

import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


A__						=							logging.get_logger(__name__)




def 					_lowercase     (				a_				:					Optional[Any]=None					,a_				:					Union[str, Any]=None				)				->	Any:




					'''simple docstring'''







					return field(default_factory=lambda: default					,metadata=a_				)


@dataclass
class 				__UpperCamelCase				:
	_lowercase  :   List[str]       				=  list_field(
	    default=[]  ,						metadata={
	        "help": (
	            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
	            " of all available models"
	        )
	    }  ,						)

	_lowercase  :   List[int]       				=  list_field(
	    default=[8]  ,						metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}			)

	_lowercase  :   List[int]       				=  list_field(
	    default=[8, 3_2, 1_2_8, 5_1_2]  ,						metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}  ,						)

	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}  ,						)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}  ,						)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}			)
	_lowercase  :   bool       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Use FP16 to accelerate inference."}			)
	_lowercase  :   bool       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Benchmark training of model"}			)
	_lowercase  :   bool       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Verbose memory tracing"}			)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}  ,						)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={
	        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
	    }  ,						)
	_lowercase  :   bool       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Trace memory line by line"}			)
	_lowercase  :   bool       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Save result to a CSV file"}			)
	_lowercase  :   bool       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Save all print statements in a log file"}			)
	_lowercase  :   bool       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Whether to print environment information"}			)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={
	        "help": (
	            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
	            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
	            " for debugging / testing and on TPU."
	        )
	    }  ,						)
	_lowercase  :   str       				=  field(
	    default=F'''inference_time_{round(time()			)}.csv'''  ,						metadata={"help": "CSV filename used if saving time results to csv."}  ,						)
	_lowercase  :   str       				=  field(
	    default=F'''inference_memory_{round(time()			)}.csv'''  ,						metadata={"help": "CSV filename used if saving memory results to csv."}  ,						)
	_lowercase  :   str       				=  field(
	    default=F'''train_time_{round(time()			)}.csv'''  ,						metadata={"help": "CSV filename used if saving time results to csv for training."}  ,						)
	_lowercase  :   str       				=  field(
	    default=F'''train_memory_{round(time()			)}.csv'''  ,						metadata={"help": "CSV filename used if saving memory results to csv for training."}  ,						)
	_lowercase  :   str       				=  field(
	    default=F'''env_info_{round(time()			)}.csv'''  ,						metadata={"help": "CSV filename used if saving environment information."}  ,						)
	_lowercase  :   str       				=  field(
	    default=F'''log_{round(time()			)}.csv'''  ,						metadata={"help": "Log filename used if print statements are saved in log."}  ,						)
	_lowercase  :   int       				=  field(default=3  ,						metadata={"help": "Times an experiment will be run."}			)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={
	        "help": (
	            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
	            " model weights."
	        )
	    }  ,						)







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						warnings.warn(
						    F'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
						    ' are deprecated in general and it is advised to use external Benchmarking libraries '
						    ' to benchmark Transformer models.'  , __UpperCamelCase  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						return json.dumps(dataclasses.asdict(self      )  , indent=2      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						if len(self.models      ) <= 0:
											raise ValueError(
											    'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
											    ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'      )
						return self.models







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						if not self.multi_process:
											return False
						elif self.is_tpu:
											logger.info('Multiprocessing is currently not possible on TPU.'      )
											return False
						else:
											return True







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 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[int]       				=  "time_series_transformer"
	_lowercase  :   Dict       				=  {
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	    "num_hidden_layers": "encoder_layers",
	}







	def __init__(   self: str  , __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 = 1_00  , __UpperCamelCase: float = 0.02  , __UpperCamelCase: Any=True  , **__UpperCamelCase: Optional[Any]  , ):

						'''simple docstring'''





						__magic_name__							=    prediction_length
						__magic_name__							=    context_length or prediction_length
						__magic_name__							=    distribution_output
						__magic_name__							=    loss
						__magic_name__							=    input_size
						__magic_name__							=    num_time_features
						__magic_name__							=    lags_sequence
						__magic_name__							=    scaling
						__magic_name__							=    num_dynamic_real_features
						__magic_name__							=    num_static_real_features
						__magic_name__							=    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`'      )
											__magic_name__							=    cardinality
						else:
											__magic_name__							=    [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`'      )
											__magic_name__							=    embedding_dimension
						else:
											__magic_name__							=    [min(50  , (cat + 1) // 2      ) for cat in self.cardinality]
						__magic_name__							=    num_parallel_samples

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

						__magic_name__							=    dropout
						__magic_name__							=    attention_dropout
						__magic_name__							=    activation_dropout
						__magic_name__							=    encoder_layerdrop
						__magic_name__							=    decoder_layerdrop

						__magic_name__							=    activation_function
						__magic_name__							=    init_std

						__magic_name__							=    use_cache

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







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''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
						)




