import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


_A								=   logging.getLogger(__name__)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_=2  ,       SCREAMING_SNAKE_CASE_=3  ,       SCREAMING_SNAKE_CASE_=16  ,       SCREAMING_SNAKE_CASE_ = 10  ,       SCREAMING_SNAKE_CASE_ = 2							):

     def get_dataset(SCREAMING_SNAKE_CASE_							):
          lowercase_					:       Any     				=					torch.randn(batch_size * n_batches  ,       1							)
          return TensorDataset(SCREAMING_SNAKE_CASE_  ,       a * x + b + 0.1 * torch.randn(batch_size * n_batches  ,       1							)							)

     lowercase_					:       Dict     				=					get_dataset(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Dict     				=					get_dataset(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       List[str]     				=					DataLoader(SCREAMING_SNAKE_CASE_  ,       shuffle=SCREAMING_SNAKE_CASE_  ,       batch_size=SCREAMING_SNAKE_CASE_  ,       num_workers=4							)
     lowercase_					:       List[str]     				=					DataLoader(SCREAMING_SNAKE_CASE_  ,       shuffle=SCREAMING_SNAKE_CASE_  ,       batch_size=SCREAMING_SNAKE_CASE_  ,       num_workers=4							)
     return (train_dataloader, valid_dataloader)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=None							):
     lowercase_					:       Any     				=					[]
     for epoch in range(SCREAMING_SNAKE_CASE_							):
          # Train quickly
          model.train()
          for batch in dataloader:
               lowercase_					:       List[str]     				=					batch
               lowercase_					:       Dict     				=					model(SCREAMING_SNAKE_CASE_							)
               lowercase_					:       Dict     				=					torch.nn.functional.mse_loss(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
               accelerator.backward(SCREAMING_SNAKE_CASE_							)
               optimizer.step()
               optimizer.zero_grad()
          rands.append(random.random()							)  # Introduce some randomness
          if scheduler is not None:
               scheduler.step()
     return rands


class       UpperCAmelCase__  (       nn.Module			):






   """simple docstring"""
   def __init__(self				)    ->     Tuple:
        super().__init__()
        lowercase_					:       Any     				=					nn.Parameter(torch.randn(1				)				)
        lowercase_					:       Any     				=					nn.Parameter(torch.randn(1				)				)





   def 		_lowerCamelCase (self       ,				_a				)    ->     Optional[Any]:
        return x * self.a + self.b



class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42				)
             lowercase_					:       Union[str, Any]     				=					DummyModel()
             lowercase_					:       Optional[int]     				=					torch.optim.Adam(params=model.parameters()       ,				lr=1e-3				)
             lowercase_					:       Optional[int]     				=					dummy_dataloaders()
             lowercase_					:       Tuple     				=					ProjectConfiguration(total_limit=1       ,				project_dir=_a       ,				automatic_checkpoint_naming=_a				)
             # Train baseline
             lowercase_					:       Optional[int]     				=					Accelerator(project_config=_a				)
             lowercase_					:       Tuple     				=					accelerator.prepare(
                 _a       ,				_a       ,				_a       ,				_a				)
             # Save initial
             accelerator.save_state()

             # Save second state
             accelerator.save_state()
             self.assertEqual(len(os.listdir(accelerator.project_dir				)				)       ,				1				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42				)
             lowercase_					:       Optional[int]     				=					DummyModel()
             lowercase_					:       str     				=					torch.optim.Adam(params=model.parameters()       ,				lr=1e-3				)
             lowercase_					:       str     				=					dummy_dataloaders()
             # Train baseline
             lowercase_					:       Any     				=					Accelerator()
             lowercase_					:       Tuple     				=					accelerator.prepare(
                 _a       ,				_a       ,				_a       ,				_a				)
             # Save initial
             lowercase_					:       str     				=					os.path.join(_a       ,				'initial'				)
             accelerator.save_state(_a				)
             (lowercase_)					:       str     				=					model.a.item(), model.b.item()
             lowercase_					:       Any     				=					optimizer.state_dict()
             lowercase_					:       str     				=					train(3       ,				_a       ,				_a       ,				_a       ,				_a				)
             (lowercase_)					:       str     				=					model.a.item(), model.b.item()
             lowercase_					:       Optional[Any]     				=					optimizer.state_dict()

             # Train partially
             set_seed(42				)
             lowercase_					:       Optional[int]     				=					DummyModel()
             lowercase_					:       List[Any]     				=					torch.optim.Adam(params=model.parameters()       ,				lr=1e-3				)
             lowercase_					:       Dict     				=					dummy_dataloaders()
             lowercase_					:       Optional[int]     				=					Accelerator()
             lowercase_					:       Optional[Any]     				=					accelerator.prepare(
                 _a       ,				_a       ,				_a       ,				_a				)
             accelerator.load_state(_a				)
             (lowercase_)					:       List[Any]     				=					model.a.item(), model.b.item()
             lowercase_					:       str     				=					optimizer.state_dict()
             self.assertEqual(_a       ,				_a				)
             self.assertEqual(_a       ,				_a				)
             self.assertEqual(_a       ,				_a				)

             lowercase_					:       Union[str, Any]     				=					train(2       ,				_a       ,				_a       ,				_a       ,				_a				)
             # Save everything
             lowercase_					:       int     				=					os.path.join(_a       ,				'checkpoint'				)
             accelerator.save_state(_a				)

             # Load everything back in and make sure all states work
             accelerator.load_state(_a				)
             test_rands += train(1       ,				_a       ,				_a       ,				_a       ,				_a				)
             (lowercase_)					:       Any     				=					model.a.item(), model.b.item()
             lowercase_					:       str     				=					optimizer.state_dict()
             self.assertEqual(_a       ,				_a				)
             self.assertEqual(_a       ,				_a				)
             self.assertEqual(_a       ,				_a				)
             self.assertEqual(_a       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42				)
             lowercase_					:       str     				=					DummyModel()
             lowercase_					:       str     				=					torch.optim.Adam(params=model.parameters()       ,				lr=1e-3				)
             lowercase_					:       int     				=					dummy_dataloaders()
             lowercase_					:       List[str]     				=					ProjectConfiguration(automatic_checkpoint_naming=_a				)

             # Train baseline
             lowercase_					:       List[str]     				=					Accelerator(project_dir=_a       ,				project_config=_a				)
             lowercase_					:       List[Any]     				=					accelerator.prepare(
                 _a       ,				_a       ,				_a       ,				_a				)
             # Save initial
             accelerator.save_state()
             (lowercase_)					:       str     				=					model.a.item(), model.b.item()
             lowercase_					:       Optional[int]     				=					optimizer.state_dict()
             lowercase_					:       Any     				=					train(3       ,				_a       ,				_a       ,				_a       ,				_a				)
             (lowercase_)					:       Dict     				=					model.a.item(), model.b.item()
             lowercase_					:       List[str]     				=					optimizer.state_dict()

             # Train partially
             set_seed(42				)
             lowercase_					:       Tuple     				=					DummyModel()
             lowercase_					:       List[Any]     				=					torch.optim.Adam(params=model.parameters()       ,				lr=1e-3				)
             lowercase_					:       Union[str, Any]     				=					dummy_dataloaders()
             lowercase_					:       List[Any]     				=					ProjectConfiguration(iteration=1       ,				automatic_checkpoint_naming=_a				)
             lowercase_					:       Optional[Any]     				=					Accelerator(project_dir=_a       ,				project_config=_a				)
             lowercase_					:       List[Any]     				=					accelerator.prepare(
                 _a       ,				_a       ,				_a       ,				_a				)
             accelerator.load_state(os.path.join(_a       ,				'checkpoints'       ,				'checkpoint_0'				)				)
             (lowercase_)					:       Optional[int]     				=					model.a.item(), model.b.item()
             lowercase_					:       Dict     				=					optimizer.state_dict()
             self.assertEqual(_a       ,				_a				)
             self.assertEqual(_a       ,				_a				)
             self.assertEqual(_a       ,				_a				)

             lowercase_					:       List[Any]     				=					train(2       ,				_a       ,				_a       ,				_a       ,				_a				)
             # Save everything
             accelerator.save_state()

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(_a       ,				'checkpoints'       ,				'checkpoint_1'				)				)
             test_rands += train(1       ,				_a       ,				_a       ,				_a       ,				_a				)
             (lowercase_)					:       List[str]     				=					model.a.item(), model.b.item()
             lowercase_					:       Union[str, Any]     				=					optimizer.state_dict()
             self.assertEqual(_a       ,				_a				)
             self.assertEqual(_a       ,				_a				)
             self.assertEqual(_a       ,				_a				)
             self.assertEqual(_a       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_					:       Optional[Any]     				=					torch.tensor([1, 2, 3]				)
        lowercase_					:       List[str]     				=					torch.tensor([2, 3, 4]				)
        lowercase_					:       Union[str, Any]     				=					DummyModel()
        lowercase_					:       int     				=					torch.optim.Adam(net.parameters()				)
        lowercase_					:       Tuple     				=					Accelerator()
        with self.assertRaises(_a				) as ve:
             accelerator.register_for_checkpointing(_a       ,				_a       ,				_a       ,				_a				)
        lowercase_					:       List[Any]     				=					str(ve.exception				)
        self.assertTrue('Item at index 0' in message				)
        self.assertTrue('Item at index 1' in message				)
        self.assertFalse('Item at index 2' in message				)
        self.assertFalse('Item at index 3' in message				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42				)
             lowercase_					:       Optional[int]     				=					DummyModel()
             lowercase_					:       int     				=					torch.optim.Adam(params=model.parameters()       ,				lr=1e-3				)
             lowercase_					:       Dict     				=					torch.optim.lr_scheduler.StepLR(_a       ,				step_size=1       ,				gamma=0.99				)
             lowercase_					:       List[Any]     				=					dummy_dataloaders()
             lowercase_					:       str     				=					ProjectConfiguration(automatic_checkpoint_naming=_a				)
             # Train baseline
             lowercase_					:       int     				=					Accelerator(project_dir=_a       ,				project_config=_a				)
             lowercase_					:       Optional[Any]     				=					accelerator.prepare(
                 _a       ,				_a       ,				_a       ,				_a       ,				_a				)
             # Save initial
             accelerator.save_state()
             lowercase_					:       Dict     				=					scheduler.state_dict()
             train(3       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a				)
             self.assertNotEqual(_a       ,				scheduler.state_dict()				)

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(_a       ,				'checkpoints'       ,				'checkpoint_0'				)				)
             self.assertEqual(_a       ,				scheduler.state_dict()				)
   def 		_lowerCamelCase (self				)    ->     Any:
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42				)
             lowercase_					:       Union[str, Any]     				=					DummyModel()
             lowercase_					:       List[Any]     				=					ProjectConfiguration(automatic_checkpoint_naming=_a       ,				total_limit=2				)
             # Train baseline
             lowercase_					:       int     				=					Accelerator(project_dir=_a       ,				project_config=_a				)
             lowercase_					:       Tuple     				=					accelerator.prepare(_a				)
             # Save 3 states:
             for _ in range(11				):
                  accelerator.save_state()
             self.assertTrue(not os.path.exists(os.path.join(_a       ,				'checkpoints'       ,				'checkpoint_0'				)				)				)
             self.assertTrue(os.path.exists(os.path.join(_a       ,				'checkpoints'       ,				'checkpoint_9'				)				)				)
             self.assertTrue(os.path.exists(os.path.join(_a       ,				'checkpoints'       ,				'checkpoint_10'				)				)				)





   @require_cuda
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       Tuple     				=					['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__				)]
        execute_subprocess_async(_a       ,				env=os.environ.copy()				)


if __name__ == "__main__":
 _A								=   '/tmp/accelerate/state_checkpointing'
 _A								=   DummyModel()
 _A								=   torch.optim.Adam(params=model.parameters(), lr=1e-3)
 _A								=   torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
 _A				, _A								=   dummy_dataloaders()
 _A								=   ProjectConfiguration(automatic_checkpoint_naming=True)
 # Train baseline
 _A								=   Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
 if accelerator.process_index == 0:
  if os.path.exists(savedir):
   shutil.rmtree(savedir)
  os.makedirs(savedir)
 _A				, _A				, _A				, _A				, _A								=   accelerator.prepare(
     model, optimizer, train_dataloader, valid_dataloader, scheduler
 )
 _A				, _A								=   accelerator.prepare(model, optimizer)
 train(3, model, train_dataloader, optimizer, accelerator, scheduler)
 # Check that the intial optimizer is loaded on the GPU
 for group in optimizer.param_groups:
  _A								=   group['params'][0].device
  break
 assert param_device.type == accelerator.device.type
 _A								=   model.cpu()
 accelerator.wait_for_everyone()
 accelerator.save_state()
 accelerator.wait_for_everyone()

 # Check CPU state
 accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
 for group in optimizer.param_groups:
  _A								=   group['params'][0].device
  break
 assert (
     param_device.type == torch.device('cpu').type
 ), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

 # Check device state
 model.to(accelerator.device)
 accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
 for group in optimizer.param_groups:
  _A								=   group['params'][0].device
  break
 assert (
     param_device.type == accelerator.device.type
 ), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

 # Check error
 with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
  accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
 accelerator.wait_for_everyone()
 if accelerator.process_index == 0:
  shutil.rmtree(savedir)
 accelerator.wait_for_everyone()





'''simple docstring'''




import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
 from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor


class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a=13       ,				_a=32       ,				_a=3       ,				_a=4       ,				_a=[10, 20, 30, 40]       ,				_a=[2, 2, 3, 2]       ,				_a=True       ,				_a=True       ,				_a=37       ,				_a="gelu"       ,				_a=10       ,				_a=0.02       ,				_a=["stage2", "stage3", "stage4"]       ,				_a=[2, 3, 4]       ,				_a=None       ,				)    ->     List[str]:
        lowercase_					:       Optional[int]     				=					parent
        lowercase_					:       int     				=					batch_size
        lowercase_					:       Union[str, Any]     				=					image_size
        lowercase_					:       Optional[Any]     				=					num_channels
        lowercase_					:       Any     				=					num_stages
        lowercase_					:       Optional[Any]     				=					hidden_sizes
        lowercase_					:       Any     				=					depths
        lowercase_					:       Tuple     				=					is_training
        lowercase_					:       int     				=					use_labels
        lowercase_					:       Tuple     				=					intermediate_size
        lowercase_					:       Any     				=					hidden_act
        lowercase_					:       Union[str, Any]     				=					num_labels
        lowercase_					:       Union[str, Any]     				=					initializer_range
        lowercase_					:       Optional[Any]     				=					out_features
        lowercase_					:       str     				=					out_indices
        lowercase_					:       Dict     				=					scope
   def 		_lowerCamelCase (self				)    ->     Any:
        lowercase_					:       Optional[Any]     				=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

        lowercase_					:       int     				=					None
        if self.use_labels:
             lowercase_					:       Any     				=					ids_tensor([self.batch_size]       ,				self.num_labels				)

        lowercase_					:       str     				=					self.get_config()

        return config, pixel_values, labels
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        return ConvNextVaConfig(
            num_channels=self.num_channels       ,				hidden_sizes=self.hidden_sizes       ,				depths=self.depths       ,				num_stages=self.num_stages       ,				hidden_act=self.hidden_act       ,				is_decoder=_a       ,				initializer_range=self.initializer_range       ,				out_features=self.out_features       ,				out_indices=self.out_indices       ,				num_labels=self.num_labels       ,				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Tuple:
        lowercase_					:       Optional[int]     				=					ConvNextVaModel(config=_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       List[Any]     				=					model(_a				)
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape       ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Any:
        lowercase_					:       int     				=					ConvNextVaForImageClassification(_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       Dict     				=					model(_a       ,				labels=_a				)
        self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Optional[Any]:
        lowercase_					:       Any     				=					ConvNextVaBackbone(config=_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       Union[str, Any]     				=					model(_a				)

        # 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, self.hidden_sizes[1], 4, 4]				)

        # verify channels
        self.parent.assertEqual(len(model.channels				)       ,				len(config.out_features				)				)
        self.parent.assertListEqual(model.channels       ,				config.hidden_sizes[1:]				)

        # verify backbone works with out_features=None
        lowercase_					:       int     				=					None
        lowercase_					:       List[str]     				=					ConvNextVaBackbone(config=_a				)
        model.to(_a				)
        model.eval()
        lowercase_					:       Union[str, Any]     				=					model(_a				)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps				)       ,				1				)
        self.parent.assertListEqual(list(result.feature_maps[0].shape				)       ,				[self.batch_size, self.hidden_sizes[-1], 1, 1]				)

        # verify channels
        self.parent.assertEqual(len(model.channels				)       ,				1				)
        self.parent.assertListEqual(model.channels       ,				[config.hidden_sizes[-1]]				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       Union[str, Any]     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       str     				=					config_and_inputs
        lowercase_					:       Optional[int]     				=					{'pixel_values': pixel_values}
        return config, inputs_dict





   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_					:       Optional[Any]     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       Optional[Any]     				=					config_and_inputs
        lowercase_					:       Dict     				=					{'pixel_values': pixel_values, 'labels': labels}
        return config, inputs_dict



@require_torch
class       UpperCAmelCase__  (       _snake_case					,     _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       List[str]     =    (
       (
           ConvNextVaModel,
           ConvNextVaForImageClassification,
           ConvNextVaBackbone,
       )
       if is_torch_available()
       else ()
   )
   A							:       Optional[int]     =    (
       {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
       if is_torch_available()
       else {}
   )

   A							:       Optional[int]     =    False
   A							:       str     =    False
   A							:       int     =    False
   A							:       Dict     =    False
   A							:       Optional[int]     =    False
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       Dict     				=					ConvNextVaModelTester(self				)
        lowercase_					:       List[str]     				=					ConfigTester(self       ,				config_class=_a       ,				has_text_modality=_a       ,				hidden_size=37				)
   def 		_lowerCamelCase (self				)    ->     int:
        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				)    ->     List[Any]:
        return
   @unittest.skip(reason='ConvNextV2 does not use inputs_embeds'				)
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        pass
   @unittest.skip(reason='ConvNextV2 does not support input and output embeddings'				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        pass
   @unittest.skip(reason='ConvNextV2 does not use feedforward chunking'				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        pass
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        if not self.model_tester.is_training:
             return

        for model_class in self.all_model_classes:
             lowercase_				,lowercase_					:       List[Any]     				=					self.model_tester.prepare_config_and_inputs_with_labels()
             lowercase_					:       Dict     				=					True

             if model_class.__name__ in [
                 *get_values(_a				),
                 *get_values(_a				),
             ]:
                  continue

             lowercase_					:       str     				=					model_class(_a				)
             model.to(_a				)
             model.train()
             lowercase_					:       Tuple     				=					self._prepare_for_class(_a       ,				_a       ,				return_labels=_a				)
             lowercase_					:       List[Any]     				=					model(**_a				).loss
             loss.backward()
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        if not self.model_tester.is_training:
             return

        for model_class in self.all_model_classes:
             lowercase_				,lowercase_					:       Optional[Any]     				=					self.model_tester.prepare_config_and_inputs_with_labels()
             lowercase_					:       Optional[int]     				=					False
             lowercase_					:       Union[str, Any]     				=					True

             if (
                 model_class.__name__
                 in [*get_values(_a				), *get_values(_a				)]
                 or not model_class.supports_gradient_checkpointing
             ):
                  continue

             lowercase_					:       Union[str, Any]     				=					model_class(_a				)
             model.to(_a				)
             model.gradient_checkpointing_enable()
             model.train()
             lowercase_					:       List[Any]     				=					self._prepare_for_class(_a       ,				_a       ,				return_labels=_a				)
             lowercase_					:       Optional[int]     				=					model(**_a				).loss
             loss.backward()
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_				,lowercase_					:       int     				=					self.model_tester.prepare_config_and_inputs_for_common()

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

             lowercase_					:       List[str]     				=					['pixel_values']
             self.assertListEqual(arg_names[:1]       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       List[Any]     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        def check_hidden_states_output(_a       ,				_a       ,				_a				):
             lowercase_					:       Tuple     				=					model_class(_a				)
             model.to(_a				)
             model.eval()

             with torch.no_grad():
                  lowercase_					:       str     				=					model(**self._prepare_for_class(_a       ,				_a				)				)

             lowercase_					:       str     				=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             lowercase_					:       Optional[int]     				=					self.model_tester.num_stages
             self.assertEqual(len(_a				)       ,				expected_num_stages + 1				)

             # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]				)       ,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,				)

        lowercase_				,lowercase_					:       Optional[int]     				=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_					:       Optional[Any]     				=					True
             check_hidden_states_output(_a       ,				_a       ,				_a				)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowercase_					:       int     				=					True

             check_hidden_states_output(_a       ,				_a       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Dict     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_a				)





   @slow
   def 		_lowerCamelCase (self				)    ->     Any:
        for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_					:       Optional[int]     				=					ConvNextVaModel.from_pretrained(_a				)
             self.assertIsNotNone(_a				)



def _UpperCamelCase   (       ):
     lowercase_					:       Union[str, Any]     				=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
     return image






@require_torch
@require_vision
class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   @cached_property
   def 		_lowerCamelCase (self				)    ->     str:
        return AutoImageProcessor.from_pretrained('facebook/convnextv2-tiny-1k-224'				) if is_vision_available() else None





   @slow
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       List[Any]     				=					ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'				).to(_a				)

        lowercase_					:       Any     				=					self.default_image_processor
        lowercase_					:       Union[str, Any]     				=					prepare_img()
        lowercase_					:       List[str]     				=					preprocessor(images=_a       ,				return_tensors='pt'				).to(_a				)

        # forward pass
        with torch.no_grad():
             lowercase_					:       List[Any]     				=					model(**_a				)

        # verify the logits
        lowercase_					:       str     				=					torch.Size((1, 1_000)				)
        self.assertEqual(outputs.logits.shape       ,				_a				)

        lowercase_					:       Tuple     				=					torch.tensor([0.99_96, 0.19_66, -0.43_86]				).to(_a				)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				_a       ,				atol=1e-4				)				)




