

'''simple docstring'''



from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed


def       snake_case   (	snake_case      : Dict					,		snake_case      : Union[str, Any]					,		snake_case      : Tuple					,		snake_case      : Union[str, Any]			) ->  Union[str, Any]:


    """simple docstring"""


    for param, grad_param in zip(model_a.parameters()					,		model_b.parameters()			):
        if not param.requires_grad:
            continue
        if not did_step:
            # Grads should not be in sync
            assert (
                torch.allclose(param.grad					,		grad_param.grad			) is False
            ), F'Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'
        else:
            # Grads should be in sync
            assert (
                torch.allclose(param.grad					,		grad_param.grad			) is True
            ), F'Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'


def       snake_case   (	snake_case      : List[Any]					,		snake_case      : Tuple					,		snake_case      : List[str]					,		snake_case      : Tuple					,		snake_case      : Union[str, Any]=True			) ->  List[Any]:


    """simple docstring"""


    model.train()
    lowerCAmelCase 						=							model(snake_case			)
    lowerCAmelCase 						=							F.mse_loss(snake_case					,		target.to(output.device			)			)
    if not do_backward:
        loss /= accelerator.gradient_accumulation_steps
        loss.backward()
    else:
        accelerator.backward(snake_case			)


def       snake_case   (	snake_case      : List[Any]					,		snake_case      : Dict=False			) ->  Dict:


    """simple docstring"""


    set_seed(42			)
    lowerCAmelCase 						=							RegressionModel()
    lowerCAmelCase 						=							deepcopy(snake_case			)
    lowerCAmelCase 						=							RegressionDataset(length=80			)
    lowerCAmelCase 						=							DataLoader(snake_case					,		batch_size=16			)
    model.to(accelerator.device			)
    if sched:
        lowerCAmelCase 						=							AdamW(params=model.parameters()					,		lr=1e-3			)
        lowerCAmelCase 						=							AdamW(params=ddp_model.parameters()					,		lr=1e-3			)
        lowerCAmelCase 						=							LambdaLR(snake_case					,		lr_lambda=lambda snake_case			: epoch**0.65			)
        lowerCAmelCase 						=							LambdaLR(snake_case					,		lr_lambda=lambda snake_case			: epoch**0.65			)
    # Make a copy of `model`
    if sched:
        lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase 						=							accelerator.prepare(snake_case					,		snake_case					,		snake_case					,		snake_case			)
    else:
        lowerCAmelCase						,   lowerCAmelCase 						=							accelerator.prepare(snake_case					,		snake_case			)
    if sched:
        return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
    return model, ddp_model, dataloader


def       snake_case   (	snake_case      : List[str]			) ->  Dict:


    """simple docstring"""


    lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase 						=							get_training_setup(snake_case			)
    # Use a single batch
    lowerCAmelCase						,   lowerCAmelCase 						=							next(iter(snake_case			)			).values()
    for iteration in range(3			):
        # Gather the distributed inputs and targs for the base model
        lowerCAmelCase						,   lowerCAmelCase 						=							accelerator.gather((ddp_input, ddp_target)			)
        lowerCAmelCase						,   lowerCAmelCase 						=							input.to(accelerator.device			), target.to(accelerator.device			)
        # Perform our initial ground truth step in non "DDP"
        step_model(snake_case					,		snake_case					,		snake_case					,		snake_case			)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(snake_case			):
                step_model(snake_case					,		snake_case					,		snake_case					,		snake_case			)
        else:
            # Sync grads
            step_model(snake_case					,		snake_case					,		snake_case					,		snake_case			)

        # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
        check_model_parameters(snake_case					,		snake_case					,		snake_case					,		snake_case			)
        for param, ddp_param in zip(model.parameters()					,		ddp_model.parameters()			):
            if not param.requires_grad:
                continue
            assert torch.allclose(
                param.grad					,		ddp_param.grad			), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1337 + iteration			)
        lowerCAmelCase 						=							ddp_input[torch.randperm(len(snake_case			)			)]


def       snake_case   (	snake_case      : Tuple			) ->  str:


    """simple docstring"""


    lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase 						=							get_training_setup(snake_case			)
    # Use a single batch
    lowerCAmelCase						,   lowerCAmelCase 						=							next(iter(snake_case			)			).values()
    for iteration in range(3			):
        # Gather the distributed inputs and targs for the base model
        lowerCAmelCase						,   lowerCAmelCase 						=							accelerator.gather((ddp_input, ddp_target)			)
        lowerCAmelCase						,   lowerCAmelCase 						=							input.to(accelerator.device			), target.to(accelerator.device			)
        # Perform our initial ground truth step in non "DDP"
        step_model(snake_case					,		snake_case					,		snake_case					,		snake_case			)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(snake_case			):
                step_model(snake_case					,		snake_case					,		snake_case					,		snake_case			)
        else:
            # Sync grads
            step_model(snake_case					,		snake_case					,		snake_case					,		snake_case			)

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters()					,		ddp_model.parameters()			):
            if not param.requires_grad:
                continue
            if iteration % 2 == 0:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad					,		ddp_param.grad			) is False
                ), F'Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
            else:
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad					,		ddp_param.grad			) is True
                ), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1337 + iteration			)
        lowerCAmelCase 						=							ddp_input[torch.randperm(len(snake_case			)			)]


def       snake_case   (	snake_case      : Union[str, Any]=False					,		snake_case      : str=False			) ->  Union[str, Any]:


    """simple docstring"""


    lowerCAmelCase 						=							Accelerator(
        split_batches=snake_case					,		dispatch_batches=snake_case					,		gradient_accumulation_steps=2			)
    # Test that context manager behaves properly
    lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase 						=							get_training_setup(snake_case			)
    for iteration, batch in enumerate(snake_case			):
        lowerCAmelCase						,   lowerCAmelCase 						=							batch.values()
        # Gather the distributed inputs and targs for the base model
        lowerCAmelCase						,   lowerCAmelCase 						=							accelerator.gather((ddp_input, ddp_target)			)
        lowerCAmelCase						,   lowerCAmelCase 						=							input.to(accelerator.device			), target.to(accelerator.device			)
        # Perform our initial ground truth step in non "DDP"
        step_model(snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case			)
        # Do "gradient accumulation" (noop)
        with accelerator.accumulate(snake_case			):
            step_model(snake_case					,		snake_case					,		snake_case					,		snake_case			)

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters()					,		ddp_model.parameters()			):
            if not param.requires_grad:
                continue
            if ((iteration + 1) % 2 == 0) or (iteration == len(snake_case			) - 1):
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad					,		ddp_param.grad			) is True
                ), F'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
            else:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad					,		ddp_param.grad			) is False
                ), F'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1337 + iteration			)
        lowerCAmelCase 						=							ddp_input[torch.randperm(len(snake_case			)			)]
    GradientState._reset_state()


def       snake_case   (	snake_case      : Tuple=False					,		snake_case      : Any=False			) ->  int:


    """simple docstring"""


    lowerCAmelCase 						=							Accelerator(
        split_batches=snake_case					,		dispatch_batches=snake_case					,		gradient_accumulation_steps=2			)
    # Test that context manager behaves properly
    lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase 						=							get_training_setup(snake_case					,		snake_case			)
    for iteration, batch in enumerate(snake_case			):
        lowerCAmelCase						,   lowerCAmelCase 						=							batch.values()
        # Gather the distributed inputs and targs for the base model
        lowerCAmelCase						,   lowerCAmelCase 						=							accelerator.gather((ddp_input, ddp_target)			)
        lowerCAmelCase						,   lowerCAmelCase 						=							input.to(accelerator.device			), target.to(accelerator.device			)
        # Perform our initial ground truth step in non "DDP"
        model.train()
        ddp_model.train()
        step_model(snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case			)
        opt.step()

        if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(snake_case			)):
            if split_batches:
                sched.step()
            else:
                for _ in range(accelerator.num_processes			):
                    sched.step()
        opt.zero_grad()
        # Perform gradient accumulation under wrapper
        with accelerator.accumulate(snake_case			):
            step_model(snake_case					,		snake_case					,		snake_case					,		snake_case			)
            ddp_opt.step()
            ddp_sched.step()
            ddp_opt.zero_grad()

        # Learning rates should be the same
        assert (
            opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
        ), F'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'
        lowerCAmelCase 						=							(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(snake_case			))
        if accelerator.num_processes > 1:
            check_model_parameters(snake_case					,		snake_case					,		snake_case					,		snake_case			)
        # Shuffle ddp_input on each iteration
        torch.manual_seed(1337 + iteration			)
    GradientState._reset_state()


def       snake_case   (	) ->  List[str]:


    """simple docstring"""


    lowerCAmelCase 						=							Accelerator()

    lowerCAmelCase 						=							RegressionDataset(length=80			)
    lowerCAmelCase 						=							DataLoader(snake_case					,		batch_size=16			)
    lowerCAmelCase 						=							RegressionDataset(length=96			)
    lowerCAmelCase 						=							DataLoader(snake_case					,		batch_size=16			)
    lowerCAmelCase						,   lowerCAmelCase 						=							accelerator.prepare(snake_case					,		snake_case			)
    assert accelerator.gradient_state.active_dataloader is None
    for iteration, _ in enumerate(snake_case			):
        assert id(accelerator.gradient_state.active_dataloader			) == id(snake_case			)
        if iteration < len(snake_case			) - 1:
            assert not accelerator.gradient_state.end_of_dataloader
            if iteration == 1:
                for batch_num, _ in enumerate(snake_case			):
                    assert id(accelerator.gradient_state.active_dataloader			) == id(snake_case			)
                    if batch_num < len(snake_case			) - 1:
                        assert not accelerator.gradient_state.end_of_dataloader
                    else:
                        assert accelerator.gradient_state.end_of_dataloader
        else:
            assert accelerator.gradient_state.end_of_dataloader
    assert accelerator.gradient_state.active_dataloader is None


def       snake_case   (	) ->  int:


    """simple docstring"""


    lowerCAmelCase 						=							Accelerator()
    lowerCAmelCase 						=							accelerator.state
    if state.local_process_index == 0:
        print('**Test `accumulate` gradient accumulation with dataloader break**'			)
    test_dataloader_break()
    if state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print('**Test NOOP `no_sync` context manager**'			)
        test_noop_sync(snake_case			)
    if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
        if state.local_process_index == 0:
            print('**Test Distributed `no_sync` context manager**'			)
        test_distributed_sync(snake_case			)
    if state.distributed_type == DistributedType.MULTI_GPU:
        for split_batch in [True, False]:
            for dispatch_batches in [True, False]:
                if state.local_process_index == 0:
                    print(
                        '**Test `accumulate` gradient accumulation, '					,		F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'					,		)
                test_gradient_accumulation(snake_case					,		snake_case			)

    # Currently will break on torch 2.0 +, need to investigate why
    if is_torch_version('<'					,		'2.0'			) or state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print(
                '**Test `accumulate` gradient accumulation with optimizer and scheduler, '					,		'`split_batches=False`, `dispatch_batches=False`**'					,		)
        test_gradient_accumulation_with_opt_and_scheduler()
        if state.distributed_type == DistributedType.MULTI_GPU:
            for split_batch in [True, False]:
                for dispatch_batches in [True, False]:
                    if not split_batch and not dispatch_batches:
                        continue
                    if state.local_process_index == 0:
                        print(
                            '**Test `accumulate` gradient accumulation with optimizer and scheduler, '					,		F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'					,		)
                    test_gradient_accumulation_with_opt_and_scheduler(snake_case					,		snake_case			)


def       snake_case   (	snake_case      : int			) ->  Optional[Any]:


    """simple docstring"""


    main()


if __name__ == "__main__":
     main()








'''simple docstring'''



import inspect
import unittest

from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
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 ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
     from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor



class 				_snake_case					:

  def __init__(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=13      , _SCREAMING_SNAKE_CASE=32      , _SCREAMING_SNAKE_CASE=3      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=[10, 20, 30, 40]      , _SCREAMING_SNAKE_CASE=[2, 2, 3, 2]      , _SCREAMING_SNAKE_CASE=True      , _SCREAMING_SNAKE_CASE=True      , _SCREAMING_SNAKE_CASE=37      , _SCREAMING_SNAKE_CASE="gelu"      , _SCREAMING_SNAKE_CASE=10      , _SCREAMING_SNAKE_CASE=0.02      , _SCREAMING_SNAKE_CASE=["stage2", "stage3", "stage4"]      , _SCREAMING_SNAKE_CASE=[2, 3, 4]      , _SCREAMING_SNAKE_CASE=None      , ):





      '''simple docstring'''

      lowerCAmelCase 						=							parent
      lowerCAmelCase 						=							batch_size
      lowerCAmelCase 						=							image_size
      lowerCAmelCase 						=							num_channels
      lowerCAmelCase 						=							num_stages
      lowerCAmelCase 						=							hidden_sizes
      lowerCAmelCase 						=							depths
      lowerCAmelCase 						=							is_training
      lowerCAmelCase 						=							use_labels
      lowerCAmelCase 						=							intermediate_size
      lowerCAmelCase 						=							hidden_act
      lowerCAmelCase 						=							num_labels
      lowerCAmelCase 						=							initializer_range
      lowerCAmelCase 						=							out_features
      lowerCAmelCase 						=							out_indices
      lowerCAmelCase 						=							scope

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return ConvNextConfig(
          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=_SCREAMING_SNAKE_CASE      , initializer_range=self.initializer_range      , out_features=self.out_features      , out_indices=self.out_indices      , num_labels=self.num_labels      , )

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							ConvNextModel(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE	)
      # 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   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							ConvNextForImageClassification(_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_labels)	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							ConvNextBackbone(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      lowerCAmelCase 						=							model(_SCREAMING_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, 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
      lowerCAmelCase 						=							None
      lowerCAmelCase 						=							ConvNextBackbone(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      lowerCAmelCase 						=							model(_SCREAMING_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, 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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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



@require_torch
class 				_snake_case					(   a_     , a_     , unittest.TestCase			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     (
      (
          ConvNextModel,
          ConvNextForImageClassification,
          ConvNextBackbone,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE			:       int				=     (
      {'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE			:       Tuple				=     True
  SCREAMING_SNAKE_CASE			:       Union[str, Any]				=     False
  SCREAMING_SNAKE_CASE			:       Tuple				=     False
  SCREAMING_SNAKE_CASE			:       List[str]				=     False
  SCREAMING_SNAKE_CASE			:       Optional[int]				=     False

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							ConvNextModelTester(self	)
      lowerCAmelCase 						=							ConfigTester(self      , config_class=_SCREAMING_SNAKE_CASE      , has_text_modality=_SCREAMING_SNAKE_CASE      , hidden_size=37	)

  def   _SCREAMING_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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return

  @unittest.skip(reason='ConvNext does not use inputs_embeds'	)
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      pass

  @unittest.skip(reason='ConvNext does not support input and output embeddings'	)
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      pass

  @unittest.skip(reason='ConvNext does not use feedforward chunking'	)
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      pass

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          lowerCAmelCase 						=							model_class(_SCREAMING_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]      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      def check_hidden_states_output(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
          lowerCAmelCase 						=							model_class(_SCREAMING_SNAKE_CASE	)
          model.to(_SCREAMING_SNAKE_CASE	)
          model.eval()

          with torch.no_grad():
              lowerCAmelCase 						=							model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)	)

          lowerCAmelCase 						=							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          lowerCAmelCase 						=							self.model_tester.num_stages
          self.assertEqual(len(_SCREAMING_SNAKE_CASE	)      , expected_num_stages + 1	)

          # ConvNext'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]      , )

      lowerCAmelCase						,   lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          lowerCAmelCase 						=							True
          check_hidden_states_output(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

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

          check_hidden_states_output(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE	)


  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase 						=							ConvNextModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
          self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)



def       snake_case   (	) ->  List[str]:


    """simple docstring"""


    lowerCAmelCase 						=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
    return image



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

  @cached_property
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return AutoImageProcessor.from_pretrained('facebook/convnext-tiny-224'	) if is_vision_available() else None


  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							ConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224'	).to(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.default_image_processor
      lowerCAmelCase 						=							prepare_img()
      lowerCAmelCase 						=							image_processor(images=_SCREAMING_SNAKE_CASE      , return_tensors='pt'	).to(_SCREAMING_SNAKE_CASE	)

      # forward pass
      with torch.no_grad():
          lowerCAmelCase 						=							model(**_SCREAMING_SNAKE_CASE	)

      # verify the logits
      lowerCAmelCase 						=							torch.Size((1, 10_00)	)
      self.assertEqual(outputs.logits.shape      , _SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							torch.tensor([-0.0_260, -0.4_739, 0.1_911]	).to(_SCREAMING_SNAKE_CASE	)

      self.assertTrue(torch.allclose(outputs.logits[0, :3]      , _SCREAMING_SNAKE_CASE      , atol=1e-4	)	)



@require_torch
class 				_snake_case					(   unittest.TestCase     , a_			):
  SCREAMING_SNAKE_CASE			:       Dict				=     (ConvNextBackbone,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE			:       Tuple				=     ConvNextConfig

  SCREAMING_SNAKE_CASE			:       Dict				=     False

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							ConvNextModelTester(self	)





