






'''simple docstring'''






import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
      import torch

      from transformers import (
          YosoForMaskedLM,
          YosoForMultipleChoice,
          YosoForQuestionAnswering,
          YosoForSequenceClassification,
          YosoForTokenClassification,
          YosoModel,
      )
      from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST




class a_     :







   def __init__(self       , __a       , __a=1_3       , __a=7       , __a=True       , __a=True       , __a=True       , __a=True       , __a=9_9       , __a=3_2       , __a=5       , __a=4       , __a=3_7       , __a="gelu"       , __a=0.1       , __a=0.1       , __a=5_1_2       , __a=1_6       , __a=2       , __a=0.02       , __a=3       , __a=4       , __a=None       , )      ->   int:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			parent
      __snake_case					:   Dict  				=			batch_size
      __snake_case					:   Union[str, Any]  				=			seq_length
      __snake_case					:   str  				=			is_training
      __snake_case					:   Optional[Any]  				=			use_input_mask
      __snake_case					:   Dict  				=			use_token_type_ids
      __snake_case					:   Union[str, Any]  				=			use_labels
      __snake_case					:   Optional[Any]  				=			vocab_size
      __snake_case					:   Optional[Any]  				=			hidden_size
      __snake_case					:   int  				=			num_hidden_layers
      __snake_case					:   Any  				=			num_attention_heads
      __snake_case					:   Optional[Any]  				=			intermediate_size
      __snake_case					:   List[str]  				=			hidden_act
      __snake_case					:   List[Any]  				=			hidden_dropout_prob
      __snake_case					:   Optional[Any]  				=			attention_probs_dropout_prob
      __snake_case					:   str  				=			max_position_embeddings
      __snake_case					:   str  				=			type_vocab_size
      __snake_case					:   Dict  				=			type_sequence_label_size
      __snake_case					:   int  				=			initializer_range
      __snake_case					:   Dict  				=			num_labels
      __snake_case					:   Optional[int]  				=			num_choices
      __snake_case					:   Dict  				=			scope







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[Any]:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			ids_tensor([self.batch_size, self.seq_length]       , self.vocab_size)

      __snake_case					:   Optional[Any]  				=			None
      if self.use_input_mask:
         __snake_case					:   Union[str, Any]  				=			random_attention_mask([self.batch_size, self.seq_length])

      __snake_case					:   Tuple  				=			None
      if self.use_token_type_ids:
         __snake_case					:   Union[str, Any]  				=			ids_tensor([self.batch_size, self.seq_length]       , self.type_vocab_size)

      __snake_case					:   Dict  				=			None
      __snake_case					:   List[str]  				=			None
      __snake_case					:   List[str]  				=			None
      if self.use_labels:
         __snake_case					:   Union[str, Any]  				=			ids_tensor([self.batch_size]       , self.type_sequence_label_size)
         __snake_case					:   Tuple  				=			ids_tensor([self.batch_size, self.seq_length]       , self.num_labels)
         __snake_case					:   str  				=			ids_tensor([self.batch_size]       , self.num_choices)

      __snake_case					:   List[str]  				=			self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      return YosoConfig(
          vocab_size=self.vocab_size       , hidden_size=self.hidden_size       , num_hidden_layers=self.num_hidden_layers       , 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       , max_position_embeddings=self.max_position_embeddings       , type_vocab_size=self.type_vocab_size       , is_decoder=__a       , initializer_range=self.initializer_range       , )







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      __snake_case					:   Tuple  				=			self.get_config()
      __snake_case					:   Tuple  				=			3_0_0
      return config







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      (
          (
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,
      )					:   Optional[Any]  				=			self.prepare_config_and_inputs()

      __snake_case					:   Optional[int]  				=			True
      __snake_case					:   Optional[int]  				=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
      __snake_case					:   Dict  				=			ids_tensor([self.batch_size, self.seq_length]       , vocab_size=2)

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   Dict:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			YosoModel(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Optional[Any]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a)
      __snake_case					:   int  				=			model(__a       , token_type_ids=__a)
      __snake_case					:   Optional[Any]  				=			model(__a)
      self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a       , __a       , __a       , )      ->   int:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			True
      __snake_case					:   Dict  				=			YosoModel(__a)
      model.to(__a)
      model.eval()
      __snake_case					:   List[str]  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , encoder_hidden_states=__a       , encoder_attention_mask=__a       , )
      __snake_case					:   List[Any]  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , encoder_hidden_states=__a       , )
      __snake_case					:   List[Any]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a)
      self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			YosoForMaskedLM(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Union[str, Any]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.vocab_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			YosoForQuestionAnswering(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   str  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , start_positions=__a       , end_positions=__a       , )
      self.parent.assertEqual(result.start_logits.shape       , (self.batch_size, self.seq_length))
      self.parent.assertEqual(result.end_logits.shape       , (self.batch_size, self.seq_length))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   str  				=			self.num_labels
      __snake_case					:   Dict  				=			YosoForSequenceClassification(__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Optional[int]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_labels))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   Dict:
      """simple docstring"""






      __snake_case					:   str  				=			self.num_labels
      __snake_case					:   Dict  				=			YosoForTokenClassification(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Tuple  				=			model(__a       , attention_mask=__a       , token_type_ids=__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.num_labels))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   str:
      """simple docstring"""






      __snake_case					:   int  				=			self.num_choices
      __snake_case					:   Tuple  				=			YosoForMultipleChoice(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Any  				=			input_ids.unsqueeze(1).expand(-1       , self.num_choices       , -1).contiguous()
      __snake_case					:   Tuple  				=			token_type_ids.unsqueeze(1).expand(-1       , self.num_choices       , -1).contiguous()
      __snake_case					:   List[str]  				=			input_mask.unsqueeze(1).expand(-1       , self.num_choices       , -1).contiguous()
      __snake_case					:   Optional[int]  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , labels=__a       , )
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_choices))







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      __snake_case					:   int  				=			self.prepare_config_and_inputs()
      (
          (
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,
      )					:   Union[str, Any]  				=			config_and_inputs
      __snake_case					:   Optional[int]  				=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict




@require_torch
class a_     (     UpperCamelCase_		,   UpperCamelCase_		,   unittest.TestCase				):
   _snake_case	   =				(
       (
           YosoModel,
           YosoForMaskedLM,
           YosoForMultipleChoice,
           YosoForQuestionAnswering,
           YosoForSequenceClassification,
           YosoForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   _snake_case	   =				False
   _snake_case	   =				False
   _snake_case	   =				False

   _snake_case	   =				()
   _snake_case	   =				(
       {
           """feature-extraction""": YosoModel,
           """fill-mask""": YosoForMaskedLM,
           """question-answering""": YosoForQuestionAnswering,
           """text-classification""": YosoForSequenceClassification,
           """token-classification""": YosoForTokenClassification,
           """zero-shot""": YosoForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			YosoModelTester(self)
      __snake_case					:   List[Any]  				=			ConfigTester(self       , config_class=__a       , hidden_size=3_7)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      self.config_tester.run_common_tests()







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         __snake_case					:   List[Any]  				=			type
         self.model_tester.create_and_check_model(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   str  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   List[str]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__a)







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __snake_case					:   Tuple  				=			YosoModel.from_pretrained(__a)
         self.assertIsNotNone(__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Any:
      """simple docstring"""






      return




@require_torch
class a_     (     unittest.TestCase				):







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   str  				=			YosoModel.from_pretrained('uw-madison/yoso-4096')
      __snake_case					:   str  				=			torch.tensor([[0, 1, 2, 3, 4, 5]])

      with torch.no_grad():
         __snake_case					:   str  				=			model(__a)[0]

      __snake_case					:   Union[str, Any]  				=			torch.Size((1, 6, 7_6_8))
      self.assertEqual(output.shape       , __a)

      __snake_case					:   Dict  				=			torch.tensor(
          [[[-0.0_611, 0.1_242, 0.0_840], [0.0_280, -0.0_048, 0.1_125], [0.0_106, 0.0_226, 0.0_751]]])

      self.assertTrue(torch.allclose(output[:, :3, :3]       , __a       , atol=1E-4))







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096')
      __snake_case					:   Tuple  				=			torch.tensor([[0, 1, 2, 3, 4, 5]])

      with torch.no_grad():
         __snake_case					:   Any  				=			model(__a)[0]

      __snake_case					:   List[str]  				=			5_0_2_6_5

      __snake_case					:   List[str]  				=			torch.Size((1, 6, vocab_size))
      self.assertEqual(output.shape       , __a)

      __snake_case					:   str  				=			torch.tensor(
          [[[-2.1_313, -3.7_285, -2.2_407], [-2.7_047, -3.3_314, -2.6_408], [0.0_629, -2.5_166, -0.3_356]]])

      self.assertTrue(torch.allclose(output[:, :3, :3]       , __a       , atol=1E-4))







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   Dict  				=			YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096')
      __snake_case					:   Optional[Any]  				=			torch.arange(4_0_9_6).unsqueeze(0)

      with torch.no_grad():
         __snake_case					:   Dict  				=			model(__a)[0]

      __snake_case					:   List[str]  				=			5_0_2_6_5

      __snake_case					:   List[Any]  				=			torch.Size((1, 4_0_9_6, vocab_size))
      self.assertEqual(output.shape       , __a)

      __snake_case					:   Tuple  				=			torch.tensor(
          [[[-2.3_914, -4.3_742, -5.0_956], [-4.0_988, -4.2_384, -7.0_406], [-3.1_427, -3.7_192, -6.6_800]]])

      self.assertTrue(torch.allclose(output[:, :3, :3]       , __a       , atol=1E-4))







'''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

__A     						= logging.get_logger(__name__)




def 							_SCREAMING_SNAKE_CASE	(					)     ->			Optional[Any]:







   """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 a_     (     UpperCamelCase_				):







   def __init__(self       , __a)      ->   Optional[Any]:
      """simple docstring"""






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




class a_     (     UpperCamelCase_				):







   def __init__(self       , __a)      ->   List[str]:
      """simple docstring"""






      super().__init__(__a)
      __snake_case					:   Dict  				=			None
      __snake_case					:   Optional[Any]  				=			[]







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






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







   def      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   Dict  				=			self.get_value(__a)
      if val is None:
         return False
      else:
         return val == "auto"







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a=None       , __a=True)      ->   int:
      """simple docstring"""






      __snake_case				,__snake_case					:   str  				=			self.find_config_node(__a)
      if config is None:
         return

      if config.get(__a) == "auto":
         __snake_case					:   List[Any]  				=			hf_val
         return

      if not must_match:
         return

      __snake_case					:   Optional[Any]  				=			config.get(__a)
      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}""")

   _snake_case	   =				partialmethod(UpperCamelCase_		,   must_match=UpperCamelCase_				)







   def      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   Union[str, Any]:
      """simple docstring"""






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







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a)      ->   Any:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			[
          'zero_optimization.reduce_bucket_size',
          'zero_optimization.stage3_prefetch_bucket_size',
          'zero_optimization.stage3_param_persistence_threshold',
      ]
      __snake_case					:   int  				=			[x for x in hidden_size_based_keys if self.is_auto(__a)]

      if len(__a) > 0:
         if hasattr(model.config       , 'hidden_size'):
            __snake_case					:   Optional[int]  				=			model.config.hidden_size
         elif hasattr(model.config       , 'hidden_sizes'):
            # if there are many hidden sizes pick the largest one
            __snake_case					:   Tuple  				=			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'       , __a       , 'num_training_steps (calculated)')
      self.fill_match('scheduler.params.warmup_num_steps'       , args.get_warmup_steps(__a)       , 'warmup_steps')

      if len(self.mismatches) > 0:
         __snake_case					:   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'.""")


__A     						= None




def 							_SCREAMING_SNAKE_CASE	(					A	:							Dict	)     ->			Union[str, Any]:







   """simple docstring"""
   # 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)
   __snake_case					:   List[Any]  				=			weakref.ref(A	)




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







   """simple docstring"""
   # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
   global _hf_deepspeed_config_weak_ref
   __snake_case					:   Optional[Any]  				=			None




def 							_SCREAMING_SNAKE_CASE	(					)     ->			str:







   """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 							_SCREAMING_SNAKE_CASE	(					)     ->			List[Any]:







   """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 							_SCREAMING_SNAKE_CASE	(					A	:							Dict	,		A	:							int	,		A	:							Optional[Any]	,		A	:							Optional[Any]	,		A	:							str	)     ->			Union[str, Any]:







   """simple docstring"""
   from accelerate.utils import DummyOptim, DummyScheduler

   __snake_case					:   Tuple  				=			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)

   __snake_case					:   Any  				=			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.'	)
      __snake_case					:   Union[str, Any]  				=			DummyOptim(params=A	)
   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.
      __snake_case					:   Optional[int]  				=			trainer.create_optimizer()
      # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
      __snake_case					:   Dict  				=			True

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

   return optimizer, lr_scheduler




def 							_SCREAMING_SNAKE_CASE	(					A	:							Dict	,		A	:							List[Any]	,		A	:							str=False	)     ->			Union[str, Any]:







   """simple docstring"""
   from deepspeed.utils import logger as ds_logger

   __snake_case					:   int  				=			trainer.model
   __snake_case					:   Any  				=			trainer.args

   __snake_case					:   List[str]  				=			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(A	,		A	,		A	)

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

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

   return optimizer, lr_scheduler





def 							_SCREAMING_SNAKE_CASE	(					A	:							Any	,		A	:							int	)     ->			List[Any]:







   """simple docstring"""
   # 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

   __snake_case					:   int  				=			sorted(glob.glob(F"""{checkpoint_path}/global_step*"""	)	)

   if len(A	) > 0:
      logger.info(F"""Attempting to resume from {checkpoint_path}"""	)
      # this magically updates self.optimizer and self.lr_scheduler
      __snake_case				,__snake_case					:   Optional[Any]  				=			deepspeed_engine.load_checkpoint(
          A	,		load_optimizer_states=A	,		load_lr_scheduler_states=A	)
      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}"""	)