


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase__     			=							{
    '''auto_factory''': ['''get_values'''],
    '''configuration_auto''': ['''ALL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CONFIG_MAPPING''', '''MODEL_NAMES_MAPPING''', '''AutoConfig'''],
    '''feature_extraction_auto''': ['''FEATURE_EXTRACTOR_MAPPING''', '''AutoFeatureExtractor'''],
    '''image_processing_auto''': ['''IMAGE_PROCESSOR_MAPPING''', '''AutoImageProcessor'''],
    '''processing_auto''': ['''PROCESSOR_MAPPING''', '''AutoProcessor'''],
    '''tokenization_auto''': ['''TOKENIZER_MAPPING''', '''AutoTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__     			=							[
      '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
      '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING''',
      '''MODEL_FOR_AUDIO_XVECTOR_MAPPING''',
      '''MODEL_FOR_BACKBONE_MAPPING''',
      '''MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING''',
      '''MODEL_FOR_CAUSAL_LM_MAPPING''',
      '''MODEL_FOR_CTC_MAPPING''',
      '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
      '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING''',
      '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
      '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING''',
      '''MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING''',
      '''MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
      '''MODEL_FOR_MASKED_LM_MAPPING''',
      '''MODEL_FOR_MASK_GENERATION_MAPPING''',
      '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
      '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
      '''MODEL_FOR_OBJECT_DETECTION_MAPPING''',
      '''MODEL_FOR_PRETRAINING_MAPPING''',
      '''MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
      '''MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
      '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
      '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
      '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
      '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
      '''MODEL_FOR_TEXT_ENCODING_MAPPING''',
      '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
      '''MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING''',
      '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING''',
      '''MODEL_FOR_VISION_2_SEQ_MAPPING''',
      '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING''',
      '''MODEL_MAPPING''',
      '''MODEL_WITH_LM_HEAD_MAPPING''',
      '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
      '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING''',
      '''AutoModel''',
      '''AutoBackbone''',
      '''AutoModelForAudioClassification''',
      '''AutoModelForAudioFrameClassification''',
      '''AutoModelForAudioXVector''',
      '''AutoModelForCausalLM''',
      '''AutoModelForCTC''',
      '''AutoModelForDepthEstimation''',
      '''AutoModelForImageClassification''',
      '''AutoModelForImageSegmentation''',
      '''AutoModelForInstanceSegmentation''',
      '''AutoModelForMaskGeneration''',
      '''AutoModelForTextEncoding''',
      '''AutoModelForMaskedImageModeling''',
      '''AutoModelForMaskedLM''',
      '''AutoModelForMultipleChoice''',
      '''AutoModelForNextSentencePrediction''',
      '''AutoModelForObjectDetection''',
      '''AutoModelForPreTraining''',
      '''AutoModelForQuestionAnswering''',
      '''AutoModelForSemanticSegmentation''',
      '''AutoModelForSeq2SeqLM''',
      '''AutoModelForSequenceClassification''',
      '''AutoModelForSpeechSeq2Seq''',
      '''AutoModelForTableQuestionAnswering''',
      '''AutoModelForTokenClassification''',
      '''AutoModelForUniversalSegmentation''',
      '''AutoModelForVideoClassification''',
      '''AutoModelForVision2Seq''',
      '''AutoModelForVisualQuestionAnswering''',
      '''AutoModelForDocumentQuestionAnswering''',
      '''AutoModelWithLMHead''',
      '''AutoModelForZeroShotImageClassification''',
      '''AutoModelForZeroShotObjectDetection''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__     			=							[
      '''TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
      '''TF_MODEL_FOR_CAUSAL_LM_MAPPING''',
      '''TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
      '''TF_MODEL_FOR_MASK_GENERATION_MAPPING''',
      '''TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
      '''TF_MODEL_FOR_MASKED_LM_MAPPING''',
      '''TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
      '''TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
      '''TF_MODEL_FOR_PRETRAINING_MAPPING''',
      '''TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
      '''TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
      '''TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
      '''TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
      '''TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
      '''TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
      '''TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
      '''TF_MODEL_FOR_TEXT_ENCODING_MAPPING''',
      '''TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
      '''TF_MODEL_FOR_VISION_2_SEQ_MAPPING''',
      '''TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
      '''TF_MODEL_MAPPING''',
      '''TF_MODEL_WITH_LM_HEAD_MAPPING''',
      '''TFAutoModel''',
      '''TFAutoModelForAudioClassification''',
      '''TFAutoModelForCausalLM''',
      '''TFAutoModelForImageClassification''',
      '''TFAutoModelForMaskedImageModeling''',
      '''TFAutoModelForMaskedLM''',
      '''TFAutoModelForMaskGeneration''',
      '''TFAutoModelForMultipleChoice''',
      '''TFAutoModelForNextSentencePrediction''',
      '''TFAutoModelForPreTraining''',
      '''TFAutoModelForDocumentQuestionAnswering''',
      '''TFAutoModelForQuestionAnswering''',
      '''TFAutoModelForSemanticSegmentation''',
      '''TFAutoModelForSeq2SeqLM''',
      '''TFAutoModelForSequenceClassification''',
      '''TFAutoModelForSpeechSeq2Seq''',
      '''TFAutoModelForTableQuestionAnswering''',
      '''TFAutoModelForTextEncoding''',
      '''TFAutoModelForTokenClassification''',
      '''TFAutoModelForVision2Seq''',
      '''TFAutoModelForZeroShotImageClassification''',
      '''TFAutoModelWithLMHead''',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__     			=							[
      '''FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
      '''FLAX_MODEL_FOR_CAUSAL_LM_MAPPING''',
      '''FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
      '''FLAX_MODEL_FOR_MASKED_LM_MAPPING''',
      '''FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
      '''FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
      '''FLAX_MODEL_FOR_PRETRAINING_MAPPING''',
      '''FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
      '''FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
      '''FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
      '''FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
      '''FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
      '''FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING''',
      '''FLAX_MODEL_MAPPING''',
      '''FlaxAutoModel''',
      '''FlaxAutoModelForCausalLM''',
      '''FlaxAutoModelForImageClassification''',
      '''FlaxAutoModelForMaskedLM''',
      '''FlaxAutoModelForMultipleChoice''',
      '''FlaxAutoModelForNextSentencePrediction''',
      '''FlaxAutoModelForPreTraining''',
      '''FlaxAutoModelForQuestionAnswering''',
      '''FlaxAutoModelForSeq2SeqLM''',
      '''FlaxAutoModelForSequenceClassification''',
      '''FlaxAutoModelForSpeechSeq2Seq''',
      '''FlaxAutoModelForTokenClassification''',
      '''FlaxAutoModelForVision2Seq''',
  ]


if TYPE_CHECKING:
  from .auto_factory import get_values
  from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
  from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
  from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
  from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
  from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_auto import (
        MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
        MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
        MODEL_FOR_AUDIO_XVECTOR_MAPPING,
        MODEL_FOR_BACKBONE_MAPPING,
        MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
        MODEL_FOR_CAUSAL_LM_MAPPING,
        MODEL_FOR_CTC_MAPPING,
        MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
        MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
        MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
        MODEL_FOR_MASK_GENERATION_MAPPING,
        MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
        MODEL_FOR_MASKED_LM_MAPPING,
        MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        MODEL_FOR_OBJECT_DETECTION_MAPPING,
        MODEL_FOR_PRETRAINING_MAPPING,
        MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
        MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_TEXT_ENCODING_MAPPING,
        MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
        MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
        MODEL_FOR_VISION_2_SEQ_MAPPING,
        MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
        MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
        MODEL_MAPPING,
        MODEL_WITH_LM_HEAD_MAPPING,
        AutoBackbone,
        AutoModel,
        AutoModelForAudioClassification,
        AutoModelForAudioFrameClassification,
        AutoModelForAudioXVector,
        AutoModelForCausalLM,
        AutoModelForCTC,
        AutoModelForDepthEstimation,
        AutoModelForDocumentQuestionAnswering,
        AutoModelForImageClassification,
        AutoModelForImageSegmentation,
        AutoModelForInstanceSegmentation,
        AutoModelForMaskedImageModeling,
        AutoModelForMaskedLM,
        AutoModelForMaskGeneration,
        AutoModelForMultipleChoice,
        AutoModelForNextSentencePrediction,
        AutoModelForObjectDetection,
        AutoModelForPreTraining,
        AutoModelForQuestionAnswering,
        AutoModelForSemanticSegmentation,
        AutoModelForSeqaSeqLM,
        AutoModelForSequenceClassification,
        AutoModelForSpeechSeqaSeq,
        AutoModelForTableQuestionAnswering,
        AutoModelForTextEncoding,
        AutoModelForTokenClassification,
        AutoModelForUniversalSegmentation,
        AutoModelForVideoClassification,
        AutoModelForVisionaSeq,
        AutoModelForVisualQuestionAnswering,
        AutoModelForZeroShotImageClassification,
        AutoModelForZeroShotObjectDetection,
        AutoModelWithLMHead,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_auto import (
        TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_MASK_GENERATION_MAPPING,
        TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
        TF_MODEL_FOR_MASKED_LM_MAPPING,
        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        TF_MODEL_FOR_PRETRAINING_MAPPING,
        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
        TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_MAPPING,
        TF_MODEL_WITH_LM_HEAD_MAPPING,
        TFAutoModel,
        TFAutoModelForAudioClassification,
        TFAutoModelForCausalLM,
        TFAutoModelForDocumentQuestionAnswering,
        TFAutoModelForImageClassification,
        TFAutoModelForMaskedImageModeling,
        TFAutoModelForMaskedLM,
        TFAutoModelForMaskGeneration,
        TFAutoModelForMultipleChoice,
        TFAutoModelForNextSentencePrediction,
        TFAutoModelForPreTraining,
        TFAutoModelForQuestionAnswering,
        TFAutoModelForSemanticSegmentation,
        TFAutoModelForSeqaSeqLM,
        TFAutoModelForSequenceClassification,
        TFAutoModelForSpeechSeqaSeq,
        TFAutoModelForTableQuestionAnswering,
        TFAutoModelForTextEncoding,
        TFAutoModelForTokenClassification,
        TFAutoModelForVisionaSeq,
        TFAutoModelForZeroShotImageClassification,
        TFAutoModelWithLMHead,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_auto import (
        FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
        FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_MASKED_LM_MAPPING,
        FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        FLAX_MODEL_FOR_PRETRAINING_MAPPING,
        FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
        FLAX_MODEL_MAPPING,
        FlaxAutoModel,
        FlaxAutoModelForCausalLM,
        FlaxAutoModelForImageClassification,
        FlaxAutoModelForMaskedLM,
        FlaxAutoModelForMultipleChoice,
        FlaxAutoModelForNextSentencePrediction,
        FlaxAutoModelForPreTraining,
        FlaxAutoModelForQuestionAnswering,
        FlaxAutoModelForSeqaSeqLM,
        FlaxAutoModelForSequenceClassification,
        FlaxAutoModelForSpeechSeqaSeq,
        FlaxAutoModelForTokenClassification,
        FlaxAutoModelForVisionaSeq,
    )

else:
  import sys

  UpperCAmelCase__     			=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin
def        UpperCAmelCase_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Dict:







 """simple docstring"""


 _lowercase							   =Accelerator(step_scheduler_with_optimizer=__snake_case  ,		split_batches=__snake_case		)
 _lowercase							   =torch.nn.Linear(2  ,		4		)
 _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=1.0		)
 _lowercase							   =torch.optim.lr_scheduler.OneCycleLR(__snake_case  ,		max_lr=0.01  ,		steps_per_epoch=2  ,		epochs=1		)
 _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

 # Optimizer has stepped
 scheduler.step()
 if step_scheduler_with_optimizer or (num_processes == 1):
  assert (
      scheduler.scheduler.last_epoch == num_processes
  ), F"Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"
 else:
  assert (
      scheduler.scheduler.last_epoch != num_processes
  ), F"Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"
def        UpperCAmelCase_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Optional[int]:







 """simple docstring"""


 _lowercase							   =Accelerator(step_scheduler_with_optimizer=__snake_case  ,		split_batches=__snake_case		)
 _lowercase							   =torch.nn.Linear(2  ,		4		)
 _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=1.0		)
 _lowercase							   =torch.optim.lr_scheduler.LambdaLR(__snake_case  ,		lr_lambda=lambda __snake_case		: 1 - n / 10		)
 _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

 # Optimizer has stepped
 _lowercase							   =False
 scheduler.step()
 _lowercase							   =1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

 # Optimizer has not stepped
 _lowercase							   =True
 scheduler.step()
 if not step_scheduler_with_optimizer:
  _lowercase							   =1 - 2 / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"
def        UpperCAmelCase_      (							__snake_case = 2		)  ->    Optional[int]:







 """simple docstring"""


 from transformers import get_linear_schedule_with_warmup

 _lowercase							   =[1, 2, 4]
 for num_steps in steps:
  _lowercase							   =GradientAccumulationPlugin(num_steps=__snake_case  ,		adjust_scheduler=num_steps > 1		)
  _lowercase							   =Accelerator(gradient_accumulation_plugin=__snake_case		)
  _lowercase							   =torch.nn.Linear(2  ,		4		)
  _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=10.0		)
  _lowercase							   =get_linear_schedule_with_warmup(optimizer=__snake_case  ,		num_warmup_steps=0  ,		num_training_steps=20		)

  _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

  for i in range(10 * num_steps		):
   with accelerator.accumulate(__snake_case		):
    optimizer.step()
    scheduler.step()

   if i == (10 * num_steps - 2):
    assert (
        scheduler.get_last_lr()[0] != 0
    ), F"Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"
  assert (
      scheduler.get_last_lr()[0] == 0
  ), F"Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"
  GradientState._reset_state()







@require_cpu
class    lowerCamelCase__     ( unittest.TestCase):





     def   __A      (self )  ->     Optional[Any]:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 )	,  num_processes=1 )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Any:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 )	,  num_processes=1 )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Union[str, Any]:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Dict:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Optional[Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     List[str]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     def   __A      (self )  ->     Union[str, Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     @require_huggingface_suite
     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 ) )
      debug_launcher(UpperCAmelCase )

