
from typing import List, Optional, Union

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


SCREAMING_SNAKE_CASE							:Optional[Any]							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:str							 =				{
    '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      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	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					: Tuple							,A					: Optional[int] = None							,A					: Optional[int] = None							,A					: str = "student_t"							,A					: str = "nll"							,A					: int = 1							,A					: List[int] = [1, 2, 3, 4, 5, 6, 7]							,A					: Optional[Union[str, bool]] = "mean"							,A					: int = 0							,A					: int = 0							,A					: int = 0							,A					: int = 0							,A					: Optional[List[int]] = None							,A					: Optional[List[int]] = None							,A					: int = 32							,A					: int = 32							,A					: int = 2							,A					: int = 2							,A					: int = 2							,A					: int = 2							,A					: bool = True							,A					: str = "gelu"							,A					: int = 64							,A					: float = 0.1							,A					: float = 0.1							,A					: float = 0.1							,A					: float = 0.1							,A					: float = 0.1							,A					: int = 1_00							,A					: float = 0.02							,A					: Union[str, Any]=True							,**A					: Optional[int]							,):
					# time series specific configuration
					__A   			= prediction_length
					__A   			= context_length or prediction_length
					__A   			= distribution_output
					__A   			= loss
					__A   			= input_size
					__A   			= num_time_features
					__A   			= lags_sequence
					__A   			= scaling
					__A   			= num_dynamic_real_features
					__A   			= num_static_real_features
					__A   			= num_static_categorical_features
					if cardinality 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`" )
									__A   			= cardinality
					else:
									__A   			= [0]
					if embedding_dimension 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`" )
									__A   			= embedding_dimension
					else:
									__A   			= [min(50							,(cat + 1) // 2 ) for cat in self.cardinality]
					__A   			= num_parallel_samples

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

					__A   			= dropout
					__A   			= attention_dropout
					__A   			= activation_dropout
					__A   			= encoder_layerdrop
					__A   			= decoder_layerdrop

					__A   			= activation_function
					__A   			= init_std

					__A   			= use_cache

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







	@property
	def 				UpperCamelCase_  (       self					: Optional[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
					)


import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__A				=logging.get_logger(__name__)





def 			lowerCamelCase_ (				):
				# Get the sagemaker specific mp parameters from smp_options variable.
				lowerCamelCase_						=			os.getenv("SM_HP_MP_PARAMETERS"	, "{}"    )
				try:
								# Parse it and check the field "partitions" is included, it is required for model parallel.
								lowerCamelCase_						=			json.loads(lowerCamelCase__    )
								if "partitions" not in smp_options:
												return False
				except json.JSONDecodeError:
								return False

				# Get the sagemaker specific framework parameters from mpi_options variable.
				lowerCamelCase_						=			os.getenv("SM_FRAMEWORK_PARAMS"	, "{}"    )
				try:
								# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
								lowerCamelCase_						=			json.loads(lowerCamelCase__    )
								if not mpi_options.get("sagemaker_mpi_enabled"	, lowerCamelCase__    ):
												return False
				except json.JSONDecodeError:
								return False
				# Lastly, check if the `smdistributed` module is present.
				return importlib.util.find_spec("smdistributed"    ) is not None


if is_sagemaker_model_parallel_available():
			import smdistributed.modelparallel.torch as smp

			smp.init()





@dataclass
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = field(
	    default=''			,       metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}			,       )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					super().__post_init__()
					warnings.warn(
					    "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
					    "`TrainingArguments` instead."						,  lowercase						,  )



	@cached_property
	def     SCREAMING_SNAKE_CASE_(    self )				->       "torch.device":
					logger.info("PyTorch: setting up devices" )
					if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
									logger.warning(
									    "torch.distributed process group is initialized, but local_rank == -1. "
									    "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch" )
					if self.no_cuda:
									lowerCamelCase_						=			torch.device("cpu" )
									lowerCamelCase_						=			0
					elif is_sagemaker_model_parallel_available():
									lowerCamelCase_						=			smp.local_rank()
									lowerCamelCase_						=			torch.device("cuda"						,  lowercase )
									lowerCamelCase_						=			1
					elif is_sagemaker_dp_enabled():
									import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

									torch.distributed.init_process_group(backend="smddp"						,  timeout=self.ddp_timeout_delta )
									lowerCamelCase_						=			int(os.getenv("SMDATAPARALLEL_LOCAL_RANK" ) )
									lowerCamelCase_						=			torch.device("cuda"						,  self.local_rank )
									lowerCamelCase_						=			1
					elif self.local_rank == -1:
									# if n_gpu is > 1 we'll use nn.DataParallel.
									# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
									# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
									# trigger an error that a device index is missing. Index 0 takes into account the
									# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
									# will use the first GPU in that env, i.e. GPU#1
									lowerCamelCase_						=			torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
									# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
									# the default value.
									lowerCamelCase_						=			torch.cuda.device_count()
					else:
									# Here, we'll use torch.distributed.
									# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
									if not torch.distributed.is_initialized():
													torch.distributed.init_process_group(backend="nccl"						,  timeout=self.ddp_timeout_delta )
									lowerCamelCase_						=			torch.device("cuda"						,  self.local_rank )
									lowerCamelCase_						=			1

					if device.type == "cuda":
									torch.cuda.set_device(lowercase )

					return device



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					if is_sagemaker_model_parallel_available():
									return smp.dp_size()

					return super().world_size



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					return not is_sagemaker_model_parallel_available()



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					return False


