





import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
    from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DPTImageProcessor



class  SCREAMING_SNAKE_CASE_  :


  """simple docstring"""





  def __init__(  self	,  A	,  A=2	,  A=32	,  A=16	,  A=3	,  A=True	,  A=True	,  A=32	,  A=4	,  A=[0, 1, 2, 3]	,  A=4	,  A=37	,  A="gelu"	,  A=0.1	,  A=0.1	,  A=0.02	,  A=3	,  A=False	,  A=None	,  )		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       parent
       __magic_name__				=       batch_size
       __magic_name__				=       image_size
       __magic_name__				=       patch_size
       __magic_name__				=       num_channels
       __magic_name__				=       is_training
       __magic_name__				=       use_labels
       __magic_name__				=       hidden_size
       __magic_name__				=       num_hidden_layers
       __magic_name__				=       backbone_out_indices
       __magic_name__				=       num_attention_heads
       __magic_name__				=       intermediate_size
       __magic_name__				=       hidden_act
       __magic_name__				=       hidden_dropout_prob
       __magic_name__				=       attention_probs_dropout_prob
       __magic_name__				=       initializer_range
       __magic_name__				=       num_labels
       __magic_name__				=       scope
       __magic_name__				=       is_hybrid
       # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
       __magic_name__				=       (image_size // patch_size) ** 2
       __magic_name__				=       num_patches + 1





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





       '''simple docstring'''







       __magic_name__				=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       __magic_name__				=       None
       if self.use_labels:
            __magic_name__				=       ids_tensor([self.batch_size, self.image_size, self.image_size]	,  self.num_labels						)

       __magic_name__				=       self.get_config()

       return config, pixel_values, labels





  def 			__A      (  self						)		->   int:





       '''simple docstring'''







       return DPTConfig(
           image_size=self.image_size	,  patch_size=self.patch_size	,  num_channels=self.num_channels	,  hidden_size=self.hidden_size	,  num_hidden_layers=self.num_hidden_layers	,  backbone_out_indices=self.backbone_out_indices	,  num_attention_heads=self.num_attention_heads	,  intermediate_size=self.intermediate_size	,  hidden_act=self.hidden_act	,  hidden_dropout_prob=self.hidden_dropout_prob	,  attention_probs_dropout_prob=self.attention_probs_dropout_prob	,  is_decoder=A	,  initializer_range=self.initializer_range	,  is_hybrid=self.is_hybrid	,  )





  def 			__A      (  self	,  A	,  A	,  A						)		->   Optional[int]:





       '''simple docstring'''







       __magic_name__				=       DPTModel(config=A						)
       model.to(A						)
       model.eval()
       __magic_name__				=       model(A						)
       self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.seq_length, self.hidden_size)						)





  def 			__A      (  self	,  A	,  A	,  A						)		->   int:





       '''simple docstring'''







       __magic_name__				=       self.num_labels
       __magic_name__				=       DPTForDepthEstimation(A						)
       model.to(A						)
       model.eval()
       __magic_name__				=       model(A						)
       self.parent.assertEqual(result.predicted_depth.shape	,  (self.batch_size, self.image_size, self.image_size)						)





  def 			__A      (  self	,  A	,  A	,  A						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       self.num_labels
       __magic_name__				=       DPTForSemanticSegmentation(A						)
       model.to(A						)
       model.eval()
       __magic_name__				=       model(A	,  labels=A						)
       self.parent.assertEqual(
           result.logits.shape	,  (self.batch_size, self.num_labels, self.image_size, self.image_size)						)







  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







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



@require_torch
class  SCREAMING_SNAKE_CASE_  ( __lowerCAmelCase   ,							__lowerCAmelCase   ,							unittest.TestCase			):


  """simple docstring"""





  _a								=							(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  _a								=							(
      {
          """depth-estimation""": DPTForDepthEstimation,
          """feature-extraction""": DPTModel,
          """image-segmentation""": DPTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  _a								=							False
  _a								=							False
  _a								=							False





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       DPTModelTester(self						)
       __magic_name__				=       ConfigTester(self	,  config_class=A	,  has_text_modality=A	,  hidden_size=37						)





  def 			__A      (  self						)		->   Optional[Any]:





       '''simple docstring'''







       self.config_tester.run_common_tests()





  @unittest.skip(reason='''DPT does not use inputs_embeds'''						)
  def 			__A      (  self						)		->   Optional[int]:





       '''simple docstring'''







       pass





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            __magic_name__				=       model_class(A						)
            self.assertIsInstance(model.get_input_embeddings()	,  (nn.Module)						)
            __magic_name__				=       model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(A	,  nn.Linear						)						)





  def 			__A      (  self						)		->   int:





       '''simple docstring'''







       __magic_name__				=       self.model_tester.prepare_config_and_inputs_for_common()

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

            __magic_name__				=       ['pixel_values']
            self.assertListEqual(arg_names[:1]	,  A						)





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A						)





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





       '''simple docstring'''







       __magic_name__				=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*A						)





  def 			__A      (  self						)		->   Optional[Any]:





       '''simple docstring'''







       __magic_name__				=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*A						)





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





       '''simple docstring'''







       for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                 continue

            __magic_name__				=       self.model_tester.prepare_config_and_inputs_for_common()
            __magic_name__				=       True

            if model_class in get_values(A						):
                 continue

            __magic_name__				=       model_class(A						)
            model.to(A						)
            model.train()
            __magic_name__				=       self._prepare_for_class(A	,  A	,  return_labels=A						)
            __magic_name__				=       model(**A						).loss
            loss.backward()





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





       '''simple docstring'''







       for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                 continue

            __magic_name__				=       self.model_tester.prepare_config_and_inputs_for_common()
            __magic_name__				=       False
            __magic_name__				=       True

            if model_class in get_values(A						) or not model_class.supports_gradient_checkpointing:
                 continue
            __magic_name__				=       model_class(A						)
            model.to(A						)
            model.gradient_checkpointing_enable()
            model.train()
            __magic_name__				=       self._prepare_for_class(A	,  A	,  return_labels=A						)
            __magic_name__				=       model(**A						).loss
            loss.backward()





  def 			__A      (  self						)		->   str:





       '''simple docstring'''







       __magic_name__				=       self.model_tester.prepare_config_and_inputs_for_common()

       __magic_name__				=       _config_zero_init(A						)
       for model_class in self.all_model_classes:
            __magic_name__				=       model_class(config=A						)
            # Skip the check for the backbone
            __magic_name__				=       []
            for name, module in model.named_modules():
                 if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                      __magic_name__				=       [F'{name}.{key}' for key in module.state_dict().keys()]
                      break

            for name, param in model.named_parameters():
                 if param.requires_grad:
                      if name in backbone_params:
                           continue
                      self.assertIn(
                          ((param.data.mean() * 1E9).round() / 1E9).item()	,  [0.0, 1.0]	,  msg=F'Parameter {name} of model {model_class} seems not properly initialized'	,  )





  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''						)
  def 			__A      (  self						)		->   Any:





       '''simple docstring'''







       pass







  @slow
  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __magic_name__				=       DPTModel.from_pretrained(A						)
            self.assertIsNotNone(A						)




def   _SCREAMING_SNAKE_CASE      (	):
     __magic_name__				=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
     return image



@require_torch
@require_vision
@slow
class  SCREAMING_SNAKE_CASE_  ( unittest.TestCase			):


  """simple docstring"""





  def 			__A      (  self						)		->   Tuple:





       '''simple docstring'''







       __magic_name__				=       DPTImageProcessor.from_pretrained('''Intel/dpt-large'''						)
       __magic_name__				=       DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''						).to(A						)

       __magic_name__				=       prepare_img()
       __magic_name__				=       image_processor(images=A	,  return_tensors='''pt'''						).to(A						)

       # forward pass
       with torch.no_grad():
            __magic_name__				=       model(**A						)
            __magic_name__				=       outputs.predicted_depth

       # verify the predicted depth
       __magic_name__				=       torch.Size((1, 3_84, 3_84)						)
       self.assertEqual(predicted_depth.shape	,  A						)

       __magic_name__				=       torch.tensor(
           [[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]						).to(A						)

       self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]	,  A	,  atol=1E-4						)						)





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





       '''simple docstring'''







       __magic_name__				=       DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''						)
       __magic_name__				=       DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''						).to(A						)

       __magic_name__				=       prepare_img()
       __magic_name__				=       image_processor(images=A	,  return_tensors='''pt'''						).to(A						)

       # forward pass
       with torch.no_grad():
            __magic_name__				=       model(**A						)

       # verify the logits
       __magic_name__				=       torch.Size((1, 1_50, 4_80, 4_80)						)
       self.assertEqual(outputs.logits.shape	,  A						)

       __magic_name__				=       torch.tensor(
           [[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]						).to(A						)

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







  def 			__A      (  self						)		->   Optional[Any]:





       '''simple docstring'''







       __magic_name__				=       DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''						)
       __magic_name__				=       DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''						).to(A						)

       __magic_name__				=       prepare_img()
       __magic_name__				=       image_processor(images=A	,  return_tensors='''pt'''						).to(A						)

       # forward pass
       with torch.no_grad():
            __magic_name__				=       model(**A						)

       __magic_name__				=       outputs.logits.detach().cpu()

       __magic_name__				=       image_processor.post_process_semantic_segmentation(outputs=A	,  target_sizes=[(5_00, 3_00)]						)
       __magic_name__				=       torch.Size((5_00, 3_00)						)
       self.assertEqual(segmentation[0].shape	,  A						)

       __magic_name__				=       image_processor.post_process_semantic_segmentation(outputs=A						)
       __magic_name__				=       torch.Size((4_80, 4_80)						)
       self.assertEqual(segmentation[0].shape	,  A						)
"""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

lowercase__   			=      logging.get_logger(__name__)







def 		__a  (      )      ->Dict:
  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   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase)      ->     int:





        '''simple docstring'''





        set_hf_deepspeed_config(self)
        dep_version_check('accelerate')
        dep_version_check('deepspeed')
        super().__init__(lowercase)







class   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase)      ->     List[Any]:





        '''simple docstring'''





        super().__init__(lowercase)
        a__:	List[Any]     				=      None
        a__:	int     				=      []



      def     lowerCamelCase_  (		self)      ->     Any:





        '''simple docstring'''





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



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Optional[Any]:





        '''simple docstring'''





        a__:	Optional[int]     				=      self.get_value(lowercase)
        if val is None:
          return False
        else:
          return val == "auto"



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase       ,     lowercase=None       ,     lowercase=True)      ->     int:





        '''simple docstring'''





        a__				,       a__:	int     				=      self.find_config_node(lowercase)
        if config is None:
          return

        if config.get(lowercase) == "auto":
          a__:	Optional[int]     				=      hf_val
          return

        if not must_match:
          return

        a__:	List[Any]     				=      config.get(lowercase)
        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}')

      a__								=						partialmethod(__lowerCAmelCase			, must_match=__lowerCAmelCase							)



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Dict:





        '''simple docstring'''





        a__:	str     				=      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'       ,     lowercase       ,     '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__:	Tuple     				=      'apex' if args.fpaa_backend == 'apex' else 'amp'
        else:
          a__:	Optional[int]     				=      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__:	Union[str, Any]     				=      self.config.get('checkpoint'       ,     {})
          a__:	Tuple     				=      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__:	Optional[int]     				=      torch.bfloataa
        elif self.is_false('fp16.enabled'):
          a__:	Tuple     				=      torch.floataa
        else:
          a__:	Tuple     				=      torch.floataa



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase       ,     lowercase)      ->     Any:





        '''simple docstring'''





        a__:	List[Any]     				=      [
            'zero_optimization.reduce_bucket_size',
            'zero_optimization.stage3_prefetch_bucket_size',
            'zero_optimization.stage3_param_persistence_threshold',
        ]
        a__:	Union[str, Any]     				=      [x for x in hidden_size_based_keys if self.is_auto(lowercase)]

        if len(lowercase) > 0:
          if hasattr(model.config       ,     'hidden_size'):
            a__:	Any     				=      model.config.hidden_size
          elif hasattr(model.config       ,     'hidden_sizes'):
            # if there are many hidden sizes pick the largest one
            a__:	List[str]     				=      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'       ,     lowercase       ,     'num_training_steps (calculated)')
        self.fill_match('scheduler.params.warmup_num_steps'       ,     args.get_warmup_steps(lowercase)       ,     'warmup_steps')

        if len(self.mismatches) > 0:
          a__:	int     				=      '\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\'.')







lowercase__   			=      None







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->List[Any]:
  # 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__:	Any     				=      weakref.ref(_SCREAMING_SNAKE_CASE  )







def 		__a  (      )      ->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__:	Any     				=      None







def 		__a  (      )      ->Union[str, 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 		__a  (      )      ->Tuple:
  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 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Dict:
  from accelerate.utils import DummyOptim, DummyScheduler

  a__:	Optional[Any]     				=      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__:	Dict     				=      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__:	str     				=      DummyOptim(params=_SCREAMING_SNAKE_CASE  )
  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__:	Any     				=      trainer.create_optimizer()
    # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
    a__:	str     				=      True

  a__:	Dict     				=      None
  if "scheduler" in config:
    a__:	List[str]     				=      DummyScheduler(_SCREAMING_SNAKE_CASE  )
  else:
    if isinstance(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
      raise ValueError(
          'Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '
          'Please configure a scheduler in the DeepSpeed config.'  )
    a__:	Tuple     				=      trainer.create_scheduler(num_training_steps=_SCREAMING_SNAKE_CASE						,	optimizer=_SCREAMING_SNAKE_CASE  )

  return optimizer, lr_scheduler







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=False  )      ->Dict:
  from deepspeed.utils import logger as ds_logger

  a__:	Optional[int]     				=      trainer.model
  a__:	List[Any]     				=      trainer.args

  a__:	Optional[int]     				=      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(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  # 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__:	Optional[int]     				=      None, None
    a__:	Union[str, Any]     				=      None
  else:
    a__:	List[Any]     				=      None  # important for when deepspeed_init is used as re-init
    a__:	Union[str, Any]     				=      list(filter(lambda _SCREAMING_SNAKE_CASE  : p.requires_grad						,	model.parameters()  )  )
    a__				,       a__:	Optional[int]     				=      deepspeed_optim_sched(
        _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

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

  return optimizer, lr_scheduler







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->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__:	Tuple     				=      sorted(glob.glob(F'{checkpoint_path}/global_step*'  )  )

  if len(_SCREAMING_SNAKE_CASE  ) > 0:
    logger.info(F'Attempting to resume from {checkpoint_path}'  )
    # this magically updates self.optimizer and self.lr_scheduler
    a__				,       a__:	Union[str, Any]     				=      deepspeed_engine.load_checkpoint(
        _SCREAMING_SNAKE_CASE						,	load_optimizer_states=_SCREAMING_SNAKE_CASE						,	load_lr_scheduler_states=_SCREAMING_SNAKE_CASE  )
    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}'  )


