






"""simple docstring"""
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


lowerCAmelCase__    						=			logging.getLogger(__name__)





def 				lowercase__		(					lowerCamelCase=2,						lowerCamelCase=3,						lowerCamelCase=16,						lowerCamelCase = 10,						lowerCamelCase = 2       ):

				def get_dataset(lowerCamelCase       ):
								_SCREAMING_SNAKE_CASE   :		str       		=    torch.randn(batch_size * n_batches,						1       )
								return TensorDataset(lowerCamelCase,						a * x + b + 0.1 * torch.randn(batch_size * n_batches,						1       )       )

				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    get_dataset(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    get_dataset(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    DataLoader(lowerCamelCase,						shuffle=lowerCamelCase,						batch_size=lowerCamelCase,						num_workers=4       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    DataLoader(lowerCamelCase,						shuffle=lowerCamelCase,						batch_size=lowerCamelCase,						num_workers=4       )
				return (train_dataloader, valid_dataloader)





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase=None       ):
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    []
				for epoch in range(lowerCamelCase       ):
								# Train quickly
								model.train()
								for batch in dataloader:
												_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    batch
												_SCREAMING_SNAKE_CASE   :		Tuple       		=    model(lowerCamelCase       )
												_SCREAMING_SNAKE_CASE   :		str       		=    torch.nn.functional.mse_loss(lowerCamelCase,						lowerCamelCase       )
												accelerator.backward(lowerCamelCase       )
												optimizer.step()
												optimizer.zero_grad()
								rands.append(random.random()       )  # Introduce some randomness
								if scheduler is not None:
												scheduler.step()
				return rands


class _lowerCAmelCase						(    nn.Module       ):







						def __init__(    self							)		->							List[str]:
										super().__init__()
										_SCREAMING_SNAKE_CASE   :		Any       		=    nn.Parameter(torch.randn(1							)							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    nn.Parameter(torch.randn(1							)							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							Any:
										return x * self.a + self.b







class _lowerCAmelCase						(    unittest.TestCase       ):







						def 			A      (    self							)		->							List[Any]:
										with tempfile.TemporaryDirectory() as tmpdir:
														set_seed(4_2							)
														_SCREAMING_SNAKE_CASE   :		Dict       		=    DummyModel()
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    torch.optim.Adam(params=model.parameters()   ,				lr=1e-3							)
														_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    dummy_dataloaders()
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    ProjectConfiguration(total_limit=1   ,				project_dir=lowerCAmelCase_   ,				automatic_checkpoint_naming=lowerCAmelCase_							)
														# Train baseline
														_SCREAMING_SNAKE_CASE   :		Dict       		=    Accelerator(project_config=lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    accelerator.prepare(
														    lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														# Save initial
														accelerator.save_state()

														# Save second state
														accelerator.save_state()
														self.assertEqual(len(os.listdir(accelerator.project_dir							)							)   ,				1							)







						def 			A      (    self							)		->							str:
										with tempfile.TemporaryDirectory() as tmpdir:
														set_seed(4_2							)
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    DummyModel()
														_SCREAMING_SNAKE_CASE   :		Dict       		=    torch.optim.Adam(params=model.parameters()   ,				lr=1e-3							)
														_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    dummy_dataloaders()
														# Train baseline
														_SCREAMING_SNAKE_CASE   :		int       		=    Accelerator()
														_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    accelerator.prepare(
														    lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														# Save initial
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    os.path.join(lowerCAmelCase_   ,				'initial'							)
														accelerator.save_state(lowerCAmelCase_							)
														((_SCREAMING_SNAKE_CASE)		,						(_SCREAMING_SNAKE_CASE))   :		List[str]       		=    model.a.item(), model.b.item()
														_SCREAMING_SNAKE_CASE   :		Any       		=    optimizer.state_dict()
														_SCREAMING_SNAKE_CASE   :		Dict       		=    train(3   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														((_SCREAMING_SNAKE_CASE)		,						(_SCREAMING_SNAKE_CASE))   :		int       		=    model.a.item(), model.b.item()
														_SCREAMING_SNAKE_CASE   :		int       		=    optimizer.state_dict()

														# Train partially
														set_seed(4_2							)
														_SCREAMING_SNAKE_CASE   :		str       		=    DummyModel()
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.optim.Adam(params=model.parameters()   ,				lr=1e-3							)
														_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    dummy_dataloaders()
														_SCREAMING_SNAKE_CASE   :		Dict       		=    Accelerator()
														_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    accelerator.prepare(
														    lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														accelerator.load_state(lowerCAmelCase_							)
														((_SCREAMING_SNAKE_CASE)		,						(_SCREAMING_SNAKE_CASE))   :		Tuple       		=    model.a.item(), model.b.item()
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    optimizer.state_dict()
														self.assertEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)
														self.assertEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)
														self.assertEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)

														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    train(2   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														# Save everything
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    os.path.join(lowerCAmelCase_   ,				'checkpoint'							)
														accelerator.save_state(lowerCAmelCase_							)

														# Load everything back in and make sure all states work
														accelerator.load_state(lowerCAmelCase_							)
														test_rands += train(1   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														((_SCREAMING_SNAKE_CASE)		,						(_SCREAMING_SNAKE_CASE))   :		List[str]       		=    model.a.item(), model.b.item()
														_SCREAMING_SNAKE_CASE   :		Any       		=    optimizer.state_dict()
														self.assertEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)
														self.assertEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)
														self.assertEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)
														self.assertEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)







						def 			A      (    self							)		->							Union[str, Any]:
										with tempfile.TemporaryDirectory() as tmpdir:
														set_seed(4_2							)
														_SCREAMING_SNAKE_CASE   :		int       		=    DummyModel()
														_SCREAMING_SNAKE_CASE   :		str       		=    torch.optim.Adam(params=model.parameters()   ,				lr=1e-3							)
														_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    dummy_dataloaders()
														_SCREAMING_SNAKE_CASE   :		str       		=    ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase_							)

														# Train baseline
														_SCREAMING_SNAKE_CASE   :		Dict       		=    Accelerator(project_dir=lowerCAmelCase_   ,				project_config=lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		str       		=    accelerator.prepare(
														    lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														# Save initial
														accelerator.save_state()
														((_SCREAMING_SNAKE_CASE)		,						(_SCREAMING_SNAKE_CASE))   :		Optional[int]       		=    model.a.item(), model.b.item()
														_SCREAMING_SNAKE_CASE   :		int       		=    optimizer.state_dict()
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    train(3   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														((_SCREAMING_SNAKE_CASE)		,						(_SCREAMING_SNAKE_CASE))   :		Union[str, Any]       		=    model.a.item(), model.b.item()
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    optimizer.state_dict()

														# Train partially
														set_seed(4_2							)
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    DummyModel()
														_SCREAMING_SNAKE_CASE   :		str       		=    torch.optim.Adam(params=model.parameters()   ,				lr=1e-3							)
														_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    dummy_dataloaders()
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    ProjectConfiguration(iteration=1   ,				automatic_checkpoint_naming=lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    Accelerator(project_dir=lowerCAmelCase_   ,				project_config=lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    accelerator.prepare(
														    lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														accelerator.load_state(os.path.join(lowerCAmelCase_   ,				'checkpoints'   ,				'checkpoint_0'							)							)
														((_SCREAMING_SNAKE_CASE)		,						(_SCREAMING_SNAKE_CASE))   :		int       		=    model.a.item(), model.b.item()
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    optimizer.state_dict()
														self.assertEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)
														self.assertEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)
														self.assertEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)

														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    train(2   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														# Save everything
														accelerator.save_state()

														# Load everything back in and make sure all states work
														accelerator.load_state(os.path.join(lowerCAmelCase_   ,				'checkpoints'   ,				'checkpoint_1'							)							)
														test_rands += train(1   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														((_SCREAMING_SNAKE_CASE)		,						(_SCREAMING_SNAKE_CASE))   :		str       		=    model.a.item(), model.b.item()
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    optimizer.state_dict()
														self.assertEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)
														self.assertEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)
														self.assertEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)
														self.assertEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE   :		int       		=    torch.tensor([1, 2, 3]							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    torch.tensor([2, 3, 4]							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    DummyModel()
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    torch.optim.Adam(net.parameters()							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    Accelerator()
										with self.assertRaises(lowerCAmelCase_							) as ve:
														accelerator.register_for_checkpointing(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		str       		=    str(ve.exception							)
										self.assertTrue('Item at index 0' in message							)
										self.assertTrue('Item at index 1' in message							)
										self.assertFalse('Item at index 2' in message							)
										self.assertFalse('Item at index 3' in message							)







						def 			A      (    self							)		->							str:
										with tempfile.TemporaryDirectory() as tmpdir:
														set_seed(4_2							)
														_SCREAMING_SNAKE_CASE   :		str       		=    DummyModel()
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    torch.optim.Adam(params=model.parameters()   ,				lr=1e-3							)
														_SCREAMING_SNAKE_CASE   :		Dict       		=    torch.optim.lr_scheduler.StepLR(lowerCAmelCase_   ,				step_size=1   ,				gamma=0.99							)
														_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    dummy_dataloaders()
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase_							)
														# Train baseline
														_SCREAMING_SNAKE_CASE   :		str       		=    Accelerator(project_dir=lowerCAmelCase_   ,				project_config=lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Any       		=    accelerator.prepare(
														    lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														# Save initial
														accelerator.save_state()
														_SCREAMING_SNAKE_CASE   :		str       		=    scheduler.state_dict()
														train(3   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														self.assertNotEqual(lowerCAmelCase_   ,				scheduler.state_dict()							)

														# Load everything back in and make sure all states work
														accelerator.load_state(os.path.join(lowerCAmelCase_   ,				'checkpoints'   ,				'checkpoint_0'							)							)
														self.assertEqual(lowerCAmelCase_   ,				scheduler.state_dict()							)







						def 			A      (    self							)		->							Dict:
										with tempfile.TemporaryDirectory() as tmpdir:
														set_seed(4_2							)
														_SCREAMING_SNAKE_CASE   :		str       		=    DummyModel()
														_SCREAMING_SNAKE_CASE   :		int       		=    ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase_   ,				total_limit=2							)
														# Train baseline
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    Accelerator(project_dir=lowerCAmelCase_   ,				project_config=lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    accelerator.prepare(lowerCAmelCase_							)
														# Save 3 states:
														for _ in range(1_1							):
																		accelerator.save_state()
														self.assertTrue(not os.path.exists(os.path.join(lowerCAmelCase_   ,				'checkpoints'   ,				'checkpoint_0'							)							)							)
														self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_   ,				'checkpoints'   ,				'checkpoint_9'							)							)							)
														self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase_   ,				'checkpoints'   ,				'checkpoint_10'							)							)							)







						@require_cuda
						def 			A      (    self							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__							)]
										execute_subprocess_async(lowerCAmelCase_   ,				env=os.environ.copy()							)


if __name__ == "__main__":
			lowerCAmelCase__    						=			'/tmp/accelerate/state_checkpointing'
			lowerCAmelCase__    						=			DummyModel()
			lowerCAmelCase__    						=			torch.optim.Adam(params=model.parameters(), lr=1E-3)
			lowerCAmelCase__    						=			torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
			lowerCAmelCase__						, lowerCAmelCase__    						=			dummy_dataloaders()
			lowerCAmelCase__    						=			ProjectConfiguration(automatic_checkpoint_naming=True)
			# Train baseline
			lowerCAmelCase__    						=			Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
			if accelerator.process_index == 0:
						if os.path.exists(savedir):
									shutil.rmtree(savedir)
						os.makedirs(savedir)
			lowerCAmelCase__						, lowerCAmelCase__						, lowerCAmelCase__						, lowerCAmelCase__						, lowerCAmelCase__    						=			accelerator.prepare(
			    model, optimizer, train_dataloader, valid_dataloader, scheduler
			)
			lowerCAmelCase__						, lowerCAmelCase__    						=			accelerator.prepare(model, optimizer)
			train(3, model, train_dataloader, optimizer, accelerator, scheduler)
			# Check that the intial optimizer is loaded on the GPU
			for group in optimizer.param_groups:
						lowerCAmelCase__    						=			group['params'][0].device
						break
			assert param_device.type == accelerator.device.type
			lowerCAmelCase__    						=			model.cpu()
			accelerator.wait_for_everyone()
			accelerator.save_state()
			accelerator.wait_for_everyone()

			# Check CPU state
			accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
			for group in optimizer.param_groups:
						lowerCAmelCase__    						=			group['params'][0].device
						break
			assert (
			    param_device.type == torch.device('cpu').type
			), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

			# Check device state
			model.to(accelerator.device)
			accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
			for group in optimizer.param_groups:
						lowerCAmelCase__    						=			group['params'][0].device
						break
			assert (
			    param_device.type == accelerator.device.type
			), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

			# Check error
			with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
						accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
			accelerator.wait_for_everyone()
			if accelerator.process_index == 0:
						shutil.rmtree(savedir)
			accelerator.wait_for_everyone()









"""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
										)

