'''simple docstring'''


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      ,		snake_case_      ,		snake_case_=2      ,		snake_case_=3_2      ,		snake_case_=1_6      ,		snake_case_=3      ,		snake_case_=True      ,		snake_case_=True      ,		snake_case_=3_2      ,		snake_case_=4      ,		snake_case_=[0, 1, 2, 3]      ,		snake_case_=4      ,		snake_case_=3_7      ,		snake_case_="gelu"      ,		snake_case_=0.1      ,		snake_case_=0.1      ,		snake_case_=0.02      ,		snake_case_=3      ,		snake_case_=False      ,		snake_case_=None      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   str   =      parent
       UpperCAmelCase_							:   Dict   =      batch_size
       UpperCAmelCase_							:   Dict   =      image_size
       UpperCAmelCase_							:   int   =      patch_size
       UpperCAmelCase_							:   str   =      num_channels
       UpperCAmelCase_							:   Optional[Any]   =      is_training
       UpperCAmelCase_							:   List[str]   =      use_labels
       UpperCAmelCase_							:   Dict   =      hidden_size
       UpperCAmelCase_							:   Optional[Any]   =      num_hidden_layers
       UpperCAmelCase_							:   List[Any]   =      backbone_out_indices
       UpperCAmelCase_							:   Union[str, Any]   =      num_attention_heads
       UpperCAmelCase_							:   List[Any]   =      intermediate_size
       UpperCAmelCase_							:   Optional[Any]   =      hidden_act
       UpperCAmelCase_							:   List[Any]   =      hidden_dropout_prob
       UpperCAmelCase_							:   Optional[Any]   =      attention_probs_dropout_prob
       UpperCAmelCase_							:   int   =      initializer_range
       UpperCAmelCase_							:   Any   =      num_labels
       UpperCAmelCase_							:   int   =      scope
       UpperCAmelCase_							:   Union[str, Any]   =      is_hybrid
       # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
       UpperCAmelCase_							:   Optional[Any]   =      (image_size // patch_size) ** 2
       UpperCAmelCase_							:   Union[str, Any]   =      num_patches + 1


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   int   =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       UpperCAmelCase_							:   Union[str, Any]   =      None
       if self.use_labels:
            UpperCAmelCase_							:   Dict   =      ids_tensor([self.batch_size, self.image_size, self.image_size]      ,		self.num_labels						)

       UpperCAmelCase_							:   Optional[int]   =      self.get_config()

       return config, pixel_values, labels


  def _UpperCamelCase     ( self						):




       '''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=snake_case_      ,		initializer_range=self.initializer_range      ,		is_hybrid=self.is_hybrid      ,		)


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Any   =      DPTModel(config=snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:   int   =      model(snake_case_						)
       self.parent.assertEqual(result.last_hidden_state.shape      ,		(self.batch_size, self.seq_length, self.hidden_size)						)


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[Any]   =      self.num_labels
       UpperCAmelCase_							:   Any   =      DPTForDepthEstimation(snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:   Tuple   =      model(snake_case_						)
       self.parent.assertEqual(result.predicted_depth.shape      ,		(self.batch_size, self.image_size, self.image_size)						)


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      self.num_labels
       UpperCAmelCase_							:   Union[str, Any]   =      DPTForSemanticSegmentation(snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:   str   =      model(snake_case_      ,		labels=snake_case_						)
       self.parent.assertEqual(
           result.logits.shape      ,		(self.batch_size, self.num_labels, self.image_size, self.image_size)						)






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   str   =      self.prepare_config_and_inputs()
       UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_							:   int   =      config_and_inputs
       UpperCAmelCase_							:   Optional[int]   =      {'pixel_values': pixel_values}
       return config, inputs_dict







@require_torch
class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_						,	lowerCamelCase_						,	unittest.TestCase       ):







  '''simple docstring'''






  lowerCamelCase_		:List[str]						    =		(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  lowerCamelCase_		:List[Any]						    =		(
      {
          '''depth-estimation''': DPTForDepthEstimation,
          '''feature-extraction''': DPTModel,
          '''image-segmentation''': DPTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  lowerCamelCase_		:Optional[int]						    =		False
  lowerCamelCase_		:Union[str, Any]						    =		False
  lowerCamelCase_		:Optional[int]						    =		False


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   str   =      DPTModelTester(self						)
       UpperCAmelCase_							:   str   =      ConfigTester(self      ,		config_class=snake_case_      ,		has_text_modality=snake_case_      ,		hidden_size=3_7						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       self.config_tester.run_common_tests()


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




       '''simple docstring'''

       pass


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

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

       for model_class in self.all_model_classes:
            UpperCAmelCase_							:   Optional[Any]   =      model_class(snake_case_						)
            self.assertIsInstance(model.get_input_embeddings()      ,		(nn.Module)						)
            UpperCAmelCase_							:   Optional[int]   =      model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(snake_case_      ,		nn.Linear						)						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_   ,			UpperCAmelCase_							:   Optional[Any]   =      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCAmelCase_							:   int   =      model_class(snake_case_						)
            UpperCAmelCase_							:   Tuple   =      inspect.signature(model.forward						)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCAmelCase_							:   List[Any]   =      [*signature.parameters.keys()]

            UpperCAmelCase_							:   Tuple   =      ['pixel_values']
            self.assertListEqual(arg_names[:1]      ,		snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Union[str, Any]   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Dict   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[Any]   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

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

            UpperCAmelCase_   ,			UpperCAmelCase_							:   List[str]   =      self.model_tester.prepare_config_and_inputs_for_common()
            UpperCAmelCase_							:   Dict   =      True

            if model_class in get_values(snake_case_						):
                 continue

            UpperCAmelCase_							:   List[str]   =      model_class(snake_case_						)
            model.to(snake_case_						)
            model.train()
            UpperCAmelCase_							:   str   =      self._prepare_for_class(snake_case_      ,		snake_case_      ,		return_labels=snake_case_						)
            UpperCAmelCase_							:   Tuple   =      model(**snake_case_						).loss
            loss.backward()


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

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

            UpperCAmelCase_   ,			UpperCAmelCase_							:   str   =      self.model_tester.prepare_config_and_inputs_for_common()
            UpperCAmelCase_							:   Union[str, Any]   =      False
            UpperCAmelCase_							:   Dict   =      True

            if model_class in get_values(snake_case_						) or not model_class.supports_gradient_checkpointing:
                 continue
            UpperCAmelCase_							:   Union[str, Any]   =      model_class(snake_case_						)
            model.to(snake_case_						)
            model.gradient_checkpointing_enable()
            model.train()
            UpperCAmelCase_							:   str   =      self._prepare_for_class(snake_case_      ,		snake_case_      ,		return_labels=snake_case_						)
            UpperCAmelCase_							:   int   =      model(**snake_case_						).loss
            loss.backward()


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_   ,			UpperCAmelCase_							:   Optional[int]   =      self.model_tester.prepare_config_and_inputs_for_common()

       UpperCAmelCase_							:   Dict   =      _config_zero_init(snake_case_						)
       for model_class in self.all_model_classes:
            UpperCAmelCase_							:   List[str]   =      model_class(config=snake_case_						)
            # Skip the check for the backbone
            UpperCAmelCase_							:   str   =      []
            for name, module in model.named_modules():
                 if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                      UpperCAmelCase_							:   Any   =      [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 _UpperCamelCase     ( self						):




       '''simple docstring'''

       pass






  @slow
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase_							:   Optional[Any]   =      DPTModel.from_pretrained(snake_case_						)
            self.assertIsNotNone(snake_case_						)







def 					_lowerCamelCase    (      ):



     """simple docstring"""





     UpperCAmelCase_							:   int   =      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 _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Union[str, Any]   =      DPTImageProcessor.from_pretrained('Intel/dpt-large'						)
       UpperCAmelCase_							:   Optional[int]   =      DPTForDepthEstimation.from_pretrained('Intel/dpt-large'						).to(snake_case_						)

       UpperCAmelCase_							:   Any   =      prepare_img()
       UpperCAmelCase_							:   Optional[int]   =      image_processor(images=snake_case_      ,		return_tensors='pt'						).to(snake_case_						)

       # forward pass
       with torch.no_grad():
            UpperCAmelCase_							:   Optional[Any]   =      model(**snake_case_						)
            UpperCAmelCase_							:   List[Any]   =      outputs.predicted_depth

       # verify the predicted depth
       UpperCAmelCase_							:   int   =      torch.Size((1, 3_8_4, 3_8_4)						)
       self.assertEqual(predicted_depth.shape      ,		snake_case_						)

       UpperCAmelCase_							:   Union[str, Any]   =      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(snake_case_						)

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


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[int]   =      DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'						)
       UpperCAmelCase_							:   Optional[int]   =      DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'						).to(snake_case_						)

       UpperCAmelCase_							:   int   =      prepare_img()
       UpperCAmelCase_							:   Optional[Any]   =      image_processor(images=snake_case_      ,		return_tensors='pt'						).to(snake_case_						)

       # forward pass
       with torch.no_grad():
            UpperCAmelCase_							:   Optional[Any]   =      model(**snake_case_						)

       # verify the logits
       UpperCAmelCase_							:   Tuple   =      torch.Size((1, 1_5_0, 4_8_0, 4_8_0)						)
       self.assertEqual(outputs.logits.shape      ,		snake_case_						)

       UpperCAmelCase_							:   Tuple   =      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(snake_case_						)

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






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Any   =      DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'						)
       UpperCAmelCase_							:   Dict   =      DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'						).to(snake_case_						)

       UpperCAmelCase_							:   Dict   =      prepare_img()
       UpperCAmelCase_							:   int   =      image_processor(images=snake_case_      ,		return_tensors='pt'						).to(snake_case_						)

       # forward pass
       with torch.no_grad():
            UpperCAmelCase_							:   Dict   =      model(**snake_case_						)

       UpperCAmelCase_							:   Optional[Any]   =      outputs.logits.detach().cpu()

       UpperCAmelCase_							:   Any   =      image_processor.post_process_semantic_segmentation(outputs=snake_case_      ,		target_sizes=[(5_0_0, 3_0_0)]						)
       UpperCAmelCase_							:   Tuple   =      torch.Size((5_0_0, 3_0_0)						)
       self.assertEqual(segmentation[0].shape      ,		snake_case_						)

       UpperCAmelCase_							:   Optional[int]   =      image_processor.post_process_semantic_segmentation(outputs=snake_case_						)
       UpperCAmelCase_							:   Tuple   =      torch.Size((4_8_0, 4_8_0)						)
       self.assertEqual(segmentation[0].shape      ,		snake_case_						)







'''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__     :			Dict      			=  logging.get_logger(__name__)


def 					_lowerCamelCase    (      ):



     """simple docstring"""





     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      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  def __init__( self      ,		snake_case_						):




       '''simple docstring'''

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







class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  def __init__( self      ,		snake_case_						):




       '''simple docstring'''

       super().__init__(snake_case_						)
       UpperCAmelCase_							:   Tuple   =      None
       UpperCAmelCase_							:   Any   =      []


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

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


  def _UpperCamelCase     ( self      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      self.get_value(snake_case_						)
       if val is None:
            return False
       else:
            return val == "auto"


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_=None      ,		snake_case_=True						):




       '''simple docstring'''

       UpperCAmelCase_   ,			UpperCAmelCase_							:   int   =      self.find_config_node(snake_case_						)
       if config is None:
            return

       if config.get(snake_case_						) == "auto":
            UpperCAmelCase_							:   int   =      hf_val
            return

       if not must_match:
            return

       UpperCAmelCase_							:   Optional[Any]   =      config.get(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}'''						)

  lowerCamelCase_		:Tuple						    =		partialmethod(lowerCamelCase_						,	must_match=lowerCamelCase_       )


  def _UpperCamelCase     ( self      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Union[str, Any]   =      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'      ,		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:
            UpperCAmelCase_							:   Any   =      'apex' if args.fpaa_backend == 'apex' else 'amp'
       else:
            UpperCAmelCase_							:   Union[str, Any]   =      None

       if args.save_on_each_node:
            # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
            UpperCAmelCase_							:   List[str]   =      self.config.get('checkpoint'      ,		{}						)
            UpperCAmelCase_							:   str   =      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'						):
            UpperCAmelCase_							:   Dict   =      torch.bfloataa
       elif self.is_false('fp16.enabled'						):
            UpperCAmelCase_							:   List[Any]   =      torch.floataa
       else:
            UpperCAmelCase_							:   Optional[int]   =      torch.floataa






  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      [
           'zero_optimization.reduce_bucket_size',
           'zero_optimization.stage3_prefetch_bucket_size',
           'zero_optimization.stage3_param_persistence_threshold',
       ]
       UpperCAmelCase_							:   str   =      [x for x in hidden_size_based_keys if self.is_auto(snake_case_						)]

       if len(snake_case_						) > 0:
            if hasattr(model.config      ,		'hidden_size'						):
                 UpperCAmelCase_							:   List[Any]   =      model.config.hidden_size
            elif hasattr(model.config      ,		'hidden_sizes'						):
                 # if there are many hidden sizes pick the largest one
                 UpperCAmelCase_							:   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'      ,		1_0 * hidden_size						)

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

       if len(self.mismatches						) > 0:
            UpperCAmelCase_							:   List[Any]   =      '\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__     :			List[str]      			=  None


def 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[int]   ):



     """simple docstring"""





     global _hf_deepspeed_config_weak_ref
     # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
     UpperCAmelCase_							:   List[str]   =      weakref.ref(lowerCamelCase_   )


def 					_lowerCamelCase    (      ):



     """simple docstring"""





     global _hf_deepspeed_config_weak_ref
     UpperCAmelCase_							:   int   =      None


def 					_lowerCamelCase    (      ):



     """simple docstring"""





     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 					_lowerCamelCase    (      ):



     """simple docstring"""





     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 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[int]	,     lowerCamelCase_					:							Optional[Any]	,     lowerCamelCase_					:							int	,     lowerCamelCase_					:							Union[str, Any]	,     lowerCamelCase_					:							Union[str, Any]   ):



     """simple docstring"""





     from accelerate.utils import DummyOptim, DummyScheduler

     UpperCAmelCase_							:   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)

     UpperCAmelCase_							:   int   =      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.'   )
          UpperCAmelCase_							:   int   =      DummyOptim(params=lowerCamelCase_   )
     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.
          UpperCAmelCase_							:   Tuple   =      trainer.create_optimizer()
          # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
          UpperCAmelCase_							:   Any   =      True

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

     return optimizer, lr_scheduler


def 					_lowerCamelCase    (      lowerCamelCase_					:							List[str]	,     lowerCamelCase_					:							str	,     lowerCamelCase_					:							str=False   ):



     """simple docstring"""





     from deepspeed.utils import logger as ds_logger

     UpperCAmelCase_							:   List[str]   =      trainer.model
     UpperCAmelCase_							:   List[Any]   =      trainer.args

     UpperCAmelCase_							:   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(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_   )

     # 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'   )
          UpperCAmelCase_   ,			UpperCAmelCase_							:   List[Any]   =      None, None
          UpperCAmelCase_							:   str   =      None
     else:
          UpperCAmelCase_							:   Union[str, Any]   =      None  # important for when deepspeed_init is used as re-init
          UpperCAmelCase_							:   Tuple   =      list(filter(lambda lowerCamelCase_   : p.requires_grad	,     model.parameters()   )   )
          UpperCAmelCase_   ,			UpperCAmelCase_							:   List[str]   =      deepspeed_optim_sched(
              lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_   )

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

     return optimizer, lr_scheduler





def 					_lowerCamelCase    (      lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							int   ):



     """simple docstring"""





     import glob

     UpperCAmelCase_							:   Dict   =      sorted(glob.glob(F'''{checkpoint_path}/global_step*'''   )   )

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






