






import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  DebertaTokenizer
  _lowerCamelCase    =  True
  _lowerCamelCase    =  DebertaTokenizerFast


  def  lowerCamelCase       (      self		:			Tuple							)						->	int:


         """simple docstring"""
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         _UpperCAmelCase							=    [
             """l""",
             """o""",
             """w""",
             """e""",
             """r""",
             """s""",
             """t""",
             """i""",
             """d""",
             """n""",
             """\u0120""",
             """\u0120l""",
             """\u0120n""",
             """\u0120lo""",
             """\u0120low""",
             """er""",
             """\u0120lowest""",
             """\u0120newer""",
             """\u0120wider""",
             """[UNK]""",
         ]
         _UpperCAmelCase							=    dict(zip(lowerCamelCase       ,				range(len(lowerCamelCase							)							)							)							)
         _UpperCAmelCase							=    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
         _UpperCAmelCase							=    {"""unk_token""": """[UNK]"""}

         _UpperCAmelCase							=    os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES["""vocab_file"""]							)
         _UpperCAmelCase							=    os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES["""merges_file"""]							)
         with open(self.vocab_file       ,				"""w"""       ,				encoding="""utf-8"""							) as fp:
                fp.write(json.dumps(lowerCamelCase							) + """\n"""							)
         with open(self.merges_file       ,				"""w"""       ,				encoding="""utf-8"""							) as fp:
                fp.write("""\n""".join(lowerCamelCase							)							)


  def  lowerCamelCase       (      self		:			Optional[int]       ,				**lowerCamelCase		:			str							)						->	List[str]:


         """simple docstring"""
         kwargs.update(self.special_tokens_map							)
         return self.tokenizer_class.from_pretrained(self.tmpdirname       ,				**lowerCamelCase							)


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


         """simple docstring"""
         _UpperCAmelCase							=    """lower newer"""
         _UpperCAmelCase							=    """lower newer"""
         return input_text, output_text


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_tokenizer()
         _UpperCAmelCase							=    """lower newer"""
         _UpperCAmelCase							=    ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
         _UpperCAmelCase							=    tokenizer.tokenize(lowerCamelCase							)
         self.assertListEqual(lowerCamelCase       ,				lowerCamelCase							)

         _UpperCAmelCase							=    tokens + [tokenizer.unk_token]
         _UpperCAmelCase							=    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase							)       ,				lowerCamelCase							)


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


         """simple docstring"""
         _UpperCAmelCase							=    self.get_tokenizer()
         _UpperCAmelCase							=    tokenizer("""Hello"""       ,				"""World"""							)
         _UpperCAmelCase							=    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
         self.assertListEqual(tokd["""token_type_ids"""]       ,				lowerCamelCase							)


  @slow
  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""							)

         _UpperCAmelCase							=    tokenizer.encode("""sequence builders"""       ,				add_special_tokens=lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.encode("""multi-sequence build"""       ,				add_special_tokens=lowerCamelCase							)

         _UpperCAmelCase							=    tokenizer.encode(
             """sequence builders"""       ,				add_special_tokens=lowerCamelCase       ,				add_prefix_space=lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.encode(
             """sequence builders"""       ,				"""multi-sequence build"""       ,				add_special_tokens=lowerCamelCase       ,				add_prefix_space=lowerCamelCase							)

         _UpperCAmelCase							=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase       ,				lowerCamelCase							)

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode





  @slow
  def  lowerCamelCase       (      self		:			str							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    [self.tokenizer_class]
         if self.test_rust_tokenizer:
                tokenizer_classes.append(self.rust_tokenizer_class							)

         for tokenizer_class in tokenizer_classes:
                _UpperCAmelCase							=    tokenizer_class.from_pretrained("""microsoft/deberta-base"""							)

                _UpperCAmelCase							=    [
                    """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                    """ALBERT incorporates two parameter reduction techniques""",
                    """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                    """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                    """ vocabulary embedding.""",
                ]

                _UpperCAmelCase							=    tokenizer(lowerCamelCase       ,				padding=lowerCamelCase							)
                _UpperCAmelCase							=    [tokenizer.decode(lowerCamelCase       ,				skip_special_tokens=lowerCamelCase							) for seq in encoding["""input_ids"""]]

                # fmt: off
                _UpperCAmelCase							=    {
                    """input_ids""": [
                        [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [1, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [1, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
                    ],
                    """token_type_ids""": [
                        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                    ],
                    """attention_mask""": [
                        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                    ]
                }
                # fmt: on

                _UpperCAmelCase							=    [
                    """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                    """ALBERT incorporates two parameter reduction techniques""",
                    """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                    """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                    """ vocabulary embedding.""",
                ]

                self.assertDictEqual(encoding.data       ,				lowerCamelCase							)

                for expected, decoded in zip(lowerCamelCase       ,				lowerCamelCase							):
                       self.assertEqual(lowerCamelCase       ,				lowerCamelCase							)







from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )				->						List[str]:
       for param, grad_param in zip(model_a.parameters()   ,      model_b.parameters() ):
              if not param.requires_grad:
                     continue
              if not did_step:
                     # Grads should not be in sync
                     assert (
                         torch.allclose(param.grad   ,      grad_param.grad ) is False
                     ), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
              else:
                     # Grads should be in sync
                     assert (
                         torch.allclose(param.grad   ,      grad_param.grad ) is True
                     ), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case=True )				->						Optional[int]:
       model.train()
       _UpperCAmelCase							=    model(__snake_case )
       _UpperCAmelCase							=    F.mse_loss(__snake_case   ,      target.to(output.device ) )
       if not do_backward:
              loss /= accelerator.gradient_accumulation_steps
              loss.backward()
       else:
              accelerator.backward(__snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case=False )				->						Optional[int]:
       set_seed(4_2 )
       _UpperCAmelCase							=    RegressionModel()
       _UpperCAmelCase							=    deepcopy(__snake_case )
       _UpperCAmelCase							=    RegressionDataset(length=8_0 )
       _UpperCAmelCase							=    DataLoader(__snake_case   ,      batch_size=1_6 )
       model.to(accelerator.device )
       if sched:
              _UpperCAmelCase							=    AdamW(params=model.parameters()   ,      lr=1E-3 )
              _UpperCAmelCase							=    AdamW(params=ddp_model.parameters()   ,      lr=1E-3 )
              _UpperCAmelCase							=    LambdaLR(__snake_case   ,      lr_lambda=lambda __snake_case : epoch**0.65 )
              _UpperCAmelCase							=    LambdaLR(__snake_case   ,      lr_lambda=lambda __snake_case : epoch**0.65 )
       # Make a copy of `model`
       if sched:
              _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.prepare(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
       else:
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.prepare(__snake_case   ,      __snake_case )
       if sched:
              return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
       return model, ddp_model, dataloader




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Dict:
       # Test when on a single CPU or GPU that the context manager does nothing
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case )
       # Use a single batch
       _UpperCAmelCase	,			_UpperCAmelCase							=    next(iter(__snake_case ) ).values()
       for iteration in range(3 ):
              # Gather the distributed inputs and targs for the base model
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.gather((ddp_input, ddp_target) )
              _UpperCAmelCase	,			_UpperCAmelCase							=    input.to(accelerator.device ), target.to(accelerator.device )
              # Perform our initial ground truth step in non "DDP"
              step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(__snake_case ):
                            step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              else:
                     # Sync grads
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )

              # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
              check_model_parameters(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              for param, ddp_param in zip(model.parameters()   ,      ddp_model.parameters() ):
                     if not param.requires_grad:
                            continue
                     assert torch.allclose(
                         param.grad   ,      ddp_param.grad ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

              # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration )
              _UpperCAmelCase							=    ddp_input[torch.randperm(len(__snake_case ) )]




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						int:
       # Test on distributed setup that context manager behaves properly
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case )
       # Use a single batch
       _UpperCAmelCase	,			_UpperCAmelCase							=    next(iter(__snake_case ) ).values()
       for iteration in range(3 ):
              # Gather the distributed inputs and targs for the base model
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.gather((ddp_input, ddp_target) )
              _UpperCAmelCase	,			_UpperCAmelCase							=    input.to(accelerator.device ), target.to(accelerator.device )
              # Perform our initial ground truth step in non "DDP"
              step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(__snake_case ):
                            step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              else:
                     # Sync grads
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )

              # DDP model and model should only be in sync when not (iteration % 2 == 0)
              for param, ddp_param in zip(model.parameters()   ,      ddp_model.parameters() ):
                     if not param.requires_grad:
                            continue
                     if iteration % 2 == 0:
                            # Grads should not be in sync
                            assert (
                                torch.allclose(param.grad   ,      ddp_param.grad ) is False
                            ), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
                     else:
                            # Grads should be in sync
                            assert (
                                torch.allclose(param.grad   ,      ddp_param.grad ) is True
                            ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration )
              _UpperCAmelCase							=    ddp_input[torch.randperm(len(__snake_case ) )]




def       _SCREAMING_SNAKE_CASE (							__snake_case=False   ,      __snake_case=False )				->						Any:
       _UpperCAmelCase							=    Accelerator(
           split_batches=__snake_case   ,      dispatch_batches=__snake_case   ,      gradient_accumulation_steps=2 )
       # Test that context manager behaves properly
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case )
       for iteration, batch in enumerate(__snake_case ):
              _UpperCAmelCase	,			_UpperCAmelCase							=    batch.values()
              # Gather the distributed inputs and targs for the base model
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.gather((ddp_input, ddp_target) )
              _UpperCAmelCase	,			_UpperCAmelCase							=    input.to(accelerator.device ), target.to(accelerator.device )
              # Perform our initial ground truth step in non "DDP"
              step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Do "gradient accumulation" (noop)
              with accelerator.accumulate(__snake_case ):
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )

              # DDP model and model should only be in sync when not (iteration % 2 == 0)
              for param, ddp_param in zip(model.parameters()   ,      ddp_model.parameters() ):
                     if not param.requires_grad:
                            continue
                     if ((iteration + 1) % 2 == 0) or (iteration == len(__snake_case ) - 1):
                            # Grads should be in sync
                            assert (
                                torch.allclose(param.grad   ,      ddp_param.grad ) is True
                            ), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
                     else:
                            # Grads should not be in sync
                            assert (
                                torch.allclose(param.grad   ,      ddp_param.grad ) is False
                            ), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration )
              _UpperCAmelCase							=    ddp_input[torch.randperm(len(__snake_case ) )]
       GradientState._reset_state()




def       _SCREAMING_SNAKE_CASE (							__snake_case=False   ,      __snake_case=False )				->						Dict:
       _UpperCAmelCase							=    Accelerator(
           split_batches=__snake_case   ,      dispatch_batches=__snake_case   ,      gradient_accumulation_steps=2 )
       # Test that context manager behaves properly
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case   ,      __snake_case )
       for iteration, batch in enumerate(__snake_case ):
              _UpperCAmelCase	,			_UpperCAmelCase							=    batch.values()
              # Gather the distributed inputs and targs for the base model
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.gather((ddp_input, ddp_target) )
              _UpperCAmelCase	,			_UpperCAmelCase							=    input.to(accelerator.device ), target.to(accelerator.device )
              # Perform our initial ground truth step in non "DDP"
              model.train()
              ddp_model.train()
              step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              opt.step()

              if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__snake_case )):
                     if split_batches:
                            sched.step()
                     else:
                            for _ in range(accelerator.num_processes ):
                                   sched.step()
              opt.zero_grad()
              # Perform gradient accumulation under wrapper
              with accelerator.accumulate(__snake_case ):
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
                     ddp_opt.step()
                     ddp_sched.step()
                     ddp_opt.zero_grad()

              # Learning rates should be the same
              assert (
                  opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
              ), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
              _UpperCAmelCase							=    (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__snake_case ))
              if accelerator.num_processes > 1:
                     check_model_parameters(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration )
       GradientState._reset_state()




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

       _UpperCAmelCase							=    RegressionDataset(length=8_0 )
       _UpperCAmelCase							=    DataLoader(__snake_case   ,      batch_size=1_6 )
       _UpperCAmelCase							=    RegressionDataset(length=9_6 )
       _UpperCAmelCase							=    DataLoader(__snake_case   ,      batch_size=1_6 )
       _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.prepare(__snake_case   ,      __snake_case )
       assert accelerator.gradient_state.active_dataloader is None
       for iteration, _ in enumerate(__snake_case ):
              assert id(accelerator.gradient_state.active_dataloader ) == id(__snake_case )
              if iteration < len(__snake_case ) - 1:
                     assert not accelerator.gradient_state.end_of_dataloader
                     if iteration == 1:
                            for batch_num, _ in enumerate(__snake_case ):
                                   assert id(accelerator.gradient_state.active_dataloader ) == id(__snake_case )
                                   if batch_num < len(__snake_case ) - 1:
                                          assert not accelerator.gradient_state.end_of_dataloader
                                   else:
                                          assert accelerator.gradient_state.end_of_dataloader
              else:
                     assert accelerator.gradient_state.end_of_dataloader
       assert accelerator.gradient_state.active_dataloader is None




def       _SCREAMING_SNAKE_CASE (							)				->						List[Any]:
       _UpperCAmelCase							=    Accelerator()
       _UpperCAmelCase							=    accelerator.state
       if state.local_process_index == 0:
              print("""**Test `accumulate` gradient accumulation with dataloader break**""" )
       test_dataloader_break()
       if state.distributed_type == DistributedType.NO:
              if state.local_process_index == 0:
                     print("""**Test NOOP `no_sync` context manager**""" )
              test_noop_sync(__snake_case )
       if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
              if state.local_process_index == 0:
                     print("""**Test Distributed `no_sync` context manager**""" )
              test_distributed_sync(__snake_case )
       if state.distributed_type == DistributedType.MULTI_GPU:
              for split_batch in [True, False]:
                     for dispatch_batches in [True, False]:
                            if state.local_process_index == 0:
                                   print(
                                       """**Test `accumulate` gradient accumulation, """   ,      f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""   ,      )
                            test_gradient_accumulation(__snake_case   ,      __snake_case )

    # Currently will break on torch 2.0 +, need to investigate why
       if is_torch_version("""<"""   ,      """2.0""" ) or state.distributed_type == DistributedType.NO:
              if state.local_process_index == 0:
                     print(
                         """**Test `accumulate` gradient accumulation with optimizer and scheduler, """   ,      """`split_batches=False`, `dispatch_batches=False`**"""   ,      )
              test_gradient_accumulation_with_opt_and_scheduler()
              if state.distributed_type == DistributedType.MULTI_GPU:
                     for split_batch in [True, False]:
                            for dispatch_batches in [True, False]:
                                   if not split_batch and not dispatch_batches:
                                          continue
                                   if state.local_process_index == 0:
                                          print(
                                              """**Test `accumulate` gradient accumulation with optimizer and scheduler, """   ,      f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""   ,      )
                                   test_gradient_accumulation_with_opt_and_scheduler(__snake_case   ,      __snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Tuple:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
  main()