor

        lowerCamelCase      						=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
        lowerCamelCase      						=    image_processor(images=__snake_case						,	return_tensors='pt'     ).to(__snake_case     )

        # forward pass
        with torch.no_grad():
         lowerCamelCase      						=    model(**__snake_case     )

        # verify the logits
        lowerCamelCase      						=    torch.Size((1, 1000)     )
        self.assertEqual(outputs.logits.shape						,	__snake_case     )
        lowerCamelCase      						=    torch.tensor([0.3805, -0.8676, -0.3912]     ).to(__snake_case     )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]						,	__snake_case						,	atol=1e-4     )     )


@require_torch
@require_natten
class     lowerCAmelCase				(    unittest.TestCase   ,				__UpperCamelCase				):


       '''simple docstring'''







       snake_case    							=	(NatBackbone,) if is_torch_available() else ()
       snake_case    							=	NatConfig

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







        '''simple docstring'''

        lowerCamelCase      						=    NatModelTester(self     )




