






"""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       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''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       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=True		):







       '''simple docstring'''
       model.train()
       UpperCAmelCase	    =			model(lowerCAmelCase		)
       UpperCAmelCase	    =			F.mse_loss(lowerCAmelCase	,      target.to(output.device		)		)
       if not do_backward:
              loss /= accelerator.gradient_accumulation_steps
              loss.backward()
       else:
              accelerator.backward(lowerCAmelCase		)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase=False		):







       '''simple docstring'''
       set_seed(42		)
       UpperCAmelCase	    =			RegressionModel()
       UpperCAmelCase	    =			deepcopy(lowerCAmelCase		)
       UpperCAmelCase	    =			RegressionDataset(length=80		)
       UpperCAmelCase	    =			DataLoader(lowerCAmelCase	,      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(lowerCAmelCase	,      lr_lambda=lambda lowerCAmelCase		: epoch**0.65		)
              UpperCAmelCase	    =			LambdaLR(lowerCAmelCase	,      lr_lambda=lambda lowerCAmelCase		: epoch**0.65		)
       # Make a copy of `model`
       if sched:
              UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			accelerator.prepare(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
       else:
              UpperCAmelCase    ,       UpperCAmelCase	    =			accelerator.prepare(lowerCAmelCase	,      lowerCAmelCase		)
       if sched:
              return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
       return model, ddp_model, dataloader






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       # Test when on a single CPU or GPU that the context manager does nothing
       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			get_training_setup(lowerCAmelCase		)
       # Use a single batch
       UpperCAmelCase    ,       UpperCAmelCase	    =			next(iter(lowerCAmelCase		)		).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(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(lowerCAmelCase		):
                            step_model(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
              else:
                     # Sync grads
                     step_model(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)

              # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
              check_model_parameters(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
              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(1337 + iteration		)
              UpperCAmelCase	    =			ddp_input[torch.randperm(len(lowerCAmelCase		)		)]






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       # Test on distributed setup that context manager behaves properly
       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			get_training_setup(lowerCAmelCase		)
       # Use a single batch
       UpperCAmelCase    ,       UpperCAmelCase	    =			next(iter(lowerCAmelCase		)		).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(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(lowerCAmelCase		):
                            step_model(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
              else:
                     # Sync grads
                     step_model(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)

              # 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(1337 + iteration		)
              UpperCAmelCase	    =			ddp_input[torch.randperm(len(lowerCAmelCase		)		)]






def       _lowerCAmelCase       (      lowerCAmelCase=False	,      lowerCAmelCase=False		):







       '''simple docstring'''
       UpperCAmelCase	    =			Accelerator(
           split_batches=lowerCAmelCase	,      dispatch_batches=lowerCAmelCase	,      gradient_accumulation_steps=2		)
       # Test that context manager behaves properly
       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			get_training_setup(lowerCAmelCase		)
       for iteration, batch in enumerate(lowerCAmelCase		):
              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(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
              # Do "gradient accumulation" (noop)
              with accelerator.accumulate(lowerCAmelCase		):
                     step_model(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)

              # 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(lowerCAmelCase		) - 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(1337 + iteration		)
              UpperCAmelCase	    =			ddp_input[torch.randperm(len(lowerCAmelCase		)		)]
       GradientState._reset_state()






def       _lowerCAmelCase       (      lowerCAmelCase=False	,      lowerCAmelCase=False		):







       '''simple docstring'''
       UpperCAmelCase	    =			Accelerator(
           split_batches=lowerCAmelCase	,      dispatch_batches=lowerCAmelCase	,      gradient_accumulation_steps=2		)
       # Test that context manager behaves properly
       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			get_training_setup(lowerCAmelCase	,      lowerCAmelCase		)
       for iteration, batch in enumerate(lowerCAmelCase		):
              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(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
              opt.step()

              if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowerCAmelCase		)):
                     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(lowerCAmelCase		):
                     step_model(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
                     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(lowerCAmelCase		))
              if accelerator.num_processes > 1:
                     check_model_parameters(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
              # Shuffle ddp_input on each iteration
              torch.manual_seed(1337 + iteration		)
       GradientState._reset_state()






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			Accelerator()

       UpperCAmelCase	    =			RegressionDataset(length=80		)
       UpperCAmelCase	    =			DataLoader(lowerCAmelCase	,      batch_size=16		)
       UpperCAmelCase	    =			RegressionDataset(length=96		)
       UpperCAmelCase	    =			DataLoader(lowerCAmelCase	,      batch_size=16		)
       UpperCAmelCase    ,       UpperCAmelCase	    =			accelerator.prepare(lowerCAmelCase	,      lowerCAmelCase		)
       assert accelerator.gradient_state.active_dataloader is None
       for iteration, _ in enumerate(lowerCAmelCase		):
              assert id(accelerator.gradient_state.active_dataloader		) == id(lowerCAmelCase		)
              if iteration < len(lowerCAmelCase		) - 1:
                     assert not accelerator.gradient_state.end_of_dataloader
                     if iteration == 1:
                            for batch_num, _ in enumerate(lowerCAmelCase		):
                                   assert id(accelerator.gradient_state.active_dataloader		) == id(lowerCAmelCase		)
                                   if batch_num < len(lowerCAmelCase		) - 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       (      ):







       '''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(lowerCAmelCase		)
       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(lowerCAmelCase		)
       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(lowerCAmelCase	,      lowerCAmelCase		)

    # 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(lowerCAmelCase	,      lowerCAmelCase		)






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
 main()








"""simple docstring"""







from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_	:    str            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Optional[int]            =      {
    '''huggingface/time-series-transformer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}





class   UpperCamelCase_ (       a_  ):
  _A       :					int				=	'time_series_transformer'
  _A       :					List[Any]				=	{
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
      'num_hidden_layers': 'encoder_layers',
  }



  def __init__(      self ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = "student_t" ,		snake_case__ = "nll" ,		snake_case__ = 1 ,		snake_case__ = [1, 2, 3, 4, 5, 6, 7] ,		snake_case__ = "mean" ,		snake_case__ = 0 ,		snake_case__ = 0 ,		snake_case__ = 0 ,		snake_case__ = 0 ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = 32 ,		snake_case__ = 32 ,		snake_case__ = 2 ,		snake_case__ = 2 ,		snake_case__ = 2 ,		snake_case__ = 2 ,		snake_case__ = True ,		snake_case__ = "gelu" ,		snake_case__ = 64 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 1_00 ,		snake_case__ = 0.02 ,		snake_case__=True ,		**snake_case__ ,		)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			prediction_length
         UpperCAmelCase	    =			context_length or prediction_length
         UpperCAmelCase	    =			distribution_output
         UpperCAmelCase	    =			loss
         UpperCAmelCase	    =			input_size
         UpperCAmelCase	    =			num_time_features
         UpperCAmelCase	    =			lags_sequence
         UpperCAmelCase	    =			scaling
         UpperCAmelCase	    =			num_dynamic_real_features
         UpperCAmelCase	    =			num_static_real_features
         UpperCAmelCase	    =			num_static_categorical_features
         if cardinality and num_static_categorical_features > 0:
                if len(snake_case__				) != num_static_categorical_features:
                       raise ValueError(
                           """The cardinality should be a list of the same length as `num_static_categorical_features`"""				)
                UpperCAmelCase	    =			cardinality
         else:
                UpperCAmelCase	    =			[0]
         if embedding_dimension and num_static_categorical_features > 0:
                if len(snake_case__				) != num_static_categorical_features:
                       raise ValueError(
                           """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""				)
                UpperCAmelCase	    =			embedding_dimension
         else:
                UpperCAmelCase	    =			[min(50 ,		(cat + 1) // 2				) for cat in self.cardinality]
         UpperCAmelCase	    =			num_parallel_samples

         # Transformer architecture configuration
         UpperCAmelCase	    =			input_size * len(snake_case__				) + self._number_of_features
         UpperCAmelCase	    =			d_model
         UpperCAmelCase	    =			encoder_attention_heads
         UpperCAmelCase	    =			decoder_attention_heads
         UpperCAmelCase	    =			encoder_ffn_dim
         UpperCAmelCase	    =			decoder_ffn_dim
         UpperCAmelCase	    =			encoder_layers
         UpperCAmelCase	    =			decoder_layers

         UpperCAmelCase	    =			dropout
         UpperCAmelCase	    =			attention_dropout
         UpperCAmelCase	    =			activation_dropout
         UpperCAmelCase	    =			encoder_layerdrop
         UpperCAmelCase	    =			decoder_layerdrop

         UpperCAmelCase	    =			activation_function
         UpperCAmelCase	    =			init_std

         UpperCAmelCase	    =			use_cache

         super().__init__(is_encoder_decoder=snake_case__ ,		**snake_case__				)



  @property
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         return (
             sum(self.embedding_dimension				)
             + self.num_dynamic_real_features
             + self.num_time_features
             + self.num_static_real_features
             + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
         )
