
'''simple docstring'''


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType


class     lowercase__						(	__UpperCAmelCase ):






   '''simple docstring'''







   def __init__(      self			, lowerCamelCase__ ):

      '''simple docstring'''







      UpperCamelCase	  =	data







   def __iter__(      self ):

      '''simple docstring'''







      for element in self.data:
         yield element




def 			__snake_case  (     _UpperCAmelCase       :							List[str]=True):
   UpperCamelCase	  =	Accelerator(even_batches=_UpperCAmelCase)
   assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
   return accelerator



def 			__snake_case  (     _UpperCAmelCase       :							Tuple,   _UpperCAmelCase       :							Tuple,   _UpperCAmelCase       :							Union[str, Any],   _UpperCAmelCase       :							List[Any] = False):
   if iterable:
      UpperCamelCase	  =	DummyIterableDataset(torch.as_tensor(range(_UpperCAmelCase)))
   else:
      UpperCamelCase	  =	TensorDataset(torch.as_tensor(range(_UpperCAmelCase)))

   UpperCamelCase	  =	DataLoader(_UpperCAmelCase,   batch_size=_UpperCAmelCase)
   UpperCamelCase	  =	accelerator.prepare(_UpperCAmelCase)

   return dl



def 			__snake_case  (     _UpperCAmelCase       :							Optional[int],   _UpperCAmelCase       :							int,   _UpperCAmelCase       :							List[Any],   _UpperCAmelCase       :							List[Any],   _UpperCAmelCase       :							Optional[int],   ):
   UpperCamelCase	  =	create_dataloader(accelerator=_UpperCAmelCase,   dataset_size=_UpperCAmelCase,   batch_size=_UpperCAmelCase)

   UpperCamelCase	  =	[len(batch[0]) for batch in dl]

   if accelerator.process_index == 0:
      assert batch_sizes == process_0_expected_batch_sizes
   elif accelerator.process_index == 1:
      assert batch_sizes == process_1_expected_batch_sizes



def 			__snake_case  (     ):
   UpperCamelCase	  =	create_accelerator()

   # without padding, we would expect a different number of batches
   verify_dataloader_batch_sizes(
       _UpperCAmelCase,   dataset_size=3,   batch_size=1,   process_0_expected_batch_sizes=[1, 1],   process_1_expected_batch_sizes=[1, 1],   )

   # without padding, we would expect the same number of batches, but different sizes
   verify_dataloader_batch_sizes(
       _UpperCAmelCase,   dataset_size=7,   batch_size=2,   process_0_expected_batch_sizes=[2, 2],   process_1_expected_batch_sizes=[2, 2],   )



def 			__snake_case  (     ):
   UpperCamelCase	  =	create_accelerator(even_batches=_UpperCAmelCase)

   verify_dataloader_batch_sizes(
       _UpperCAmelCase,   dataset_size=3,   batch_size=1,   process_0_expected_batch_sizes=[1, 1],   process_1_expected_batch_sizes=[1],   )

   verify_dataloader_batch_sizes(
       _UpperCAmelCase,   dataset_size=7,   batch_size=2,   process_0_expected_batch_sizes=[2, 2],   process_1_expected_batch_sizes=[2, 1],   )



def 			__snake_case  (     ):
   UpperCamelCase	  =	create_accelerator(even_batches=_UpperCAmelCase)

   UpperCamelCase	  =	torch.nn.Linear(1,   1)
   UpperCamelCase	  =	accelerator.prepare(_UpperCAmelCase)

   UpperCamelCase	  =	create_dataloader(_UpperCAmelCase,   dataset_size=3,   batch_size=1)

   UpperCamelCase	  =	[]
   with accelerator.join_uneven_inputs([ddp_model]):
      for batch_idx, batch in enumerate(_UpperCAmelCase):
         UpperCamelCase	  =	ddp_model(batch[0].float())
         UpperCamelCase	  =	output.sum()
         loss.backward()
         batch_idxs.append(_UpperCAmelCase)

   accelerator.wait_for_everyone()

   if accelerator.process_index == 0:
      assert batch_idxs == [0, 1]
   elif accelerator.process_index == 1:
      assert batch_idxs == [0]



def 			__snake_case  (     _UpperCAmelCase       :							Optional[int]):
   with warnings.catch_warnings(record=_UpperCAmelCase) as w:
      with accelerator.join_uneven_inputs([Mock()]):
         pass

      assert issubclass(w[-1].category,   _UpperCAmelCase)
      assert "only supported for multi-GPU" in str(w[-1].message)



def 			__snake_case  (     ):
   UpperCamelCase	  =	True
   UpperCamelCase	  =	False
   UpperCamelCase	  =	create_accelerator(even_batches=_UpperCAmelCase)
   UpperCamelCase	  =	torch.nn.Linear(1,   1)
   UpperCamelCase	  =	accelerator.prepare(_UpperCAmelCase)
   UpperCamelCase	  =	create_dataloader(_UpperCAmelCase,   dataset_size=3,   batch_size=1)
   UpperCamelCase	  =	create_dataloader(_UpperCAmelCase,   dataset_size=3,   batch_size=1)

   with accelerator.join_uneven_inputs([ddp_model],   even_batches=_UpperCAmelCase):
      UpperCamelCase	  =	train_dl.batch_sampler.even_batches
      UpperCamelCase	  =	valid_dl.batch_sampler.even_batches

   assert train_dl_overridden_value == overridden_even_batches
   assert valid_dl_overridden_value == overridden_even_batches
   assert train_dl.batch_sampler.even_batches == default_even_batches
   assert valid_dl.batch_sampler.even_batches == default_even_batches



def 			__snake_case  (     ):
   UpperCamelCase	  =	True
   UpperCamelCase	  =	False
   UpperCamelCase	  =	create_accelerator(even_batches=_UpperCAmelCase)
   UpperCamelCase	  =	torch.nn.Linear(1,   1)
   UpperCamelCase	  =	accelerator.prepare(_UpperCAmelCase)
   create_dataloader(_UpperCAmelCase,   dataset_size=3,   batch_size=1,   iterable=_UpperCAmelCase)
   UpperCamelCase	  =	create_dataloader(_UpperCAmelCase,   dataset_size=3,   batch_size=1)

   with warnings.catch_warnings():
      warnings.filterwarnings('''ignore''')
      try:
         with accelerator.join_uneven_inputs([ddp_model],   even_batches=_UpperCAmelCase):
            UpperCamelCase	  =	batch_dl.batch_sampler.even_batches
      except AttributeError:
         # ensure attribute error is not raised when processing iterable dl
         raise AssertionError

   assert batch_dl_overridden_value == overridden_even_batches
   assert batch_dl.batch_sampler.even_batches == default_even_batches



def 			__snake_case  (     ):
   UpperCamelCase	  =	create_accelerator()
   UpperCamelCase	  =	torch.nn.Linear(1,   1)
   UpperCamelCase	  =	accelerator.prepare(_UpperCAmelCase)
   create_dataloader(_UpperCAmelCase,   dataset_size=3,   batch_size=1,   iterable=_UpperCAmelCase)

   with warnings.catch_warnings(record=_UpperCAmelCase) as w:
      with accelerator.join_uneven_inputs([ddp_model],   even_batches=_UpperCAmelCase):
         pass

      assert issubclass(w[-1].category,   _UpperCAmelCase)
      assert "only supported for map-style datasets" in str(w[-1].message)



def 			__snake_case  (     ):
   UpperCamelCase	  =	create_accelerator()

   accelerator.print('''Test that even_batches variable ensures uniform batches across processes''')
   test_default_ensures_even_batch_sizes()

   accelerator.print('''Run tests with even_batches disabled''')
   test_can_disable_even_batches()

   accelerator.print('''Test joining uneven inputs''')
   test_can_join_uneven_inputs()

   accelerator.print('''Test overriding even_batches when joining uneven inputs''')
   test_join_can_override_even_batches()

   accelerator.print('''Test overriding even_batches for mixed dataloader types''')
   test_join_can_override_for_mixed_type_dataloaders()

   accelerator.print('''Test overriding even_batches raises a warning for iterable dataloaders''')
   test_join_raises_warning_for_iterable_when_overriding_even_batches()

   accelerator.print('''Test join with non DDP distributed raises warning''')
   UpperCamelCase	  =	accelerator.state.distributed_type
   UpperCamelCase	  =	DistributedType.FSDP
   test_join_raises_warning_for_non_ddp_distributed(_UpperCAmelCase)
   UpperCamelCase	  =	original_state


if __name__ == "__main__":
   main()













"""simple docstring"""
from typing import List, Optional, Union

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


lowerCAmelCase__    						=			logging.get_logger(__name__)

lowerCAmelCase__    						=			{
    '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 _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Any 					=				'time_series_transformer'
						SCREAMING_SNAKE_CASE_:					List[str] 					=				{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}







						def __init__(    self   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = "student_t"   ,				lowerCAmelCase_ = "nll"   ,				lowerCAmelCase_ = 1   ,				lowerCAmelCase_ = [1, 2, 3, 4, 5, 6, 7]   ,				lowerCAmelCase_ = "mean"   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = 3_2   ,				lowerCAmelCase_ = 3_2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = "gelu"   ,				lowerCAmelCase_ = 6_4   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 1_0_0   ,				lowerCAmelCase_ = 0.02   ,				lowerCAmelCase_=True   ,				**lowerCAmelCase_   ,				)		->							str:
										# time series specific configuration
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    prediction_length
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    context_length or prediction_length
										_SCREAMING_SNAKE_CASE   :		Any       		=    distribution_output
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    loss
										_SCREAMING_SNAKE_CASE   :		Any       		=    input_size
										_SCREAMING_SNAKE_CASE   :		Any       		=    num_time_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    lags_sequence
										_SCREAMING_SNAKE_CASE   :		Dict       		=    scaling
										_SCREAMING_SNAKE_CASE   :		Any       		=    num_dynamic_real_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_static_real_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_static_categorical_features
										if cardinality and num_static_categorical_features > 0:
														if len(lowerCAmelCase_							) != num_static_categorical_features:
																		raise ValueError(
																		    'The cardinality should be a list of the same length as `num_static_categorical_features`'							)
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    cardinality
										else:
														_SCREAMING_SNAKE_CASE   :		Dict       		=    [0]
										if embedding_dimension and num_static_categorical_features > 0:
														if len(lowerCAmelCase_							) != num_static_categorical_features:
																		raise ValueError(
																		    'The embedding dimension should be a list of the same length as `num_static_categorical_features`'							)
														_SCREAMING_SNAKE_CASE   :		int       		=    embedding_dimension
										else:
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    [min(5_0   ,				(cat + 1) // 2							) for cat in self.cardinality]
										_SCREAMING_SNAKE_CASE   :		str       		=    num_parallel_samples

										# Transformer architecture configuration
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    input_size * len(lowerCAmelCase_							) + self._number_of_features
										_SCREAMING_SNAKE_CASE   :		int       		=    d_model
										_SCREAMING_SNAKE_CASE   :		str       		=    encoder_attention_heads
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    decoder_attention_heads
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    encoder_ffn_dim
										_SCREAMING_SNAKE_CASE   :		Dict       		=    decoder_ffn_dim
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    encoder_layers
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    decoder_layers

										_SCREAMING_SNAKE_CASE   :		Dict       		=    dropout
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    attention_dropout
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    activation_dropout
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    encoder_layerdrop
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    decoder_layerdrop

										_SCREAMING_SNAKE_CASE   :		Dict       		=    activation_function
										_SCREAMING_SNAKE_CASE   :		str       		=    init_std

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    use_cache

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







						@property
						def 			A      (    self							)		->							int:
										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
										)

