"""simple docstring"""





from typing import List, Optional, Union

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


__A  =				logging.get_logger(__name__)

__A  =				{
    '''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  _snake_case     (					a__		):
						snake_case__			  =			"time_series_transformer"
						snake_case__			  =			{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						    "num_hidden_layers": "encoder_layers",
						}






						def __init__(       self      :			List[Any]							,					UpperCAmelCase      :			Optional[int] = None							,					UpperCAmelCase      :			Optional[int] = None							,					UpperCAmelCase      :			str = "student_t"							,					UpperCAmelCase      :			str = "nll"							,					UpperCAmelCase      :			int = 1							,					UpperCAmelCase      :			List[int] = [1, 2, 3, 4, 5, 6, 7]							,					UpperCAmelCase      :			Optional[Union[str, bool]] = "mean"							,					UpperCAmelCase      :			int = 0							,					UpperCAmelCase      :			int = 0							,					UpperCAmelCase      :			int = 0							,					UpperCAmelCase      :			int = 0							,					UpperCAmelCase      :			Optional[List[int]] = None							,					UpperCAmelCase      :			Optional[List[int]] = None							,					UpperCAmelCase      :			int = 32							,					UpperCAmelCase      :			int = 32							,					UpperCAmelCase      :			int = 2							,					UpperCAmelCase      :			int = 2							,					UpperCAmelCase      :			int = 2							,					UpperCAmelCase      :			int = 2							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			str = "gelu"							,					UpperCAmelCase      :			int = 64							,					UpperCAmelCase      :			float = 0.1							,					UpperCAmelCase      :			float = 0.1							,					UpperCAmelCase      :			float = 0.1							,					UpperCAmelCase      :			float = 0.1							,					UpperCAmelCase      :			float = 0.1							,					UpperCAmelCase      :			int = 100							,					UpperCAmelCase      :			float = 0.0_2							,					UpperCAmelCase      :			Tuple=True							,					**UpperCAmelCase      :			Any							,					):
													# time series specific configuration
													__lowerCamelCase					:		Tuple		   =     prediction_length
													__lowerCamelCase					:		Optional[Any]		   =     context_length or prediction_length
													__lowerCamelCase					:		Optional[Any]		   =     distribution_output
													__lowerCamelCase					:		int		   =     loss
													__lowerCamelCase					:		Tuple		   =     input_size
													__lowerCamelCase					:		Any		   =     num_time_features
													__lowerCamelCase					:		List[str]		   =     lags_sequence
													__lowerCamelCase					:		List[str]		   =     scaling
													__lowerCamelCase					:		List[str]		   =     num_dynamic_real_features
													__lowerCamelCase					:		Union[str, Any]		   =     num_static_real_features
													__lowerCamelCase					:		Tuple		   =     num_static_categorical_features
													if cardinality and num_static_categorical_features > 0:
																				if len(UpperCAmelCase    ) != num_static_categorical_features:
																											raise ValueError(
																											    "The cardinality should be a list of the same length as `num_static_categorical_features`"    )
																				__lowerCamelCase					:		Tuple		   =     cardinality
													else:
																				__lowerCamelCase					:		Dict		   =     [0]
													if embedding_dimension and num_static_categorical_features > 0:
																				if len(UpperCAmelCase    ) != num_static_categorical_features:
																											raise ValueError(
																											    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"    )
																				__lowerCamelCase					:		int		   =     embedding_dimension
													else:
																				__lowerCamelCase					:		List[str]		   =     [min(50							,					(cat + 1) // 2    ) for cat in self.cardinality]
													__lowerCamelCase					:		List[str]		   =     num_parallel_samples

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

													__lowerCamelCase					:		Tuple		   =     dropout
													__lowerCamelCase					:		Tuple		   =     attention_dropout
													__lowerCamelCase					:		Any		   =     activation_dropout
													__lowerCamelCase					:		List[str]		   =     encoder_layerdrop
													__lowerCamelCase					:		int		   =     decoder_layerdrop

													__lowerCamelCase					:		Optional[int]		   =     activation_function
													__lowerCamelCase					:		Optional[int]		   =     init_std

													__lowerCamelCase					:		int		   =     use_cache

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







						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													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
													)
"""simple docstring"""





import gc
import threading
import time

import psutil
import torch
class  _snake_case     :






						def __init__(       self      :			str    ):
													__lowerCamelCase					:		Optional[Any]		   =     psutil.Process()
													__lowerCamelCase					:		List[Any]		   =     False






						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		List[Any]		   =     -1

													while True:
																				__lowerCamelCase					:		Union[str, Any]		   =     max(self.process.memory_info().rss							,					self.cpu_memory_peak    )

																				# can't sleep or will not catch the peak right (this comment is here on purpose)
																				if not self.peak_monitoring:
																											break






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[Any]		   =     True
													__lowerCamelCase					:		Union[str, Any]		   =     threading.Thread(target=self.peak_monitor    )
													__lowerCamelCase					:		Optional[Any]		   =     True
													self.thread.start()







						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase					:		str		   =     False
													self.thread.join()
													return self.cpu_memory_peak

__A  =				PeakCPUMemory()


def    lowercase_   (   ) ->      Union[str, Any]:



							'''simple docstring'''

							__lowerCamelCase					:		Any		   =     {"time": time.time()}

							gc.collect()
							torch.cuda.empty_cache()

							# CPU mem
							__lowerCamelCase					:		Optional[int]		   =     psutil.Process().memory_info().rss
							cpu_peak_tracker.start()

							# GPU mem
							for i in range(torch.cuda.device_count()      ):
														__lowerCamelCase					:		int		   =     torch.cuda.memory_allocated(_lowerCamelCase      )
							torch.cuda.reset_peak_memory_stats()

							return measures


def    lowercase_   (   _lowerCamelCase:	Optional[Any]      ) ->      Tuple:



							'''simple docstring'''

							__lowerCamelCase					:		Tuple		   =     {"time": time.time() - start_measures["time"]}

							gc.collect()
							torch.cuda.empty_cache()

							# CPU mem
							__lowerCamelCase					:		Dict		   =     (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
							__lowerCamelCase					:		Tuple		   =     (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

							# GPU mem
							for i in range(torch.cuda.device_count()      ):
														__lowerCamelCase					:		Any		   =     (torch.cuda.memory_allocated(_lowerCamelCase      ) - start_measures[str(_lowerCamelCase      )]) / 2**20
														__lowerCamelCase					:		Optional[int]		   =     (torch.cuda.max_memory_allocated(_lowerCamelCase      ) - start_measures[str(_lowerCamelCase      )]) / 2**20

							return measures







def    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	List[Any]      ) ->      Optional[int]:



							'''simple docstring'''

							print(F"""{description}:"""      )
							print(F"""- Time: {measures["time"]:.2f}s"""      )
							for i in range(torch.cuda.device_count()      ):
														print(F"""- GPU {i} allocated: {measures[str(_lowerCamelCase      )]:.2f}MiB"""      )
														__lowerCamelCase					:		List[Any]		   =     measures[F"""{i}-peak"""]
														print(F"""- GPU {i} peak: {peak:.2f}MiB"""      )
							print(F"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB"""      )
							print(F"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB"""      )