

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCamelCase__					: Tuple								=      16
UpperCamelCase__					: Tuple								=      32


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    if args.with_tracking:
        SCREAMING_SNAKE_CASE_													=  Accelerator(
            cpu=args.cpu	,				mixed_precision=args.mixed_precision	,				log_with='all'	,				project_dir=args.project_dir    )
    else:
        SCREAMING_SNAKE_CASE_													=  Accelerator(cpu=args.cpu	,				mixed_precision=args.mixed_precision    )

    if hasattr(args.checkpointing_steps	,				'isdigit'    ):
        if args.checkpointing_steps == "epoch":
            SCREAMING_SNAKE_CASE_													=  args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            SCREAMING_SNAKE_CASE_													=  int(args.checkpointing_steps    )
        else:
            raise ValueError(
                f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""    )
    else:
        SCREAMING_SNAKE_CASE_													=  None
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    SCREAMING_SNAKE_CASE_													=  config['lr']
    SCREAMING_SNAKE_CASE_													=  int(config['num_epochs']    )
    SCREAMING_SNAKE_CASE_													=  int(config['seed']    )
    SCREAMING_SNAKE_CASE_													=  int(config['batch_size']    )

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        SCREAMING_SNAKE_CASE_													=  os.path.split(_SCREAMING_SNAKE_CASE    )[-1].split('.'    )[0]
        accelerator.init_trackers(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )

    SCREAMING_SNAKE_CASE_													=  AutoTokenizer.from_pretrained('bert-base-cased'    )
    SCREAMING_SNAKE_CASE_													=  load_dataset('glue'	,				'mrpc'    )
    SCREAMING_SNAKE_CASE_													=  evaluate.load('glue'	,				'mrpc'    )

    def tokenize_function(_SCREAMING_SNAKE_CASE   :						int    ):
        # max_length=None => use the model max length (it's actually the default)
        SCREAMING_SNAKE_CASE_													=  tokenizer(examples['sentence1']	,				examples['sentence2']	,				truncation=_SCREAMING_SNAKE_CASE	,				max_length=_SCREAMING_SNAKE_CASE    )
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        SCREAMING_SNAKE_CASE_													=  datasets.map(
            _SCREAMING_SNAKE_CASE	,				batched=_SCREAMING_SNAKE_CASE	,				remove_columns=['idx', 'sentence1', 'sentence2']	,				)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    SCREAMING_SNAKE_CASE_													=  tokenized_datasets.rename_column('label'	,				'labels'    )

    # If the batch size is too big we use gradient accumulation
    SCREAMING_SNAKE_CASE_													=  1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        SCREAMING_SNAKE_CASE_													=  batch_size // MAX_GPU_BATCH_SIZE
        SCREAMING_SNAKE_CASE_													=  MAX_GPU_BATCH_SIZE

    def collate_fn(_SCREAMING_SNAKE_CASE   :						Dict    ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        SCREAMING_SNAKE_CASE_													=  128 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            SCREAMING_SNAKE_CASE_													=  16
        elif accelerator.mixed_precision != "no":
            SCREAMING_SNAKE_CASE_													=  8
        else:
            SCREAMING_SNAKE_CASE_													=  None

        return tokenizer.pad(
            _SCREAMING_SNAKE_CASE	,				padding='longest'	,				max_length=_SCREAMING_SNAKE_CASE	,				pad_to_multiple_of=_SCREAMING_SNAKE_CASE	,				return_tensors='pt'	,				)

    # Instantiate dataloaders.
    SCREAMING_SNAKE_CASE_													=  DataLoader(
        tokenized_datasets['train']	,				shuffle=_SCREAMING_SNAKE_CASE	,				collate_fn=_SCREAMING_SNAKE_CASE	,				batch_size=_SCREAMING_SNAKE_CASE    )
    SCREAMING_SNAKE_CASE_													=  DataLoader(
        tokenized_datasets['validation']	,				shuffle=_SCREAMING_SNAKE_CASE	,				collate_fn=_SCREAMING_SNAKE_CASE	,				batch_size=_SCREAMING_SNAKE_CASE    )

    set_seed(_SCREAMING_SNAKE_CASE    )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    SCREAMING_SNAKE_CASE_													=  AutoModelForSequenceClassification.from_pretrained('bert-base-cased'	,				return_dict=_SCREAMING_SNAKE_CASE    )

    # 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).
    SCREAMING_SNAKE_CASE_													=  model.to(accelerator.device    )

    # Instantiate optimizer
    SCREAMING_SNAKE_CASE_													=  AdamW(params=model.parameters()	,				lr=_SCREAMING_SNAKE_CASE    )

    # Instantiate scheduler
    SCREAMING_SNAKE_CASE_													=  get_linear_schedule_with_warmup(
        optimizer=_SCREAMING_SNAKE_CASE	,				num_warmup_steps=100	,				num_training_steps=(len(_SCREAMING_SNAKE_CASE    ) * num_epochs) // gradient_accumulation_steps	,				)

    # 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.
    SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  accelerator.prepare(
        _SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )

    # We need to keep track of how many total steps we have iterated over
    SCREAMING_SNAKE_CASE_													=  0
    # We also need to keep track of the stating epoch so files are named properly
    SCREAMING_SNAKE_CASE_													=  0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""    )
            accelerator.load_state(args.resume_from_checkpoint    )
            SCREAMING_SNAKE_CASE_													=  os.path.basename(args.resume_from_checkpoint    )
        else:
            # Get the most recent checkpoint
            SCREAMING_SNAKE_CASE_													=  [f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
            dirs.sort(key=os.path.getctime    )
            SCREAMING_SNAKE_CASE_													=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        SCREAMING_SNAKE_CASE_													=  os.path.splitext(_SCREAMING_SNAKE_CASE    )[0]

        if "epoch" in training_difference:
            SCREAMING_SNAKE_CASE_													=  int(training_difference.replace('epoch_'	,				''    )    ) + 1
            SCREAMING_SNAKE_CASE_													=  None
        else:
            SCREAMING_SNAKE_CASE_													=  int(training_difference.replace('step_'	,				''    )    )
            SCREAMING_SNAKE_CASE_													=  resume_step // len(_SCREAMING_SNAKE_CASE    )
            resume_step -= starting_epoch * len(_SCREAMING_SNAKE_CASE    )

    # Now we train the model
    for epoch in range(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    ):
        model.train()
        if args.with_tracking:
            SCREAMING_SNAKE_CASE_													=  0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            SCREAMING_SNAKE_CASE_													=  accelerator.skip_first_batches(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            SCREAMING_SNAKE_CASE_													=  train_dataloader
        for step, batch in enumerate(_SCREAMING_SNAKE_CASE    ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device    )
            SCREAMING_SNAKE_CASE_													=  model(**_SCREAMING_SNAKE_CASE    )
            SCREAMING_SNAKE_CASE_													=  outputs.loss
            SCREAMING_SNAKE_CASE_													=  loss / gradient_accumulation_steps
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(_SCREAMING_SNAKE_CASE    )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

            if isinstance(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    ):
                SCREAMING_SNAKE_CASE_													=  f"""step_{overall_step}"""
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        SCREAMING_SNAKE_CASE_													=  os.path.join(args.output_dir	,				_SCREAMING_SNAKE_CASE    )
                    accelerator.save_state(_SCREAMING_SNAKE_CASE    )

        model.eval()
        for step, batch in enumerate(_SCREAMING_SNAKE_CASE    ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device    )
            with torch.no_grad():
                SCREAMING_SNAKE_CASE_													=  model(**_SCREAMING_SNAKE_CASE    )
            SCREAMING_SNAKE_CASE_													=  outputs.logits.argmax(dim=-1    )
            SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  accelerator.gather_for_metrics((predictions, batch['labels'])    )
            metric.add_batch(
                predictions=_SCREAMING_SNAKE_CASE	,				references=_SCREAMING_SNAKE_CASE	,				)

        SCREAMING_SNAKE_CASE_													=  metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"""epoch {epoch}:"""	,				_SCREAMING_SNAKE_CASE    )
        if args.with_tracking:
            accelerator.log(
                {
                    'accuracy': eval_metric['accuracy'],
                    'f1': eval_metric['f1'],
                    'train_loss': total_loss.item() / len(_SCREAMING_SNAKE_CASE    ),
                    'epoch': epoch,
                }	,				step=_SCREAMING_SNAKE_CASE	,				)

        if checkpointing_steps == "epoch":
            SCREAMING_SNAKE_CASE_													=  f"""epoch_{epoch}"""
            if args.output_dir is not None:
                SCREAMING_SNAKE_CASE_													=  os.path.join(args.output_dir	,				_SCREAMING_SNAKE_CASE    )
            accelerator.save_state(_SCREAMING_SNAKE_CASE    )

    if args.with_tracking:
        accelerator.end_training()


def    _UpperCAmelCase    (					):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  argparse.ArgumentParser(description='Simple example of training script.'    )
    parser.add_argument(
        '--mixed_precision'	,				type=_SCREAMING_SNAKE_CASE	,				default=_SCREAMING_SNAKE_CASE	,				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('--cpu'	,				action='store_true'	,				help='If passed, will train on the CPU.'    )
    parser.add_argument(
        '--checkpointing_steps'	,				type=_SCREAMING_SNAKE_CASE	,				default=_SCREAMING_SNAKE_CASE	,				help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'	,				)
    parser.add_argument(
        '--resume_from_checkpoint'	,				type=_SCREAMING_SNAKE_CASE	,				default=_SCREAMING_SNAKE_CASE	,				help='If the training should continue from a checkpoint folder.'	,				)
    parser.add_argument(
        '--with_tracking'	,				action='store_true'	,				help='Whether to load in all available experiment trackers from the environment and use them for logging.'	,				)
    parser.add_argument(
        '--output_dir'	,				type=_SCREAMING_SNAKE_CASE	,				default='.'	,				help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'	,				)
    parser.add_argument(
        '--project_dir'	,				type=_SCREAMING_SNAKE_CASE	,				default='logs'	,				help='Location on where to store experiment tracking logs` and relevent project information'	,				)
    SCREAMING_SNAKE_CASE_													=  parser.parse_args()
    SCREAMING_SNAKE_CASE_													=  {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       main()





from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
       from PIL import Image

       from transformers import ConvNextImageProcessor



class  __snake_case					:

  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=0.0_2  ,						_A=3  ,						_A=None  ,						):
      SCREAMING_SNAKE_CASE_													=  parent
      SCREAMING_SNAKE_CASE_													=  batch_size
      SCREAMING_SNAKE_CASE_													=  image_size
      SCREAMING_SNAKE_CASE_													=  num_channels
      SCREAMING_SNAKE_CASE_													=  num_stages
      SCREAMING_SNAKE_CASE_													=  hidden_sizes
      SCREAMING_SNAKE_CASE_													=  depths
      SCREAMING_SNAKE_CASE_													=  is_training
      SCREAMING_SNAKE_CASE_													=  use_labels
      SCREAMING_SNAKE_CASE_													=  intermediate_size
      SCREAMING_SNAKE_CASE_													=  hidden_act
      SCREAMING_SNAKE_CASE_													=  type_sequence_label_size
      SCREAMING_SNAKE_CASE_													=  initializer_range
      SCREAMING_SNAKE_CASE_													=  scope

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      SCREAMING_SNAKE_CASE_													=  None
      if self.use_labels:
          SCREAMING_SNAKE_CASE_													=  ids_tensor([self.batch_size]  ,						self.type_sequence_label_size)

      SCREAMING_SNAKE_CASE_													=  self.get_config()

      return config, pixel_values, labels

  def   lowerCAmelCase__       (   self):
      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=_A  ,						initializer_range=self.initializer_range  ,						)

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  TFConvNextModel(config=_A)
      SCREAMING_SNAKE_CASE_													=  model(_A  ,						training=_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):
      SCREAMING_SNAKE_CASE_													=  self.type_sequence_label_size
      SCREAMING_SNAKE_CASE_													=  TFConvNextForImageClassification(_A)
      SCREAMING_SNAKE_CASE_													=  model(_A  ,						labels=_A  ,						training=_A)
      self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size))


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






@require_tf
class  __snake_case					(      lowerCAmelCase__       ,	lowerCAmelCase__       ,	unittest.TestCase    ):
  __lowerCAmelCase       :		str  	=					(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
  __lowerCAmelCase       :		List[str]  	=					(
      {'feature-extraction': TFConvNextModel, 'image-classification': TFConvNextForImageClassification}
      if is_tf_available()
      else {}
  )

  __lowerCAmelCase       :		Union[str, Any]  	=					False
  __lowerCAmelCase       :		Dict  	=					False
  __lowerCAmelCase       :		Optional[int]  	=					False
  __lowerCAmelCase       :		Optional[Any]  	=					False
  __lowerCAmelCase       :		List[Any]  	=					False

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  TFConvNextModelTester(self)
      SCREAMING_SNAKE_CASE_													=  ConfigTester(
          self  ,						config_class=_A  ,						has_text_modality=_A  ,						hidden_size=37  ,						)

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

  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices('GPU')) == 0  ,						reason='TF does not support backprop for grouped convolutions on CPU.'  ,						)
  @slow
  def   lowerCAmelCase__       (   self):
      super().test_keras_fit()

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

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()

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

          SCREAMING_SNAKE_CASE_													=  ['pixel_values']
          self.assertListEqual(arg_names[:1]  ,						_A)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_A)

  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices('GPU')) == 0  ,						reason='TF does not support backprop for grouped convolutions on CPU.'  ,						)
  def   lowerCAmelCase__       (   self):
      super().test_dataset_conversion()

  def   lowerCAmelCase__       (   self):
      def check_hidden_states_output(_A  ,						_A  ,						_A):
          SCREAMING_SNAKE_CASE_													=  model_class(_A)

          SCREAMING_SNAKE_CASE_													=  model(**self._prepare_for_class(_A  ,						_A))
          SCREAMING_SNAKE_CASE_													=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          SCREAMING_SNAKE_CASE_													=  self.model_tester.num_stages
          self.assertEqual(len(_A)  ,						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]  ,						)

      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE_													=  True
          check_hidden_states_output(_A  ,						_A  ,						_A)

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

          check_hidden_states_output(_A  ,						_A  ,						_A)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()

      def check_equivalence(_A  ,						_A  ,						_A  ,						_A={}):
          SCREAMING_SNAKE_CASE_													=  model(_A  ,						return_dict=_A  ,						**_A)
          SCREAMING_SNAKE_CASE_													=  model(_A  ,						return_dict=_A  ,						**_A).to_tuple()

          def recursive_check(_A  ,						_A):
              if isinstance(_A  ,						(List, Tuple)):
                  for tuple_iterable_value, dict_iterable_value in zip(_A  ,						_A):
                      recursive_check(_A  ,						_A)
              elif tuple_object is None:
                  return
              else:
                  self.assertTrue(
                      all(tf.equal(_A  ,						_A))  ,						msg=(
                          'Tuple and dict output are not equal. Difference:'
                          f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}"""
                      )  ,						)

              recursive_check(_A  ,						_A)

      for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE_													=  model_class(_A)

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          check_equivalence(_A  ,						_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          check_equivalence(_A  ,						_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          check_equivalence(_A  ,						_A  ,						_A  ,						{'output_hidden_states': True})

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          check_equivalence(_A  ,						_A  ,						_A  ,						{'output_hidden_states': True})

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_A)


  @slow
  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  TFConvNextModel.from_pretrained('facebook/convnext-tiny-224')
      self.assertIsNotNone(_A)






def    _UpperCAmelCase    (					):





    """simple docstring"""


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





@require_tf
@require_vision
class  __snake_case					(      unittest.TestCase    ):

  @cached_property
  def   lowerCAmelCase__       (   self):
      return ConvNextImageProcessor.from_pretrained('facebook/convnext-tiny-224') if is_vision_available() else None


  @slow
  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224')

      SCREAMING_SNAKE_CASE_													=  self.default_image_processor
      SCREAMING_SNAKE_CASE_													=  prepare_img()
      SCREAMING_SNAKE_CASE_													=  image_processor(images=_A  ,						return_tensors='tf')

      # forward pass
      SCREAMING_SNAKE_CASE_													=  model(**_A)

      # verify the logits
      SCREAMING_SNAKE_CASE_													=  tf.TensorShape((1, 1000))
      self.assertEqual(outputs.logits.shape  ,						_A)

      SCREAMING_SNAKE_CASE_													=  tf.constant([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1])

      tf.debugging.assert_near(outputs.logits[0, :3]  ,						_A  ,						atol=1E-4)


