



'''simple docstring'''

# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


UpperCAmelCase     		=   open  # noqa: we just need to have a builtin inside this module to test it properly




'''simple docstring'''

from typing import List, Optional, Union

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


UpperCAmelCase     		=   logging.get_logger(__name__)

UpperCAmelCase     		=   {
    '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(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   List[Any]									=		"time_series_transformer"
						UpperCAmelCase							:   int									=		{
						    "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_ = "mean"     ,	A_ = 0     ,	A_ = 0     ,	A_ = 0     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = 32     ,	A_ = 32     ,	A_ = 2     ,	A_ = 2     ,	A_ = 2     ,	A_ = 2     ,	A_ = True     ,	A_ = "gelu"     ,	A_ = 64     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 100     ,	A_ = 0.0_2     ,	A_=True     ,	**A_     ,	)     ->     Optional[Any]:
							# time series specific configuration
							lowerCAmelCase   =     prediction_length
							lowerCAmelCase   =     context_length or prediction_length
							lowerCAmelCase   =     distribution_output
							lowerCAmelCase   =     loss
							lowerCAmelCase   =     input_size
							lowerCAmelCase   =     num_time_features
							lowerCAmelCase   =     lags_sequence
							lowerCAmelCase   =     scaling
							lowerCAmelCase   =     num_dynamic_real_features
							lowerCAmelCase   =     num_static_real_features
							lowerCAmelCase   =     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`"""				)
								lowerCAmelCase   =     cardinality
							else:
								lowerCAmelCase   =     [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`"""				)
								lowerCAmelCase   =     embedding_dimension
							else:
								lowerCAmelCase   =     [min(50     ,	(cat + 1) // 2				) for cat in self.cardinality]
							lowerCAmelCase   =     num_parallel_samples

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

							lowerCAmelCase   =     dropout
							lowerCAmelCase   =     attention_dropout
							lowerCAmelCase   =     activation_dropout
							lowerCAmelCase   =     encoder_layerdrop
							lowerCAmelCase   =     decoder_layerdrop

							lowerCAmelCase   =     activation_function
							lowerCAmelCase   =     init_std

							lowerCAmelCase   =     use_cache

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




						@property
						def     __snake_case   ( self				)     ->     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
							)