
import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel




class        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: Any			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: Any=13			,							UpperCamelCase__			: str=7			,							UpperCamelCase__			: Tuple=True			,							UpperCamelCase__			: Optional[Any]=True			,							UpperCamelCase__			: Tuple=False			,							UpperCamelCase__			: int=True			,							UpperCamelCase__			: int=99			,							UpperCamelCase__			: List[Any]=32			,							UpperCamelCase__			: Tuple=5			,							UpperCamelCase__			: Optional[int]=4			,							UpperCamelCase__			: str=37			,							UpperCamelCase__			: Optional[Any]="gelu"			,							UpperCamelCase__			: Union[str, Any]=0.1			,							UpperCamelCase__			: Union[str, Any]=0.1			,							UpperCamelCase__			: str=512			,							UpperCamelCase__			: Optional[Any]=16			,							UpperCamelCase__			: str=2			,							UpperCamelCase__			: Optional[int]=0.02			,							UpperCamelCase__			: int=3			,							UpperCamelCase__			: Optional[int]=4			,							UpperCamelCase__			: str=None			,							)			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       parent
    __magic_name__				     =       batch_size
    __magic_name__				     =       seq_length
    __magic_name__				     =       is_training
    __magic_name__				     =       use_input_mask
    __magic_name__				     =       use_token_type_ids
    __magic_name__				     =       use_labels
    __magic_name__				     =       vocab_size
    __magic_name__				     =       hidden_size
    __magic_name__				     =       num_hidden_layers
    __magic_name__				     =       num_attention_heads
    __magic_name__				     =       intermediate_size
    __magic_name__				     =       hidden_act
    __magic_name__				     =       hidden_dropout_prob
    __magic_name__				     =       attention_probs_dropout_prob
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       type_vocab_size
    __magic_name__				     =       type_sequence_label_size
    __magic_name__				     =       initializer_range
    __magic_name__				     =       num_labels
    __magic_name__				     =       num_choices
    __magic_name__				     =       scope
  def 							_lowercase							( self			: Optional[Any]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size    )

    __magic_name__				     =       None
    if self.use_input_mask:
      __magic_name__				     =       random_attention_mask([self.batch_size, self.seq_length]    )

    __magic_name__				     =       None
    if self.use_token_type_ids:
      __magic_name__				     =       ids_tensor([self.batch_size, self.seq_length]			,							self.type_vocab_size    )

    __magic_name__				     =       None
    __magic_name__				     =       None
    __magic_name__				     =       None
    if self.use_labels:
      __magic_name__				     =       ids_tensor([self.batch_size]			,							self.type_sequence_label_size    )
      __magic_name__				     =       ids_tensor([self.batch_size, self.seq_length]			,							self.num_labels    )
      __magic_name__				     =       ids_tensor([self.batch_size]			,							self.num_choices    )

    __magic_name__				     =       self.get_config()

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  def 							_lowercase							( self			: List[str]    )			->	str:







    """simple docstring"""






    return OpenLlamaConfig(
        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=UpperCamelCase__			,							initializer_range=self.initializer_range			,							use_stable_embedding=UpperCamelCase__			,							)
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: List[str]			,							UpperCamelCase__			: List[Any]			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: int			,							UpperCamelCase__			: int			,							UpperCamelCase__			: Optional[Any]			,							UpperCamelCase__			: List[Any]    )			->	str:







    """simple docstring"""






    __magic_name__				     =       OpenLlamaModel(config=UpperCamelCase__    )
    model.to(UpperCamelCase__    )
    model.eval()
    __magic_name__				     =       model(UpperCamelCase__			,							attention_mask=UpperCamelCase__    )
    __magic_name__				     =       model(UpperCamelCase__    )
    self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)    )
  def 							_lowercase							( self			: Optional[int]			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: int			,							UpperCamelCase__			: str			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: str			,							UpperCamelCase__			: int			,							UpperCamelCase__			: List[Any]			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: List[str]			,							)			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       True
    __magic_name__				     =       OpenLlamaModel(UpperCamelCase__    )
    model.to(UpperCamelCase__    )
    model.eval()
    __magic_name__				     =       model(
        UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							encoder_hidden_states=UpperCamelCase__			,							encoder_attention_mask=UpperCamelCase__			,							)
    __magic_name__				     =       model(
        UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							encoder_hidden_states=UpperCamelCase__			,							)
    __magic_name__				     =       model(UpperCamelCase__			,							attention_mask=UpperCamelCase__    )
    self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)    )
  def 							_lowercase							( self			: Optional[int]			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: Any			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: Optional[Any]			,							UpperCamelCase__			: Any			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: Any			,							UpperCamelCase__			: Tuple			,							)			->	Dict:







    """simple docstring"""






    __magic_name__				     =       OpenLlamaForCausalLM(config=UpperCamelCase__    )
    model.to(UpperCamelCase__    )
    model.eval()
    __magic_name__				     =       model(UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							labels=UpperCamelCase__    )
    self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.seq_length, self.vocab_size)    )
  def 							_lowercase							( self			: Union[str, Any]			,							UpperCamelCase__			: Optional[int]			,							UpperCamelCase__			: Optional[int]			,							UpperCamelCase__			: Optional[int]			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: Optional[Any]			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: str			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: List[str]			,							)			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       True
    __magic_name__				     =       True
    __magic_name__				     =       OpenLlamaForCausalLM(config=UpperCamelCase__    )
    model.to(UpperCamelCase__    )
    model.eval()

    # first forward pass
    __magic_name__				     =       model(
        UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							encoder_hidden_states=UpperCamelCase__			,							encoder_attention_mask=UpperCamelCase__			,							use_cache=UpperCamelCase__			,							)
    __magic_name__				     =       outputs.past_key_values

    # create hypothetical multiple next token and extent to next_input_ids
    __magic_name__				     =       ids_tensor((self.batch_size, 3)			,							config.vocab_size    )
    __magic_name__				     =       ids_tensor((self.batch_size, 3)			,							vocab_size=2    )

    # append to next input_ids and
    __magic_name__				     =       torch.cat([input_ids, next_tokens]			,							dim=-1    )
    __magic_name__				     =       torch.cat([input_mask, next_mask]			,							dim=-1    )

    __magic_name__				     =       model(
        UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							encoder_hidden_states=UpperCamelCase__			,							encoder_attention_mask=UpperCamelCase__			,							output_hidden_states=UpperCamelCase__			,							)["""hidden_states"""][0]
    __magic_name__				     =       model(
        UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							encoder_hidden_states=UpperCamelCase__			,							encoder_attention_mask=UpperCamelCase__			,							past_key_values=UpperCamelCase__			,							output_hidden_states=UpperCamelCase__			,							)["""hidden_states"""][0]

    # select random slice
    __magic_name__				     =       ids_tensor((1,)			,							output_from_past.shape[-1]    ).item()
    __magic_name__				     =       output_from_no_past[:, -3:, random_slice_idx].detach()
    __magic_name__				     =       output_from_past[:, :, random_slice_idx].detach()

    self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

    # test that outputs are equal for slice
    self.parent.assertTrue(torch.allclose(UpperCamelCase__			,							UpperCamelCase__			,							atol=1E-3    )    )





  def 							_lowercase							( self			: Optional[Any]    )			->	int:







    """simple docstring"""






    __magic_name__				     =       self.prepare_config_and_inputs()
    (
        (
        __magic_name__
    )     ,    (
        __magic_name__
    )     ,    (
        __magic_name__
    )     ,    (
        __magic_name__
    )     ,    (
        __magic_name__
    )     ,    (
        __magic_name__
    )     ,    (
        __magic_name__
    )     ,    
    )				     =       config_and_inputs
    __magic_name__				     =       {"""input_ids""": input_ids, """attention_mask""": input_mask}
    return config, inputs_dict







@require_torch
class        UpperCAmelCase_			( _A  ,       _A  ,       _A  ,       unittest.TestCase     ):


  '''simple docstring'''







  a__           =		(
      (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
  )
  a__           =		(OpenLlamaForCausalLM,) if is_torch_available() else ()
  a__           =		(
      {
          """feature-extraction""": OpenLlamaModel,
          """text-classification""": OpenLlamaForSequenceClassification,
          """text-generation""": OpenLlamaForCausalLM,
          """zero-shot""": OpenLlamaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  a__           =		False
  a__           =		False
  def 							_lowercase							( self			: Union[str, Any]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       OpenLlamaModelTester(self    )
    __magic_name__				     =       ConfigTester(self			,							config_class=UpperCamelCase__			,							hidden_size=37    )
  def 							_lowercase							( self			: List[Any]    )			->	Tuple:







    """simple docstring"""






    self.config_tester.run_common_tests()
  def 							_lowercase							( self			: str    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
      __magic_name__				     =       type
      self.model_tester.create_and_check_model(*UpperCamelCase__    )
  def 							_lowercase							( self			: Optional[Any]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()
    __magic_name__				     =       3
    __magic_name__				     =       input_dict["""input_ids"""]
    __magic_name__				     =       input_ids.ne(1    ).to(UpperCamelCase__    )
    __magic_name__				     =       ids_tensor([self.model_tester.batch_size]			,							self.model_tester.type_sequence_label_size    )
    __magic_name__				     =       OpenLlamaForSequenceClassification(UpperCamelCase__    )
    model.to(UpperCamelCase__    )
    model.eval()
    __magic_name__				     =       model(UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							labels=UpperCamelCase__    )
    self.assertEqual(result.logits.shape			,							(self.model_tester.batch_size, self.model_tester.num_labels)    )
  def 							_lowercase							( self			: List[str]    )			->	Tuple:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()
    __magic_name__				     =       3
    __magic_name__				     =       """single_label_classification"""
    __magic_name__				     =       input_dict["""input_ids"""]
    __magic_name__				     =       input_ids.ne(1    ).to(UpperCamelCase__    )
    __magic_name__				     =       ids_tensor([self.model_tester.batch_size]			,							self.model_tester.type_sequence_label_size    )
    __magic_name__				     =       OpenLlamaForSequenceClassification(UpperCamelCase__    )
    model.to(UpperCamelCase__    )
    model.eval()
    __magic_name__				     =       model(UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							labels=UpperCamelCase__    )
    self.assertEqual(result.logits.shape			,							(self.model_tester.batch_size, self.model_tester.num_labels)    )
  def 							_lowercase							( self			: str    )			->	Dict:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()
    __magic_name__				     =       3
    __magic_name__				     =       """multi_label_classification"""
    __magic_name__				     =       input_dict["""input_ids"""]
    __magic_name__				     =       input_ids.ne(1    ).to(UpperCamelCase__    )
    __magic_name__				     =       ids_tensor(
        [self.model_tester.batch_size, config.num_labels]			,							self.model_tester.type_sequence_label_size    ).to(torch.float    )
    __magic_name__				     =       OpenLlamaForSequenceClassification(UpperCamelCase__    )
    model.to(UpperCamelCase__    )
    model.eval()
    __magic_name__				     =       model(UpperCamelCase__			,							attention_mask=UpperCamelCase__			,							labels=UpperCamelCase__    )
    self.assertEqual(result.logits.shape			,							(self.model_tester.batch_size, self.model_tester.num_labels)    )
  @unittest.skip("""Open-Llama buffers include complex numbers, which breaks this test"""    )
  def 							_lowercase							( self			: str    )			->	int:







    """simple docstring"""






    pass





  @parameterized.expand([("""linear""",), ("""dynamic""",)]    )
  def 							_lowercase							( self			: Tuple			,							UpperCamelCase__			: List[Any]    )			->	str:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()
    __magic_name__				     =       ids_tensor([1, 10]			,							config.vocab_size    )
    __magic_name__				     =       ids_tensor([1, int(config.max_position_embeddings * 1.5    )]			,							config.vocab_size    )

    set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
    __magic_name__				     =       OpenLlamaModel(UpperCamelCase__    )
    original_model.to(UpperCamelCase__    )
    original_model.eval()
    __magic_name__				     =       original_model(UpperCamelCase__    ).last_hidden_state
    __magic_name__				     =       original_model(UpperCamelCase__    ).last_hidden_state

    set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
    __magic_name__				     =       {"""type""": scaling_type, """factor""": 10.0}
    __magic_name__				     =       OpenLlamaModel(UpperCamelCase__    )
    scaled_model.to(UpperCamelCase__    )
    scaled_model.eval()
    __magic_name__				     =       scaled_model(UpperCamelCase__    ).last_hidden_state
    __magic_name__				     =       scaled_model(UpperCamelCase__    ).last_hidden_state

    # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
    # maximum sequence length, so the outputs for the short input should match.
    if scaling_type == "dynamic":
      self.assertTrue(torch.allclose(UpperCamelCase__			,							UpperCamelCase__			,							atol=1E-5    )    )
    else:
      self.assertFalse(torch.allclose(UpperCamelCase__			,							UpperCamelCase__			,							atol=1E-5    )    )

    # The output should be different for long inputs
    self.assertFalse(torch.allclose(UpperCamelCase__			,							UpperCamelCase__			,							atol=1E-5    )    )


'''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()




