






import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# 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)
#   - FSDP
#
# 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      =				1_6
__UpperCAmelCase      =				3_2





def 						A_					(						lowercase_			)				->Dict:







 """simple docstring"""

 return int(x / 2**2_0			)
class 	a_:



     """simple docstring"""



     def __enter__(		self					:	Optional[int])							->	Tuple:

      """simple docstring"""
      gc.collect()
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
      SCREAMING_SNAKE_CASE				    =		torch.cuda.memory_allocated()
      SCREAMING_SNAKE_CASE				    =		psutil.Process()

      SCREAMING_SNAKE_CASE				    =		self.cpu_mem_used()
      SCREAMING_SNAKE_CASE				    =		True
      SCREAMING_SNAKE_CASE				    =		threading.Thread(target=self.peak_monitor_func)
      SCREAMING_SNAKE_CASE				    =		True
      peak_monitor_thread.start()
      return self





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

      """simple docstring"""
      return self.process.memory_info().rss





     def     __UpperCamelCase						(		self					:	List[str])							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		-1

      while True:
       SCREAMING_SNAKE_CASE				    =		max(self.cpu_mem_used()  ,  self.cpu_peak)

       # can't sleep or will not catch the peak right (this comment is here on purpose)
       # time.sleep(0.001) # 1msec

       if not self.peak_monitoring:
        break





     def __exit__(		self					:	int  ,  *lowerCAmelCase__					:	Tuple)							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		False

      gc.collect()
      torch.cuda.empty_cache()
      SCREAMING_SNAKE_CASE				    =		torch.cuda.memory_allocated()
      SCREAMING_SNAKE_CASE				    =		torch.cuda.max_memory_allocated()
      SCREAMING_SNAKE_CASE				    =		bamb(self.end - self.begin)
      SCREAMING_SNAKE_CASE				    =		bamb(self.peak - self.begin)

      SCREAMING_SNAKE_CASE				    =		self.cpu_mem_used()
      SCREAMING_SNAKE_CASE				    =		bamb(self.cpu_end - self.cpu_begin)
      SCREAMING_SNAKE_CASE				    =		bamb(self.cpu_peak - self.cpu_begin)
      # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      __UpperCAmelCase      =				mocked_dataloaders  # noqa: F811





def 						A_					(						lowercase_	,  lowercase_			)				->Dict:







 """simple docstring"""

 if os.environ.get('TESTING_MOCKED_DATALOADERS'	,  lowercase_			) == "1":
  SCREAMING_SNAKE_CASE				    =		2

 # New Code #
 # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
 SCREAMING_SNAKE_CASE				    =		FullyShardedDataParallelPlugin(
     state_dict_config=FullStateDictConfig(offload_to_cpu=lowercase_	,  ranka_only=lowercase_			)	,  optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowercase_	,  ranka_only=lowercase_			)	,  )

 # Initialize accelerator
 if args.with_tracking:
  SCREAMING_SNAKE_CASE				    =		Accelerator(
      cpu=args.cpu	,  mixed_precision=args.mixed_precision	,  log_with='wandb'	,  project_dir=args.logging_dir	,  fsdp_plugin=lowercase_	,  )
 else:
  SCREAMING_SNAKE_CASE				    =		Accelerator(fsdp_plugin=lowercase_			)
 accelerator.print(accelerator.distributed_type			)

 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				    =		vars(lowercase_			)
  accelerator.init_trackers('fsdp_glue_no_trainer'	,  lowercase_			)

 SCREAMING_SNAKE_CASE				    =		AutoTokenizer.from_pretrained(args.model_name_or_path			)
 SCREAMING_SNAKE_CASE				    =		load_dataset('glue'	,  'mrpc'			)
 SCREAMING_SNAKE_CASE				    =		evaluate.load('glue'	,  'mrpc'			)

 def tokenize_function(lowercase_			):
  # max_length=None => use the model max length (it's actually the default)
  SCREAMING_SNAKE_CASE				    =		tokenizer(examples['sentence1']	,  examples['sentence2']	,  truncation=lowercase_	,  max_length=lowercase_			)
  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(
      lowercase_	,  batched=lowercase_	,  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(lowercase_			):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  SCREAMING_SNAKE_CASE				    =		1_2_8 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				    =		1_6
  elif accelerator.mixed_precision != "no":
   SCREAMING_SNAKE_CASE				    =		8
  else:
   SCREAMING_SNAKE_CASE				    =		None

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

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

 set_seed(lowercase_			)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 SCREAMING_SNAKE_CASE				    =		AutoModelForSequenceClassification.from_pretrained(
     args.model_name_or_path	,  return_dict=lowercase_	,  low_cpu_mem_usage=lowercase_			)

 # New Code #
 # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
 SCREAMING_SNAKE_CASE				    =		accelerator.prepare(lowercase_			)
 accelerator.print(lowercase_			)

 # Instantiate optimizer
 # New Code #
 # For FSDP feature, at present it doesn't support multiple parameter groups,
 # so we need to create a single parameter group for the whole model
 SCREAMING_SNAKE_CASE				    =		torch.optim.AdamW(params=model.parameters()	,  lr=lowercase_	,  weight_decay=2e-4			)

 # Instantiate scheduler
 SCREAMING_SNAKE_CASE				    =		get_linear_schedule_with_warmup(
     optimizer=lowercase_	,  num_warmup_steps=1_0	,  num_training_steps=(len(lowercase_			) * num_epochs) // gradient_accumulation_steps	,  )

 # New Code #
 # For FSDP feature, prepare everything except the model as we have already prepared the model
 # before creating the optimizer
 # 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				    =		accelerator.prepare(
     lowercase_	,  lowercase_	,  lowercase_	,  lowercase_			)

 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(lowercase_			)[0]

  if "epoch" in training_difference:
   num_epochs -= int(training_difference.replace('epoch_'	,  ''			)			)
   SCREAMING_SNAKE_CASE				    =		None
  else:
   SCREAMING_SNAKE_CASE				    =		int(training_difference.replace('step_'	,  ''			)			)
   num_epochs -= resume_step // len(lowercase_			)
   # If resuming by step, we also need to know exactly how far into the DataLoader we went
   SCREAMING_SNAKE_CASE				    =		(num_epochs * len(lowercase_			)) - resume_step

    # Now we train the model
 for epoch in range(lowercase_			):
  # New Code #
  # context manager to track the peak memory usage during the training epoch
  with TorchTracemalloc() as tracemalloc:
   model.train()
   if args.with_tracking:
    SCREAMING_SNAKE_CASE				    =		0
   for step, batch in enumerate(lowercase_			):
    # We need to skip steps until we reach the resumed step
    if args.resume_from_checkpoint and epoch == 0:
     if resume_step is not None and step < resume_step:
      pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device			)
    SCREAMING_SNAKE_CASE				    =		model(**lowercase_			)
    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(lowercase_			)
    if step % gradient_accumulation_steps == 0:
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()
     # accelerator.print(lr_scheduler.get_lr())

    overall_step += 1

    if isinstance(lowercase_	,  lowercase_			):
     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	,  lowercase_			)
      accelerator.save_state(lowercase_			)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin			)			)			)
  accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used			)			)
  accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked			)			)
  accelerator.print(
      'Total Peak Memory consumed during the train (max): {}'.format(
          tracemalloc.peaked + bamb(tracemalloc.begin			)			)			)
  # Logging the peak memory usage of the GPU to the tracker
  if args.with_tracking:
   accelerator.log(
       {
           'train_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin			),
       }	,  step=lowercase_	,  )

  # New Code #
  # context manager to track the peak memory usage during the evaluation
  with TorchTracemalloc() as tracemalloc:
   model.eval()
   for step, batch in enumerate(lowercase_			):
    # 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(**lowercase_			)
    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=lowercase_	,  references=lowercase_	,  )

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

   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	,  lowercase_			)
    accelerator.save_state(lowercase_			)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print('Memory before entering the eval : {}'.format(bamb(tracemalloc.begin			)			)			)
  accelerator.print('Memory consumed at the end of the eval (end-begin): {}'.format(tracemalloc.used			)			)
  accelerator.print('Peak Memory consumed during the eval (max-begin): {}'.format(tracemalloc.peaked			)			)
  accelerator.print(
      'Total Peak Memory consumed during the eval (max): {}'.format(tracemalloc.peaked + bamb(tracemalloc.begin			)			)			)
  # Logging the peak memory usage of the GPU to the tracker
  if args.with_tracking:
   accelerator.log(
       {
           'eval_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin			),
       }	,  step=lowercase_	,  )

 if args.with_tracking:
  accelerator.end_training()





def 						A_					(						)				->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		argparse.ArgumentParser(description='Simple example of training script.'			)
 parser.add_argument(
     '--mixed_precision'	,  type=lowercase_	,  default=lowercase_	,  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=lowercase_	,  default=lowercase_	,  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=lowercase_	,  default=lowercase_	,  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=lowercase_	,  default='.'	,  help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'	,  )
 parser.add_argument(
     '--logging_dir'	,  type=lowercase_	,  default='logs'	,  help='Location on where to store experiment tracking logs`'	,  )
 parser.add_argument(
     '--model_name_or_path'	,  type=lowercase_	,  help='Path to pretrained model or model identifier from huggingface.co/models.'	,  required=lowercase_	,  )
 SCREAMING_SNAKE_CASE				    =		parser.parse_args()
 SCREAMING_SNAKE_CASE				    =		{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
 training_function(lowercase_	,  lowercase_			)


if __name__ == "__main__":
      main()







'''simple docstring'''

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 lowerCamelCase   :

				def __init__(       self,						lowercase_,						lowercase_=13,						lowercase_=32,						lowercase_=3,						lowercase_=4,						lowercase_=[10, 20, 30, 40],						lowercase_=[2, 2, 3, 2],						lowercase_=True,						lowercase_=True,						lowercase_=37,						lowercase_="gelu",						lowercase_=10,						lowercase_=0.02,						lowercase_=3,						lowercase_=None,						)    ->							Dict:
					snake_case							=					parent
					snake_case							=					batch_size
					snake_case							=					image_size
					snake_case							=					num_channels
					snake_case							=					num_stages
					snake_case							=					hidden_sizes
					snake_case							=					depths
					snake_case							=					is_training
					snake_case							=					use_labels
					snake_case							=					intermediate_size
					snake_case							=					hidden_act
					snake_case							=					type_sequence_label_size
					snake_case							=					initializer_range
					snake_case							=					scope

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case							=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

					snake_case							=					self.get_config()

					return config, pixel_values, labels

				def 					_lowerCamelCase    (       self )    ->							Any:
					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=lowercase_,						initializer_range=self.initializer_range,						)

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_ )    ->							List[Any]:
					snake_case							=					TFConvNextModel(config=lowercase_ )
					snake_case							=					model(lowercase_,						training=lowercase_ )
					# 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,						lowercase_,						lowercase_,						lowercase_ )    ->							Union[str, Any]:
					snake_case							=					self.type_sequence_label_size
					snake_case							=					TFConvNextForImageClassification(lowercase_ )
					snake_case							=					model(lowercase_,						labels=lowercase_,						training=lowercase_ )
					self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.type_sequence_label_size) )






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







@require_tf
class lowerCamelCase   (    __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase				):
				snake_case_							     = (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
				snake_case_							     = (
				    {'''feature-extraction''': TFConvNextModel, '''image-classification''': TFConvNextForImageClassification}
				    if is_tf_available()
				    else {}
				)

				snake_case_							     = False
				snake_case_							     = False
				snake_case_							     = False
				snake_case_							     = False
				snake_case_							     = False

				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					TFConvNextModelTester(self )
					snake_case							=					ConfigTester(
					    self,						config_class=lowercase_,						has_text_modality=lowercase_,						hidden_size=37,						)

				@unittest.skip(reason='ConvNext does not use inputs_embeds' )
				def 					_lowerCamelCase    (       self )    ->							Tuple:
					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 )    ->							Optional[Any]:
					super().test_keras_fit()

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

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

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

						snake_case							=					['pixel_values']
						self.assertListEqual(arg_names[:1],						lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowercase_ )

				@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 )    ->							Optional[int]:
					super().test_dataset_conversion()

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					def check_hidden_states_output(lowercase_,						lowercase_,						lowercase_ ):
						snake_case							=					model_class(lowercase_ )

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

						snake_case							=					self.model_tester.num_stages
						self.assertEqual(len(lowercase_ ),						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],						)

					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						snake_case							=					True
						check_hidden_states_output(lowercase_,						lowercase_,						lowercase_ )

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

						check_hidden_states_output(lowercase_,						lowercase_,						lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

					def check_equivalence(lowercase_,						lowercase_,						lowercase_,						lowercase_={} ):
						snake_case							=					model(lowercase_,						return_dict=lowercase_,						**lowercase_ )
						snake_case							=					model(lowercase_,						return_dict=lowercase_,						**lowercase_ ).to_tuple()

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

							recursive_check(lowercase_,						lowercase_ )

					for model_class in self.all_model_classes:
						snake_case							=					model_class(lowercase_ )

						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_ )
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_ )
						check_equivalence(lowercase_,						lowercase_,						lowercase_ )

						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						check_equivalence(lowercase_,						lowercase_,						lowercase_ )

						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_ )
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_ )
						check_equivalence(lowercase_,						lowercase_,						lowercase_,						{'output_hidden_states': True} )

						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						check_equivalence(lowercase_,						lowercase_,						lowercase_,						{'output_hidden_states': True} )

				def 					_lowerCamelCase    (       self )    ->							List[str]:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*lowercase_ )






				@slow
				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					TFConvNextModel.from_pretrained('facebook/convnext-tiny-224' )
					self.assertIsNotNone(lowercase_ )

def      __magic_name__							(			)      ->		Optional[int]:
	snake_case							=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
	return image







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

				@cached_property
				def 					_lowerCamelCase    (       self )    ->							List[str]:
					return ConvNextImageProcessor.from_pretrained('facebook/convnext-tiny-224' ) if is_vision_available() else None






				@slow
				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224' )

					snake_case							=					self.default_image_processor
					snake_case							=					prepare_img()
					snake_case							=					image_processor(images=lowercase_,						return_tensors='tf' )

					# forward pass
					snake_case							=					model(**lowercase_ )

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

					snake_case							=					tf.constant([-0.0_260, -0.4_739, 0.1_911] )

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





