






"""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     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''time_series_transformer'''
	__magic_name__			   =       {
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	    '''num_hidden_layers''': '''encoder_layers''',
	}





	def __init__(   self      :				Dict			,			lowerCAmelCase_      :				Optional[int] = None			,			lowerCAmelCase_      :				Optional[int] = None			,			lowerCAmelCase_      :				str = "student_t"			,			lowerCAmelCase_      :				str = "nll"			,			lowerCAmelCase_      :				int = 1			,			lowerCAmelCase_      :				List[int] = [1, 2, 3, 4, 5, 6, 7]			,			lowerCAmelCase_      :				Optional[Union[str, bool]] = "mean"			,			lowerCAmelCase_      :				int = 0			,			lowerCAmelCase_      :				int = 0			,			lowerCAmelCase_      :				int = 0			,			lowerCAmelCase_      :				int = 0			,			lowerCAmelCase_      :				Optional[List[int]] = None			,			lowerCAmelCase_      :				Optional[List[int]] = None			,			lowerCAmelCase_      :				int = 32			,			lowerCAmelCase_      :				int = 32			,			lowerCAmelCase_      :				int = 2			,			lowerCAmelCase_      :				int = 2			,			lowerCAmelCase_      :				int = 2			,			lowerCAmelCase_      :				int = 2			,			lowerCAmelCase_      :				bool = True			,			lowerCAmelCase_      :				str = "gelu"			,			lowerCAmelCase_      :				int = 64			,			lowerCAmelCase_      :				float = 0.1			,			lowerCAmelCase_      :				float = 0.1			,			lowerCAmelCase_      :				float = 0.1			,			lowerCAmelCase_      :				float = 0.1			,			lowerCAmelCase_      :				float = 0.1			,			lowerCAmelCase_      :				int = 100			,			lowerCAmelCase_      :				float = 0.0_2			,			lowerCAmelCase_      :				Any=True			,			**lowerCAmelCase_      :				List[str]			,			)		->    Optional[int]:
				# time series specific configuration
				UpperCAmelCase_  :				Dict      =      prediction_length
				UpperCAmelCase_  :				Any      =      context_length or prediction_length
				UpperCAmelCase_  :				List[Any]      =      distribution_output
				UpperCAmelCase_  :				int      =      loss
				UpperCAmelCase_  :				Optional[Any]      =      input_size
				UpperCAmelCase_  :				str      =      num_time_features
				UpperCAmelCase_  :				Any      =      lags_sequence
				UpperCAmelCase_  :				Tuple      =      scaling
				UpperCAmelCase_  :				Tuple      =      num_dynamic_real_features
				UpperCAmelCase_  :				Union[str, Any]      =      num_static_real_features
				UpperCAmelCase_  :				Union[str, 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`"		)
							UpperCAmelCase_  :				str      =      cardinality
				else:
							UpperCAmelCase_  :				Any      =      [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`"		)
							UpperCAmelCase_  :				int      =      embedding_dimension
				else:
							UpperCAmelCase_  :				str      =      [min(50			,			(cat + 1) // 2		) for cat in self.cardinality]
				UpperCAmelCase_  :				Any      =      num_parallel_samples

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

				UpperCAmelCase_  :				Any      =      dropout
				UpperCAmelCase_  :				Optional[int]      =      attention_dropout
				UpperCAmelCase_  :				Dict      =      activation_dropout
				UpperCAmelCase_  :				Any      =      encoder_layerdrop
				UpperCAmelCase_  :				List[str]      =      decoder_layerdrop

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

				UpperCAmelCase_  :				List[str]      =      use_cache

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





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    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
				)











"""simple docstring"""
import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowerCamelCase_       			=   pytest.mark.integration

@pytest.mark.parametrize("path"			,["paws", "csv"]		)
def        snake_case   (     A__			,A__		):
			inspect_dataset(A__			,A__		)
			UpperCAmelCase_  :				Tuple      =      path + ".py"
			assert script_name in os.listdir(A__		)
			assert "__pycache__" not in os.listdir(A__		)

@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"		)
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"		)
@pytest.mark.parametrize("path"			,["accuracy"]		)
def        snake_case   (     A__			,A__		):
			inspect_metric(A__			,A__		)
			UpperCAmelCase_  :				List[str]      =      path + ".py"
			assert script_name in os.listdir(A__		)
			assert "__pycache__" not in os.listdir(A__		)

@pytest.mark.parametrize(
    "path, config_name, expected_splits"			,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				Dict      =      get_dataset_config_info(A__			,config_name=A__		)
			assert info.config_name == config_name
			assert list(info.splits.keys()		) == expected_splits

@pytest.mark.parametrize(
    "path, config_name, expected_exception"			,[
        ("paws", None, ValueError),
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			with pytest.raises(A__		):
						get_dataset_config_info(A__			,config_name=A__		)

@pytest.mark.parametrize(
    "path, expected"			,[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ]			,)
def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				str      =      get_dataset_config_names(A__		)
			assert expected in config_names

@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config"			,[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				List[str]      =      get_dataset_infos(A__		)
			assert list(infos.keys()		) == expected_configs
			UpperCAmelCase_  :				List[str]      =      expected_configs[0]
			assert expected_config in infos
			UpperCAmelCase_  :				int      =      infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()		) == expected_splits_in_first_config

@pytest.mark.parametrize(
    "path, expected_config, expected_splits"			,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				Optional[Any]      =      get_dataset_infos(A__		)
			assert expected_config in infos
			UpperCAmelCase_  :				Union[str, Any]      =      infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()		) == expected_splits




@pytest.mark.parametrize(
    "path, config_name, expected_exception"			,[
        ("paws", None, ValueError),
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			with pytest.raises(A__		):
						get_dataset_split_names(A__			,config_name=A__		)



