'''simple docstring'''




import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
  import torch

__snake_case   :      str						    =  logging.get_logger(__name__)





def      _UpperCAmelCase    (							)	->      Tuple:
    return importlib.util.find_spec('''deepspeed'''   ) is not None


if is_accelerate_available() and is_deepspeed_available():
  from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
  # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
  # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
  from builtins import object as DeepSpeedConfig




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE )		->     Dict:
           # set global weakref object
           set_hf_deepspeed_config(self )
           dep_version_check('''accelerate''' )
           dep_version_check('''deepspeed''' )
           super().__init__(_SCREAMING_SNAKE_CASE )




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE )		->     Optional[int]:
           super().__init__(_SCREAMING_SNAKE_CASE )
           A_     =   None
           A_     =   []

       def 					__A       (      self )		->     int:
           if self._dtype is None:
               raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype''' )
           return self._dtype

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     Optional[Any]:
           A_     =   self.get_value(_SCREAMING_SNAKE_CASE )
           if val is None:
               return False
           else:
               return val == "auto"

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE=None    ,     _SCREAMING_SNAKE_CASE=True )		->     int:
           A_   ,A_     =   self.find_config_node(_SCREAMING_SNAKE_CASE )
           if config is None:
               return

           if config.get(_SCREAMING_SNAKE_CASE ) == "auto":
               A_     =   hf_val
               return

           if not must_match:
               return

           A_     =   config.get(_SCREAMING_SNAKE_CASE )
           if ds_val is not None and ds_val != hf_val:
               self.mismatches.append(F'''- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}''' )

       __lowercase					:					Dict    =				partialmethod(_UpperCamelCase						,      must_match=_UpperCamelCase     )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     Optional[int]:
           A_     =   args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
           self.fill_match(
               '''train_micro_batch_size_per_gpu'''    ,     args.per_device_train_batch_size    ,     '''per_device_train_batch_size''' )
           self.fill_match('''gradient_accumulation_steps'''    ,     args.gradient_accumulation_steps    ,     '''gradient_accumulation_steps''' )
           self.fill_match('''train_batch_size'''    ,     _SCREAMING_SNAKE_CASE    ,     '''train_batch_size (calculated)''' )
           self.fill_match('''gradient_clipping'''    ,     args.max_grad_norm    ,     '''max_grad_norm''' )

           self.fill_match('''optimizer.params.lr'''    ,     args.learning_rate    ,     '''learning_rate''' )
           self.fill_match('''optimizer.params.betas'''    ,     [args.adam_betaa, args.adam_betaa]    ,     '''adam_beta1+adam_beta2''' )
           self.fill_match('''optimizer.params.eps'''    ,     args.adam_epsilon    ,     '''adam_epsilon''' )
           self.fill_match('''optimizer.params.weight_decay'''    ,     args.weight_decay    ,     '''weight_decay''' )

           self.fill_only('''scheduler.params.warmup_min_lr'''    ,     0 )  # not a trainer arg
           self.fill_match('''scheduler.params.warmup_max_lr'''    ,     args.learning_rate    ,     '''learning_rate''' )
           # total_num_steps - will get set in trainer_config_finalize

           # fp16
           if args.fpaa or args.fpaa_full_eval:
               A_     =   '''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
           else:
               A_     =   None

           if args.save_on_each_node:
               # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
               A_     =   self.config.get('''checkpoint'''    ,     {} )
               A_     =   args.save_on_each_node

           # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
           # any here unless the user did the work
           self.fill_match(
               '''fp16.enabled'''    ,     ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == '''amp''')    ,     '''fp16|fp16_full_eval+fp16_backend(amp)'''    ,     )

           # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
           # ZeRO features
           self.fill_match('''amp.enabled'''    ,     fpaa_backend == '''apex'''    ,     '''fp16+fp16_backend(apex)''' )
           self.fill_match('''amp.opt_level'''    ,     args.fpaa_opt_level    ,     '''fp16_opt_level''' )

           self.fill_match('''bf16.enabled'''    ,     (args.bfaa or args.bfaa_full_eval)    ,     '''bf16|bf16_full_eval''' )

           # deepspeed's default mode is fp16 unless there is a config that says differently
           if self.is_true('''bf16.enabled''' ):
               A_     =   torch.bfloataa
           elif self.is_false('''fp16.enabled''' ):
               A_     =   torch.floataa
           else:
               A_     =   torch.floataa

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Any:
           A_     =   [
               '''zero_optimization.reduce_bucket_size''',
               '''zero_optimization.stage3_prefetch_bucket_size''',
               '''zero_optimization.stage3_param_persistence_threshold''',
           ]
           A_     =   [x for x in hidden_size_based_keys if self.is_auto(_SCREAMING_SNAKE_CASE )]

           if len(_SCREAMING_SNAKE_CASE ) > 0:
               if hasattr(model.config    ,     '''hidden_size''' ):
                   A_     =   model.config.hidden_size
               elif hasattr(model.config    ,     '''hidden_sizes''' ):
                   # if there are many hidden sizes pick the largest one
                   A_     =   max(model.config.hidden_sizes )
               else:
                   raise ValueError(
                       '''The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '''
                       '''therefore it\'s not possible to automatically fill out the following `auto` entries '''
                       F'''in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing '''
                       '''`auto` values for these keys with an integer value of your choice.''' )

               self.fill_only('''zero_optimization.reduce_bucket_size'''    ,     hidden_size * hidden_size )
               if self.is_zeroa():
                   # automatically assign the optimal config values based on model config
                   self.fill_only('''zero_optimization.stage3_prefetch_bucket_size'''    ,     0.9 * hidden_size * hidden_size )
                   self.fill_only('''zero_optimization.stage3_param_persistence_threshold'''    ,     10 * hidden_size )

        # scheduler
           self.fill_match('''scheduler.params.total_num_steps'''    ,     _SCREAMING_SNAKE_CASE    ,     '''num_training_steps (calculated)''' )
           self.fill_match('''scheduler.params.warmup_num_steps'''    ,     args.get_warmup_steps(_SCREAMING_SNAKE_CASE )    ,     '''warmup_steps''' )

           if len(self.mismatches ) > 0:
               A_     =   '''\n'''.join(self.mismatches )
               raise ValueError(
                   '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
                   F''' values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to \'auto\'.''' )
__snake_case   :      Optional[int]						    =  None





def      _UpperCAmelCase    (							_UpperCamelCase    :	int   )	->      Tuple:
    # this is a special weakref global object to allow us to get to Deepspeed config from APIs
    # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
    global _hf_deepspeed_config_weak_ref
    # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
    A_     =   weakref.ref(_UpperCamelCase   )





def      _UpperCAmelCase    (							)	->      List[Any]:
    # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
    global _hf_deepspeed_config_weak_ref
    A_     =   None





def      _UpperCAmelCase    (							)	->      Optional[Any]:
    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().is_zeroa()
    else:
        return False





def      _UpperCAmelCase    (							)	->      str:
    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().config
    else:
        return None





def      _UpperCAmelCase    (							_UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	List[str],   _UpperCamelCase    :	Optional[Any],   _UpperCamelCase    :	Optional[int],   _UpperCamelCase    :	int   )	->      Tuple:
    from accelerate.utils import DummyOptim, DummyScheduler

    A_     =   hf_deepspeed_config.config

    # Optimizer + Scheduler
    # Currently supported combos:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Yes
    # 3. DS scheduler + HF optimizer: Yes
    # 4. HF scheduler + DS optimizer: No
    #
    # Unless Offload is enabled in which case it's:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Mostly*
    # 3. DS scheduler + HF optimizer: Mostly*
    # 4. HF scheduler + DS optimizer: No
    #
    # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

    A_     =   None
    if "optimizer" in config:
        if args.adafactor:
            raise ValueError(
                '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
                '''Only one optimizer can be configured.'''   )
        A_     =   DummyOptim(params=_UpperCamelCase   )
    else:
        if hf_deepspeed_config.is_offload():
            logger.info(
                '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
                ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''   )

        # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
        # But trainer uses AdamW by default.
        A_     =   trainer.create_optimizer()
        # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
        A_     =   True

    A_     =   None
    if "scheduler" in config:
        A_     =   DummyScheduler(_UpperCamelCase   )
    else:
        if isinstance(_UpperCamelCase,   _UpperCamelCase   ):
            raise ValueError(
                '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
                '''Please configure a scheduler in the DeepSpeed config.'''   )
        A_     =   trainer.create_scheduler(num_training_steps=_UpperCamelCase,   optimizer=_UpperCamelCase   )

    return optimizer, lr_scheduler





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple,   _UpperCamelCase    :	Dict,   _UpperCamelCase    :	Any=False   )	->      List[Any]:
    from deepspeed.utils import logger as ds_logger

    A_     =   trainer.model
    A_     =   trainer.args

    A_     =   trainer.accelerator.state.deepspeed_plugin.hf_ds_config

    # resume config update - some bits like `model` and `num_training_steps` only become available during train
    hf_deepspeed_config.trainer_config_finalize(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )

    # set the Deepspeed log level consistent with the Trainer
    ds_logger.setLevel(args.get_process_log_level()   )

    if inference:
        # only Z3 makes sense for the inference
        if not hf_deepspeed_config.is_zeroa():
            raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''   )

        # in case the training config is re-used for inference
        hf_deepspeed_config.del_config_sub_tree('''optimizer'''   )
        hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''   )
        A_   ,A_     =   None, None
        A_     =   None
    else:
        A_     =   None  # important for when deepspeed_init is used as re-init
        A_     =   list(filter(lambda _UpperCamelCase   : p.requires_grad,   model.parameters()   )   )
        A_   ,A_     =   deepspeed_optim_sched(
            _UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )

    # keep for quick debug:
    # from pprint import pprint; pprint(config)

    return optimizer, lr_scheduler





def      _UpperCAmelCase    (							_UpperCamelCase    :	int,   _UpperCamelCase    :	Dict   )	->      int:
    # it's possible that the user is trying to resume from model_path, which doesn't necessarily
    # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
    # a resume from a checkpoint and not just a local pretrained weight. So we check here if the
    # path contains what looks like a deepspeed checkpoint
    import glob

    A_     =   sorted(glob.glob(F'''{checkpoint_path}/global_step*'''   )   )

    if len(_UpperCamelCase   ) > 0:
        logger.info(F'''Attempting to resume from {checkpoint_path}'''   )
        # this magically updates self.optimizer and self.lr_scheduler
        A_   ,A_     =   deepspeed_engine.load_checkpoint(
            _UpperCamelCase,   load_optimizer_states=_UpperCamelCase,   load_lr_scheduler_states=_UpperCamelCase   )
        if load_path is None:
            raise ValueError(F'''[deepspeed] failed to resume from checkpoint {checkpoint_path}'''   )
    else:
        raise ValueError(F'''Can\'t find a valid checkpoint at {checkpoint_path}'''   )









import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType



@require_torch
class SCREAMING_SNAKE_CASE	:
  """simple docstring"""






  lowerCamelCase    :			List[Any]						      =()
  lowerCamelCase    :			Union[str, Any]						      =True







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






     """simple docstring"""







     __lowerCAmelCase  :			List[Any]					    =						self.config_class

     # test default config
     __lowerCAmelCase  :			Union[str, Any]					    =						config_class()
     self.assertIsNotNone(lowerCAmelCase    )
     __lowerCAmelCase  :			Optional[int]					    =						["""stem"""] + [f'''stage{idx}''' for idx in range(1       ,							len(config.depths    ) + 1    )]
     self.assertEqual(config.stage_names       ,							lowerCAmelCase    )
     self.assertTrue(set(config.out_features    ).issubset(set(config.stage_names    )    )    )

     # Test out_features and out_indices are correctly set
     # out_features and out_indices both None
     __lowerCAmelCase  :			str					    =						config_class(out_features=lowerCAmelCase       ,							out_indices=lowerCAmelCase    )
     self.assertEqual(config.out_features       ,							[config.stage_names[-1]]    )
     self.assertEqual(config.out_indices       ,							[len(config.stage_names    ) - 1]    )

     # out_features and out_indices both set
     __lowerCAmelCase  :			str					    =						config_class(out_features=["""stem""", """stage1"""]       ,							out_indices=[0, 1]    )
     self.assertEqual(config.out_features       ,							["""stem""", """stage1"""]    )
     self.assertEqual(config.out_indices       ,							[0, 1]    )

     # Only out_features set
     __lowerCAmelCase  :			Any					    =						config_class(out_features=["""stage1""", """stage3"""]    )
     self.assertEqual(config.out_features       ,							["""stage1""", """stage3"""]    )
     self.assertEqual(config.out_indices       ,							[1, 3]    )

     # Only out_indices set
     __lowerCAmelCase  :			str					    =						config_class(out_indices=[0, 2]    )
     self.assertEqual(config.out_features       ,							[config.stage_names[0], config.stage_names[2]]    )
     self.assertEqual(config.out_indices       ,							[0, 2]    )

     # Error raised when out_indices do not correspond to out_features
     with self.assertRaises(lowerCAmelCase    ):
        __lowerCAmelCase  :			Dict					    =						config_class(out_features=["""stage1""", """stage2"""]       ,							out_indices=[0, 2]    )







  def        SCREAMING_SNAKE_CASE    (			self :    str    )							->	Optional[int]:






     """simple docstring"""







     __lowerCAmelCase    ,__lowerCAmelCase  :			Union[str, Any]					    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase  :			List[Any]					    =						model_class(lowerCAmelCase    )
        __lowerCAmelCase  :			str					    =						inspect.signature(model.forward    )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        __lowerCAmelCase  :			List[Any]					    =						[*signature.parameters.keys()]
        __lowerCAmelCase  :			Union[str, Any]					    =						["""pixel_values"""]
        self.assertListEqual(arg_names[:1]       ,							lowerCAmelCase    )







  def        SCREAMING_SNAKE_CASE    (			self :    List[Any]    )							->	Tuple:






     """simple docstring"""







     __lowerCAmelCase    ,__lowerCAmelCase  :			Dict					    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase  :			List[Any]					    =						model_class(lowerCAmelCase    )
        self.assertEqual(len(model.channels    )       ,							len(config.out_features    )    )
        __lowerCAmelCase  :			List[Any]					    =						model.num_features
        __lowerCAmelCase  :			List[str]					    =						[config.stage_names.index(lowerCAmelCase    ) for feat in config.out_features]
        __lowerCAmelCase  :			Union[str, Any]					    =						[num_features[idx] for idx in out_indices]
        self.assertListEqual(model.channels       ,							lowerCAmelCase    )

        __lowerCAmelCase  :			int					    =						copy.deepcopy(lowerCAmelCase    )
        __lowerCAmelCase  :			str					    =						None
        __lowerCAmelCase  :			List[Any]					    =						model_class(lowerCAmelCase    )
        self.assertEqual(len(model.channels    )       ,							1    )
        self.assertListEqual(model.channels       ,							[num_features[-1]]    )

        __lowerCAmelCase  :			Optional[Any]					    =						copy.deepcopy(lowerCAmelCase    )
        __lowerCAmelCase  :			List[Any]					    =						None
        __lowerCAmelCase  :			Any					    =						model_class(lowerCAmelCase    )
        self.assertEqual(len(model.channels    )       ,							1    )
        self.assertListEqual(model.channels       ,							[num_features[-1]]    )







  def        SCREAMING_SNAKE_CASE    (			self :    Union[str, Any]    )							->	Optional[int]:






     """simple docstring"""







     __lowerCAmelCase    ,__lowerCAmelCase  :			str					    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase  :			List[Any]					    =						model_class(lowerCAmelCase    )
        model.to(lowerCAmelCase    )
        model.eval()
        __lowerCAmelCase  :			str					    =						model(**lowerCAmelCase    )

        self.assertEqual(len(result.feature_maps    )       ,							len(config.out_features    )    )
        self.assertEqual(len(model.channels    )       ,							len(config.out_features    )    )
        self.assertEqual(len(result.feature_maps    )       ,							len(config.out_indices    )    )
        self.assertEqual(len(model.channels    )       ,							len(config.out_indices    )    )

        # Check output of last stage is taken if out_features=None, out_indices=None
        __lowerCAmelCase  :			Dict					    =						copy.deepcopy(lowerCAmelCase    )
        __lowerCAmelCase  :			int					    =						None
        __lowerCAmelCase  :			Any					    =						model_class(lowerCAmelCase    )
        model.to(lowerCAmelCase    )
        model.eval()
        __lowerCAmelCase  :			Optional[int]					    =						model(**lowerCAmelCase    )

        self.assertEqual(len(result.feature_maps    )       ,							1    )
        self.assertEqual(len(model.channels    )       ,							1    )

        __lowerCAmelCase  :			List[Any]					    =						copy.deepcopy(lowerCAmelCase    )
        __lowerCAmelCase  :			Optional[Any]					    =						None
        __lowerCAmelCase  :			Union[str, Any]					    =						model_class(lowerCAmelCase    )
        model.to(lowerCAmelCase    )
        model.eval()
        __lowerCAmelCase  :			Dict					    =						model(**lowerCAmelCase    )

        self.assertEqual(len(result.feature_maps    )       ,							1    )
        self.assertEqual(len(model.channels    )       ,							1    )

        # Check backbone can be initialized with fresh weights
        __lowerCAmelCase  :			Union[str, Any]					    =						copy.deepcopy(lowerCAmelCase    )
        __lowerCAmelCase  :			int					    =						False
        __lowerCAmelCase  :			Tuple					    =						model_class(lowerCAmelCase    )
        model.to(lowerCAmelCase    )
        model.eval()
        __lowerCAmelCase  :			Any					    =						model(**lowerCAmelCase    )







  def        SCREAMING_SNAKE_CASE    (			self :    List[str]    )							->	str:






     """simple docstring"""







     __lowerCAmelCase    ,__lowerCAmelCase  :			Any					    =						self.model_tester.prepare_config_and_inputs_for_common()

     for backbone_class in self.all_model_classes:
        __lowerCAmelCase  :			List[Any]					    =						backbone_class(lowerCAmelCase    )

        self.assertTrue(hasattr(lowerCAmelCase       ,							"""backbone_type"""    )    )
        self.assertTrue(hasattr(lowerCAmelCase       ,							"""stage_names"""    )    )
        self.assertTrue(hasattr(lowerCAmelCase       ,							"""num_features"""    )    )
        self.assertTrue(hasattr(lowerCAmelCase       ,							"""out_indices"""    )    )
        self.assertTrue(hasattr(lowerCAmelCase       ,							"""out_features"""    )    )
        self.assertTrue(hasattr(lowerCAmelCase       ,							"""out_feature_channels"""    )    )
        self.assertTrue(hasattr(lowerCAmelCase       ,							"""channels"""    )    )

        self.assertIsInstance(backbone.backbone_type       ,							lowerCAmelCase    )
        # Verify num_features has been initialized in the backbone init
        self.assertIsNotNone(backbone.num_features    )
        self.assertTrue(len(backbone.channels    ) == len(backbone.out_indices    )    )
        self.assertTrue(len(backbone.stage_names    ) == len(backbone.num_features    )    )
        self.assertTrue(len(backbone.channels    ) <= len(backbone.num_features    )    )
        self.assertTrue(len(backbone.out_feature_channels    ) == len(backbone.stage_names    )    )







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






     """simple docstring"""







     __lowerCAmelCase    ,__lowerCAmelCase  :			int					    =						self.model_tester.prepare_config_and_inputs_for_common()
     __lowerCAmelCase  :			Union[str, Any]					    =						inputs_dict["""pixel_values"""].shape[0]

     for backbone_class in self.all_model_classes:
        __lowerCAmelCase  :			Union[str, Any]					    =						backbone_class(lowerCAmelCase    )
        backbone.to(lowerCAmelCase    )
        backbone.eval()

        __lowerCAmelCase  :			Tuple					    =						backbone(**lowerCAmelCase    )

        # Test default outputs and verify feature maps
        self.assertIsInstance(outputs.feature_maps       ,							lowerCAmelCase    )
        self.assertTrue(len(outputs.feature_maps    ) == len(backbone.channels    )    )
        for feature_map, n_channels in zip(outputs.feature_maps       ,							backbone.channels    ):
           self.assertTrue(feature_map.shape[:2]       ,							(batch_size, n_channels)    )
        self.assertIsNone(outputs.hidden_states    )
        self.assertIsNone(outputs.attentions    )

        # Test output_hidden_states=True
        __lowerCAmelCase  :			Optional[Any]					    =						backbone(**lowerCAmelCase       ,							output_hidden_states=lowerCAmelCase    )
        self.assertIsNotNone(outputs.hidden_states    )
        self.assertTrue(len(outputs.hidden_states    )       ,							len(backbone.stage_names    )    )
        for hidden_state, n_channels in zip(outputs.hidden_states       ,							backbone.channels    ):
           self.assertTrue(hidden_state.shape[:2]       ,							(batch_size, n_channels)    )

        # Test output_attentions=True
        if self.has_attentions:
           __lowerCAmelCase  :			Optional[Any]					    =						backbone(**lowerCAmelCase       ,							output_attentions=lowerCAmelCase    )
           self.assertIsNotNone(outputs.attentions    )


