
'''simple docstring'''
from typing import List, Optional, Union

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


lowerCAmelCase					:    Dict           = logging.get_logger(__name__)

lowerCAmelCase					:    Dict           = {
    '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 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""time_series_transformer"""
				lowerCAmelCase_     			=							{
				    """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_ = 0.02						,				A_=True						,				**A_						,				)->		Tuple:







											'''simple docstring'''






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

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

											UpperCamelCase   			=     dropout
											UpperCamelCase   			=     attention_dropout
											UpperCamelCase   			=     activation_dropout
											UpperCamelCase   			=     encoder_layerdrop
											UpperCamelCase   			=     decoder_layerdrop

											UpperCamelCase   			=     activation_function
											UpperCamelCase   			=     init_std

											UpperCamelCase   			=     use_cache

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







				@property
				def 						UpperCAmelCase_    (       self     )->		int:







											'''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
											)






'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=snake_case_)
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							field(default="""language-modeling"""							,       metadata={"""include_in_asdict_even_if_is_default""": True})

				lowerCAmelCase_     			=							Features({"""text""": Value("""string""")})
				lowerCAmelCase_     			=							Features({})
				lowerCAmelCase_     			=							"text"







				@property
				def 						UpperCAmelCase_    (       self     )->		Dict[str, str]:







											'''simple docstring'''






											return {self.text_column: "text"}




