

"""simple docstring"""




import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory

def 							__SCREAMING_SNAKE_CASE							(			)				->    Dict:
				raise RuntimeError("CUDA out of memory."	)

class 			a					(		nn.Module			):


						def __init__(    self		):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase    :						List[str]							=      nn.Linear(3  ,   4		)
										_UpperCAmelCase    :						str							=      nn.BatchNormad(4		)
										_UpperCAmelCase    :						int							=      nn.Linear(4  ,   5		)





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										return self.lineara(self.batchnorm(self.lineara(snake_case__		)		)		)







class 			a					(		unittest.TestCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      []

										@find_executable_batch_size(starting_batch_size=128		)
										def mock_training_loop_function(A_		):
														nonlocal batch_sizes
														batch_sizes.append(snake_case__		)
														if batch_size != 8:
																		raise_fake_out_of_memory()

										mock_training_loop_function()
										self.assertListEqual(snake_case__  ,   [128, 64, 32, 16, 8]		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      []

										@find_executable_batch_size(starting_batch_size=128		)
										def mock_training_loop_function(A_  ,   A_		):
														nonlocal batch_sizes
														batch_sizes.append(snake_case__		)
														if batch_size != 8:
																		raise_fake_out_of_memory()
														return batch_size, arga

										_UpperCAmelCase    :						str							=      mock_training_loop_function("hello"		)
										self.assertListEqual(snake_case__  ,   [128, 64, 32, 16, 8]		)
										self.assertListEqual([bs, arga]  ,   [8, "hello"]		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										@find_executable_batch_size(starting_batch_size=0		)
										def mock_training_loop_function(A_		):
														pass

										with self.assertRaises(snake_case__		) as cm:
														mock_training_loop_function()
														self.assertIn("No executable batch size found, reached zero."  ,   cm.exception.args[0]		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										@find_executable_batch_size(starting_batch_size=16		)
										def mock_training_loop_function(A_		):
														if batch_size > 0:
																		raise_fake_out_of_memory()
														pass

										with self.assertRaises(snake_case__		) as cm:
														mock_training_loop_function()
														self.assertIn("No executable batch size found, reached zero."  ,   cm.exception.args[0]		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										@find_executable_batch_size(starting_batch_size=128		)
										def mock_training_loop_function(A_  ,   A_  ,   A_		):
														if batch_size != 8:
																		raise raise_fake_out_of_memory()

										with self.assertRaises(snake_case__		) as cm:
														mock_training_loop_function(128  ,   "hello"  ,   "world"		)
														self.assertIn("Batch size was passed into `f`"  ,   cm.exception.args[0]		)
														self.assertIn("`f(arg1=\'hello\', arg2=\'world\')"  ,   cm.exception.args[0]		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										@find_executable_batch_size(starting_batch_size=16		)
										def mock_training_loop_function(A_		):
														raise ValueError("Oops, we had an error!"		)

										with self.assertRaises(snake_case__		) as cm:
														mock_training_loop_function()
														self.assertIn("Oops, we had an error!"  ,   cm.exception.args[0]		)





						@require_cuda
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      torch.cuda.memory_allocated()
										_UpperCAmelCase    :						Union[str, Any]							=      ModelForTest()
										model.cuda()
										self.assertGreater(torch.cuda.memory_allocated()  ,   snake_case__		)
										_UpperCAmelCase    :						List[Any]							=      release_memory(snake_case__		)
										self.assertEqual(torch.cuda.memory_allocated()  ,   snake_case__		)





from typing import List, Optional

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


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}

class 			a					(		UpperCAmelCase			):
						_lowercase        =     "autoformer"
						_lowercase        =     {
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						    "num_hidden_layers": "encoder_layers",
						}


						def __init__(    self  ,   A_ = None  ,   A_ = None  ,   A_ = "student_t"  ,   A_ = "nll"  ,   A_ = 1  ,   A_ = [1, 2, 3, 4, 5, 6, 7]  ,   A_ = True  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = None  ,   A_ = None  ,   A_ = 64  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 32  ,   A_ = 32  ,   A_ = "gelu"  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 100  ,   A_ = 0.02  ,   A_ = True  ,   A_=True  ,   A_ = 10  ,   A_ = 25  ,   A_ = 3  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      prediction_length
										_UpperCAmelCase    :						Dict							=      context_length if context_length is not None else prediction_length
										_UpperCAmelCase    :						Tuple							=      distribution_output
										_UpperCAmelCase    :						List[Any]							=      loss
										_UpperCAmelCase    :						Optional[Any]							=      input_size
										_UpperCAmelCase    :						int							=      num_time_features
										_UpperCAmelCase    :						str							=      lags_sequence
										_UpperCAmelCase    :						Union[str, Any]							=      scaling
										_UpperCAmelCase    :						Union[str, Any]							=      num_dynamic_real_features
										_UpperCAmelCase    :						int							=      num_static_real_features
										_UpperCAmelCase    :						int							=      num_static_categorical_features
										if cardinality is not None and num_static_categorical_features > 0:
														if len(A_		) != num_static_categorical_features:
																		raise ValueError(
																		    "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
														_UpperCAmelCase    :						Union[str, Any]							=      cardinality
										else:
														_UpperCAmelCase    :						Any							=      [0]
										if embedding_dimension is not None and num_static_categorical_features > 0:
														if len(A_		) != num_static_categorical_features:
																		raise ValueError(
																		    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
														_UpperCAmelCase    :						int							=      embedding_dimension
										else:
														_UpperCAmelCase    :						int							=      [min(50  ,   (cat + 1) // 2		) for cat in self.cardinality]
										_UpperCAmelCase    :						Union[str, Any]							=      num_parallel_samples

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

										_UpperCAmelCase    :						Optional[Any]							=      dropout
										_UpperCAmelCase    :						Union[str, Any]							=      attention_dropout
										_UpperCAmelCase    :						Dict							=      activation_dropout
										_UpperCAmelCase    :						Dict							=      encoder_layerdrop
										_UpperCAmelCase    :						int							=      decoder_layerdrop

										_UpperCAmelCase    :						Union[str, Any]							=      activation_function
										_UpperCAmelCase    :						str							=      init_std

										_UpperCAmelCase    :						Tuple							=      use_cache

										# Autoformer
										_UpperCAmelCase    :						str							=      label_length
										_UpperCAmelCase    :						Any							=      moving_average
										_UpperCAmelCase    :						Optional[Any]							=      autocorrelation_factor

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





						@property
						def     _UpperCAmelCase			(    self		):







										'''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
										)


