"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers.models.xglm.modeling_tf_xglm import (
         TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFXGLMForCausalLM,
         TFXGLMModel,
     )
@require_tf
class 						__A							:
     _UpperCamelCase							:			Any							=		XGLMConfig
     _UpperCamelCase							:			List[Any]							=		{}
     _UpperCamelCase							:			Optional[int]							=		"gelu"






     def __init__(		self				,     a__				,     a__=14				,     a__=7				,     a__=True				,     a__=True				,     a__=True				,     a__=99				,     a__=32				,     a__=2				,     a__=4				,     a__=37				,     a__="gelu"				,     a__=0.1				,     a__=0.1				,     a__=512				,     a__=0.0_2				,     ):
       _lowerCAmelCase						:       Optional[int]  			=     parent
       _lowerCAmelCase						:       int  			=     batch_size
       _lowerCAmelCase						:       Optional[Any]  			=     seq_length
       _lowerCAmelCase						:       Any  			=     is_training
       _lowerCAmelCase						:       Optional[int]  			=     use_input_mask
       _lowerCAmelCase						:       str  			=     use_labels
       _lowerCAmelCase						:       Any  			=     vocab_size
       _lowerCAmelCase						:       Optional[int]  			=     d_model
       _lowerCAmelCase						:       int  			=     num_hidden_layers
       _lowerCAmelCase						:       Union[str, Any]  			=     num_attention_heads
       _lowerCAmelCase						:       Union[str, Any]  			=     ffn_dim
       _lowerCAmelCase						:       Any  			=     activation_function
       _lowerCAmelCase						:       Tuple  			=     activation_dropout
       _lowerCAmelCase						:       int  			=     attention_dropout
       _lowerCAmelCase						:       Optional[int]  			=     max_position_embeddings
       _lowerCAmelCase						:       str  			=     initializer_range
       _lowerCAmelCase						:       List[Any]  			=     None
       _lowerCAmelCase						:       Tuple  			=     0
       _lowerCAmelCase						:       str  			=     2
       _lowerCAmelCase						:       Optional[int]  			=     1






     def     __A  (		self  ):
       return XGLMConfig.from_pretrained("""facebook/xglm-564M"""  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     tf.clip_by_value(
           ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size  )				,     clip_value_min=0				,     clip_value_max=3  )

       _lowerCAmelCase						:       Tuple  			=     None
       if self.use_input_mask:
         _lowerCAmelCase						:       Dict  			=     random_attention_mask([self.batch_size, self.seq_length]  )

       _lowerCAmelCase						:       int  			=     self.get_config()

       _lowerCAmelCase						:       int  			=     floats_tensor([self.num_hidden_layers, self.num_attention_heads]				,     2  )

       return (
           config,
           input_ids,
           input_mask,
           head_mask,
       )






     def     __A  (		self  ):
       return XGLMConfig(
           vocab_size=self.vocab_size				,     d_model=self.hidden_size				,     num_layers=self.num_hidden_layers				,     attention_heads=self.num_attention_heads				,     ffn_dim=self.ffn_dim				,     activation_function=self.activation_function				,     activation_dropout=self.activation_dropout				,     attention_dropout=self.attention_dropout				,     max_position_embeddings=self.max_position_embeddings				,     initializer_range=self.initializer_range				,     use_cache=a__				,     bos_token_id=self.bos_token_id				,     eos_token_id=self.eos_token_id				,     pad_token_id=self.pad_token_id				,     return_dict=a__				,     )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.prepare_config_and_inputs()

       (
           (
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	
       )						:       List[str]  			=     config_and_inputs

       _lowerCAmelCase						:       Tuple  			=     {
           """input_ids""": input_ids,
           """head_mask""": head_mask,
       }

       return config, inputs_dict







@require_tf
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Optional[Any]							=		(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
     _UpperCamelCase							:			List[str]							=		(TFXGLMForCausalLM,) if is_tf_available() else ()
     _UpperCamelCase							:			Optional[Any]							=		(
         {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
     )
     _UpperCamelCase							:			Any							=		False
     _UpperCamelCase							:			int							=		False
     _UpperCamelCase							:			Union[str, Any]							=		False






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     TFXGLMModelTester(self  )
       _lowerCAmelCase						:       Optional[Any]  			=     ConfigTester(self				,     config_class=a__				,     n_embd=37  )






     def     __A  (		self  ):
       self.config_tester.run_common_tests()






     @slow
     def     __A  (		self  ):
       for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:       Any  			=     TFXGLMModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )






     @unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""  )
     def     __A  (		self  ):
       super().test_resize_token_embeddings()







@require_tf
class 						__A							( unittest.TestCase		):






     @slow
     def     __A  (		self				,     a__=True  ):
       _lowerCAmelCase						:       List[str]  			=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""  )
       _lowerCAmelCase						:       Tuple  			=     tf.convert_to_tensor([[2, 268, 9865]]				,     dtype=tf.intaa  )  # The dog
       # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
       # fmt: off
       _lowerCAmelCase						:       Any  			=     [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
       # fmt: on
       _lowerCAmelCase						:       str  			=     model.generate(a__				,     do_sample=a__				,     num_beams=1  )
       if verify_outputs:
         self.assertListEqual(output_ids[0].numpy().tolist()				,     a__  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""  )
       _lowerCAmelCase						:       Tuple  			=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""  )

       tf.random.set_seed(0  )
       _lowerCAmelCase						:       List[Any]  			=     tokenizer("""Today is a nice day and"""				,     return_tensors="""tf"""  )
       _lowerCAmelCase						:       Optional[Any]  			=     tokenized.input_ids
       # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
       with tf.device(""":/CPU:0"""  ):
         _lowerCAmelCase						:       List[str]  			=     model.generate(a__				,     do_sample=a__				,     seed=[7, 0]  )
       _lowerCAmelCase						:       int  			=     tokenizer.decode(output_ids[0]				,     skip_special_tokens=a__  )

       _lowerCAmelCase						:       Union[str, Any]  			=     (
           """Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
       )
       self.assertEqual(a__				,     a__  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""  )
       _lowerCAmelCase						:       List[Any]  			=     XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""  )

       _lowerCAmelCase						:       Optional[Any]  			=     """left"""

       # use different length sentences to test batching
       _lowerCAmelCase						:       List[Any]  			=     [
           """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
           """left-padding, such as in batched generation. The output for the sequence below should be the same """
           """regardless of whether left padding is applied or not. When""",
           """Hello, my dog is a little""",
       ]

       _lowerCAmelCase						:       int  			=     tokenizer(a__				,     return_tensors="""tf"""				,     padding=a__  )
       _lowerCAmelCase						:       Optional[int]  			=     inputs["""input_ids"""]

       _lowerCAmelCase						:       List[Any]  			=     model.generate(input_ids=a__				,     attention_mask=inputs["""attention_mask"""]				,     max_new_tokens=12  )

       _lowerCAmelCase						:       Any  			=     tokenizer(sentences[0]				,     return_tensors="""tf"""  ).input_ids
       _lowerCAmelCase						:       List[Any]  			=     model.generate(input_ids=a__				,     max_new_tokens=12  )

       _lowerCAmelCase						:       str  			=     tokenizer(sentences[1]				,     return_tensors="""tf"""  ).input_ids
       _lowerCAmelCase						:       Optional[Any]  			=     model.generate(input_ids=a__				,     max_new_tokens=12  )

       _lowerCAmelCase						:       Any  			=     tokenizer.batch_decode(a__				,     skip_special_tokens=a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     tokenizer.decode(output_non_padded[0]				,     skip_special_tokens=a__  )
       _lowerCAmelCase						:       Optional[int]  			=     tokenizer.decode(output_padded[0]				,     skip_special_tokens=a__  )

       _lowerCAmelCase						:       Dict  			=     [
           """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
           """left-padding, such as in batched generation. The output for the sequence below should be the same """
           """regardless of whether left padding is applied or not. When left padding is applied, the sequence will be """
           """a single""",
           """Hello, my dog is a little bit of a shy one, but he is very friendly""",
       ]
       self.assertListEqual(a__				,     a__  )
       self.assertListEqual(a__				,     [non_padded_sentence, padded_sentence]  )





'''simple docstring'''





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 	lowerCAmelCase_				(					snake_case_       : Dict					,    snake_case_       : Union[str, Any]					,    snake_case_       : Optional[Any]					,    snake_case_       : List[Any]			)    ->      List[Any]:


     '''simple docstring'''




     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 	lowerCAmelCase_				(					snake_case_       : Any					,    snake_case_       : Tuple					,    snake_case_       : Any					,    snake_case_       : List[Any]					,    snake_case_       : str=True			)    ->      Optional[Any]:


     '''simple docstring'''




     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 	lowerCAmelCase_				(					snake_case_       : Optional[Any]					,    snake_case_       : Any=False			)    ->      Dict:


     '''simple docstring'''




     set_seed(42			)
     UpperCAmelCase_           =   RegressionModel()
     UpperCAmelCase_           =   deepcopy(snake_case_			)
     UpperCAmelCase_           =   RegressionDataset(length=80			)
     UpperCAmelCase_           =   DataLoader(snake_case_					,    batch_size=16			)
     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 	lowerCAmelCase_				(					snake_case_       : Any			)    ->      int:


     '''simple docstring'''




     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(13_37 + iteration			)
          UpperCAmelCase_           =   ddp_input[torch.randperm(len(snake_case_			)			)]





def 	lowerCAmelCase_				(					snake_case_       : Tuple			)    ->      str:


     '''simple docstring'''




     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(13_37 + iteration			)
          UpperCAmelCase_           =   ddp_input[torch.randperm(len(snake_case_			)			)]





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]=False					,    snake_case_       : str=False			)    ->      List[str]:


     '''simple docstring'''




     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(13_37 + iteration			)
          UpperCAmelCase_           =   ddp_input[torch.randperm(len(snake_case_			)			)]
     GradientState._reset_state()





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]=False					,    snake_case_       : Tuple=False			)    ->      Union[str, Any]:


     '''simple docstring'''




     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(13_37 + iteration			)
     GradientState._reset_state()





def 	lowerCAmelCase_				(					)    ->      List[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   Accelerator()

     UpperCAmelCase_           =   RegressionDataset(length=80			)
     UpperCAmelCase_           =   DataLoader(snake_case_					,    batch_size=16			)
     UpperCAmelCase_           =   RegressionDataset(length=96			)
     UpperCAmelCase_           =   DataLoader(snake_case_					,    batch_size=16			)
     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 	lowerCAmelCase_				(					)    ->      str:


     '''simple docstring'''




     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 	lowerCAmelCase_				(					snake_case_       : Dict			)    ->      int:


     '''simple docstring'''




     main()


if __name__ == "__main__":
    main()




